diff --git a/.Rbuildignore b/.Rbuildignore index 162d5905c08e7678e64fca3f9325fa85a8ca83e1..991bd9585381064b0e307eedee9b1e7393724f13 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,6 +1,5 @@ ^.*\.Rproj$ ^\.Rproj\.user$ -^deps.* ^.github.* ^.vscode.* tools/torchgen/.Rbuildignore @@ -12,11 +11,15 @@ tools/torchgen/.Rbuildignore ^lantern$ .*\.tmp ^vignettes/examples/.* -^deps/.* ^bench/.* ^docker/.* ^test.html ^LICENSE\.md$ ^.*torchpkg.dll$ +^deps.* +^public/.* # uncomment below for CRAN submission -# ^inst/deps/.* +^inst/deps/.* +^cran-comments\.md$ +^CRAN-RELEASE$ + diff --git a/.github/workflows/lantern.yaml b/.github/workflows/lantern.yaml index b99b527fb0453eb5004fa80dd8c988ddba14a811..d2474876e0d8d644a7117ac006786345ee6e9ac7 100644 --- a/.github/workflows/lantern.yaml +++ b/.github/workflows/lantern.yaml @@ -4,6 +4,7 @@ on: push: branches: - master + - 'cran/**' jobs: build: diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index 4fec726d95c82c241bf3afe5b7a338e6ebce12c1..6cf1dfa1994a39a7e335c5fd941f894109239d79 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -25,7 +25,7 @@ jobs: install: 1 runs-on: ${{ matrix.os }} name: ${{ matrix.os }} - timeout-minutes: 30 + timeout-minutes: 45 env: R_REMOTES_NO_ERRORS_FROM_WARNINGS: true INSTALL_TORCH: ${{ matrix.install }} @@ -54,7 +54,7 @@ jobs: Rscript tools/buildlantern.R - name: Check run: | - rcmdcheck::rcmdcheck(args = c("--no-multiarch", "--no-manual"), error_on = "warning", check_dir = "check") + rcmdcheck::rcmdcheck(args = c("--no-multiarch", "--no-manual"), error_on = "error", check_dir = "check") shell: Rscript {0} - name: Install run: | diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5f50d55b4de3946397da43728c6f6616bb447a2e --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,35 @@ +on: + push: + branches: master + +name: pkgdown + +jobs: + pkgdown: + runs-on: macOS-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + TORCH_TEST: 1 + TORCH_INSTALL: 1 + steps: + - uses: actions/checkout@v2 + + - uses: r-lib/actions/setup-r@master + + - uses: r-lib/actions/setup-pandoc@master + + - name: Install dependencies + run: | + install.packages("remotes") + remotes::install_deps(dependencies = TRUE) + install.packages("pkgdown") + shell: Rscript {0} + + - name: Install package + run: R CMD INSTALL . + + - name: Deploy package + run: | + git config --local user.email "actions@github.com" + git config --local user.name "GitHub Actions" + Rscript -e 'Sys.setenv(TORCH_TEST = 1);pkgdown::deploy_to_branch(new_process = FALSE)' \ No newline at end of file diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml new file mode 100644 index 0000000000000000000000000000000000000000..053eec76385f667cd58e6cfac2151e5a83de7bad --- /dev/null +++ b/.github/workflows/website.yaml @@ -0,0 +1,35 @@ +on: + push: + branches: + - blogdown + workflow_dispatch: + workflow_run: + workflows: ["pkgdown"] + branches: ["master"] + types: + - completed + +name: Merge websites + +jobs: + pkgdown: + runs-on: macOS-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + TORCH_TEST: 1 + steps: + - uses: actions/checkout@v2 + with: + ref: 'blogdown' + - name: Deploy package + run: | + git config --local user.email "actions@github.com" + git config --local user.name "GitHub Actions" + mkdir static/docs + cd static/docs + git clone -b gh-pages https://github.com/mlverse/torch.git . + rm -r .git/ + cd ../.. + git add -A + git commit --allow-empty -m "Update site" + git push --force origin HEAD:website \ No newline at end of file diff --git a/.gitignore b/.gitignore index b7691041fd9506aee2547dfb8fad7e75fde77ddf..8f55aaa057e5fba0698a46e6caa318442df0a453 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,5 @@ test.html lantern/.idea* lantern/cmake-build* check/ +docs/ +public diff --git a/DESCRIPTION b/DESCRIPTION index dfc9257909e48fb2ced6eb5fdf01bb1a2025453b..09e79cced7f9b7aeddb0e3aaaf2b70fc84cd4645 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,18 +1,20 @@ Package: torch Type: Package Title: Tensors and Neural Networks with 'GPU' Acceleration -Version: 0.0.1 +Version: 0.0.3.9000 Authors@R: c( - person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre")), - person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut")), + person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")), + person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cph")), person("Dmitriy", "Selivanov", role = c("ctb")), person("Athos", "Damiani", role = c("ctb")), person(family = "RStudio", role = c("cph")) ) -Description: Provides functionality similar to 'PyTorch' but written entirely in R and - the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration. +Description: Provides functionality to define and train neural networks similar to + 'PyTorch' by Paszke et al (2019) but written entirely in R + using the 'libtorch' library. Also supports low-level tensor operations and + 'GPU' acceleration. License: MIT + file LICENSE -URL: http://mlverse.github.io/torch, https://github.com/mlverse/torch +URL: https://torch.mlverse.org/docs, https://github.com/mlverse/torch BugReports: https://github.com/mlverse/torch/issues Encoding: UTF-8 LazyData: true @@ -26,7 +28,9 @@ Imports: rlang, methods, utils, - stats + stats, + bit64, + magrittr RoxygenNote: 7.1.1 Roxygen: list(markdown = TRUE) Suggests: @@ -34,9 +38,8 @@ Suggests: covr, knitr, rmarkdown, - bit64, - magrittr, - glue + glue, + palmerpenguins VignetteBuilder: knitr Collate: 'R7.R' @@ -68,6 +71,7 @@ Collate: 'nn-activation.R' 'nn-batchnorm.R' 'nn-conv.R' + 'nn-distance.R' 'nn-dropout.R' 'nn-init.R' 'nn-linear.R' @@ -107,11 +111,13 @@ Collate: 'storage.R' 'tensor_list.R' 'tensor_options.R' + 'type-info.R' 'utils-data-collate.R' 'utils-data-dataloader.R' 'utils-data-enum.R' 'utils-data-fetcher.R' 'utils-data-sampler.R' + 'utils-pipe.R' 'utils.R' 'variable_list.R' 'with-indices.R' diff --git a/NAMESPACE b/NAMESPACE index 2b70b1ba13cf085dbcc0b7e491d1b42bdebe796b..d2965ec8c1fe6a17fbdef4bc155be4346deb0685 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,6 +7,7 @@ S3method("$",enum_env) S3method("$",nn_Module) S3method("$",nn_module) S3method("$<-",nn_Module) +S3method("$<-",nn_module) S3method("%%",torch_tensor) S3method("%/%",torch_tensor) S3method("&",torch_tensor) @@ -30,6 +31,7 @@ S3method("[[",nn_Module) S3method("[[",nn_module) S3method("[[",nn_module_list) S3method("[[<-",nn_Module) +S3method("[[<-",nn_module) S3method("^",torch_tensor) S3method("|",torch_tensor) S3method(.DollarNames,torch_tensor) @@ -41,6 +43,7 @@ S3method(as.character,torch_dimname_list) S3method(as.character,torch_dtype) S3method(as.double,torch_tensor) S3method(as.integer,torch_tensor) +S3method(as.integer64,torch_tensor) S3method(as.logical,torch_tensor) S3method(as.numeric,torch_tensor) S3method(as_array,torch_tensor) @@ -74,12 +77,14 @@ S3method(sign,torch_tensor) S3method(sin,torch_tensor) S3method(sinh,torch_tensor) S3method(sqrt,torch_tensor) +S3method(str,torch_tensor) S3method(sum,torch_tensor) S3method(tan,torch_tensor) S3method(tanh,torch_tensor) S3method(torch_save,nn_module) S3method(torch_save,torch_tensor) S3method(trunc,torch_tensor) +export("%>%") export(as_array) export(autograd_backward) export(autograd_function) @@ -95,15 +100,32 @@ export(dataset) export(enumerate) export(install_torch) export(is_dataloader) +export(is_nn_buffer) +export(is_nn_module) +export(is_nn_parameter) +export(is_torch_device) export(is_torch_dtype) export(is_torch_layout) export(is_torch_memory_format) export(is_torch_qscheme) +export(is_undefined_tensor) +export(load_state_dict) +export(nn_adaptive_avg_pool1d) +export(nn_adaptive_avg_pool2d) +export(nn_adaptive_avg_pool3d) export(nn_adaptive_log_softmax_with_loss) +export(nn_adaptive_max_pool1d) +export(nn_adaptive_max_pool2d) +export(nn_adaptive_max_pool3d) +export(nn_avg_pool1d) +export(nn_avg_pool2d) +export(nn_avg_pool3d) export(nn_batch_norm1d) export(nn_batch_norm2d) export(nn_bce_loss) +export(nn_bce_with_logits_loss) export(nn_bilinear) +export(nn_buffer) export(nn_celu) export(nn_conv1d) export(nn_conv2d) @@ -111,18 +133,23 @@ export(nn_conv3d) export(nn_conv_transpose1d) export(nn_conv_transpose2d) export(nn_conv_transpose3d) +export(nn_cosine_embedding_loss) export(nn_cross_entropy_loss) +export(nn_ctc_loss) export(nn_dropout) export(nn_dropout2d) export(nn_dropout3d) export(nn_elu) export(nn_embedding) +export(nn_fractional_max_pool2d) +export(nn_fractional_max_pool3d) export(nn_gelu) export(nn_glu) export(nn_hardshrink) export(nn_hardsigmoid) export(nn_hardswish) export(nn_hardtanh) +export(nn_hinge_embedding_loss) export(nn_identity) export(nn_init_calculate_gain) export(nn_init_constant_) @@ -139,15 +166,32 @@ export(nn_init_uniform_) export(nn_init_xavier_normal_) export(nn_init_xavier_uniform_) export(nn_init_zeros_) +export(nn_kl_div_loss) +export(nn_l1_loss) export(nn_leaky_relu) export(nn_linear) export(nn_log_sigmoid) export(nn_log_softmax) +export(nn_lp_pool1d) +export(nn_lp_pool2d) +export(nn_margin_ranking_loss) export(nn_max_pool1d) export(nn_max_pool2d) +export(nn_max_pool3d) +export(nn_max_unpool1d) +export(nn_max_unpool2d) +export(nn_max_unpool3d) export(nn_module) export(nn_module_list) +export(nn_mse_loss) +export(nn_multi_margin_loss) export(nn_multihead_attention) +export(nn_multilabel_margin_loss) +export(nn_multilabel_soft_margin_loss) +export(nn_nll_loss) +export(nn_pairwise_distance) +export(nn_parameter) +export(nn_poisson_nll_loss) export(nn_prelu) export(nn_relu) export(nn_relu6) @@ -156,6 +200,8 @@ export(nn_rrelu) export(nn_selu) export(nn_sequential) export(nn_sigmoid) +export(nn_smooth_l1_loss) +export(nn_soft_margin_loss) export(nn_softmax) export(nn_softmax2d) export(nn_softmin) @@ -165,6 +211,8 @@ export(nn_softsign) export(nn_tanh) export(nn_tanhshrink) export(nn_threshold) +export(nn_triplet_margin_loss) +export(nn_triplet_margin_with_distance_loss) export(nn_utils_rnn_pack_padded_sequence) export(nn_utils_rnn_pack_sequence) export(nn_utils_rnn_pad_packed_sequence) @@ -258,6 +306,7 @@ export(nnf_rrelu) export(nnf_rrelu_) export(nnf_selu) export(nnf_selu_) +export(nnf_sigmoid) export(nnf_smooth_l1_loss) export(nnf_soft_margin_loss) export(nnf_softmax) @@ -269,6 +318,7 @@ export(nnf_tanhshrink) export(nnf_threshold) export(nnf_threshold_) export(nnf_triplet_margin_loss) +export(nnf_triplet_margin_with_distance_loss) export(nnf_unfold) export(optim_adam) export(optim_sgd) @@ -311,6 +361,7 @@ export(torch_cartesian_prod) export(torch_cat) export(torch_cdist) export(torch_ceil) +export(torch_celu) export(torch_celu_) export(torch_chain_matmul) export(torch_channels_last_format) @@ -362,6 +413,7 @@ export(torch_exp) export(torch_expm1) export(torch_eye) export(torch_fft) +export(torch_finfo) export(torch_flatten) export(torch_flip) export(torch_float) @@ -386,6 +438,7 @@ export(torch_hamming_window) export(torch_hann_window) export(torch_histc) export(torch_ifft) +export(torch_iinfo) export(torch_imag) export(torch_index_select) export(torch_int) @@ -397,6 +450,7 @@ export(torch_inverse) export(torch_irfft) export(torch_is_complex) export(torch_is_floating_point) +export(torch_is_installed) export(torch_isfinite) export(torch_isinf) export(torch_isnan) @@ -422,6 +476,7 @@ export(torch_lstsq) export(torch_lt) export(torch_lu) export(torch_lu_solve) +export(torch_manual_seed) export(torch_masked_select) export(torch_matmul) export(torch_matrix_power) @@ -479,6 +534,7 @@ export(torch_reciprocal) export(torch_reduction_mean) export(torch_reduction_none) export(torch_reduction_sum) +export(torch_relu) export(torch_relu_) export(torch_remainder) export(torch_renorm) @@ -492,6 +548,7 @@ export(torch_round) export(torch_rrelu_) export(torch_rsqrt) export(torch_save) +export(torch_selu) export(torch_selu_) export(torch_set_default_dtype) export(torch_short) @@ -546,6 +603,8 @@ export(torch_zeros_like) export(with_enable_grad) export(with_no_grad) importFrom(Rcpp,sourceCpp) +importFrom(bit64,as.integer64) +importFrom(magrittr,"%>%") importFrom(rlang,":=") importFrom(rlang,env_bind) importFrom(rlang,env_get) diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 0000000000000000000000000000000000000000..84ff6413c1f29f7f9b302108129c7a1ad2034e66 --- /dev/null +++ b/NEWS.md @@ -0,0 +1,11 @@ +# torch (development version) + +- Added many missing losses (#252) +- Implemented the `$<-` and `[[<-` operators for the `nn_module` class. (#253) +- Export `nn_parameter`, `nn_buffer`, and `is_*` auxiliary functions. +- Added a new serialization vignette. + +# torch 0.0.2 + +* Added a `NEWS.md` file to track changes to the package. +* Auto install when loading the package for the first time. diff --git a/R/RcppExports.R b/R/RcppExports.R index 4d9873b9ba6dd84f0a225db4f2c20390e63d7323..df7bcb30b1acbfcf46aec302f6212f93e7f39d87 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -6885,6 +6885,10 @@ cpp_generator_set_current_seed <- function(generator, seed) { invisible(.Call('_torch_cpp_generator_set_current_seed', PACKAGE = 'torchpkg', generator, seed)) } +cpp_torch_manual_seed <- function(seed) { + invisible(.Call('_torch_cpp_torch_manual_seed', PACKAGE = 'torchpkg', seed)) +} + enquos0 <- function(env) { .Call('_torch_enquos0', PACKAGE = 'torchpkg', env) } @@ -7037,6 +7041,10 @@ cpp_tensor_load <- function(s) { .Call('_torch_cpp_tensor_load', PACKAGE = 'torchpkg', s) } +cpp_load_state_dict <- function(path) { + .Call('_torch_cpp_load_state_dict', PACKAGE = 'torchpkg', path) +} + cpp_torch_scalar <- function(x) { .Call('_torch_cpp_torch_scalar', PACKAGE = 'torchpkg', x) } @@ -7081,8 +7089,8 @@ cpp_torch_tensor_dtype <- function(x) { .Call('_torch_cpp_torch_tensor_dtype', PACKAGE = 'torchpkg', x) } -cpp_torch_tensor <- function(x, dim, options, requires_grad) { - .Call('_torch_cpp_torch_tensor', PACKAGE = 'torchpkg', x, dim, options, requires_grad) +cpp_torch_tensor <- function(x, dim, options, requires_grad, is_integer64) { + .Call('_torch_cpp_torch_tensor', PACKAGE = 'torchpkg', x, dim, options, requires_grad, is_integer64) } cpp_as_array <- function(x) { diff --git a/R/codegen-utils.R b/R/codegen-utils.R index 76744c9e116bb1bf8386cbb94346bed9c6cc661d..523794a224ed557ad432fd6742cdf2e01cfe069e 100644 --- a/R/codegen-utils.R +++ b/R/codegen-utils.R @@ -16,17 +16,28 @@ as_1_based_dim <- function(x) { as_1_based_tensor_list <- function(x) { tensors <- x$to_r() - tensors <- lapply(tensors, function(x) x$sub(1L, 0L)) + tensors <- lapply(tensors, as_1_based_tensor) torch_tensor_list(tensors) } +as_1_based_tensor <- function(x) { + + if (!any(x$shape == 0)) { + e <- torch_min(torch_abs(x))$to(dtype = torch_int()) + if (as.numeric(e) == 0) + runtime_error("Indices/Index start at 1 and got a 0.") + } + + x - (x > 0)$to(dtype = x$dtype) +} + argument_to_torch_type <- function(obj, expected_types, arg_name) { if (is.name(obj)) return(NULL) if (any(arg_name == c("index", "indices", "dims")) && any("Tensor" == expected_types) && is_torch_tensor(obj)) - return(list(get("ptr", obj$sub(1L, alpha = 1L), inherits = FALSE), "Tensor")) + return(list(get("ptr", as_1_based_tensor(obj), inherits = FALSE), "Tensor")) if (any("Tensor" == expected_types) && is_torch_tensor(obj)) return(list(get("ptr", obj, inherits = FALSE), "Tensor")) diff --git a/R/conditions.R b/R/conditions.R index 2dbfbf852bbeed7b7932b1fc9241455cd59d659f..626b8f736083434b77a9c49378a63e38069e7d34 100644 --- a/R/conditions.R +++ b/R/conditions.R @@ -10,16 +10,16 @@ runtime_error <- function(..., env = rlang::caller_env()) { rlang::abort(glue::glue(..., .envir = env), class = "runtime_error") } -not_implemented_error <- function(...) { - rlang::abort(glue::glue(...), class = "not_implemented_error") +not_implemented_error <- function(..., env = rlang::caller_env()) { + rlang::abort(glue::glue(..., .envir = env), class = "not_implemented_error") } -warn <- function(...) { - rlang::warn(glue::glue(...), class = "warning") +warn <- function(..., env = rlang::caller_env()) { + rlang::warn(glue::glue(..., .envir = env), class = "warning") } -stop_iteration_error <- function(...) { - rlang::abort(glue::glue(...), class = "stop_iteration_error") +stop_iteration_error <- function(..., env = rlang::caller_env()) { + rlang::abort(glue::glue(..., .envir = env), class = "stop_iteration_error") } inform <- rlang::inform diff --git a/R/creation-ops.R b/R/creation-ops.R index 5c1c78594c3ddbf9f9d36e5aacbfe40f8759d81e..3df0ec7e203e9db4d3b31b14188fe4854cd31078 100644 --- a/R/creation-ops.R +++ b/R/creation-ops.R @@ -18,6 +18,7 @@ resolve_size <- function(...) { size } +#' @rdname torch_ones torch_ones <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -33,6 +34,7 @@ torch_ones <- function(..., names = NULL, dtype = NULL, layout = torch_strided() do.call(.torch_ones, args) } +#' @rdname torch_ones_like torch_ones_like <- function(input, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, memory_format = torch_preserve_format()) { @@ -49,6 +51,7 @@ torch_ones_like <- function(input, dtype = NULL, layout = torch_strided(), do.call(.torch_ones_like, args) } +#' @rdname torch_rand torch_rand <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -64,6 +67,7 @@ torch_rand <- function(..., names = NULL, dtype = NULL, layout = torch_strided() do.call(.torch_rand, args) } +#' @rdname torch_rand_like torch_rand_like <- function(input, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, memory_format = torch_preserve_format()) { @@ -80,6 +84,7 @@ torch_rand_like <- function(input, dtype = NULL, layout = torch_strided(), do.call(.torch_rand_like, args) } +#' @rdname torch_randint torch_randint <- function(low, high, size, generator = NULL, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, memory_format = torch_preserve_format()) { @@ -100,6 +105,7 @@ torch_randint <- function(low, high, size, generator = NULL, dtype = NULL, layou do.call(.torch_randint, args) } +#' @rdname torch_randint_like torch_randint_like <- function(input, low, high, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { @@ -118,6 +124,7 @@ torch_randint_like <- function(input, low, high, dtype = NULL, do.call(.torch_randint_like, args) } +#' @rdname torch_randn torch_randn <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -133,6 +140,7 @@ torch_randn <- function(..., names = NULL, dtype = NULL, layout = torch_strided( do.call(.torch_randn, args) } +#' @rdname torch_randn_like torch_randn_like <- function(input, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, memory_format = torch_preserve_format()) { @@ -149,6 +157,7 @@ torch_randn_like <- function(input, dtype = NULL, layout = torch_strided(), do.call(.torch_randn_like, args) } +#' @rdname torch_randperm torch_randperm <- function(n, dtype = torch_int64(), layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -163,6 +172,7 @@ torch_randperm <- function(n, dtype = torch_int64(), layout = torch_strided(), do.call(.torch_randperm, args) } +#' @rdname torch_zeros torch_zeros <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -178,6 +188,7 @@ torch_zeros <- function(..., names = NULL, dtype = NULL, layout = torch_strided( do.call(.torch_zeros, args) } +#' @rdname torch_zeros_like torch_zeros_like <- function(input, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, memory_format = torch_preserve_format()) { @@ -194,6 +205,7 @@ torch_zeros_like <- function(input, dtype = NULL, layout = torch_strided(), do.call(.torch_zeros_like, args) } +#' @rdname torch_empty torch_empty <- function(..., names = NULL, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -209,6 +221,7 @@ torch_empty <- function(..., names = NULL, dtype = NULL, layout = torch_strided( do.call(.torch_empty, args) } +#' @rdname torch_empty_like torch_empty_like <- function(input, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, memory_format = torch_preserve_format()) { @@ -225,6 +238,7 @@ torch_empty_like <- function(input, dtype = NULL, layout = torch_strided(), do.call(.torch_empty_like, args) } +#' @rdname torch_arange torch_arange <- function(start, end, step = 1, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -241,6 +255,7 @@ torch_arange <- function(start, end, step = 1, dtype = NULL, layout = torch_stri do.call(.torch_arange, args) } +#' @rdname torch_range torch_range <- function(start, end, step = 1, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { warning("This function is deprecated in favor of torch_arange.") @@ -248,6 +263,7 @@ torch_range <- function(start, end, step = 1, dtype = NULL, layout = torch_strid device=device, requires_grad = requires_grad) } +#' @rdname torch_linspace torch_linspace <- function(start, end, steps=100, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -264,6 +280,7 @@ torch_linspace <- function(start, end, steps=100, dtype = NULL, layout = torch_s do.call(.torch_linspace, args) } +#' @rdname torch_logspace torch_logspace <- function(start, end, steps=100, base=10, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -281,6 +298,7 @@ torch_logspace <- function(start, end, steps=100, base=10, dtype = NULL, layout do.call(.torch_logspace, args) } +#' @rdname torch_eye torch_eye <- function(n, m=n, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -296,6 +314,7 @@ torch_eye <- function(n, m=n, dtype = NULL, layout = torch_strided(), do.call(.torch_eye, args) } +#' @rdname torch_empty_strided torch_empty_strided <- function(size, stride, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, pin_memory = FALSE) { args <- list( @@ -312,6 +331,7 @@ torch_empty_strided <- function(size, stride, dtype = NULL, layout = torch_strid do.call(.torch_empty_strided, args) } +#' @rdname torch_full torch_full <- function(size, fill_value, names = NULL, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE) { args <- list( @@ -328,6 +348,7 @@ torch_full <- function(size, fill_value, names = NULL, dtype = NULL, layout = to do.call(.torch_full, args) } +#' @rdname torch_full_like torch_full_like <- function(input, fill_value, dtype = NULL, layout = torch_strided(), device=NULL, requires_grad = FALSE, memory_format = torch_preserve_format()) { diff --git a/R/device.R b/R/device.R index 0489296a102b610d2d3a4d1768840f7e584d6720..f3faf0504f6c799fc996bd159f600aa9492c5a4c 100644 --- a/R/device.R +++ b/R/device.R @@ -62,6 +62,8 @@ Device <- R6::R6Class( #' #' A `torch_device` can be constructed via a string or via a string and device ordinal #' +#' @concept tensor-attributtes +#' #' @examples #' #' # Via string @@ -78,6 +80,12 @@ torch_device <- function(type, index = NULL) { Device$new(type, index) } +#' Checks if object is a device +#' +#' @param x object to check +#' @concept tensor-attributes +#' +#' @export is_torch_device <- function(x) { inherits(x, "torch_device") } diff --git a/R/dtype.R b/R/dtype.R index 60524760c950812e4051af05609eacaf6f8c15e6..8a533eaddf087bf40de8668fc41792918e8a6362 100644 --- a/R/dtype.R +++ b/R/dtype.R @@ -6,7 +6,10 @@ torch_dtype <- R6::R6Class( self$ptr <- ptr }, print = function() { - cat("torch_", cpp_dtype_to_string(self$ptr), sep = "") + cat("torch_", self$.type(), "\n", sep = "") + }, + .type = function() { + cpp_dtype_to_string(self$ptr) } ), active = list( @@ -54,6 +57,7 @@ dtype_from_string <- function(str) { #' #' @name torch_dtype #' @rdname torch_dtype +#' @concept tensor-attributes #' NULL @@ -130,6 +134,7 @@ torch_qint32 <- function() torch_dtype$new(cpp_torch_qint32()) #' Check if object is a torch data type #' #' @param x object to check. +#' @concept tensor-attributes #' #' @export is_torch_dtype <- function(x) { @@ -142,6 +147,7 @@ is_torch_dtype <- function(x) { #' `torch_float()`. #' #' @rdname default_dtype +#' @concept tensor-attributes #' #' @export torch_set_default_dtype <- function(d) { @@ -149,6 +155,7 @@ torch_set_default_dtype <- function(d) { } #' @rdname default_dtype +#' @concept tensor-attributes #' @export torch_get_default_dtype <- function() { torch_dtype$new(cpp_get_default_dtype()) diff --git a/R/gen-namespace-docs.R b/R/gen-namespace-docs.R index 41c63a453245d246ae80167b1a7720be349ea5c5..d4c3dfc10ce0e2ee999086034e2c621ace0f2783 100644 --- a/R/gen-namespace-docs.R +++ b/R/gen-namespace-docs.R @@ -1,6 +1,6 @@ #' Abs #' -#' @section abs(input, out=None) -> Tensor : +#' @section abs(input) -> Tensor : #' #' Computes the element-wise absolute value of the given `input` tensor. #' @@ -9,8 +9,7 @@ #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. #' #' @name torch_abs #' @@ -20,7 +19,7 @@ NULL #' Angle #' -#' @section angle(input, out=None) -> Tensor : +#' @section angle(input) -> Tensor : #' #' Computes the element-wise angle (in radians) of the given `input` tensor. #' @@ -29,8 +28,7 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. #' #' @name torch_angle #' @@ -40,7 +38,7 @@ NULL #' Real #' -#' @section real(input, out=None) -> Tensor : +#' @section real(input) -> Tensor : #' #' Returns the real part of the `input` tensor. If #' `input` is a real (non-complex) tensor, this function just @@ -54,8 +52,7 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. #' #' @name torch_real #' @@ -65,7 +62,7 @@ NULL #' Imag #' -#' @section imag(input, out=None) -> Tensor : +#' @section imag(input) -> Tensor : #' #' Returns the imaginary part of the `input` tensor. #' @@ -77,8 +74,7 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. #' #' @name torch_imag #' @@ -88,7 +84,7 @@ NULL #' Conj #' -#' @section conj(input, out=None) -> Tensor : +#' @section conj(input) -> Tensor : #' #' Computes the element-wise conjugate of the given `input` tensor. #' @@ -97,8 +93,7 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. #' #' @name torch_conj #' @@ -108,7 +103,7 @@ NULL #' Acos #' -#' @section acos(input, out=None) -> Tensor : +#' @section acos(input) -> Tensor : #' #' Returns a new tensor with the arccosine of the elements of `input`. #' @@ -117,8 +112,7 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. #' #' @name torch_acos #' @@ -128,20 +122,20 @@ NULL #' Avg_pool1d #' -#' @section avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor : +#' @section avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor : #' #' Applies a 1D average pooling over an input signal composed of several #' input planes. #' -#' See `~torch.nn.AvgPool1d` for details and output shape. +#' See [nn_avg_pool1d()] for details and output shape. #' #' -#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)} -#' @param kernel_size NA the size of the window. Can be a single number or a tuple `(kW,)` -#' @param stride NA the stride of the window. Can be a single number or a tuple `(sW,)`. Default: `kernel_size` -#' @param padding NA implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padW,)`. Default: 0 -#' @param ceil_mode NA when True, will use `ceil` instead of `floor` to compute the output shape. Default: ``False`` -#' @param count_include_pad NA when True, will include the zero-padding in the averaging calculation. Default: ``True`` +#' @param self input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)} +#' @param kernel_size the size of the window. Can be a single number or a tuple `(kW,)` +#' @param stride the stride of the window. Can be a single number or a tuple `(sW,)`. Default: `kernel_size` +#' @param padding implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padW,)`. Default: 0 +#' @param ceil_mode when `TRUE`, will use `ceil` instead of `floor` to compute the output shape. Default: `FALSE` +#' @param count_include_pad when `TRUE`, will include the zero-padding in the averaging calculation. Default: `TRUE` #' #' @name torch_avg_pool1d #' @@ -156,10 +150,10 @@ NULL #' Applies a 1D adaptive average pooling over an input signal composed of #' several input planes. #' -#' See `~torch.nn.AdaptiveAvgPool1d` for details and output shape. +#' See [nn_adaptive_avg_pool1d()] for details and output shape. #' -#' -#' @param output_size NA the target output size (single integer) +#' @param self the input tensor +#' @param output_size the target output size (single integer) #' #' @name torch_adaptive_avg_pool1d #' @@ -169,7 +163,7 @@ NULL #' Add #' -#' @section add(input, other, out=None) : +#' @section add(input, other, out=NULL) : #' #' Adds the scalar `other` to each element of the input `input` #' and returns a new resulting tensor. @@ -180,7 +174,7 @@ NULL #' If `input` is of type FloatTensor or DoubleTensor, `other` must be #' a real number, otherwise it should be an integer. #' -#' @section add(input, other, *, alpha=1, out=None) : +#' @section add(input, other, *, alpha=1, out=NULL) : #' #' Each element of the tensor `other` is multiplied by the scalar #' `alpha` and added to each element of the tensor `input`. @@ -196,9 +190,8 @@ NULL #' a real number, otherwise it should be an integer. #' #' -#' @param input (Tensor) the input tensor. -#' @param value (Number) the number to be added to each element of `input` -#' @param other (Tensor) the second input tensor +#' @param self (Tensor) the input tensor. +#' @param other (Tensor/Number) the second input tensor/number. #' @param alpha (Number) the scalar multiplier for `other` #' #' @name torch_add @@ -209,7 +202,7 @@ NULL #' Addmv #' -#' @section addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor : +#' @section addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor : #' #' Performs a matrix-vector product of the matrix `mat` and #' the vector `vec`. @@ -230,12 +223,12 @@ NULL #' `alpha` must be real numbers, otherwise they should be integers #' #' -#' @param input (Tensor) vector to be added +#' @param self (Tensor) vector to be added #' @param mat (Tensor) matrix to be multiplied #' @param vec (Tensor) vector to be multiplied #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta}) #' @param alpha (Number, optional) multiplier for \eqn{mat @ vec} (\eqn{\alpha}) -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_addmv #' @@ -245,7 +238,7 @@ NULL #' Addr #' -#' @section addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor : +#' @section addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor : #' #' Performs the outer-product of vectors `vec1` and `vec2` #' and adds it to the matrix `input`. @@ -267,12 +260,12 @@ NULL #' `alpha` must be real numbers, otherwise they should be integers #' #' -#' @param input (Tensor) matrix to be added +#' @param self (Tensor) matrix to be added #' @param vec1 (Tensor) the first vector of the outer product #' @param vec2 (Tensor) the second vector of the outer product #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta}) #' @param alpha (Number, optional) multiplier for \eqn{\mbox{vec1} \otimes \mbox{vec2}} (\eqn{\alpha}) -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_addr #' @@ -293,11 +286,11 @@ NULL #' `numpy.allclose `_ #' #' -#' @param input (Tensor) first tensor to compare +#' @param self (Tensor) first tensor to compare #' @param other (Tensor) second tensor to compare #' @param atol (float, optional) absolute tolerance. Default: 1e-08 #' @param rtol (float, optional) relative tolerance. Default: 1e-05 -#' @param equal_nan (bool, optional) if ``True``, then two ``NaN`` s will be compared as equal. Default: ``False`` +#' @param equal_nan (bool, optional) if `TRUE`, then two `NaN` s will be compared as equal. Default: `FALSE` #' #' @name torch_allclose #' @@ -307,10 +300,10 @@ NULL #' Arange #' -#' @section arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a 1-D tensor of size \eqn{\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil} -#' with values from the interval ``[start, end)`` taken with common difference +#' with values from the interval `[start, end)` taken with common difference #' `step` beginning from `start`. #' #' Note that non-integer `step` is subject to floating point rounding errors when @@ -322,14 +315,14 @@ NULL #' } #' #' -#' @param start (Number) the starting value for the set of points. Default: ``0``. +#' @param start (Number) the starting value for the set of points. Default: `0`. #' @param end (Number) the ending value for the set of points -#' @param step (Number) the gap between each pair of adjacent points. Default: ``1``. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input arguments. If any of `start`, `end`, or `stop` are floating-point, the `dtype` is inferred to be the default dtype, see `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to be `torch.int64`. -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param step (Number) the gap between each pair of adjacent points. Default: `1`. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input arguments. If any of `start`, `end`, or `stop` are floating-point, the `dtype` is inferred to be the default dtype, see `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to be `torch.int64`. +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_arange #' @@ -354,9 +347,9 @@ NULL #' documentation for the exact semantics of this method. #' #' -#' @param input (Tensor) the input tensor. -#' @param dim (int) the dimension to reduce. If ``None``, the argmax of the flattened input is returned. -#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=None``. +#' @param self (Tensor) the input tensor. +#' @param dim (int) the dimension to reduce. If `NULL`, the argmax of the flattened input is returned. +#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if `dim=NULL`. #' #' @name torch_argmax #' @@ -373,7 +366,7 @@ NULL #' This is the second value returned by `torch_min`. See its #' documentation for the exact semantics of this method. #' -#' @section argmin(input, dim, keepdim=False, out=None) -> LongTensor : +#' @section argmin(input, dim, keepdim=False, out=NULL) -> LongTensor : #' #' Returns the indices of the minimum values of a tensor across a dimension. #' @@ -381,9 +374,9 @@ NULL #' documentation for the exact semantics of this method. #' #' -#' @param input (Tensor) the input tensor. -#' @param dim (int) the dimension to reduce. If ``None``, the argmin of the flattened input is returned. -#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if ``dim=None``. +#' @param self (Tensor) the input tensor. +#' @param dim (int) the dimension to reduce. If `NULL`, the argmin of the flattened input is returned. +#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Ignored if `dim=NULL`. #' #' @name torch_argmin #' @@ -410,7 +403,7 @@ NULL #' advisable to use. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param size (tuple or ints) the shape of the output tensor #' @param stride (tuple or ints) the stride of the output tensor #' @param storage_offset (int, optional) the offset in the underlying storage of the output tensor @@ -423,7 +416,7 @@ NULL #' Asin #' -#' @section asin(input, out=None) -> Tensor : +#' @section asin(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the arcsine of the elements of `input`. #' @@ -432,8 +425,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_asin #' @@ -443,7 +436,7 @@ NULL #' Atan #' -#' @section atan(input, out=None) -> Tensor : +#' @section atan(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the arctangent of the elements of `input`. #' @@ -452,8 +445,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_atan #' @@ -463,7 +456,7 @@ NULL #' Baddbmm #' -#' @section baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor : +#' @section baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor : #' #' Performs a batch matrix-matrix product of matrices in `batch1` #' and `batch2`. @@ -486,12 +479,12 @@ NULL #' `alpha` must be real numbers, otherwise they should be integers. #' #' -#' @param input (Tensor) the tensor to be added +#' @param self (Tensor) the tensor to be added #' @param batch1 (Tensor) the first batch of matrices to be multiplied #' @param batch2 (Tensor) the second batch of matrices to be multiplied #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta}) #' @param alpha (Number, optional) multiplier for \eqn{\mbox{batch1} \mathbin{@} \mbox{batch2}} (\eqn{\alpha}) -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_baddbmm #' @@ -501,7 +494,7 @@ NULL #' Bartlett_window #' -#' @section bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Bartlett window function. #' @@ -520,19 +513,19 @@ NULL #' ready to be used as a periodic window with functions like #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have -#' ``torch_bartlett_window(L, periodic=True)`` equal to -#' ``torch_bartlett_window(L + 1, periodic=False)[:-1])``. +#' `torch_bartlett_window(L, periodic=TRUE)` equal to +#' `torch_bartlett_window(L + 1, periodic=False)[:-1])`. #' #' @note #' If `window_length` \eqn{=1}, the returned window contains a single value 1. #' #' #' @param window_length (int) the size of returned window -#' @param periodic (bool, optional) If True, returns a window to be used as periodic function. If False, return a symmetric window. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. -#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only ``torch_strided`` (dense layout) is supported. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic function. If False, return a symmetric window. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. +#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only `torch_strided` (dense layout) is supported. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_bartlett_window #' @@ -542,7 +535,7 @@ NULL #' Bernoulli #' -#' @section bernoulli(input, *, generator=None, out=None) -> Tensor : +#' @section bernoulli(input, *, generator=NULL, out=NULL) -> Tensor : #' #' Draws binary random numbers (0 or 1) from a Bernoulli distribution. #' @@ -561,13 +554,16 @@ NULL #' The returned `out` tensor only has values 0 or 1 and is of the same #' shape as `input`. #' -#' `out` can have integral ``dtype``, but `input` must have floating -#' point ``dtype``. +#' `out` can have integral `dtype`, but `input` must have floating +#' point `dtype`. #' #' -#' @param input (Tensor) the input tensor of probability values for the Bernoulli distribution +#' @param self (Tensor) the input tensor of probability values for the Bernoulli +#' distribution +#' @param p (Number) a probability value. If `p` is passed than it's used instead of +#' the values in `self` tensor. #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_bernoulli #' @@ -577,7 +573,7 @@ NULL #' Bincount #' -#' @section bincount(input, weights=None, minlength=0) -> Tensor : +#' @section bincount(input, weights=NULL, minlength=0) -> Tensor : #' #' Count the frequency of each value in an array of non-negative ints. #' @@ -585,14 +581,14 @@ NULL #' `input` unless `input` is empty, in which case the result is a #' tensor of size 0. If `minlength` is specified, the number of bins is at least #' `minlength` and if `input` is empty, then the result is tensor of size -#' `minlength` filled with zeros. If ``n`` is the value at position ``i``, -#' ``out[n] += weights[i]`` if `weights` is specified else -#' ``out[n] += 1``. +#' `minlength` filled with zeros. If `n` is the value at position `i`, +#' `out[n] += weights[i]` if `weights` is specified else +#' `out[n] += 1`. #' #' .. include:: cuda_deterministic.rst #' #' -#' @param input (Tensor) 1-d int tensor +#' @param self (Tensor) 1-d int tensor #' @param weights (Tensor) optional, weight for each value in the input tensor. Should be of same size as input tensor. #' @param minlength (int) optional, minimum number of bins. Should be non-negative. #' @@ -604,14 +600,14 @@ NULL #' Bitwise_not #' -#' @section bitwise_not(input, out=None) -> Tensor : +#' @section bitwise_not(input, out=NULL) -> Tensor : #' #' Computes the bitwise NOT of the given input tensor. The input tensor must be of #' integral or Boolean types. For bool tensors, it computes the logical NOT. #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_bitwise_not #' @@ -621,14 +617,14 @@ NULL #' Logical_not #' -#' @section logical_not(input, out=None) -> Tensor : +#' @section logical_not(input, out=NULL) -> Tensor : #' #' Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool -#' dtype. If the input tensor is not a bool tensor, zeros are treated as ``False`` and non-zeros are treated as ``True``. +#' dtype. If the input tensor is not a bool tensor, zeros are treated as `FALSE` and non-zeros are treated as `TRUE`. #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_logical_not #' @@ -638,15 +634,15 @@ NULL #' Logical_xor #' -#' @section logical_xor(input, other, out=None) -> Tensor : +#' @section logical_xor(input, other, out=NULL) -> Tensor : #' -#' Computes the element-wise logical XOR of the given input tensors. Zeros are treated as ``False`` and nonzeros are -#' treated as ``True``. +#' Computes the element-wise logical XOR of the given input tensors. Zeros are treated as `FALSE` and nonzeros are +#' treated as `TRUE`. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param other (Tensor) the tensor to compute XOR with -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_logical_xor #' @@ -656,15 +652,15 @@ NULL #' Logical_and #' -#' @section logical_and(input, other, out=None) -> Tensor : +#' @section logical_and(input, other, out=NULL) -> Tensor : #' -#' Computes the element-wise logical AND of the given input tensors. Zeros are treated as ``False`` and nonzeros are -#' treated as ``True``. +#' Computes the element-wise logical AND of the given input tensors. Zeros are treated as `FALSE` and nonzeros are +#' treated as `TRUE`. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param other (Tensor) the tensor to compute AND with -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_logical_and #' @@ -674,15 +670,15 @@ NULL #' Logical_or #' -#' @section logical_or(input, other, out=None) -> Tensor : +#' @section logical_or(input, other, out=NULL) -> Tensor : #' -#' Computes the element-wise logical OR of the given input tensors. Zeros are treated as ``False`` and nonzeros are -#' treated as ``True``. +#' Computes the element-wise logical OR of the given input tensors. Zeros are treated as `FALSE` and nonzeros are +#' treated as `TRUE`. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param other (Tensor) the tensor to compute OR with -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_logical_or #' @@ -692,7 +688,7 @@ NULL #' Blackman_window #' -#' @section blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Blackman window function. #' @@ -707,19 +703,19 @@ NULL #' ready to be used as a periodic window with functions like #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have -#' ``torch_blackman_window(L, periodic=True)`` equal to -#' ``torch_blackman_window(L + 1, periodic=False)[:-1])``. +#' `torch_blackman_window(L, periodic=TRUE)` equal to +#' `torch_blackman_window(L + 1, periodic=False)[:-1])`. #' #' @note #' If `window_length` \eqn{=1}, the returned window contains a single value 1. #' #' #' @param window_length (int) the size of returned window -#' @param periodic (bool, optional) If True, returns a window to be used as periodic function. If False, return a symmetric window. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. -#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only ``torch_strided`` (dense layout) is supported. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic function. If False, return a symmetric window. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. +#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only `torch_strided` (dense layout) is supported. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_blackman_window #' @@ -729,7 +725,7 @@ NULL #' Bmm #' -#' @section bmm(input, mat2, out=None) -> Tensor : +#' @section bmm(input, mat2, out=NULL) -> Tensor : #' #' Performs a batch matrix-matrix product of matrices stored in `input` #' and `mat2`. @@ -748,9 +744,9 @@ NULL #' For broadcasting matrix products, see [`torch_matmul`]. #' #' -#' @param input (Tensor) the first batch of matrices to be multiplied +#' @param self (Tensor) the first batch of matrices to be multiplied #' @param mat2 (Tensor) the second batch of matrices to be multiplied -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_bmm #' @@ -760,12 +756,11 @@ NULL #' Broadcast_tensors #' -#' @section broadcast_tensors(*tensors) -> List of Tensors : +#' @section broadcast_tensors(tensors) -> List of Tensors : #' #' Broadcasts the given tensors according to broadcasting-semantics. #' -#' -#' @param *tensors NA any number of tensors of the same type +#' @param tensors a list containing any number of tensors of the same type #' #' @name torch_broadcast_tensors #' @@ -775,7 +770,7 @@ NULL #' Cat #' -#' @section cat(tensors, dim=0, out=None) -> Tensor : +#' @section cat(tensors, dim=0, out=NULL) -> Tensor : #' #' Concatenates the given sequence of `seq` tensors in the given dimension. #' All tensors must either have the same shape (except in the concatenating @@ -789,7 +784,7 @@ NULL #' #' @param tensors (sequence of Tensors) any python sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension. #' @param dim (int, optional) the dimension over which the tensors are concatenated -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_cat #' @@ -799,7 +794,7 @@ NULL #' Ceil #' -#' @section ceil(input, out=None) -> Tensor : +#' @section ceil(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the ceil of the elements of `input`, #' the smallest integer greater than or equal to each element. @@ -809,8 +804,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_ceil #' @@ -848,7 +843,7 @@ NULL #' `dim` is not divisible by `chunks`. #' #' -#' @param input (Tensor) the tensor to split +#' @param self (Tensor) the tensor to split #' @param chunks (int) number of chunks to return #' @param dim (int) dimension along which to split the tensor #' @@ -860,7 +855,7 @@ NULL #' Clamp #' -#' @section clamp(input, min, max, out=None) -> Tensor : +#' @section clamp(input, min, max, out=NULL) -> Tensor : #' #' Clamp all elements in `input` into the range `[` `min`, `max` `]` and return #' a resulting tensor: @@ -876,14 +871,14 @@ NULL #' If `input` is of type `FloatTensor` or `DoubleTensor`, args `min` #' and `max` must be real numbers, otherwise they should be integers. #' -#' @section clamp(input, *, min, out=None) -> Tensor : +#' @section clamp(input, *, min, out=NULL) -> Tensor : #' #' Clamps all elements in `input` to be larger or equal `min`. #' #' If `input` is of type `FloatTensor` or `DoubleTensor`, `value` #' should be a real number, otherwise it should be an integer. #' -#' @section clamp(input, *, max, out=None) -> Tensor : +#' @section clamp(input, *, max, out=NULL) -> Tensor : #' #' Clamps all elements in `input` to be smaller or equal `max`. #' @@ -891,11 +886,10 @@ NULL #' should be a real number, otherwise it should be an integer. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param min (Number) lower-bound of the range to be clamped to #' @param max (Number) upper-bound of the range to be clamped to -#' @param out (Tensor, optional) the output tensor. -#' @param value (Number) minimal value of each element in the output +#' #' #' @name torch_clamp #' @@ -905,23 +899,21 @@ NULL #' Conv1d #' -#' @section conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor : +#' @section conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor : #' #' Applies a 1D convolution over an input signal composed of several input #' planes. #' -#' See `~torch.nn.Conv1d` for details and output shape. -#' -#' .. include:: cudnn_deterministic.rst +#' See [nn_conv1d()] for details and output shape. #' #' -#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)} -#' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)} -#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: ``None`` -#' @param stride NA the stride of the convolving kernel. Can be a single number or a one-element tuple `(sW,)`. Default: 1 -#' @param padding NA implicit paddings on both sides of the input. Can be a single number or a one-element tuple `(padW,)`. Default: 0 -#' @param dilation NA the spacing between kernel elements. Can be a single number or a one-element tuple `(dW,)`. Default: 1 -#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 +#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)} +#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)} +#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: `NULL` +#' @param stride the stride of the convolving kernel. Can be a single number or a one-element tuple `(sW,)`. Default: 1 +#' @param padding implicit paddings on both sides of the input. Can be a single number or a one-element tuple `(padW,)`. Default: 0 +#' @param dilation the spacing between kernel elements. Can be a single number or a one-element tuple `(dW,)`. Default: 1 +#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 #' #' @name torch_conv1d #' @@ -931,23 +923,21 @@ NULL #' Conv2d #' -#' @section conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor : +#' @section conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor : #' #' Applies a 2D convolution over an input image composed of several input #' planes. #' -#' See `~torch.nn.Conv2d` for details and output shape. -#' -#' .. include:: cudnn_deterministic.rst +#' See [nn_conv2d()] for details and output shape. #' #' -#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)} -#' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)} -#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: ``None`` -#' @param stride NA the stride of the convolving kernel. Can be a single number or a tuple `(sH, sW)`. Default: 1 -#' @param padding NA implicit paddings on both sides of the input. Can be a single number or a tuple `(padH, padW)`. Default: 0 -#' @param dilation NA the spacing between kernel elements. Can be a single number or a tuple `(dH, dW)`. Default: 1 -#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 +#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)} +#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)} +#' @param bias optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: `NULL` +#' @param stride the stride of the convolving kernel. Can be a single number or a tuple `(sH, sW)`. Default: 1 +#' @param padding implicit paddings on both sides of the input. Can be a single number or a tuple `(padH, padW)`. Default: 0 +#' @param dilation the spacing between kernel elements. Can be a single number or a tuple `(dH, dW)`. Default: 1 +#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 #' #' @name torch_conv2d #' @@ -957,23 +947,21 @@ NULL #' Conv3d #' -#' @section conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor : +#' @section conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor : #' #' Applies a 3D convolution over an input image composed of several input #' planes. #' -#' See `~torch.nn.Conv3d` for details and output shape. -#' -#' .. include:: cudnn_deterministic.rst +#' See [nn_conv3d()] for details and output shape. #' #' -#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)} -#' @param weight NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)} -#' @param bias NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: None -#' @param stride NA the stride of the convolving kernel. Can be a single number or a tuple `(sT, sH, sW)`. Default: 1 -#' @param padding NA implicit paddings on both sides of the input. Can be a single number or a tuple `(padT, padH, padW)`. Default: 0 -#' @param dilation NA the spacing between kernel elements. Can be a single number or a tuple `(dT, dH, dW)`. Default: 1 -#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 +#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)} +#' @param weight filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)} +#' @param bias optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: NULL +#' @param stride the stride of the convolving kernel. Can be a single number or a tuple `(sT, sH, sW)`. Default: 1 +#' @param padding implicit paddings on both sides of the input. Can be a single number or a tuple `(padT, padH, padW)`. Default: 0 +#' @param dilation the spacing between kernel elements. Can be a single number or a tuple `(dT, dH, dW)`. Default: 1 +#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 #' #' @name torch_conv3d #' @@ -989,7 +977,7 @@ NULL #' Input and output dimensions are (Time, Batch, Channels) - hence TBC. #' #' -#' @param input NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})} +#' @param self NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})} #' @param weight NA filter of shape (\eqn{\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}}) #' @param bias NA bias of shape (\eqn{\mbox{out\_channels}}) #' @param pad NA number of timesteps to pad. Default: 0 @@ -1002,24 +990,21 @@ NULL #' Conv_transpose1d #' -#' @section conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor : +#' @section conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor : #' #' Applies a 1D transposed convolution operator over an input signal #' composed of several input planes, sometimes also called "deconvolution". #' -#' See `~torch.nn.ConvTranspose1d` for details and output shape. -#' -#' .. include:: cudnn_deterministic.rst -#' +#' See [nn_conv_transpose1d()] for details and output shape. #' -#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)} -#' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)} -#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None -#' @param stride NA the stride of the convolving kernel. Can be a single number or a tuple ``(sW,)``. Default: 1 -#' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple ``(padW,)``. Default: 0 -#' @param output_padding NA additional size added to one side of each dimension in the output shape. Can be a single number or a tuple ``(out_padW)``. Default: 0 -#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 -#' @param dilation NA the spacing between kernel elements. Can be a single number or a tuple ``(dW,)``. Default: 1 +#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)} +#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)} +#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL +#' @param stride the stride of the convolving kernel. Can be a single number or a tuple `(sW,)`. Default: 1 +#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple `(padW,)`. Default: 0 +#' @param output_padding additional size added to one side of each dimension in the output shape. Can be a single number or a tuple `(out_padW)`. Default: 0 +#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 +#' @param dilation the spacing between kernel elements. Can be a single number or a tuple `(dW,)`. Default: 1 #' #' @name torch_conv_transpose1d #' @@ -1029,24 +1014,22 @@ NULL #' Conv_transpose2d #' -#' @section conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor : +#' @section conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor : #' #' Applies a 2D transposed convolution operator over an input image #' composed of several input planes, sometimes also called "deconvolution". #' -#' See `~torch.nn.ConvTranspose2d` for details and output shape. -#' -#' .. include:: cudnn_deterministic.rst +#' See [nn_conv_transpose2d()] for details and output shape. #' #' -#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)} -#' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)} -#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None -#' @param stride NA the stride of the convolving kernel. Can be a single number or a tuple ``(sH, sW)``. Default: 1 -#' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple ``(padH, padW)``. Default: 0 -#' @param output_padding NA additional size added to one side of each dimension in the output shape. Can be a single number or a tuple ``(out_padH, out_padW)``. Default: 0 -#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 -#' @param dilation NA the spacing between kernel elements. Can be a single number or a tuple ``(dH, dW)``. Default: 1 +#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)} +#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)} +#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL +#' @param stride the stride of the convolving kernel. Can be a single number or a tuple `(sH, sW)`. Default: 1 +#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple `(padH, padW)`. Default: 0 +#' @param output_padding additional size added to one side of each dimension in the output shape. Can be a single number or a tuple `(out_padH, out_padW)`. Default: 0 +#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 +#' @param dilation the spacing between kernel elements. Can be a single number or a tuple `(dH, dW)`. Default: 1 #' #' @name torch_conv_transpose2d #' @@ -1056,24 +1039,21 @@ NULL #' Conv_transpose3d #' -#' @section conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor : +#' @section conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor : #' #' Applies a 3D transposed convolution operator over an input image #' composed of several input planes, sometimes also called "deconvolution" #' -#' See `~torch.nn.ConvTranspose3d` for details and output shape. -#' -#' .. include:: cudnn_deterministic.rst -#' +#' See [nn_conv_transpose3d()] for details and output shape. #' -#' @param input NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)} -#' @param weight NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)} -#' @param bias NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None -#' @param stride NA the stride of the convolving kernel. Can be a single number or a tuple ``(sT, sH, sW)``. Default: 1 -#' @param padding NA ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple ``(padT, padH, padW)``. Default: 0 -#' @param output_padding NA additional size added to one side of each dimension in the output shape. Can be a single number or a tuple ``(out_padT, out_padH, out_padW)``. Default: 0 -#' @param groups NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 -#' @param dilation NA the spacing between kernel elements. Can be a single number or a tuple `(dT, dH, dW)`. Default: 1 +#' @param input input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)} +#' @param weight filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)} +#' @param bias optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL +#' @param stride the stride of the convolving kernel. Can be a single number or a tuple `(sT, sH, sW)`. Default: 1 +#' @param padding `dilation * (kernel_size - 1) - padding` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple `(padT, padH, padW)`. Default: 0 +#' @param output_padding additional size added to one side of each dimension in the output shape. Can be a single number or a tuple `(out_padT, out_padH, out_padW)`. Default: 0 +#' @param groups split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the number of groups. Default: 1 +#' @param dilation the spacing between kernel elements. Can be a single number or a tuple `(dT, dH, dW)`. Default: 1 #' #' @name torch_conv_transpose3d #' @@ -1083,7 +1063,7 @@ NULL #' Cos #' -#' @section cos(input, out=None) -> Tensor : +#' @section cos(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the cosine of the elements of `input`. #' @@ -1092,8 +1072,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_cos #' @@ -1103,7 +1083,7 @@ NULL #' Cosh #' -#' @section cosh(input, out=None) -> Tensor : +#' @section cosh(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the hyperbolic cosine of the elements of #' `input`. @@ -1113,8 +1093,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_cosh #' @@ -1124,10 +1104,10 @@ NULL #' Cummax #' -#' @section cummax(input, dim, out=None) -> (Tensor, LongTensor) : +#' @section cummax(input, dim) -> (Tensor, LongTensor) : #' -#' Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of -#' elements of `input` in the dimension `dim`. And ``indices`` is the index +#' Returns a namedtuple `(values, indices)` where `values` is the cumulative maximum of +#' elements of `input` in the dimension `dim`. And `indices` is the index #' location of each maximum value found in the dimension `dim`. #' #' \deqn{ @@ -1135,9 +1115,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to do the operation over -#' @param out (tuple, optional) the result tuple of two output tensors (values, indices) #' #' @name torch_cummax #' @@ -1147,10 +1126,10 @@ NULL #' Cummin #' -#' @section cummin(input, dim, out=None) -> (Tensor, LongTensor) : +#' @section cummin(input, dim) -> (Tensor, LongTensor) : #' -#' Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of -#' elements of `input` in the dimension `dim`. And ``indices`` is the index +#' Returns a namedtuple `(values, indices)` where `values` is the cumulative minimum of +#' elements of `input` in the dimension `dim`. And `indices` is the index #' location of each maximum value found in the dimension `dim`. #' #' \deqn{ @@ -1158,9 +1137,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to do the operation over -#' @param out (tuple, optional) the result tuple of two output tensors (values, indices) #' #' @name torch_cummin #' @@ -1170,7 +1148,7 @@ NULL #' Cumprod #' -#' @section cumprod(input, dim, out=None, dtype=None) -> Tensor : +#' @section cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor : #' #' Returns the cumulative product of elements of `input` in the dimension #' `dim`. @@ -1183,10 +1161,10 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to do the operation over -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. -#' @param out (Tensor, optional) the output tensor. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: NULL. +#' #' #' @name torch_cumprod #' @@ -1196,7 +1174,7 @@ NULL #' Cumsum #' -#' @section cumsum(input, dim, out=None, dtype=None) -> Tensor : +#' @section cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor : #' #' Returns the cumulative sum of elements of `input` in the dimension #' `dim`. @@ -1209,10 +1187,10 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to do the operation over -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. -#' @param out (Tensor, optional) the output tensor. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: NULL. +#' #' #' @name torch_cumsum #' @@ -1233,7 +1211,7 @@ NULL #' `~torch.svd` for details. #' #' -#' @param input (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more batch dimensions. +#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more batch dimensions. #' #' @name torch_det #' @@ -1268,11 +1246,11 @@ NULL #' need to be explicitly specified. #' #' -#' @param input (Tensor) the input tensor. Must be at least 1-dimensional. +#' @param self (Tensor) the input tensor. Must be at least 1-dimensional. #' @param offset (int, optional) which diagonal to consider. Default: 0 (main diagonal). #' @param dim1 (int, optional) first dimension with respect to which to take diagonal. Default: -2. #' @param dim2 (int, optional) second dimension with respect to which to take diagonal. Default: -1. -#' +#' #' @name torch_diag_embed #' #' @export @@ -1295,7 +1273,7 @@ NULL #' - If `offset` < 0, it is below the main diagonal. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param offset (int, optional) the diagonal to consider. Default: 0 (main diagonal). #' #' @name torch_diagflat @@ -1324,10 +1302,11 @@ NULL #' dimensions, so those need to be explicitly specified. #' #' -#' @param input (Tensor) the input tensor. Must be at least 2-dimensional. +#' @param self (Tensor) the input tensor. Must be at least 2-dimensional. #' @param offset (int, optional) which diagonal to consider. Default: 0 (main diagonal). #' @param dim1 (int, optional) first dimension with respect to which to take diagonal. Default: 0. #' @param dim2 (int, optional) second dimension with respect to which to take diagonal. Default: 1. +#' @param outdim dimension name if `self` is a named tensor. #' #' @name torch_diagonal #' @@ -1337,46 +1316,46 @@ NULL #' Div #' -#' @section div(input, other, out=None) -> Tensor : +#' @section div(input, other, out=NULL) -> Tensor : #' -#' Divides each element of the input ``input`` with the scalar ``other`` and +#' Divides each element of the input `input` with the scalar `other` and #' returns a new resulting tensor. #' #' @section Warning: #' Integer division using div is deprecated, and in a future release div will -#' perform true division like [`torch_true_divide`]. -#' Use [`torch_floor_divide`] (// in Python) to perform integer division, +#' perform true division like [torch_true_divide()]. +#' Use [torch_floor_divide()] to perform integer division, #' instead. #' #' \deqn{ #' \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}} #' } -#' If the `torch_dtype` of ``input`` and ``other`` differ, the +#' If the `torch_dtype` of `input` and `other` differ, the #' `torch_dtype` of the result tensor is determined following rules #' described in the type promotion documentation . If -#' ``out`` is specified, the result must be castable +#' `out` is specified, the result must be castable #' to the `torch_dtype` of the specified output tensor. Integral division #' by zero leads to undefined behavior. #' -#' @section div(input, other, out=None) -> Tensor : +#' @section div(input, other, out=NULL) -> Tensor : #' -#' Each element of the tensor ``input`` is divided by each element of the tensor -#' ``other``. The resulting tensor is returned. +#' Each element of the tensor `input` is divided by each element of the tensor +#' `other`. The resulting tensor is returned. #' #' \deqn{ #' \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i} #' } -#' The shapes of ``input`` and ``other`` must be broadcastable -#' . If the `torch_dtype` of ``input`` and -#' ``other`` differ, the `torch_dtype` of the result tensor is determined +#' The shapes of `input` and `other` must be broadcastable +#' . If the `torch_dtype` of `input` and +#' `other` differ, the `torch_dtype` of the result tensor is determined #' following rules described in the type promotion documentation -#' . If ``out`` is specified, the result must be +#' . If `out` is specified, the result must be #' castable to the `torch_dtype` of the #' specified output tensor. Integral division by zero leads to undefined behavior. #' #' -#' @param input (Tensor) the input tensor. -#' @param other (Number) the number to be divided to each element of ``input`` +#' @param self (Tensor) the input tensor. +#' @param other (Number) the number to be divided to each element of `input` #' #' @name torch_div #' @@ -1392,8 +1371,8 @@ NULL #' #' @note This function does not broadcast . #' -#' -#' +#' @param self the input tensor +#' @param tensor the other input tensor #' #' @name torch_dot #' @@ -1408,9 +1387,8 @@ NULL #' This function provides a way of computing multilinear expressions (i.e. sums of products) using the #' Einstein summation convention. #' -#' #' @param equation (string) The equation is given in terms of lower case letters (indices) to be associated with each dimension of the operands and result. The left hand side lists the operands dimensions, separated by commas. There should be one index letter per tensor dimension. The right hand side follows after `->` and gives the indices for the output. If the `->` and right hand side are omitted, it implicitly defined as the alphabetically sorted list of all indices appearing exactly once in the left hand side. The indices not apprearing in the output are summed over after multiplying the operands entries. If an index appears several times for the same operand, a diagonal is taken. Ellipses `...` represent a fixed number of dimensions. If the right hand side is inferred, the ellipsis dimensions are at the beginning of the output. -#' @param operands (Tensor) The operands to compute the Einstein sum of. +#' @param tensors (Tensor) The operands to compute the Einstein sum of. #' #' @name torch_einsum #' @@ -1420,21 +1398,19 @@ NULL #' Empty #' -#' @section empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor : +#' @section empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor : #' #' Returns a tensor filled with uninitialized data. The shape of the tensor is #' defined by the variable argument `size`. #' #' -#' @param size (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_contiguous_format``. -#' +#' @param ... a sequence of integers defining the shape of the output tensor. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param names optional character vector naming each dimension. +#' #' @name torch_empty #' #' @export @@ -1443,19 +1419,19 @@ NULL #' Empty_like #' -#' @section empty_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : +#' @section empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : #' #' Returns an uninitialized tensor with the same size as `input`. -#' ``torch_empty_like(input)`` is equivalent to -#' ``torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. +#' `torch_empty_like(input)` is equivalent to +#' `torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`. #' #' #' @param input (Tensor) the size of `input` will determine size of the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of `input`. -#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if ``None``, defaults to the layout of `input`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, defaults to the device of `input`. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_preserve_format``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if `NULL`, defaults to the dtype of `input`. +#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if `NULL`, defaults to the layout of `input`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, defaults to the device of `input`. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: `torch_preserve_format`. #' #' @name torch_empty_like #' @@ -1465,12 +1441,12 @@ NULL #' Empty_strided #' -#' @section empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor : +#' @section empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor : #' #' Returns a tensor filled with uninitialized data. The shape and strides of the tensor is #' defined by the variable argument `size` and `stride` respectively. -#' ``torch_empty_strided(size, stride)`` is equivalent to -#' ``torch_empty(size).as_strided(size, stride)``. +#' `torch_empty_strided(size, stride)` is equivalent to +#' `torch_empty(size).as_strided(size, stride)`. #' #' @section Warning: #' More than one element of the created tensor may refer to a single memory @@ -1481,11 +1457,11 @@ NULL #' #' @param size (tuple of ints) the shape of the output tensor #' @param stride (tuple of ints) the strides of the output tensor -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: ``False``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param pin_memory (bool, optional) If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: `FALSE`. #' #' @name torch_empty_strided #' @@ -1495,7 +1471,7 @@ NULL #' Erf #' -#' @section erf(input, out=None) -> Tensor : +#' @section erf(input, out=NULL) -> Tensor : #' #' Computes the error function of each element. The error function is defined as follows: #' @@ -1504,8 +1480,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_erf #' @@ -1515,7 +1491,7 @@ NULL #' Erfc #' -#' @section erfc(input, out=None) -> Tensor : +#' @section erfc(input, out=NULL) -> Tensor : #' #' Computes the complementary error function of each element of `input`. #' The complementary error function is defined as follows: @@ -1525,8 +1501,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_erfc #' @@ -1536,7 +1512,7 @@ NULL #' Exp #' -#' @section exp(input, out=None) -> Tensor : +#' @section exp(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the exponential of the elements #' of the input tensor `input`. @@ -1546,8 +1522,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_exp #' @@ -1557,7 +1533,7 @@ NULL #' Expm1 #' -#' @section expm1(input, out=None) -> Tensor : +#' @section expm1(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the exponential of the elements minus 1 #' of `input`. @@ -1567,8 +1543,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_expm1 #' @@ -1578,18 +1554,18 @@ NULL #' Eye #' -#' @section eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. #' #' #' @param n (int) the number of rows #' @param m (int, optional) the number of columns with default being `n` -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_eye #' @@ -1604,9 +1580,12 @@ NULL #' Flattens a contiguous range of dims in a tensor. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param start_dim (int) the first dim to flatten #' @param end_dim (int) the last dim to flatten +#' @param dims if tensor is named you can pass the name of the dimensions to +#' flatten +#' @param out_dim the name of the resulting dimension if a named tensor. #' #' @name torch_flatten #' @@ -1616,7 +1595,7 @@ NULL #' Floor #' -#' @section floor(input, out=None) -> Tensor : +#' @section floor(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the floor of the elements of `input`, #' the largest integer less than or equal to each element. @@ -1626,8 +1605,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_floor #' @@ -1637,7 +1616,7 @@ NULL #' Floor_divide #' -#' @section floor_divide(input, other, out=None) -> Tensor : +#' @section floor_divide(input, other, out=NULL) -> Tensor : #' #' Return the division of the inputs rounded down to the nearest integer. See [`torch_div`] #' for type promotion and broadcasting rules. @@ -1647,7 +1626,7 @@ NULL #' } #' #' -#' @param input (Tensor) the numerator tensor +#' @param self (Tensor) the numerator tensor #' @param other (Tensor or Scalar) the denominator #' #' @name torch_floor_divide @@ -1658,13 +1637,15 @@ NULL #' Frac #' -#' @section frac(input, out=None) -> Tensor : +#' @section frac(input, out=NULL) -> Tensor : #' #' Computes the fractional portion of each element in `input`. #' #' \deqn{ #' \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i}) #' } +#' +#' @param self the input tensor. #' #' #' @@ -1677,7 +1658,7 @@ NULL #' Full #' -#' @section full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a tensor of size `size` filled with `fill_value`. #' @@ -1691,11 +1672,12 @@ NULL #' #' @param size (int...) a list, tuple, or `torch_Size` of integers defining the shape of the output tensor. #' @param fill_value NA the number to fill the output tensor with. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param names optional names of the dimensions #' #' @name torch_full #' @@ -1705,22 +1687,22 @@ NULL #' Full_like #' -#' @section full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, : +#' @section full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, : #' #' memory_format=torch.preserve_format) -> Tensor #' #' Returns a tensor with the same size as `input` filled with `fill_value`. -#' ``torch_full_like(input, fill_value)`` is equivalent to -#' ``torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)``. +#' `torch_full_like(input, fill_value)` is equivalent to +#' `torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)`. #' #' #' @param input (Tensor) the size of `input` will determine size of the output tensor. -#' @param fill_value NA the number to fill the output tensor with. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of `input`. -#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if ``None``, defaults to the layout of `input`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, defaults to the device of `input`. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_preserve_format``. +#' @param fill_value the number to fill the output tensor with. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if `NULL`, defaults to the dtype of `input`. +#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if `NULL`, defaults to the layout of `input`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, defaults to the device of `input`. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: `torch_preserve_format`. #' #' @name torch_full_like #' @@ -1730,7 +1712,7 @@ NULL #' Hann_window #' -#' @section hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Hann window function. #' @@ -1746,19 +1728,19 @@ NULL #' ready to be used as a periodic window with functions like #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have -#' ``torch_hann_window(L, periodic=True)`` equal to -#' ``torch_hann_window(L + 1, periodic=False)[:-1])``. +#' `torch_hann_window(L, periodic=TRUE)` equal to +#' `torch_hann_window(L + 1, periodic=False)[:-1])`. #' #' @note #' If `window_length` \eqn{=1}, the returned window contains a single value 1. #' #' #' @param window_length (int) the size of returned window -#' @param periodic (bool, optional) If True, returns a window to be used as periodic function. If False, return a symmetric window. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. -#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only ``torch_strided`` (dense layout) is supported. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic function. If False, return a symmetric window. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. +#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only `torch_strided` (dense layout) is supported. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_hann_window #' @@ -1768,7 +1750,7 @@ NULL #' Hamming_window #' -#' @section hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Hamming window function. #' @@ -1783,8 +1765,8 @@ NULL #' ready to be used as a periodic window with functions like #' `torch_stft`. Therefore, if `periodic` is true, the \eqn{N} in #' above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have -#' ``torch_hamming_window(L, periodic=True)`` equal to -#' ``torch_hamming_window(L + 1, periodic=False)[:-1])``. +#' `torch_hamming_window(L, periodic=TRUE)` equal to +#' `torch_hamming_window(L + 1, periodic=False)[:-1])`. #' #' @note #' If `window_length` \eqn{=1}, the returned window contains a single value 1. @@ -1794,13 +1776,13 @@ NULL #' #' #' @param window_length (int) the size of returned window -#' @param periodic (bool, optional) If True, returns a window to be used as periodic function. If False, return a symmetric window. +#' @param periodic (bool, optional) If TRUE, returns a window to be used as periodic function. If False, return a symmetric window. #' @param alpha (float, optional) The coefficient \eqn{\alpha} in the equation above #' @param beta (float, optional) The coefficient \eqn{\beta} in the equation above -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. -#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only ``torch_strided`` (dense layout) is supported. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). Only floating point types are supported. +#' @param layout (`torch.layout`, optional) the desired layout of returned window tensor. Only `torch_strided` (dense layout) is supported. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_hamming_window #' @@ -1810,7 +1792,7 @@ NULL #' Ger #' -#' @section ger(input, vec2, out=None) -> Tensor : +#' @section ger(input, vec2, out=NULL) -> Tensor : #' #' Outer product of `input` and `vec2`. #' If `input` is a vector of size \eqn{n} and `vec2` is a vector of @@ -1819,9 +1801,8 @@ NULL #' @note This function does not broadcast . #' #' -#' @param input (Tensor) 1-D input vector +#' @param self (Tensor) 1-D input vector #' @param vec2 (Tensor) 1-D input vector -#' @param out (Tensor, optional) optional output matrix #' #' @name torch_ger #' @@ -1849,9 +1830,9 @@ NULL #' This method supports 1D, 2D and 3D complex-to-complex transforms, indicated #' by `signal_ndim`. `input` must be a tensor with last dimension #' of size 2, representing the real and imaginary components of complex -#' numbers, and should have at least ``signal_ndim + 1`` dimensions with optionally +#' numbers, and should have at least `signal_ndim + 1` dimensions with optionally #' arbitrary number of leading batch dimensions. If `normalized` is set to -#' ``True``, this normalizes the result by dividing it with +#' `TRUE`, this normalizes the result by dividing it with #' \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary. #' #' Returns the real and the imaginary parts together as one tensor of the same @@ -1870,9 +1851,9 @@ NULL #' `torch_backends.mkl.is_available` to check if MKL is installed. #' #' -#' @param input (Tensor) the input tensor of at least `signal_ndim` ``+ 1`` dimensions +#' @param self (Tensor) the input tensor of at least `signal_ndim` `+ 1` dimensions #' @param signal_ndim (int) the number of dimensions in each signal. `signal_ndim` can only be 1, 2 or 3 -#' @param normalized (bool, optional) controls whether to return normalized results. Default: ``False`` +#' @param normalized (bool, optional) controls whether to return normalized results. Default: `FALSE` #' #' @name torch_fft #' @@ -1899,7 +1880,7 @@ NULL #' signal, and \eqn{N_i} is the size of signal dimension \eqn{i}. #' #' The argument specifications are almost identical with [`torch_fft`]. -#' However, if `normalized` is set to ``True``, this instead returns the +#' However, if `normalized` is set to `TRUE`, this instead returns the #' results multiplied by \eqn{\sqrt{\prod_{i=1}^d N_i}}, to become a unitary #' operator. Therefore, to invert a [`torch_fft`], the `normalized` #' argument should be set identically for [`torch_fft`]. @@ -1920,9 +1901,9 @@ NULL #' `torch_backends.mkl.is_available` to check if MKL is installed. #' #' -#' @param input (Tensor) the input tensor of at least `signal_ndim` ``+ 1`` dimensions +#' @param self (Tensor) the input tensor of at least `signal_ndim` `+ 1` dimensions #' @param signal_ndim (int) the number of dimensions in each signal. `signal_ndim` can only be 1, 2 or 3 -#' @param normalized (bool, optional) controls whether to return normalized results. Default: ``False`` +#' @param normalized (bool, optional) controls whether to return normalized results. Default: `FALSE` #' #' @name torch_ifft #' @@ -1932,7 +1913,7 @@ NULL #' Rfft #' -#' @section rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor : +#' @section rfft(input, signal_ndim, normalized=False, onesided=TRUE) -> Tensor : #' #' Real-to-complex Discrete Fourier Transform #' @@ -1942,8 +1923,8 @@ NULL #' #' This method supports 1D, 2D and 3D real-to-complex transforms, indicated #' by `signal_ndim`. `input` must be a tensor with at least -#' ``signal_ndim`` dimensions with optionally arbitrary number of leading batch -#' dimensions. If `normalized` is set to ``True``, this normalizes the result +#' `signal_ndim` dimensions with optionally arbitrary number of leading batch +#' dimensions. If `normalized` is set to `TRUE`, this normalizes the result #' by dividing it with \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is #' unitary, where \eqn{N_i} is the size of signal dimension \eqn{i}. #' @@ -1955,7 +1936,7 @@ NULL #' where the index arithmetic is computed modulus the size of the corresponding #' dimension, \eqn{\ ^*} is the conjugate operator, and #' \eqn{d} = `signal_ndim`. `onesided` flag controls whether to avoid -#' redundancy in the output results. If set to ``True`` (default), the output will +#' redundancy in the output results. If set to `TRUE` (default), the output will #' not be full complex result of shape \eqn{(*, 2)}, where \eqn{*} is the shape #' of `input`, but instead the last dimension will be halfed as of size #' \eqn{\lfloor \frac{N_d}{2} \rfloor + 1}. @@ -1973,10 +1954,10 @@ NULL #' `torch_backends.mkl.is_available` to check if MKL is installed. #' #' -#' @param input (Tensor) the input tensor of at least `signal_ndim` dimensions +#' @param self (Tensor) the input tensor of at least `signal_ndim` dimensions #' @param signal_ndim (int) the number of dimensions in each signal. `signal_ndim` can only be 1, 2 or 3 -#' @param normalized (bool, optional) controls whether to return normalized results. Default: ``False`` -#' @param onesided (bool, optional) controls whether to return half of results to avoid redundancy. Default: ``True`` +#' @param normalized (bool, optional) controls whether to return normalized results. Default: `FALSE` +#' @param onesided (bool, optional) controls whether to return half of results to avoid redundancy. Default: `TRUE` #' #' @name torch_rfft #' @@ -1986,7 +1967,7 @@ NULL #' Irfft #' -#' @section irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor : +#' @section irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -> Tensor : #' #' Complex-to-real Inverse Discrete Fourier Transform #' @@ -1995,7 +1976,7 @@ NULL #' formats of the input and output. #' #' The argument specifications are almost identical with [`torch_ifft`]. -#' Similar to [`torch_ifft`], if `normalized` is set to ``True``, +#' Similar to [`torch_ifft`], if `normalized` is set to `TRUE`, #' this normalizes the result by multiplying it with #' \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary, where #' \eqn{N_i} is the size of signal dimension \eqn{i}. @@ -2004,8 +1985,8 @@ NULL #' Due to the conjugate symmetry, `input` do not need to contain the full #' complex frequency values. Roughly half of the values will be sufficient, as #' is the case when `input` is given by [`~torch.rfft`] with -#' ``rfft(signal, onesided=True)``. In such case, set the `onesided` -#' argument of this method to ``True``. Moreover, the original signal shape +#' `rfft(signal, onesided=TRUE)`. In such case, set the `onesided` +#' argument of this method to `TRUE`. Moreover, the original signal shape #' information can sometimes be lost, optionally set `signal_sizes` to be #' the size of the original signal (without the batch dimensions if in batched #' mode) to recover it with correct shape. @@ -2022,7 +2003,7 @@ NULL #' @section Warning: #' Generally speaking, input to this function should contain values #' following conjugate symmetry. Note that even if `onesided` is -#' ``True``, often symmetry on some part is still needed. When this +#' `TRUE`, often symmetry on some part is still needed. When this #' requirement is not satisfied, the behavior of [`torch_irfft`] is #' undefined. Since `torch_autograd.gradcheck` estimates numerical #' Jacobian with point perturbations, [`torch_irfft`] will almost @@ -2039,11 +2020,11 @@ NULL #' `torch_backends.mkl.is_available` to check if MKL is installed. #' #' -#' @param input (Tensor) the input tensor of at least `signal_ndim` ``+ 1`` dimensions +#' @param self (Tensor) the input tensor of at least `signal_ndim` `+ 1` dimensions #' @param signal_ndim (int) the number of dimensions in each signal. `signal_ndim` can only be 1, 2 or 3 -#' @param normalized (bool, optional) controls whether to return normalized results. Default: ``False`` -#' @param onesided (bool, optional) controls whether `input` was halfed to avoid redundancy, e.g., by [torch_rfft()]. Default: ``True`` -#' @param signal_sizes (list or `torch.Size`, optional) the size of the original signal (without batch dimension). Default: ``None`` +#' @param normalized (bool, optional) controls whether to return normalized results. Default: `FALSE` +#' @param onesided (bool, optional) controls whether `input` was halfed to avoid redundancy, e.g., by [torch_rfft()]. Default: `TRUE` +#' @param signal_sizes (list or `torch.Size`, optional) the size of the original signal (without batch dimension). Default: `NULL` #' #' @name torch_irfft #' @@ -2053,7 +2034,7 @@ NULL #' Inverse #' -#' @section inverse(input, out=None) -> Tensor : +#' @section inverse(input, out=NULL) -> Tensor : #' #' Takes the inverse of the square matrix `input`. `input` can be batches #' of 2D square tensors, in which case this function would return a tensor composed of @@ -2065,8 +2046,8 @@ NULL #' transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()` #' #' -#' @param input (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more batch dimensions -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more batch dimensions +#' #' #' @name torch_inverse #' @@ -2081,7 +2062,7 @@ NULL #' Returns a new tensor with boolean elements representing if each element is `NaN` or not. #' #' -#' @param input (Tensor) A tensor to check +#' @param self (Tensor) A tensor to check #' #' @name torch_isnan #' @@ -2093,11 +2074,11 @@ NULL #' #' @section is_floating_point(input) -> (bool) : #' -#' Returns True if the data type of `input` is a floating point data type i.e., -#' one of ``torch_float64``, ``torch.float32`` and ``torch.float16``. +#' Returns TRUE if the data type of `input` is a floating point data type i.e., +#' one of `torch_float64`, `torch.float32` and `torch.float16`. #' #' -#' @param input (Tensor) the PyTorch tensor to test +#' @param self (Tensor) the PyTorch tensor to test #' #' @name torch_is_floating_point #' @@ -2109,11 +2090,11 @@ NULL #' #' @section is_complex(input) -> (bool) : #' -#' Returns True if the data type of `input` is a complex data type i.e., -#' one of ``torch_complex64``, and ``torch.complex128``. +#' Returns TRUE if the data type of `input` is a complex data type i.e., +#' one of `torch_complex64`, and `torch.complex128`. #' #' -#' @param input (Tensor) the PyTorch tensor to test +#' @param self (Tensor) the PyTorch tensor to test #' #' @name torch_is_complex #' @@ -2123,26 +2104,25 @@ NULL #' Kthvalue #' -#' @section kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor) : +#' @section kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor) : #' -#' Returns a namedtuple ``(values, indices)`` where ``values`` is the `k` th +#' Returns a namedtuple `(values, indices)` where `values` is the `k` th #' smallest element of each row of the `input` tensor in the given dimension -#' `dim`. And ``indices`` is the index location of each element found. +#' `dim`. And `indices` is the index location of each element found. #' #' If `dim` is not given, the last dimension of the `input` is chosen. #' -#' If `keepdim` is ``True``, both the `values` and `indices` tensors +#' If `keepdim` is `TRUE`, both the `values` and `indices` tensors #' are the same size as `input`, except in the dimension `dim` where #' they are of size 1. Otherwise, `dim` is squeezed #' (see [`torch_squeeze`]), resulting in both the `values` and #' `indices` tensors having 1 fewer dimension than the `input` tensor. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param k (int) k for the k-th smallest element #' @param dim (int, optional) the dimension to find the kth value along #' @param keepdim (bool) whether the output tensor has `dim` retained or not. -#' @param out (tuple, optional) the output tuple of (Tensor, LongTensor) can be optionally given to be used as output buffers #' #' @name torch_kthvalue #' @@ -2152,7 +2132,7 @@ NULL #' Linspace #' -#' @section linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a one-dimensional tensor of `steps` #' equally spaced points between `start` and `end`. @@ -2162,12 +2142,12 @@ NULL #' #' @param start (float) the starting value for the set of points #' @param end (float) the ending value for the set of points -#' @param steps (int) number of points to sample between `start` and `end`. Default: ``100``. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param steps (int) number of points to sample between `start` and `end`. Default: `100`. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_linspace #' @@ -2177,7 +2157,7 @@ NULL #' Log #' -#' @section log(input, out=None) -> Tensor : +#' @section log(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the natural logarithm of the elements #' of `input`. @@ -2187,8 +2167,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_log #' @@ -2198,7 +2178,7 @@ NULL #' Log10 #' -#' @section log10(input, out=None) -> Tensor : +#' @section log10(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the logarithm to the base 10 of the elements #' of `input`. @@ -2208,8 +2188,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_log10 #' @@ -2219,7 +2199,7 @@ NULL #' Log1p #' -#' @section log1p(input, out=None) -> Tensor : +#' @section log1p(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the natural logarithm of (1 + `input`). #' @@ -2230,8 +2210,8 @@ NULL #' values of `input` #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_log1p #' @@ -2241,7 +2221,7 @@ NULL #' Log2 #' -#' @section log2(input, out=None) -> Tensor : +#' @section log2(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the logarithm to the base 2 of the elements #' of `input`. @@ -2251,8 +2231,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_log2 #' @@ -2267,7 +2247,7 @@ NULL #' Calculates log determinant of a square matrix or batches of square matrices. #' #' @note -#' Result is ``-inf`` if `input` has zero log determinant, and is ``nan`` if +#' Result is `-inf` if `input` has zero log determinant, and is `NaN` if #' `input` has negative determinant. #' #' @note @@ -2277,7 +2257,7 @@ NULL #' `~torch.svd` for details. #' #' -#' @param input (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more batch dimensions. +#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more batch dimensions. #' #' @name torch_logdet #' @@ -2287,7 +2267,7 @@ NULL #' Logspace #' -#' @section logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a one-dimensional tensor of `steps` points #' logarithmically spaced with base `base` between @@ -2298,13 +2278,13 @@ NULL #' #' @param start (float) the starting value for the set of points #' @param end (float) the ending value for the set of points -#' @param steps (int) number of points to sample between `start` and `end`. Default: ``100``. -#' @param base (float) base of the logarithm function. Default: ``10.0``. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param steps (int) number of points to sample between `start` and `end`. Default: `100`. +#' @param base (float) base of the logarithm function. Default: `10.0`. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_logspace #' @@ -2314,7 +2294,7 @@ NULL #' Logsumexp #' -#' @section logsumexp(input, dim, keepdim=False, out=None) : +#' @section logsumexp(input, dim, keepdim=False, out=NULL) : #' #' Returns the log of summed exponentials of each row of the `input` #' tensor in the given dimension `dim`. The computation is numerically @@ -2326,16 +2306,16 @@ NULL #' \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij}) #' } #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension(s) `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the -#' output tensor having 1 (or ``len(dim)``) fewer dimension(s). +#' output tensor having 1 (or `len(dim)`) fewer dimension(s). #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int or tuple of ints) the dimension or dimensions to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_logsumexp #' @@ -2345,7 +2325,7 @@ NULL #' Matmul #' -#' @section matmul(input, other, out=None) -> Tensor : +#' @section matmul(input, other, out=NULL) -> Tensor : #' #' Matrix product of two tensors. #' @@ -2373,9 +2353,9 @@ NULL #' The 1-dimensional dot product version of this function does not support an `out` parameter. #' #' -#' @param input (Tensor) the first tensor to be multiplied +#' @param self (Tensor) the first tensor to be multiplied #' @param other (Tensor) the second tensor to be multiplied -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_matmul #' @@ -2385,23 +2365,23 @@ NULL #' Matrix_rank #' -#' @section matrix_rank(input, tol=None, symmetric=False) -> Tensor : +#' @section matrix_rank(input, tol=NULL, symmetric=False) -> Tensor : #' #' Returns the numerical rank of a 2-D tensor. The method to compute the -#' matrix rank is done using SVD by default. If `symmetric` is ``True``, +#' matrix rank is done using SVD by default. If `symmetric` is `TRUE`, #' then `input` is assumed to be symmetric, and the computation of the #' rank is done by obtaining the eigenvalues. #' #' `tol` is the threshold below which the singular values (or the eigenvalues -#' when `symmetric` is ``True``) are considered to be 0. If `tol` is not -#' specified, `tol` is set to ``S.max() * max(S.size()) * eps`` where `S` is the -#' singular values (or the eigenvalues when `symmetric` is ``True``), and ``eps`` +#' when `symmetric` is `TRUE`) are considered to be 0. If `tol` is not +#' specified, `tol` is set to `S.max() * max(S.size()) * eps` where `S` is the +#' singular values (or the eigenvalues when `symmetric` is `TRUE`), and `eps` #' is the epsilon value for the datatype of `input`. #' #' -#' @param input (Tensor) the input 2-D tensor -#' @param tol (float, optional) the tolerance value. Default: ``None`` -#' @param symmetric (bool, optional) indicates whether `input` is symmetric. Default: ``False`` +#' @param self (Tensor) the input 2-D tensor +#' @param tol (float, optional) the tolerance value. Default: `NULL` +#' @param symmetric (bool, optional) indicates whether `input` is symmetric. Default: `FALSE` #' #' @name torch_matrix_rank #' @@ -2422,7 +2402,7 @@ NULL #' is returned. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param n (int) the power to raise the matrix to #' #' @name torch_matrix_power @@ -2435,32 +2415,32 @@ NULL #' #' @section max(input) -> Tensor : #' -#' Returns the maximum value of all elements in the ``input`` tensor. +#' Returns the maximum value of all elements in the `input` tensor. #' -#' @section max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) : +#' @section max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) : #' -#' Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum +#' Returns a namedtuple `(values, indices)` where `values` is the maximum #' value of each row of the `input` tensor in the given dimension -#' `dim`. And ``indices`` is the index location of each maximum value found +#' `dim`. And `indices` is the index location of each maximum value found #' (argmax). #' #' @section Warning: -#' ``indices`` does not necessarily contain the first occurrence of each +#' `indices` does not necessarily contain the first occurrence of each #' maximal value found, unless it is unique. #' The exact implementation details are device-specific. #' Do not expect the same result when run on CPU and GPU in general. #' -#' If ``keepdim`` is ``True``, the output tensors are of the same size -#' as ``input`` except in the dimension ``dim`` where they are of size 1. -#' Otherwise, ``dim`` is squeezed (see [`torch_squeeze`]), resulting -#' in the output tensors having 1 fewer dimension than ``input``. +#' If `keepdim` is `TRUE`, the output tensors are of the same size +#' as `input` except in the dimension `dim` where they are of size 1. +#' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting +#' in the output tensors having 1 fewer dimension than `input`. #' -#' @section max(input, other, out=None) -> Tensor : +#' @section max(input, other, out=NULL) -> Tensor : #' -#' Each element of the tensor ``input`` is compared with the corresponding -#' element of the tensor ``other`` and an element-wise maximum is taken. +#' Each element of the tensor `input` is compared with the corresponding +#' element of the tensor `other` and an element-wise maximum is taken. #' -#' The shapes of ``input`` and ``other`` don't need to match, +#' The shapes of `input` and `other` don't need to match, #' but they must be broadcastable . #' #' \deqn{ @@ -2470,9 +2450,9 @@ NULL #' follows the broadcasting rules . #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to reduce. -#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Default: ``False``. +#' @param keepdim (bool) whether the output tensor has `dim` retained or not. Default: `FALSE`. #' @param out (tuple, optional) the result tuple of two output tensors (max, max_indices) #' @param other (Tensor) the second input tensor #' @@ -2488,23 +2468,23 @@ NULL #' #' Returns the mean value of all elements in the `input` tensor. #' -#' @section mean(input, dim, keepdim=False, out=None) -> Tensor : +#' @section mean(input, dim, keepdim=False, out=NULL) -> Tensor : #' #' Returns the mean value of each row of the `input` tensor in the given #' dimension `dim`. If `dim` is a list of dimensions, #' reduce over all of them. #' #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension(s) `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the -#' output tensor having 1 (or ``len(dim)``) fewer dimension(s). +#' output tensor having 1 (or `len(dim)`) fewer dimension(s). #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int or tuple of ints) the dimension or dimensions to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. -#' @param out (Tensor, optional) the output tensor. +#' @param dtype the resulting data type. #' #' @name torch_mean #' @@ -2518,24 +2498,23 @@ NULL #' #' Returns the median value of all elements in the `input` tensor. #' -#' @section median(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) : +#' @section median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) : #' -#' Returns a namedtuple ``(values, indices)`` where ``values`` is the median +#' Returns a namedtuple `(values, indices)` where `values` is the median #' value of each row of the `input` tensor in the given dimension -#' `dim`. And ``indices`` is the index location of each median value found. +#' `dim`. And `indices` is the index location of each median value found. #' #' By default, `dim` is the last dimension of the `input` tensor. #' -#' If `keepdim` is ``True``, the output tensors are of the same size +#' If `keepdim` is `TRUE`, the output tensors are of the same size #' as `input` except in the dimension `dim` where they are of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in #' the outputs tensor having 1 fewer dimension than `input`. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. -#' @param out (tuple, optional) the result tuple of two output tensors (max, max_indices) #' #' @name torch_median #' @@ -2549,25 +2528,25 @@ NULL #' #' Returns the minimum value of all elements in the `input` tensor. #' -#' @section min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) : +#' @section min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) : #' -#' Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum +#' Returns a namedtuple `(values, indices)` where `values` is the minimum #' value of each row of the `input` tensor in the given dimension -#' `dim`. And ``indices`` is the index location of each minimum value found +#' `dim`. And `indices` is the index location of each minimum value found #' (argmin). #' #' @section Warning: -#' ``indices`` does not necessarily contain the first occurrence of each +#' `indices` does not necessarily contain the first occurrence of each #' minimal value found, unless it is unique. #' The exact implementation details are device-specific. #' Do not expect the same result when run on CPU and GPU in general. #' -#' If `keepdim` is ``True``, the output tensors are of the same size as +#' If `keepdim` is `TRUE`, the output tensors are of the same size as #' `input` except in the dimension `dim` where they are of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in #' the output tensors having 1 fewer dimension than `input`. #' -#' @section min(input, other, out=None) -> Tensor : +#' @section min(input, other, out=NULL) -> Tensor : #' #' Each element of the tensor `input` is compared with the corresponding #' element of the tensor `other` and an element-wise minimum is taken. @@ -2583,7 +2562,7 @@ NULL #' follows the broadcasting rules . #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. #' @param out (tuple, optional) the tuple of two output tensors (min, min_indices) @@ -2597,7 +2576,7 @@ NULL #' Mm #' -#' @section mm(input, mat2, out=None) -> Tensor : +#' @section mm(input, mat2, out=NULL) -> Tensor : #' #' Performs a matrix multiplication of the matrices `input` and `mat2`. #' @@ -2608,9 +2587,9 @@ NULL #' For broadcasting matrix products, see [`torch_matmul`]. #' #' -#' @param input (Tensor) the first matrix to be multiplied +#' @param self (Tensor) the first matrix to be multiplied #' @param mat2 (Tensor) the second matrix to be multiplied -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_mm #' @@ -2620,27 +2599,26 @@ NULL #' Mode #' -#' @section mode(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) : +#' @section mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) : #' -#' Returns a namedtuple ``(values, indices)`` where ``values`` is the mode +#' Returns a namedtuple `(values, indices)` where `values` is the mode #' value of each row of the `input` tensor in the given dimension #' `dim`, i.e. a value which appears most often -#' in that row, and ``indices`` is the index location of each mode value found. +#' in that row, and `indices` is the index location of each mode value found. #' #' By default, `dim` is the last dimension of the `input` tensor. #' -#' If `keepdim` is ``True``, the output tensors are of the same size as +#' If `keepdim` is `TRUE`, the output tensors are of the same size as #' `input` except in the dimension `dim` where they are of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting #' in the output tensors having 1 fewer dimension than `input`. #' -#' @note This function is not defined for ``torch_cuda.Tensor`` yet. +#' @note This function is not defined for `torch_cuda.Tensor` yet. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. -#' @param out (tuple, optional) the result tuple of two output tensors (values, indices) #' #' @name torch_mode #' @@ -2650,7 +2628,7 @@ NULL #' Mul #' -#' @section mul(input, other, out=None) : +#' @section mul(input, other, out=NULL) : #' #' Multiplies each element of the input `input` with the scalar #' `other` and returns a new resulting tensor. @@ -2661,7 +2639,7 @@ NULL #' If `input` is of type `FloatTensor` or `DoubleTensor`, `other` #' should be a real number, otherwise it should be an integer #' -#' @section mul(input, other, out=None) : +#' @section mul(input, other, out=NULL) : #' #' Each element of the tensor `input` is multiplied by the corresponding #' element of the Tensor `other`. The resulting tensor is returned. @@ -2673,13 +2651,9 @@ NULL #' \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i #' } #' -#' -#' @param {input} NA -#' @param value (Number) the number to be multiplied to each element of `input` -#' @param {out} NA -#' @param input (Tensor) the first multiplicand tensor +#' @param self (Tensor) the first multiplicand tensor #' @param other (Tensor) the second multiplicand tensor -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_mul #' @@ -2689,7 +2663,7 @@ NULL #' Mv #' -#' @section mv(input, vec, out=None) -> Tensor : +#' @section mv(input, vec, out=NULL) -> Tensor : #' #' Performs a matrix-vector product of the matrix `input` and the vector #' `vec`. @@ -2700,9 +2674,9 @@ NULL #' @note This function does not broadcast . #' #' -#' @param input (Tensor) matrix to be multiplied +#' @param self (Tensor) matrix to be multiplied #' @param vec (Tensor) vector to be multiplied -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_mv #' @@ -2726,7 +2700,7 @@ NULL #' All elements must be greater than \eqn{\frac{p - 1}{2}}, otherwise an error would be thrown. #' #' -#' @param input (Tensor) the tensor to compute the multivariate log-gamma function +#' @param self (Tensor) the tensor to compute the multivariate log-gamma function #' @param p (int) the number of dimensions #' #' @name torch_mvlgamma @@ -2744,7 +2718,7 @@ NULL #' returned tensor and `input` tensor share the same underlying storage. #' #' -#' @param input (Tensor) the tensor to narrow +#' @param self (Tensor) the tensor to narrow #' @param dim (int) the dimension along which to narrow #' @param start (int) the starting dimension #' @param length (int) the distance to the ending dimension @@ -2757,18 +2731,19 @@ NULL #' Ones #' -#' @section ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a tensor filled with the scalar value `1`, with the shape defined #' by the variable argument `size`. #' #' -#' @param size (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param ... (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param names optional names for the dimensions #' #' @name torch_ones #' @@ -2778,24 +2753,24 @@ NULL #' Ones_like #' -#' @section ones_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : +#' @section ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : #' #' Returns a tensor filled with the scalar value `1`, with the same size as -#' `input`. ``torch_ones_like(input)`` is equivalent to -#' ``torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. +#' `input`. `torch_ones_like(input)` is equivalent to +#' `torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`. #' #' @section Warning: #' As of 0.4, this function does not support an `out` keyword. As an alternative, -#' the old ``torch_ones_like(input, out=output)`` is equivalent to -#' ``torch_ones(input.size(), out=output)``. +#' the old `torch_ones_like(input, out=output)` is equivalent to +#' `torch_ones(input.size(), out=output)`. #' #' #' @param input (Tensor) the size of `input` will determine size of the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of `input`. -#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if ``None``, defaults to the layout of `input`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, defaults to the device of `input`. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_preserve_format``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if `NULL`, defaults to the dtype of `input`. +#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if `NULL`, defaults to the layout of `input`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, defaults to the device of `input`. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: `torch_preserve_format`. #' #' @name torch_ones_like #' @@ -2827,7 +2802,7 @@ NULL #' #' Computes the p-norm distance between every pair of row vectors in the input. #' This is identical to the upper triangular portion, excluding the diagonal, of -#' `torch_norm(input[:, None] - input, dim=2, p=p)`. This function will be faster +#' `torch_norm(input[:, NULL] - input, dim=2, p=p)`. This function will be faster #' if the rows are contiguous. #' #' If input has shape \eqn{N \times M} then the output will have shape @@ -2840,7 +2815,7 @@ NULL #' `scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())`. #' #' -#' @param input NA input tensor of shape \eqn{N \times M}. +#' @param self NA input tensor of shape \eqn{N \times M}. #' @param p NA p value for the p-norm distance to calculate between each vector pair \eqn{\in [0, \infty]}. #' #' @name torch_pdist @@ -2881,7 +2856,7 @@ NULL #' See `~torch.nn.PixelShuffle` for details. #' #' -#' @param input (Tensor) the input tensor +#' @param self (Tensor) the input tensor #' @param upscale_factor (int) factor to increase spatial resolution by #' #' @name torch_pixel_shuffle @@ -2908,7 +2883,7 @@ NULL #' See `~torch.svd` for more details. #' #' -#' @param input (Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions +#' @param self (Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions #' @param rcond (float) A floating point value to determine the cutoff for small singular values. Default: 1e-15 #' #' @name torch_pinverse @@ -2919,7 +2894,7 @@ NULL #' Rand #' -#' @section rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a tensor filled with random numbers from a uniform distribution #' on the interval \eqn{[0, 1)} @@ -2927,12 +2902,13 @@ NULL #' The shape of the tensor is defined by the variable argument `size`. #' #' -#' @param size (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param ... (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param names optional dimension names #' #' @name torch_rand #' @@ -2942,20 +2918,20 @@ NULL #' Rand_like #' -#' @section rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : +#' @section rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : #' #' Returns a tensor with the same size as `input` that is filled with #' random numbers from a uniform distribution on the interval \eqn{[0, 1)}. -#' ``torch_rand_like(input)`` is equivalent to -#' ``torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. +#' `torch_rand_like(input)` is equivalent to +#' `torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`. #' #' #' @param input (Tensor) the size of `input` will determine size of the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of `input`. -#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if ``None``, defaults to the layout of `input`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, defaults to the device of `input`. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_preserve_format``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if `NULL`, defaults to the dtype of `input`. +#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if `NULL`, defaults to the layout of `input`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, defaults to the device of `input`. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: `torch_preserve_format`. #' #' @name torch_rand_like #' @@ -2965,9 +2941,9 @@ NULL #' Randint #' -#' @section randint(low=0, high, size, *, generator=None, out=None, \ : +#' @section randint(low=0, high, size, *, generator=NULL, out=NULL, \ : #' -#' dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor +#' dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor #' #' Returns a tensor filled with random integers generated uniformly #' between `low` (inclusive) and `high` (exclusive). @@ -2975,20 +2951,21 @@ NULL #' The shape of the tensor is defined by the variable argument `size`. #' #' .. note: -#' With the global dtype default (``torch_float32``), this function returns -#' a tensor with dtype ``torch_int64``. +#' With the global dtype default (`torch_float32`), this function returns +#' a tensor with dtype `torch_int64`. #' #' #' @param low (int, optional) Lowest integer to be drawn from the distribution. Default: 0. #' @param high (int) One above the highest integer to be drawn from the distribution. #' @param size (tuple) a tuple defining the shape of the output tensor. #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param memory_format memory format for the resulting tensor. +#' #' @name torch_randint #' #' @export @@ -2997,7 +2974,7 @@ NULL #' Randint_like #' -#' @section randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, : +#' @section randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, : #' #' memory_format=torch.preserve_format) -> Tensor #' @@ -3006,18 +2983,17 @@ NULL #' `high` (exclusive). #' #' .. note: -#' With the global dtype default (``torch_float32``), this function returns -#' a tensor with dtype ``torch_int64``. +#' With the global dtype default (`torch_float32`), this function returns +#' a tensor with dtype `torch_int64`. #' #' #' @param input (Tensor) the size of `input` will determine size of the output tensor. #' @param low (int, optional) Lowest integer to be drawn from the distribution. Default: 0. #' @param high (int) One above the highest integer to be drawn from the distribution. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of `input`. -#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if ``None``, defaults to the layout of `input`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, defaults to the device of `input`. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_preserve_format``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if `NULL`, defaults to the dtype of `input`. +#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if `NULL`, defaults to the layout of `input`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, defaults to the device of `input`. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_randint_like #' @@ -3027,7 +3003,7 @@ NULL #' Randn #' -#' @section randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a tensor filled with random numbers from a normal distribution #' with mean `0` and variance `1` (also called the standard normal @@ -3039,12 +3015,13 @@ NULL #' The shape of the tensor is defined by the variable argument `size`. #' #' -#' @param size (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param ... (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param names optional names for the dimensions #' #' @name torch_randn #' @@ -3054,20 +3031,20 @@ NULL #' Randn_like #' -#' @section randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : +#' @section randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : #' #' Returns a tensor with the same size as `input` that is filled with #' random numbers from a normal distribution with mean 0 and variance 1. -#' ``torch_randn_like(input)`` is equivalent to -#' ``torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. +#' `torch_randn_like(input)` is equivalent to +#' `torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`. #' #' #' @param input (Tensor) the size of `input` will determine size of the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of `input`. -#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if ``None``, defaults to the layout of `input`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, defaults to the device of `input`. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_preserve_format``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if `NULL`, defaults to the dtype of `input`. +#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if `NULL`, defaults to the layout of `input`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, defaults to the device of `input`. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: `torch_preserve_format`. #' #' @name torch_randn_like #' @@ -3077,17 +3054,17 @@ NULL #' Randperm #' -#' @section randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) -> LongTensor : +#' @section randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor : #' -#' Returns a random permutation of integers from ``0`` to ``n - 1``. +#' Returns a random permutation of integers from `0` to `n - 1`. #' #' #' @param n (int) the upper bound (exclusive) -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: ``torch_int64``. -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: `torch_int64`. +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_randperm #' @@ -3097,7 +3074,7 @@ NULL #' Range #' -#' @section range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a 1-D tensor of size \eqn{\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1} #' with values from `start` to `end` with step `step`. Step is @@ -3110,14 +3087,14 @@ NULL #' This function is deprecated in favor of [`torch_arange`]. #' #' -#' @param start (float) the starting value for the set of points. Default: ``0``. +#' @param start (float) the starting value for the set of points. Default: `0`. #' @param end (float) the ending value for the set of points -#' @param step (float) the gap between each pair of adjacent points. Default: ``1``. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input arguments. If any of `start`, `end`, or `stop` are floating-point, the `dtype` is inferred to be the default dtype, see `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to be `torch.int64`. -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param step (float) the gap between each pair of adjacent points. Default: `1`. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). If `dtype` is not given, infer the data type from the other input arguments. If any of `start`, `end`, or `stop` are floating-point, the `dtype` is inferred to be the default dtype, see `~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to be `torch.int64`. +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_range #' @@ -3127,7 +3104,7 @@ NULL #' Reciprocal #' -#' @section reciprocal(input, out=None) -> Tensor : +#' @section reciprocal(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the reciprocal of the elements of `input` #' @@ -3136,8 +3113,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_reciprocal #' @@ -3147,7 +3124,7 @@ NULL #' Neg #' -#' @section neg(input, out=None) -> Tensor : +#' @section neg(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the negative of the elements of `input`. #' @@ -3156,8 +3133,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_neg #' @@ -3167,13 +3144,13 @@ NULL #' Repeat_interleave #' -#' @section repeat_interleave(input, repeats, dim=None) -> Tensor : +#' @section repeat_interleave(input, repeats, dim=NULL) -> Tensor : #' #' Repeat elements of a tensor. #' #' @section Warning: #' -#' This is different from `torch_Tensor.repeat` but similar to ``numpy.repeat``. +#' This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`. #' #' @section repeat_interleave(repeats) -> Tensor : #' @@ -3182,7 +3159,7 @@ NULL #' `1` appears `n2` times, `2` appears `n3` times, etc. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param repeats (Tensor or int) The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis. #' @param dim (int, optional) The dimension along which to repeat values. By default, use the flattened input array, and return a flat output array. #' @@ -3208,7 +3185,7 @@ NULL #' dimensions and the number of elements in `input`. #' #' -#' @param input (Tensor) the tensor to be reshaped +#' @param self (Tensor) the tensor to be reshaped #' @param shape (tuple of ints) the new shape #' #' @name torch_reshape @@ -3219,14 +3196,14 @@ NULL #' Round #' -#' @section round(input, out=None) -> Tensor : +#' @section round(input, out=NULL) -> Tensor : #' #' Returns a new tensor with each of the elements of `input` rounded #' to the closest integer. #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_round #' @@ -3239,6 +3216,10 @@ NULL #' @section rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor : #' #' In-place version of `torch_rrelu`. +#' +#' @param self the input tensor +#' @param generator random number generator +#' @inheritParams nnf_rrelu #' #' #' @@ -3248,15 +3229,26 @@ NULL #' @export NULL - -#' Relu_ +#' Relu #' -#' @section relu_(input) -> Tensor : +#' @section relu(input) -> Tensor : +#' +#' Computes the relu tranformation. +#' +#' @param self the input tensor #' -#' In-place version of `torch_relu`. +#' @name torch_relu #' +#' @export +NULL + +#' Relu_ #' +#' @section relu_(input) -> Tensor : #' +#' In-place version of [torch_relu()]. +#' +#' @param self the input tensor #' #' @name torch_relu_ #' @@ -3266,7 +3258,7 @@ NULL #' Rsqrt #' -#' @section rsqrt(input, out=None) -> Tensor : +#' @section rsqrt(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the reciprocal of the square-root of each of #' the elements of `input`. @@ -3276,25 +3268,53 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_rsqrt #' #' @export NULL +#' Selu +#' +#' @section selu(input) -> Tensor : +#' +#' Computes the selu transformation. +#' +#' @param self the input tensor +#' +#' @name torch_selu +#' +#' @export +#' +NULL + #' Selu_ #' #' @section selu_(input) -> Tensor : #' -#' In-place version of `toch_selu`. +#' In-place version of [torch_selu()]. +#' +#' @param self the input tensor #' #' +#' @name torch_selu_ #' +#' @export +NULL + +#' Celu #' -#' @name torch_selu_ +#' @section celu(input, alpha=1.) -> Tensor : +#' +#' See [nnf_celu()] for more info. +#' +#' @param self the input tensor +#' @param alpha the alpha value for the CELU formulation. Default: 1.0 +#' +#' @name torch_celu #' #' @export NULL @@ -3304,11 +3324,11 @@ NULL #' #' @section celu_(input, alpha=1.) -> Tensor : #' -#' In-place version of `torch_celu`. -#' -#' -#' -#' +#' In-place version of [torch_celu()]. +#' +#' @param self the input tensor +#' @param alpha the alpha value for the CELU formulation. Default: 1.0 +#' #' @name torch_celu_ #' #' @export @@ -3317,7 +3337,7 @@ NULL #' Sigmoid #' -#' @section sigmoid(input, out=None) -> Tensor : +#' @section sigmoid(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the sigmoid of the elements of `input`. #' @@ -3326,8 +3346,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_sigmoid #' @@ -3337,7 +3357,7 @@ NULL #' Sin #' -#' @section sin(input, out=None) -> Tensor : +#' @section sin(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the sine of the elements of `input`. #' @@ -3346,8 +3366,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_sin #' @@ -3357,7 +3377,7 @@ NULL #' Sinh #' -#' @section sinh(input, out=None) -> Tensor : +#' @section sinh(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the hyperbolic sine of the elements of #' `input`. @@ -3367,8 +3387,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_sinh #' @@ -3383,7 +3403,7 @@ NULL #' Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices. #' #' @note -#' If ``input`` has zero determinant, this returns ``(0, -inf)``. +#' If `input` has zero determinant, this returns `(0, -inf)`. #' #' @note #' Backward through `slogdet` internally uses SVD results when `input` @@ -3392,7 +3412,7 @@ NULL #' See `~torch.svd` for details. #' #' -#' @param input (Tensor) the input tensor of size ``(*, n, n)`` where ``*`` is zero or more batch dimensions. +#' @param self (Tensor) the input tensor of size `(*, n, n)` where `*` is zero or more batch dimensions. #' #' @name torch_slogdet #' @@ -3412,12 +3432,12 @@ NULL #' `split_size`. #' #' If `split_size_or_sections` is a list, then `tensor` will be split -#' into ``len(split_size_or_sections)`` chunks with sizes in `dim` according +#' into `len(split_size_or_sections)` chunks with sizes in `dim` according #' to `split_size_or_sections`. #' #' -#' @param tensor (Tensor) tensor to split. -#' @param split_size_or_sections (int) size of a single chunk or list of sizes for each chunk +#' @param self (Tensor) tensor to split. +#' @param split_size (int) size of a single chunk or list of sizes for each chunk #' @param dim (int) dimension along which to split the tensor. #' #' @name torch_split @@ -3428,7 +3448,7 @@ NULL #' Squeeze #' -#' @section squeeze(input, dim=None, out=None) -> Tensor : +#' @section squeeze(input, dim=NULL, out=NULL) -> Tensor : #' #' Returns a tensor with all the dimensions of `input` of size `1` removed. #' @@ -3438,16 +3458,16 @@ NULL #' #' When `dim` is given, a squeeze operation is done only in the given #' dimension. If `input` is of shape: \eqn{(A \times 1 \times B)}, -#' ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` +#' `squeeze(input, 0)` leaves the tensor unchanged, but `squeeze(input, 1)` #' will squeeze the tensor to the shape \eqn{(A \times B)}. #' #' @note The returned tensor shares the storage with the input tensor, #' so changing the contents of one will change the contents of the other. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int, optional) if given, the input will be squeezed only in this dimension -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_squeeze #' @@ -3457,7 +3477,7 @@ NULL #' Stack #' -#' @section stack(tensors, dim=0, out=None) -> Tensor : +#' @section stack(tensors, dim=0, out=NULL) -> Tensor : #' #' Concatenates sequence of tensors along a new dimension. #' @@ -3466,7 +3486,7 @@ NULL #' #' @param tensors (sequence of Tensors) sequence of tensors to concatenate #' @param dim (int) dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive) -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_stack #' @@ -3490,38 +3510,38 @@ NULL #' } #' where \eqn{m} is the index of the sliding window, and \eqn{\omega} is #' the frequency that \eqn{0 \leq \omega < \mbox{n\_fft}}. When -#' `onesided` is the default value ``True``, +#' `onesided` is the default value `TRUE`, #' #' * `input` must be either a 1-D time sequence or a 2-D batch of time #' sequences. #' -#' * If `hop_length` is ``None`` (default), it is treated as equal to -#' ``floor(n_fft / 4)``. +#' * If `hop_length` is `NULL` (default), it is treated as equal to +#' `floor(n_fft / 4)`. #' -#' * If `win_length` is ``None`` (default), it is treated as equal to +#' * If `win_length` is `NULL` (default), it is treated as equal to #' `n_fft`. #' #' * `window` can be a 1-D tensor of size `win_length`, e.g., from -#' `torch_hann_window`. If `window` is ``None`` (default), it is +#' `torch_hann_window`. If `window` is `NULL` (default), it is #' treated as if having \eqn{1} everywhere in the window. If #' \eqn{\mbox{win\_length} < \mbox{n\_fft}}, `window` will be padded on #' both sides to length `n_fft` before being applied. #' -#' * If `center` is ``True`` (default), `input` will be padded on +#' * If `center` is `TRUE` (default), `input` will be padded on #' both sides so that the \eqn{t}-th frame is centered at time #' \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame #' begins at time \eqn{t \times \mbox{hop\_length}}. #' #' * `pad_mode` determines the padding method used on `input` when -#' `center` is ``True``. See `torch_nn.functional.pad` for -#' all available options. Default is ``"reflect"``. +#' `center` is `TRUE`. See `torch_nn.functional.pad` for +#' all available options. Default is `"reflect"`. #' -#' * If `onesided` is ``True`` (default), only values for \eqn{\omega} +#' * If `onesided` is `TRUE` (default), only values for \eqn{\omega} #' in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]} #' are returned because the real-to-complex Fourier transform satisfies the #' conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}. #' -#' * If `normalized` is ``True`` (default is ``False``), the function +#' * If `normalized` is `TRUE` (default is `FALSE`), the function #' returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}. #' #' Returns the real and the imaginary parts together as one tensor of size @@ -3531,20 +3551,20 @@ NULL #' in the last dimension represents a complex number as the real part and the #' imaginary part. #' -#' .. warning:: -#' This function changed signature at version 0.4.1. Calling with the -#' previous signature may cause error or return incorrect result. +#' @section Warning: +#' This function changed signature at version 0.4.1. Calling with the +#' previous signature may cause error or return incorrect result. #' #' #' @param input (Tensor) the input tensor #' @param n_fft (int) size of Fourier transform -#' @param hop_length (int, optional) the distance between neighboring sliding window frames. Default: ``None`` (treated as equal to ``floor(n_fft / 4)``) -#' @param win_length (int, optional) the size of window frame and STFT filter. Default: ``None`` (treated as equal to `n_fft`) -#' @param window (Tensor, optional) the optional window function. Default: ``None`` (treated as window of all \eqn{1} s) -#' @param center (bool, optional) whether to pad `input` on both sides so that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}. Default: ``True`` -#' @param pad_mode (string, optional) controls the padding method used when `center` is ``True``. Default: ``"reflect"`` -#' @param normalized (bool, optional) controls whether to return the normalized STFT results Default: ``False`` -#' @param onesided (bool, optional) controls whether to return half of results to avoid redundancy Default: ``True`` +#' @param hop_length (int, optional) the distance between neighboring sliding window frames. Default: `NULL` (treated as equal to `floor(n_fft / 4)`) +#' @param win_length (int, optional) the size of window frame and STFT filter. Default: `NULL` (treated as equal to `n_fft`) +#' @param window (Tensor, optional) the optional window function. Default: `NULL` (treated as window of all \eqn{1} s) +#' @param center (bool, optional) whether to pad `input` on both sides so that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}. Default: `TRUE` +#' @param pad_mode (string, optional) controls the padding method used when `center` is `TRUE`. Default: `"reflect"` +#' @param normalized (bool, optional) controls whether to return the normalized STFT results Default: `FALSE` +#' @param onesided (bool, optional) controls whether to return half of results to avoid redundancy Default: `TRUE` #' #' @name torch_stft #' @@ -3554,25 +3574,25 @@ NULL #' Sum #' -#' @section sum(input, dtype=None) -> Tensor : +#' @section sum(input, dtype=NULL) -> Tensor : #' #' Returns the sum of all elements in the `input` tensor. #' -#' @section sum(input, dim, keepdim=False, dtype=None) -> Tensor : +#' @section sum(input, dim, keepdim=False, dtype=NULL) -> Tensor : #' #' Returns the sum of each row of the `input` tensor in the given #' dimension `dim`. If `dim` is a list of dimensions, #' reduce over all of them. #' #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension(s) `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the -#' output tensor having 1 (or ``len(dim)``) fewer dimension(s). +#' output tensor having 1 (or `len(dim)`) fewer dimension(s). #' #' -#' @param input (Tensor) the input tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. +#' @param self (Tensor) the input tensor. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: NULL. #' @param dim (int or tuple of ints) the dimension or dimensions to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. #' @@ -3584,7 +3604,7 @@ NULL #' Sqrt #' -#' @section sqrt(input, out=None) -> Tensor : +#' @section sqrt(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the square-root of the elements of `input`. #' @@ -3593,8 +3613,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_sqrt #' @@ -3604,13 +3624,13 @@ NULL #' Square #' -#' @section square(input, out=None) -> Tensor : +#' @section square(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the square of the elements of `input`. #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_square #' @@ -3620,35 +3640,35 @@ NULL #' Std #' -#' @section std(input, unbiased=True) -> Tensor : +#' @section std(input, unbiased=TRUE) -> Tensor : #' #' Returns the standard-deviation of all elements in the `input` tensor. #' -#' If `unbiased` is ``False``, then the standard-deviation will be calculated +#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated #' via the biased estimator. Otherwise, Bessel's correction will be used. #' -#' @section std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor : +#' @section std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor : #' #' Returns the standard-deviation of each row of the `input` tensor in the #' dimension `dim`. If `dim` is a list of dimensions, #' reduce over all of them. #' #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension(s) `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the -#' output tensor having 1 (or ``len(dim)``) fewer dimension(s). +#' output tensor having 1 (or `len(dim)`) fewer dimension(s). #' #' -#' If `unbiased` is ``False``, then the standard-deviation will be calculated +#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated #' via the biased estimator. Otherwise, Bessel's correction will be used. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param unbiased (bool) whether to use the unbiased estimation or not #' @param dim (int or tuple of ints) the dimension or dimensions to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_std #' @@ -3658,31 +3678,31 @@ NULL #' Std_mean #' -#' @section std_mean(input, unbiased=True) -> (Tensor, Tensor) : +#' @section std_mean(input, unbiased=TRUE) -> (Tensor, Tensor) : #' #' Returns the standard-deviation and mean of all elements in the `input` tensor. #' -#' If `unbiased` is ``False``, then the standard-deviation will be calculated +#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated #' via the biased estimator. Otherwise, Bessel's correction will be used. #' -#' @section std_mean(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor) : +#' @section std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor) : #' #' Returns the standard-deviation and mean of each row of the `input` tensor in the #' dimension `dim`. If `dim` is a list of dimensions, #' reduce over all of them. #' #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension(s) `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the -#' output tensor having 1 (or ``len(dim)``) fewer dimension(s). +#' output tensor having 1 (or `len(dim)`) fewer dimension(s). #' #' -#' If `unbiased` is ``False``, then the standard-deviation will be calculated +#' If `unbiased` is `FALSE`, then the standard-deviation will be calculated #' via the biased estimator. Otherwise, Bessel's correction will be used. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param unbiased (bool) whether to use the unbiased estimation or not #' @param dim (int or tuple of ints) the dimension or dimensions to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. @@ -3695,23 +3715,23 @@ NULL #' Prod #' -#' @section prod(input, dtype=None) -> Tensor : +#' @section prod(input, dtype=NULL) -> Tensor : #' #' Returns the product of all elements in the `input` tensor. #' -#' @section prod(input, dim, keepdim=False, dtype=None) -> Tensor : +#' @section prod(input, dim, keepdim=False, dtype=NULL) -> Tensor : #' #' Returns the product of each row of the `input` tensor in the given #' dimension `dim`. #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in #' the output tensor having 1 fewer dimension than `input`. #' #' -#' @param input (Tensor) the input tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. +#' @param self (Tensor) the input tensor. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: NULL. #' @param dim (int) the dimension to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. #' @@ -3729,10 +3749,10 @@ NULL #' and 1. #' #' 0-D and 1-D tensors are returned as is. When input is a 2-D tensor this -#' is equivalent to ``transpose(input, 0, 1)``. +#' is equivalent to `transpose(input, 0, 1)`. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' #' @name torch_t #' @@ -3742,7 +3762,7 @@ NULL #' Tan #' -#' @section tan(input, out=None) -> Tensor : +#' @section tan(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the tangent of the elements of `input`. #' @@ -3751,8 +3771,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_tan #' @@ -3762,7 +3782,7 @@ NULL #' Tanh #' -#' @section tanh(input, out=None) -> Tensor : +#' @section tanh(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the hyperbolic tangent of the elements #' of `input`. @@ -3772,8 +3792,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_tanh #' @@ -3783,11 +3803,8 @@ NULL #' Tensordot #' -#' @section TEST : -#' #' Returns a contraction of a and b over multiple dimensions. -#' -#' `tensordot` implements a generalized matrix product. +#' `tensordot` implements a generalized matrix product. #' #' #' @param a (Tensor) Left tensor to contract @@ -3806,8 +3823,9 @@ NULL #' #' In-place version of `torch_threshold`. #' -#' -#' +#' @param self input tensor +#' @param threshold The value to threshold at +#' @param value The value to replace with #' #' @name torch_threshold_ #' @@ -3827,7 +3845,7 @@ NULL #' of the other. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim0 (int) the first dimension to be transposed #' @param dim1 (int) the second dimension to be transposed #' @@ -3844,7 +3862,7 @@ NULL #' Reverse the order of a n-D tensor along given axis in dims. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dims (a list or tuple) axis to flip on #' #' @name torch_flip @@ -3855,7 +3873,7 @@ NULL #' Roll #' -#' @section roll(input, shifts, dims=None) -> Tensor : +#' @section roll(input, shifts, dims=NULL) -> Tensor : #' #' Roll the tensor along the given dimension(s). Elements that are shifted beyond the #' last position are re-introduced at the first position. If a dimension is not @@ -3863,7 +3881,7 @@ NULL #' to the original shape. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param shifts (int or tuple of ints) The number of places by which the elements of the tensor are shifted. If shifts is a tuple, dims must be a tuple of the same size, and each dimension will be rolled by the corresponding value #' @param dims (int or tuple of ints) Axis along which to roll #' @@ -3881,7 +3899,7 @@ NULL #' Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param k (int) number of times to rotate #' @param dims (a list or tuple) axis to rotate #' @@ -3913,7 +3931,7 @@ NULL NULL -#' True_divide +#' TRUE_divide #' #' @section true_divide(dividend, divisor) -> Tensor : #' @@ -3927,8 +3945,8 @@ NULL #' } #' #' -#' @param dividend (Tensor) the dividend -#' @param divisor (Tensor or Scalar) the divisor +#' @param self (Tensor) the dividend +#' @param other (Tensor or Scalar) the divisor #' #' @name torch_true_divide #' @@ -3938,14 +3956,14 @@ NULL #' Trunc #' -#' @section trunc(input, out=None) -> Tensor : +#' @section trunc(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the truncated integer values of #' the elements of `input`. #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_trunc #' @@ -3964,10 +3982,10 @@ NULL #' in C++. #' #' -#' @param input (Tensor) the input tensor +#' @param self (Tensor) the input tensor #' @param return_inverse (bool) Whether to also return the indices for where elements in the original input ended up in the returned unique list. #' @param return_counts (bool) Whether to also return the counts for each unique element. -#' @param dim (int) the dimension to apply unique. If ``None``, the unique of the flattened input is returned. default: ``None`` +#' @param dim (int) the dimension to apply unique. If `NULL`, the unique of the flattened input is returned. default: `NULL` #' #' @name torch_unique_consecutive #' @@ -3984,12 +4002,12 @@ NULL #' #' The returned tensor shares the same underlying data with this tensor. #' -#' A `dim` value within the range ``[-input.dim() - 1, input.dim() + 1)`` +#' A `dim` value within the range `[-input.dim() - 1, input.dim() + 1)` #' can be used. Negative `dim` will correspond to `unsqueeze` -#' applied at `dim` = ``dim + input.dim() + 1``. +#' applied at `dim` = `dim + input.dim() + 1`. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the index at which to insert the singleton dimension #' #' @name torch_unsqueeze @@ -4000,34 +4018,34 @@ NULL #' Var #' -#' @section var(input, unbiased=True) -> Tensor : +#' @section var(input, unbiased=TRUE) -> Tensor : #' #' Returns the variance of all elements in the `input` tensor. #' -#' If `unbiased` is ``False``, then the variance will be calculated via the +#' If `unbiased` is `FALSE`, then the variance will be calculated via the #' biased estimator. Otherwise, Bessel's correction will be used. #' -#' @section var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor : +#' @section var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor : #' #' Returns the variance of each row of the `input` tensor in the given #' dimension `dim`. #' #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension(s) `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the -#' output tensor having 1 (or ``len(dim)``) fewer dimension(s). +#' output tensor having 1 (or `len(dim)`) fewer dimension(s). #' #' -#' If `unbiased` is ``False``, then the variance will be calculated via the +#' If `unbiased` is `FALSE`, then the variance will be calculated via the #' biased estimator. Otherwise, Bessel's correction will be used. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param unbiased (bool) whether to use the unbiased estimation or not #' @param dim (int or tuple of ints) the dimension or dimensions to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_var #' @@ -4037,30 +4055,30 @@ NULL #' Var_mean #' -#' @section var_mean(input, unbiased=True) -> (Tensor, Tensor) : +#' @section var_mean(input, unbiased=TRUE) -> (Tensor, Tensor) : #' #' Returns the variance and mean of all elements in the `input` tensor. #' -#' If `unbiased` is ``False``, then the variance will be calculated via the +#' If `unbiased` is `FALSE`, then the variance will be calculated via the #' biased estimator. Otherwise, Bessel's correction will be used. #' -#' @section var_mean(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor) : +#' @section var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor) : #' #' Returns the variance and mean of each row of the `input` tensor in the given #' dimension `dim`. #' #' -#' If `keepdim` is ``True``, the output tensor is of the same size +#' If `keepdim` is `TRUE`, the output tensor is of the same size #' as `input` except in the dimension(s) `dim` where it is of size 1. #' Otherwise, `dim` is squeezed (see [`torch_squeeze`]), resulting in the -#' output tensor having 1 (or ``len(dim)``) fewer dimension(s). +#' output tensor having 1 (or `len(dim)`) fewer dimension(s). #' #' -#' If `unbiased` is ``False``, then the variance will be calculated via the +#' If `unbiased` is `FALSE`, then the variance will be calculated via the #' biased estimator. Otherwise, Bessel's correction will be used. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param unbiased (bool) whether to use the unbiased estimation or not #' @param dim (int or tuple of ints) the dimension or dimensions to reduce. #' @param keepdim (bool) whether the output tensor has `dim` retained or not. @@ -4091,16 +4109,16 @@ NULL #' #' @section where(condition) -> tuple of LongTensor : #' -#' ``torch_where(condition)`` is identical to -#' ``torch_nonzero(condition, as_tuple=True)``. +#' `torch_where(condition)` is identical to +#' `torch_nonzero(condition, as_tuple=TRUE)`. #' #' @note -#' See also [`torch_nonzero`]. +#' See also [torch_nonzero()]. #' #' -#' @param condition (BoolTensor) When True (nonzero), yield x, otherwise yield y -#' @param x (Tensor) values selected at indices where `condition` is ``True`` -#' @param y (Tensor) values selected at indices where `condition` is ``False`` +#' @param condition (BoolTensor) When TRUE (nonzero), yield x, otherwise yield y +#' @param self (Tensor) values selected at indices where `condition` is `TRUE` +#' @param other (Tensor) values selected at indices where `condition` is `FALSE` #' #' @name torch_where #' @@ -4110,19 +4128,20 @@ NULL #' Zeros #' -#' @section zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor : +#' @section zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor : #' #' Returns a tensor filled with the scalar value `0`, with the shape defined #' by the variable argument `size`. #' #' -#' @param size (int...) a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. -#' @param out (Tensor, optional) the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, uses a global default (see `torch_set_default_tensor_type`). -#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: ``torch_strided``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' +#' @param ... a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. +#' +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, uses a global default (see `torch_set_default_tensor_type`). +#' @param layout (`torch.layout`, optional) the desired layout of returned Tensor. Default: `torch_strided`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param names optional dimension names +#' #' @name torch_zeros #' #' @export @@ -4131,24 +4150,24 @@ NULL #' Zeros_like #' -#' @section zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : +#' @section zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor : #' #' Returns a tensor filled with the scalar value `0`, with the same size as -#' `input`. ``torch_zeros_like(input)`` is equivalent to -#' ``torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. +#' `input`. `torch_zeros_like(input)` is equivalent to +#' `torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)`. #' #' @section Warning: #' As of 0.4, this function does not support an `out` keyword. As an alternative, -#' the old ``torch_zeros_like(input, out=output)`` is equivalent to -#' ``torch_zeros(input.size(), out=output)``. +#' the old `torch_zeros_like(input, out=output)` is equivalent to +#' `torch_zeros(input.size(), out=output)`. #' #' #' @param input (Tensor) the size of `input` will determine size of the output tensor. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if ``None``, defaults to the dtype of `input`. -#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if ``None``, defaults to the layout of `input`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, defaults to the device of `input`. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. -#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: ``torch_preserve_format``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned Tensor. Default: if `NULL`, defaults to the dtype of `input`. +#' @param layout (`torch.layout`, optional) the desired layout of returned tensor. Default: if `NULL`, defaults to the layout of `input`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, defaults to the device of `input`. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. +#' @param memory_format (`torch.memory_format`, optional) the desired memory format of returned Tensor. Default: `torch_preserve_format`. #' #' @name torch_zeros_like #' @@ -4158,7 +4177,7 @@ NULL #' Poisson #' -#' @section poisson(input *, generator=None) -> Tensor : +#' @section poisson(input *, generator=NULL) -> Tensor : #' #' Returns a tensor of the same size as `input` with each element #' sampled from a Poisson distribution with rate parameter given by the corresponding @@ -4169,7 +4188,7 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor containing the rates of the Poisson distribution +#' @param self (Tensor) the input tensor containing the rates of the Poisson distribution #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling #' #' @name torch_poisson @@ -4185,12 +4204,12 @@ NULL #' Returns the matrix norm or vector norm of a given tensor. #' #' -#' @param input (Tensor) the input tensor -#' @param p (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: ``'fro'`` The following norms can be calculated: ===== ============================ ========================== ord matrix norm vector norm ===== ============================ ========================== None Frobenius norm 2-norm 'fro' Frobenius norm -- 'nuc' nuclear norm -- Other as vec norm when dim is None sum(abs(x)**ord)**(1./ord) ===== ============================ ========================== -#' @param dim (int, 2-tuple of ints, 2-list of ints, optional) If it is an int, vector norm will be calculated, if it is 2-tuple of ints, matrix norm will be calculated. If the value is None, matrix norm will be calculated when the input tensor only has two dimensions, vector norm will be calculated when the input tensor only has one dimension. If the input tensor has more than two dimensions, the vector norm will be applied to last dimension. -#' @param keepdim (bool, optional) whether the output tensors have `dim` retained or not. Ignored if `dim` = ``None`` and `out` = ``None``. Default: ``False`` -#' @param out (Tensor, optional) the output tensor. Ignored if `dim` = ``None`` and `out` = ``None``. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to 'dtype' while performing the operation. Default: None. +#' @param self (Tensor) the input tensor +#' @param p (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: `'fro'` The following norms can be calculated: ===== ============================ ========================== ord matrix norm vector norm ===== ============================ ========================== NULL Frobenius norm 2-norm 'fro' Frobenius norm -- 'nuc' nuclear norm -- Other as vec norm when dim is NULL sum(abs(x)**ord)**(1./ord) ===== ============================ ========================== +#' @param dim (int, 2-tuple of ints, 2-list of ints, optional) If it is an int, vector norm will be calculated, if it is 2-tuple of ints, matrix norm will be calculated. If the value is NULL, matrix norm will be calculated when the input tensor only has two dimensions, vector norm will be calculated when the input tensor only has one dimension. If the input tensor has more than two dimensions, the vector norm will be applied to last dimension. +#' @param keepdim (bool, optional) whether the output tensors have `dim` retained or not. Ignored if `dim` = `NULL` and `out` = `NULL`. Default: `FALSE` +#' Ignored if `dim` = `NULL` and `out` = `NULL`. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. If specified, the input tensor is casted to 'dtype' while performing the operation. Default: NULL. #' #' @name torch_norm #' @@ -4200,12 +4219,12 @@ NULL #' Pow #' -#' @section pow(input, exponent, out=None) -> Tensor : +#' @section pow(input, exponent, out=NULL) -> Tensor : #' #' Takes the power of each element in `input` with `exponent` and #' returns a tensor with the result. #' -#' `exponent` can be either a single ``float`` number or a `Tensor` +#' `exponent` can be either a single `float` number or a `Tensor` #' with the same number of elements as `input`. #' #' When `exponent` is a scalar value, the operation applied is: @@ -4221,9 +4240,9 @@ NULL #' When `exponent` is a tensor, the shapes of `input` #' and `exponent` must be broadcastable . #' -#' @section pow(self, exponent, out=None) -> Tensor : +#' @section pow(self, exponent, out=NULL) -> Tensor : #' -#' `self` is a scalar ``float`` value, and `exponent` is a tensor. +#' `self` is a scalar `float` value, and `exponent` is a tensor. #' The returned tensor `out` is of the same shape as `exponent` #' #' The operation applied is: @@ -4233,9 +4252,9 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param exponent (float or tensor) the exponent value -#' @param out (Tensor, optional) the output tensor. +#' #' @param self (float) the scalar base value for the power operation #' #' @name torch_pow @@ -4246,7 +4265,7 @@ NULL #' Addmm #' -#' @section addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor : +#' @section addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor : #' #' Performs a matrix multiplication of the matrices `mat1` and `mat2`. #' The matrix `input` is added to the final result. @@ -4266,12 +4285,12 @@ NULL #' `alpha` must be real numbers, otherwise they should be integers. #' #' -#' @param input (Tensor) matrix to be added +#' @param self (Tensor) matrix to be added #' @param mat1 (Tensor) the first matrix to be multiplied #' @param mat2 (Tensor) the second matrix to be multiplied #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta}) #' @param alpha (Number, optional) multiplier for \eqn{mat1 @ mat2} (\eqn{\alpha}) -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_addmm #' @@ -4281,7 +4300,7 @@ NULL #' Sparse_coo_tensor #' -#' @section sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor : +#' @section sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor : #' #' Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given `indices` #' with the given `values`. A sparse tensor can be `uncoalesced`, in that case, there are duplicate @@ -4289,12 +4308,12 @@ NULL #' `torch_sparse`_. #' #' -#' @param indices (array_like) Initial data for the tensor. Can be a list, tuple, NumPy ``ndarray``, scalar, and other types. Will be cast to a `torch_LongTensor` internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values. -#' @param values (array_like) Initial values for the tensor. Can be a list, tuple, NumPy ``ndarray``, scalar, and other types. +#' @param indices (array_like) Initial data for the tensor. Can be a list, tuple, NumPy `ndarray`, scalar, and other types. Will be cast to a `torch_LongTensor` internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values. +#' @param values (array_like) Initial values for the tensor. Can be a list, tuple, NumPy `ndarray`, scalar, and other types. #' @param size (list, tuple, or `torch.Size`, optional) Size of the sparse tensor. If not provided the size will be inferred as the minimum size big enough to hold all non-zero elements. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if None, infers data type from `values`. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: ``False``. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if NULL, infers data type from `values`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if NULL, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param requires_grad (bool, optional) If autograd should record operations on the returned tensor. Default: `FALSE`. #' #' @name torch_sparse_coo_tensor #' @@ -4311,7 +4330,7 @@ NULL #' Returns a tuple of all slices along a given dimension, already without it. #' #' -#' @param input (Tensor) the tensor to unbind +#' @param self (Tensor) the tensor to unbind #' @param dim (int) dimension to remove #' #' @name torch_unbind @@ -4327,10 +4346,10 @@ NULL #' Converts a float tensor to quantized tensor with given scale and zero point. #' #' -#' @param input (Tensor) float tensor to quantize +#' @param self (Tensor) float tensor to quantize #' @param scale (float) scale to apply in quantization formula #' @param zero_point (int) offset in integer value that maps to float zero -#' @param dtype (`torch.dtype`) the desired data type of returned tensor. Has to be one of the quantized dtypes: ``torch_quint8``, ``torch.qint8``, ``torch.qint32`` +#' @param dtype (`torch.dtype`) the desired data type of returned tensor. Has to be one of the quantized dtypes: `torch_quint8`, `torch.qint8`, `torch.qint32` #' #' @name torch_quantize_per_tensor #' @@ -4345,11 +4364,11 @@ NULL #' Converts a float tensor to per-channel quantized tensor with given scales and zero points. #' #' -#' @param input (Tensor) float tensor to quantize -#' @param scales (Tensor) float 1D tensor of scales to use, size should match ``input.size(axis)`` -#' @param zero_points (int) integer 1D tensor of offset to use, size should match ``input.size(axis)`` +#' @param self (Tensor) float tensor to quantize +#' @param scales (Tensor) float 1D tensor of scales to use, size should match `input.size(axis)` +#' @param zero_points (int) integer 1D tensor of offset to use, size should match `input.size(axis)` #' @param axis (int) dimension on which apply per-channel quantization -#' @param dtype (`torch.dtype`) the desired data type of returned tensor. Has to be one of the quantized dtypes: ``torch_quint8``, ``torch.qint8``, ``torch.qint32`` +#' @param dtype (`torch.dtype`) the desired data type of returned tensor. Has to be one of the quantized dtypes: `torch_quint8`, `torch.qint8`, `torch.qint32` #' #' @name torch_quantize_per_channel #' @@ -4367,7 +4386,7 @@ NULL #' #' #' @param tensors (list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be -#' @param treated (1,) +#' treated (1,). #' #' @name torch_meshgrid #' @@ -4377,13 +4396,9 @@ NULL #' Cartesian_prod #' -#' @section TEST : -#' -#' Do cartesian product of the given sequence of tensors. The behavior is similar to -#' python's `itertools.product`. -#' +#' Do cartesian product of the given sequence of tensors. #' -#' @param *tensors NA any number of 1 dimensional tensors. +#' @param tensors a list containing any number of 1 dimensional tensors. #' #' @name torch_cartesian_prod #' @@ -4397,10 +4412,10 @@ NULL #' #' Compute combinations of length \eqn{r} of the given tensor. The behavior is similar to #' python's `itertools.combinations` when `with_replacement` is set to `False`, and -#' `itertools.combinations_with_replacement` when `with_replacement` is set to `True`. +#' `itertools.combinations_with_replacement` when `with_replacement` is set to `TRUE`. #' #' -#' @param input (Tensor) 1D vector. +#' @param self (Tensor) 1D vector. #' @param r (int, optional) number of elements to combine #' @param with_replacement (boolean, optional) whether to allow duplication in combination #' @@ -4418,9 +4433,9 @@ NULL #' operation on the provided input tensors. See type promotion documentation #' for more information on the type promotion logic. #' -#' #' @param tensor1 (Tensor or Number) an input tensor or number #' @param tensor2 (Tensor or Number) an input tensor or number +#' #' #' @name torch_result_type #' @@ -4466,15 +4481,15 @@ NULL #' Bitwise_and #' -#' @section bitwise_and(input, other, out=None) -> Tensor : +#' @section bitwise_and(input, other, out=NULL) -> Tensor : #' #' Computes the bitwise AND of `input` and `other`. The input tensor must be of #' integral or Boolean types. For bool tensors, it computes the logical AND. #' #' -#' @param input NA the first input tensor +#' @param self NA the first input tensor #' @param other NA the second input tensor -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_bitwise_and #' @@ -4484,15 +4499,15 @@ NULL #' Bitwise_or #' -#' @section bitwise_or(input, other, out=None) -> Tensor : +#' @section bitwise_or(input, other, out=NULL) -> Tensor : #' #' Computes the bitwise OR of `input` and `other`. The input tensor must be of #' integral or Boolean types. For bool tensors, it computes the logical OR. #' #' -#' @param input NA the first input tensor +#' @param self NA the first input tensor #' @param other NA the second input tensor -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_bitwise_or #' @@ -4502,15 +4517,15 @@ NULL #' Bitwise_xor #' -#' @section bitwise_xor(input, other, out=None) -> Tensor : +#' @section bitwise_xor(input, other, out=NULL) -> Tensor : #' #' Computes the bitwise XOR of `input` and `other`. The input tensor must be of #' integral or Boolean types. For bool tensors, it computes the logical XOR. #' #' -#' @param input NA the first input tensor +#' @param self NA the first input tensor #' @param other NA the second input tensor -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_bitwise_xor #' @@ -4520,7 +4535,7 @@ NULL #' Addbmm #' -#' @section addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor : +#' @section addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor : #' #' Performs a batch matrix-matrix product of matrices stored #' in `batch1` and `batch2`, @@ -4546,9 +4561,9 @@ NULL #' @param batch1 (Tensor) the first batch of matrices to be multiplied #' @param batch2 (Tensor) the second batch of matrices to be multiplied #' @param beta (Number, optional) multiplier for `input` (\eqn{\beta}) -#' @param input (Tensor) matrix to be added +#' @param self (Tensor) matrix to be added #' @param alpha (Number, optional) multiplier for `batch1 @ batch2` (\eqn{\alpha}) -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_addbmm #' @@ -4558,7 +4573,7 @@ NULL #' Diag #' -#' @section diag(input, diagonal=0, out=None) -> Tensor : +#' @section diag(input, diagonal=0, out=NULL) -> Tensor : #' #' - If `input` is a vector (1-D tensor), then returns a 2-D square tensor #' with the elements of `input` as the diagonal. @@ -4572,9 +4587,9 @@ NULL #' - If `diagonal` < 0, it is below the main diagonal. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param diagonal (int, optional) the diagonal to consider -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_diag #' @@ -4584,7 +4599,7 @@ NULL #' Cross #' -#' @section cross(input, other, dim=-1, out=None) -> Tensor : +#' @section cross(input, other, dim=-1, out=NULL) -> Tensor : #' #' Returns the cross product of vectors in dimension `dim` of `input` #' and `other`. @@ -4596,10 +4611,10 @@ NULL #' size 3. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param other (Tensor) the second input tensor #' @param dim (int, optional) the dimension to take the cross-product in. -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_cross #' @@ -4609,7 +4624,7 @@ NULL #' Triu #' -#' @section triu(input, diagonal=0, out=None) -> Tensor : +#' @section triu(input, diagonal=0, out=NULL) -> Tensor : #' #' Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices #' `input`, the other elements of the result tensor `out` are set to 0. @@ -4626,9 +4641,9 @@ NULL #' \eqn{d_{1}, d_{2}} are the dimensions of the matrix. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param diagonal (int, optional) the diagonal to consider -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_triu #' @@ -4638,7 +4653,7 @@ NULL #' Tril #' -#' @section tril(input, diagonal=0, out=None) -> Tensor : +#' @section tril(input, diagonal=0, out=NULL) -> Tensor : #' #' Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices #' `input`, the other elements of the result tensor `out` are set to 0. @@ -4655,9 +4670,9 @@ NULL #' \eqn{d_{1}, d_{2}} are the dimensions of the matrix. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param diagonal (int, optional) the diagonal to consider -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_tril #' @@ -4686,16 +4701,16 @@ NULL #' where \eqn{d_{1}, d_{2}} are the dimensions of the matrix. #' #' @note -#' When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to +#' When running on CUDA, `row * col` must be less than \eqn{2^{59}} to #' prevent overflow during calculation. #' #' -#' @param row (``int``) number of rows in the 2-D matrix. -#' @param col (``int``) number of columns in the 2-D matrix. -#' @param offset (``int``) diagonal offset from the main diagonal. Default: if not provided, 0. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, ``torch_long``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param layout (`torch.layout`, optional) currently only support ``torch_strided``. +#' @param row (`int`) number of rows in the 2-D matrix. +#' @param col (`int`) number of columns in the 2-D matrix. +#' @param offset (`int`) diagonal offset from the main diagonal. Default: if not provided, 0. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, `torch_long`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param layout (`torch.layout`, optional) currently only support `torch_strided`. #' #' @name torch_tril_indices #' @@ -4724,16 +4739,16 @@ NULL #' where \eqn{d_{1}, d_{2}} are the dimensions of the matrix. #' #' @note -#' When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to +#' When running on CUDA, `row * col` must be less than \eqn{2^{59}} to #' prevent overflow during calculation. #' #' -#' @param row (``int``) number of rows in the 2-D matrix. -#' @param col (``int``) number of columns in the 2-D matrix. -#' @param offset (``int``) diagonal offset from the main diagonal. Default: if not provided, 0. -#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if ``None``, ``torch_long``. -#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if ``None``, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. -#' @param layout (`torch.layout`, optional) currently only support ``torch_strided``. +#' @param row (`int`) number of rows in the 2-D matrix. +#' @param col (`int`) number of columns in the 2-D matrix. +#' @param offset (`int`) diagonal offset from the main diagonal. Default: if not provided, 0. +#' @param dtype (`torch.dtype`, optional) the desired data type of returned tensor. Default: if `NULL`, `torch_long`. +#' @param device (`torch.device`, optional) the desired device of returned tensor. Default: if `NULL`, uses the current device for the default tensor type (see `torch_set_default_tensor_type`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. +#' @param layout (`torch.layout`, optional) currently only support `torch_strided`. #' #' @name torch_triu_indices #' @@ -4747,8 +4762,7 @@ NULL #' #' Returns the sum of the elements of the diagonal of the input 2-D matrix. #' -#' -#' +#' @param self the input tensor #' #' @name torch_trace #' @@ -4758,7 +4772,7 @@ NULL #' Ne #' -#' @section ne(input, other, out=None) -> Tensor : +#' @section ne(input, other, out=NULL) -> Tensor : #' #' Computes \eqn{input \neq other} element-wise. #' @@ -4766,9 +4780,8 @@ NULL #' broadcastable with the first argument. #' #' -#' @param input (Tensor) the tensor to compare +#' @param self (Tensor) the tensor to compare #' @param other (Tensor or float) the tensor or value to compare -#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor` #' #' @name torch_ne #' @@ -4778,7 +4791,7 @@ NULL #' Eq #' -#' @section eq(input, other, out=None) -> Tensor : +#' @section eq(input, other, out=NULL) -> Tensor : #' #' Computes element-wise equality #' @@ -4786,9 +4799,9 @@ NULL #' broadcastable with the first argument. #' #' -#' @param input (Tensor) the tensor to compare +#' @param self (Tensor) the tensor to compare #' @param other (Tensor or float) the tensor or value to compare -#' @param out (Tensor, optional) the output tensor. Must be a `ByteTensor` +#' Must be a `ByteTensor` #' #' @name torch_eq #' @@ -4798,7 +4811,7 @@ NULL #' Ge #' -#' @section ge(input, other, out=None) -> Tensor : +#' @section ge(input, other, out=NULL) -> Tensor : #' #' Computes \eqn{\mbox{input} \geq \mbox{other}} element-wise. #' @@ -4806,9 +4819,8 @@ NULL #' broadcastable with the first argument. #' #' -#' @param input (Tensor) the tensor to compare +#' @param self (Tensor) the tensor to compare #' @param other (Tensor or float) the tensor or value to compare -#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor` #' #' @name torch_ge #' @@ -4818,7 +4830,7 @@ NULL #' Le #' -#' @section le(input, other, out=None) -> Tensor : +#' @section le(input, other, out=NULL) -> Tensor : #' #' Computes \eqn{\mbox{input} \leq \mbox{other}} element-wise. #' @@ -4826,9 +4838,8 @@ NULL #' broadcastable with the first argument. #' #' -#' @param input (Tensor) the tensor to compare +#' @param self (Tensor) the tensor to compare #' @param other (Tensor or float) the tensor or value to compare -#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor` #' #' @name torch_le #' @@ -4838,7 +4849,7 @@ NULL #' Gt #' -#' @section gt(input, other, out=None) -> Tensor : +#' @section gt(input, other, out=NULL) -> Tensor : #' #' Computes \eqn{\mbox{input} > \mbox{other}} element-wise. #' @@ -4846,9 +4857,8 @@ NULL #' broadcastable with the first argument. #' #' -#' @param input (Tensor) the tensor to compare +#' @param self (Tensor) the tensor to compare #' @param other (Tensor or float) the tensor or value to compare -#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor` #' #' @name torch_gt #' @@ -4858,7 +4868,7 @@ NULL #' Lt #' -#' @section lt(input, other, out=None) -> Tensor : +#' @section lt(input, other, out=NULL) -> Tensor : #' #' Computes \eqn{\mbox{input} < \mbox{other}} element-wise. #' @@ -4866,9 +4876,8 @@ NULL #' broadcastable with the first argument. #' #' -#' @param input (Tensor) the tensor to compare +#' @param self (Tensor) the tensor to compare #' @param other (Tensor or float) the tensor or value to compare -#' @param out (Tensor, optional) the output tensor that must be a `BoolTensor` #' #' @name torch_lt #' @@ -4885,8 +4894,8 @@ NULL #' takes the same shape as the indices. #' #' -#' @param input (Tensor) the input tensor. -#' @param indices (LongTensor) the indices into tensor +#' @param self (Tensor) the input tensor. +#' @param index (LongTensor) the indices into tensor #' #' @name torch_take #' @@ -4896,7 +4905,7 @@ NULL #' Index_select #' -#' @section index_select(input, dim, index, out=None) -> Tensor : +#' @section index_select(input, dim, index, out=NULL) -> Tensor : #' #' Returns a new tensor which indexes the `input` tensor along dimension #' `dim` using the entries in `index` which is a `LongTensor`. @@ -4911,10 +4920,10 @@ NULL #' storage if necessary. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int) the dimension in which we index #' @param index (LongTensor) the 1-D tensor containing the indices to index -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_index_select #' @@ -4924,7 +4933,7 @@ NULL #' Masked_select #' -#' @section masked_select(input, mask, out=None) -> Tensor : +#' @section masked_select(input, mask, out=NULL) -> Tensor : #' #' Returns a new 1-D tensor which indexes the `input` tensor according to #' the boolean mask `mask` which is a `BoolTensor`. @@ -4936,9 +4945,9 @@ NULL #' as the original tensor #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param mask (BoolTensor) the tensor containing the binary mask to index with -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_masked_select #' @@ -4952,16 +4961,16 @@ NULL #' [`torch_nonzero(..., as_tuple=False) `] (default) returns a #' 2-D tensor where each row is the index for a nonzero value. #' -#' [`torch_nonzero(..., as_tuple=True) `] returns a tuple of 1-D -#' index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]`` -#' gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor +#' [`torch_nonzero(..., as_tuple=TRUE) `] returns a tuple of 1-D +#' index tensors, allowing for advanced indexing, so `x[x.nonzero(as_tuple=TRUE)]` +#' gives all nonzero values of tensor `x`. Of the returned tuple, each index tensor #' contains nonzero indices for a certain dimension. #' #' See below for more details on the two behaviors. #' -#' @section nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors : +#' @section nonzero(input, *, out=NULL, as_tuple=False) -> LongTensor or tuple of LongTensors : #' -#' **When** `as_tuple` **is ``False`` (default)**: +#' **When** `as_tuple` **is `FALSE` (default)**: #' #' Returns a tensor containing the indices of all non-zero elements of #' `input`. Each row in the result contains the indices of a non-zero @@ -4972,7 +4981,7 @@ NULL #' `out` is of size \eqn{(z \times n)}, where \eqn{z} is the total number of #' non-zero elements in the `input` tensor. #' -#' **When** `as_tuple` **is ``True``**: +#' **When** `as_tuple` **is `TRUE`**: #' #' Returns a tuple of 1-D tensors, one for each dimension in `input`, #' each containing the indices (in that dimension) of all non-zero elements of @@ -4986,8 +4995,7 @@ NULL #' value, it is treated as a one-dimensional tensor with one element. #' #' -#' @param input (Tensor) the input tensor. -#' @param out (LongTensor, optional) the output tensor containing indices +#' @param self (Tensor) the input tensor. #' #' @name torch_nonzero #' @@ -4997,7 +5005,7 @@ NULL #' Gather #' -#' @section gather(input, dim, index, out=None, sparse_grad=False) -> Tensor : +#' @section gather(input, dim, index, sparse_grad=FALSE) -> Tensor : #' #' Gathers values along an axis specified by `dim`. #' @@ -5009,16 +5017,15 @@ NULL #' #' If `input` is an n-dimensional tensor with size #' \eqn{(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})} -#' and ``dim = i``, then `index` must be an \eqn{n}-dimensional tensor with +#' and `dim = i`, then `index` must be an \eqn{n}-dimensional tensor with #' size \eqn{(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})} where \eqn{y \geq 1} #' and `out` will have the same size as `index`. #' #' -#' @param input (Tensor) the source tensor +#' @param self (Tensor) the source tensor #' @param dim (int) the axis along which to index #' @param index (LongTensor) the indices of elements to gather -#' @param out (Tensor, optional) the destination tensor -#' @param sparse_grad (bool,optional) If ``True``, gradient w.r.t. `input` will be a sparse tensor. +#' @param sparse_grad (bool,optional) If `TRUE`, gradient w.r.t. `input` will be a sparse tensor. #' #' @name torch_gather #' @@ -5028,7 +5035,7 @@ NULL #' Addcmul #' -#' @section addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor : +#' @section addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor : #' #' Performs the element-wise multiplication of `tensor1` #' by `tensor2`, multiply the result by the scalar `value` @@ -5044,11 +5051,11 @@ NULL #' a real number, otherwise an integer. #' #' -#' @param input (Tensor) the tensor to be added +#' @param self (Tensor) the tensor to be added #' @param tensor1 (Tensor) the tensor to be multiplied #' @param tensor2 (Tensor) the tensor to be multiplied #' @param value (Number, optional) multiplier for \eqn{tensor1 .* tensor2} -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_addcmul #' @@ -5058,7 +5065,7 @@ NULL #' Addcdiv #' -#' @section addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor : +#' @section addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor : #' #' Performs the element-wise division of `tensor1` by `tensor2`, #' multiply the result by the scalar `value` and add it to `input`. @@ -5086,11 +5093,11 @@ NULL #' a real number, otherwise an integer. #' #' -#' @param input (Tensor) the tensor to be added +#' @param self (Tensor) the tensor to be added #' @param tensor1 (Tensor) the numerator tensor #' @param tensor2 (Tensor) the denominator tensor #' @param value (Number, optional) multiplier for \eqn{\mbox{tensor1} / \mbox{tensor2}} -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_addcdiv #' @@ -5100,7 +5107,7 @@ NULL #' Lstsq #' -#' @section lstsq(input, A, out=None) -> Tensor : +#' @section lstsq(input, A, out=NULL) -> Tensor : #' #' Computes the solution to the least squares and least norm problems for a full #' rank matrix \eqn{A} of size \eqn{(m \times n)} and a matrix \eqn{B} of @@ -5129,9 +5136,8 @@ NULL #' The case when \eqn{m < n} is not supported on the GPU. #' #' -#' @param input (Tensor) the matrix \eqn{B} +#' @param self (Tensor) the matrix \eqn{B} #' @param A (Tensor) the \eqn{m} by \eqn{n} matrix \eqn{A} -#' @param out (tuple, optional) the optional destination tensor #' #' @name torch_lstsq #' @@ -5141,7 +5147,7 @@ NULL #' Triangular_solve #' -#' @section triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) : +#' @section triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor) : #' #' Solves a system of equations with a triangular coefficient matrix \eqn{A} #' and multiple right-hand sides \eqn{b}. @@ -5154,11 +5160,11 @@ NULL #' batched outputs `X` #' #' -#' @param input (Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where \eqn{*} is zero of more batch dimensions (\eqn{b}) +#' @param self (Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where \eqn{*} is zero of more batch dimensions (\eqn{b}) #' @param A (Tensor) the input triangular coefficient matrix of size \eqn{(*, m, m)} where \eqn{*} is zero or more batch dimensions -#' @param upper (bool, optional) whether to solve the upper-triangular system of equations (default) or the lower-triangular system of equations. Default: ``True``. -#' @param transpose (bool, optional) whether \eqn{A} should be transposed before being sent into the solver. Default: ``False``. -#' @param unitriangular (bool, optional) whether \eqn{A} is unit triangular. If True, the diagonal elements of \eqn{A} are assumed to be 1 and not referenced from \eqn{A}. Default: ``False``. +#' @param upper (bool, optional) whether to solve the upper-triangular system of equations (default) or the lower-triangular system of equations. Default: `TRUE`. +#' @param transpose (bool, optional) whether \eqn{A} should be transposed before being sent into the solver. Default: `FALSE`. +#' @param unitriangular (bool, optional) whether \eqn{A} is unit triangular. If TRUE, the diagonal elements of \eqn{A} are assumed to be 1 and not referenced from \eqn{A}. Default: `FALSE`. #' #' @name torch_triangular_solve #' @@ -5168,7 +5174,7 @@ NULL #' Symeig #' -#' @section symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) : +#' @section symeig(input, eigenvectors=False, upper=TRUE) -> (Tensor, Tensor) : #' #' This function returns eigenvalues and eigenvectors #' of a real symmetric matrix `input` or a batch of real symmetric matrices, @@ -5180,13 +5186,13 @@ NULL #' The boolean argument `eigenvectors` defines computation of #' both eigenvectors and eigenvalues or eigenvalues only. #' -#' If it is ``False``, only eigenvalues are computed. If it is ``True``, +#' If it is `FALSE`, only eigenvalues are computed. If it is `TRUE`, #' both eigenvalues and eigenvectors are computed. #' #' Since the input matrix `input` is supposed to be symmetric, #' only the upper triangular portion is used by default. #' -#' If `upper` is ``False``, then lower triangular portion is used. +#' If `upper` is `FALSE`, then lower triangular portion is used. #' #' @note The eigenvalues are returned in ascending order. If `input` is a batch of matrices, #' then the eigenvalues of each matrix in the batch is returned in ascending order. @@ -5196,13 +5202,12 @@ NULL #' #' @note Extra care needs to be taken when backward through outputs. Such #' operation is really only stable when all eigenvalues are distinct. -#' Otherwise, ``NaN`` can appear as the gradients are not properly defined. +#' Otherwise, `NaN` can appear as the gradients are not properly defined. #' #' -#' @param input (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more batch dimensions consisting of symmetric matrices. +#' @param self (Tensor) the input tensor of size \eqn{(*, n, n)} where `*` is zero or more batch dimensions consisting of symmetric matrices. #' @param eigenvectors (boolean, optional) controls whether eigenvectors have to be computed #' @param upper (boolean, optional) controls whether to consider upper-triangular or lower-triangular region -#' @param out (tuple, optional) the output tuple of (Tensor, Tensor) #' #' @name torch_symeig #' @@ -5212,7 +5217,7 @@ NULL #' Eig #' -#' @section eig(input, eigenvectors=False, out=None) -> (Tensor, Tensor) : +#' @section eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor) : #' #' Computes the eigenvalues and eigenvectors of a real square matrix. #' @@ -5221,9 +5226,8 @@ NULL #' for [`torch_symeig`] #' #' -#' @param input (Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors will be computed -#' @param eigenvectors (bool) ``True`` to compute both eigenvalues and eigenvectors; otherwise, only eigenvalues will be computed -#' @param out (tuple, optional) the output tensors +#' @param self (Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors will be computed +#' @param eigenvectors (bool) `TRUE` to compute both eigenvalues and eigenvectors; otherwise, only eigenvalues will be computed #' #' @name torch_eig #' @@ -5233,17 +5237,17 @@ NULL #' Svd #' -#' @section svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) : +#' @section svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) : #' -#' This function returns a namedtuple ``(U, S, V)`` which is the singular value +#' This function returns a namedtuple `(U, S, V)` which is the singular value #' decomposition of a input real matrix or batches of real matrices `input` such that #' \eqn{input = U \times diag(S) \times V^T}. #' -#' If `some` is ``True`` (default), the method returns the reduced singular value decomposition -#' i.e., if the last two dimensions of `input` are ``m`` and ``n``, then the returned +#' If `some` is `TRUE` (default), the method returns the reduced singular value decomposition +#' i.e., if the last two dimensions of `input` are `m` and `n`, then the returned #' `U` and `V` matrices will contain only \eqn{min(n, m)} orthonormal columns. #' -#' If `compute_uv` is ``False``, the returned `U` and `V` matrices will be zero matrices +#' If `compute_uv` is `FALSE`, the returned `U` and `V` matrices will be zero matrices #' of shape \eqn{(m \times m)} and \eqn{(n \times n)} respectively. `some` will be ignored here. #' #' @note The singular values are returned in descending order. If `input` is a batch of matrices, @@ -5258,23 +5262,22 @@ NULL #' #' @note Extra care needs to be taken when backward through `U` and `V` #' outputs. Such operation is really only stable when `input` is -#' full rank with all distinct singular values. Otherwise, ``NaN`` can +#' full rank with all distinct singular values. Otherwise, `NaN` can #' appear as the gradients are not properly defined. Also, notice that #' double backward will usually do an additional backward through `U` and #' `V` even if the original backward is only on `S`. #' -#' @note When `some` = ``False``, the gradients on `U[..., :, min(m, n):]` +#' @note When `some` = `FALSE`, the gradients on `U[..., :, min(m, n):]` #' and `V[..., :, min(m, n):]` will be ignored in backward as those vectors #' can be arbitrary bases of the subspaces. #' -#' @note When `compute_uv` = ``False``, backward cannot be performed since `U` and `V` +#' @note When `compute_uv` = `FALSE`, backward cannot be performed since `U` and `V` #' from the forward pass is required for the backward operation. #' #' -#' @param input (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more batch dimensions consisting of \eqn{m \times n} matrices. +#' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more batch dimensions consisting of \eqn{m \times n} matrices. #' @param some (bool, optional) controls the shape of returned `U` and `V` #' @param compute_uv (bool, optional) option whether to compute `U` and `V` or not -#' @param out (tuple, optional) the output tuple of tensors #' #' @name torch_svd #' @@ -5284,33 +5287,34 @@ NULL #' Cholesky #' -#' @section cholesky(input, upper=False, out=None) -> Tensor : +#' @section cholesky(input, upper=False, out=NULL) -> Tensor : #' #' Computes the Cholesky decomposition of a symmetric positive-definite #' matrix \eqn{A} or for batches of symmetric positive-definite matrices. #' -#' If `upper` is ``True``, the returned matrix ``U`` is upper-triangular, and +#' If `upper` is `TRUE`, the returned matrix `U` is upper-triangular, and #' the decomposition has the form: #' #' \deqn{ #' A = U^TU #' } -#' If `upper` is ``False``, the returned matrix ``L`` is lower-triangular, and +#' If `upper` is `FALSE`, the returned matrix `L` is lower-triangular, and #' the decomposition has the form: #' #' \deqn{ #' A = LL^T #' } -#' If `upper` is ``True``, and \eqn{A} is a batch of symmetric positive-definite +#' If `upper` is `TRUE`, and \eqn{A} is a batch of symmetric positive-definite #' matrices, then the returned tensor will be composed of upper-triangular Cholesky factors -#' of each of the individual matrices. Similarly, when `upper` is ``False``, the returned +#' of each of the individual matrices. Similarly, when `upper` is `FALSE`, the returned #' tensor will be composed of lower-triangular Cholesky factors of each of the individual #' matrices. #' #' -#' @param input (Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where `*` is zero or more batch dimensions consisting of symmetric positive-definite matrices. -#' @param upper (bool, optional) flag that indicates whether to return a upper or lower triangular matrix. Default: ``False`` -#' @param out (Tensor, optional) the output matrix +#' @param self (Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where `*` is zero or more +#' batch dimensions consisting of symmetric positive-definite matrices. +#' @param upper (bool, optional) flag that indicates whether to return a +#' upper or lower triangular matrix. Default: `FALSE` #' #' @name torch_cholesky #' @@ -5320,18 +5324,18 @@ NULL #' Cholesky_solve #' -#' @section cholesky_solve(input, input2, upper=False, out=None) -> Tensor : +#' @section cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor : #' #' Solves a linear system of equations with a positive semidefinite #' matrix to be inverted given its Cholesky factor matrix \eqn{u}. #' -#' If `upper` is ``False``, \eqn{u} is and lower triangular and `c` is +#' If `upper` is `FALSE`, \eqn{u} is and lower triangular and `c` is #' returned such that: #' #' \deqn{ #' c = (u u^T)^{{-1}} b #' } -#' If `upper` is ``True`` or not provided, \eqn{u} is upper triangular +#' If `upper` is `TRUE` or not provided, \eqn{u} is upper triangular #' and `c` is returned such that: #' #' \deqn{ @@ -5342,10 +5346,9 @@ NULL #' batched outputs `c` #' #' -#' @param input (Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)}, where \eqn{*} is zero or more batch dimensions +#' @param self (Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)}, where \eqn{*} is zero or more batch dimensions #' @param input2 (Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)}, where \eqn{*} is zero of more batch dimensions composed of upper or lower triangular Cholesky factor -#' @param upper (bool, optional) whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: ``False``. -#' @param out (Tensor, optional) the output tensor for `c` +#' @param upper (bool, optional) whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: `FALSE`. #' #' @name torch_cholesky_solve #' @@ -5355,7 +5358,7 @@ NULL #' Solve #' -#' @section torch.solve(input, A, out=None) -> (Tensor, Tensor) : +#' @section solve(input, A) -> (Tensor, Tensor) : #' #' This function returns the solution to the system of linear #' equations represented by \eqn{AX = B} and the LU factorization of @@ -5371,13 +5374,12 @@ NULL #' #' Irrespective of the original strides, the returned matrices #' `solution` and `LU` will be transposed, i.e. with strides like -#' `B.contiguous().transpose(-1, -2).stride()` and -#' `A.contiguous().transpose(-1, -2).stride()` respectively. +#' `B$contiguous()$transpose(-1, -2)$stride()` and +#' `A$contiguous()$transpose(-1, -2)$stride()` respectively. #' #' -#' @param input (Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*} is zero or more batch dimensions. +#' @param self (Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*} is zero or more batch dimensions. #' @param A (Tensor) input square matrix of size \eqn{(*, m, m)}, where \eqn{*} is zero or more batch dimensions. -#' @param out ((Tensor, Tensor) optional output tuple. #' #' @name torch_solve #' @@ -5387,19 +5389,19 @@ NULL #' Cholesky_inverse #' -#' @section cholesky_inverse(input, upper=False, out=None) -> Tensor : +#' @section cholesky_inverse(input, upper=False, out=NULL) -> Tensor : #' #' Computes the inverse of a symmetric positive-definite matrix \eqn{A} using its -#' Cholesky factor \eqn{u}: returns matrix ``inv``. The inverse is computed using -#' LAPACK routines ``dpotri`` and ``spotri`` (and the corresponding MAGMA routines). +#' Cholesky factor \eqn{u}: returns matrix `inv`. The inverse is computed using +#' LAPACK routines `dpotri` and `spotri` (and the corresponding MAGMA routines). #' -#' If `upper` is ``False``, \eqn{u} is lower triangular +#' If `upper` is `FALSE`, \eqn{u} is lower triangular #' such that the returned tensor is #' #' \deqn{ #' inv = (uu^{{T}})^{{-1}} #' } -#' If `upper` is ``True`` or not provided, \eqn{u} is upper +#' If `upper` is `TRUE` or not provided, \eqn{u} is upper #' triangular such that the returned tensor is #' #' \deqn{ @@ -5407,9 +5409,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular Cholesky factor +#' @param self (Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular Cholesky factor #' @param upper (bool, optional) whether to return a lower (default) or upper triangular matrix -#' @param out (Tensor, optional) the output tensor for `inv` #' #' @name torch_cholesky_inverse #' @@ -5419,15 +5420,15 @@ NULL #' Qr #' -#' @section qr(input, some=True, out=None) -> (Tensor, Tensor) : +#' @section qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor) : #' #' Computes the QR decomposition of a matrix or a batch of matrices `input`, #' and returns a namedtuple (Q, R) of tensors such that \eqn{\mbox{input} = Q R} #' with \eqn{Q} being an orthogonal matrix or batch of orthogonal matrices and #' \eqn{R} being an upper triangular matrix or batch of upper triangular matrices. #' -#' If `some` is ``True``, then this function returns the thin (reduced) QR factorization. -#' Otherwise, if `some` is ``False``, this function returns the complete QR factorization. +#' If `some` is `TRUE`, then this function returns the thin (reduced) QR factorization. +#' Otherwise, if `some` is `FALSE`, this function returns the complete QR factorization. #' #' @note precision may be lost if the magnitudes of the elements of `input` #' are large @@ -5437,9 +5438,8 @@ NULL #' LAPACK implementation. #' #' -#' @param input (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more batch dimensions consisting of matrices of dimension \eqn{m \times n}. -#' @param some (bool, optional) Set to ``True`` for reduced QR decomposition and ``False`` for complete QR decomposition. -#' @param out (tuple, optional) tuple of `Q` and `R` tensors satisfying `input = torch.matmul(Q, R)`. The dimensions of `Q` and `R` are \eqn{(*, m, k)} and \eqn{(*, k, n)} respectively, where \eqn{k = \min(m, n)} if `some:` is ``True`` and \eqn{k = m} otherwise. +#' @param self (Tensor) the input tensor of size \eqn{(*, m, n)} where `*` is zero or more batch dimensions consisting of matrices of dimension \eqn{m \times n}. +#' @param some (bool, optional) Set to `TRUE` for reduced QR decomposition and `FALSE` for complete QR decomposition. #' #' @name torch_qr #' @@ -5449,7 +5449,7 @@ NULL #' Geqrf #' -#' @section geqrf(input, out=None) -> (Tensor, Tensor) : +#' @section geqrf(input, out=NULL) -> (Tensor, Tensor) : #' #' This is a low-level function for calling LAPACK directly. This function #' returns a namedtuple (a, tau) as defined in `LAPACK documentation for geqrf`_ . @@ -5465,8 +5465,7 @@ NULL #' See `LAPACK documentation for geqrf`_ for further details. #' #' -#' @param input (Tensor) the input matrix -#' @param out (tuple, optional) the output tuple of (Tensor, Tensor) +#' @param self (Tensor) the input matrix #' #' @name torch_geqrf #' @@ -5485,7 +5484,7 @@ NULL #' See `LAPACK documentation for orgqr`_ for further details. #' #' -#' @param input (Tensor) the `a` from [`torch_geqrf`]. +#' @param self (Tensor) the `a` from [`torch_geqrf`]. #' @param input2 (Tensor) the `tau` from [`torch_geqrf`]. #' #' @name torch_orgqr @@ -5496,18 +5495,20 @@ NULL #' Ormqr #' -#' @section ormqr(input, input2, input3, left=True, transpose=False) -> Tensor : +#' @section ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor : #' #' Multiplies `mat` (given by `input3`) by the orthogonal `Q` matrix of the QR factorization -#' formed by [`torch_geqrf`] that is represented by `(a, tau)` (given by (`input`, `input2`)). +#' formed by [torch_geqrf()] that is represented by `(a, tau)` (given by (`input`, `input2`)). #' #' This directly calls the underlying LAPACK function `?ormqr`. -#' See `LAPACK documentation for ormqr`_ for further details. +#' See [LAPACK documentation for ormqr](https://software.intel.com/content/www/us/en/develop/documentation/mkl-developer-reference-c/top/scalapack-routines/scalapack-computational-routines/orthogonal-factorizations-scalapack-computational-routines/p-ormqr.html) for further details. #' #' -#' @param input (Tensor) the `a` from [`torch_geqrf`]. +#' @param self (Tensor) the `a` from [`torch_geqrf`]. #' @param input2 (Tensor) the `tau` from [`torch_geqrf`]. #' @param input3 (Tensor) the matrix to be multiplied. +#' @param left see LAPACK documentation +#' @param transpose see LAPACK documentation #' #' @name torch_ormqr #' @@ -5517,16 +5518,16 @@ NULL #' Lu_solve #' -#' @section lu_solve(input, LU_data, LU_pivots, out=None) -> Tensor : +#' @section lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor : #' #' Returns the LU solve of the linear system \eqn{Ax = b} using the partially pivoted #' LU factorization of A from `torch_lu`. #' #' -#' @param b (Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*} is zero or more batch dimensions. +#' @param self (Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*} is zero or more batch dimensions. #' @param LU_data (Tensor) the pivoted LU factorization of A from `torch_lu` of size \eqn{(*, m, m)}, where \eqn{*} is zero or more batch dimensions. #' @param LU_pivots (IntTensor) the pivots of the LU factorization from `torch_lu` of size \eqn{(*, m)}, where \eqn{*} is zero or more batch dimensions. The batch dimensions of `LU_pivots` must be equal to the batch dimensions of `LU_data`. -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_lu_solve #' @@ -5536,7 +5537,7 @@ NULL #' Multinomial #' -#' @section multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor : +#' @section multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor : #' #' Returns a tensor where each row contains `num_samples` indices sampled #' from the multinomial probability distribution located in the corresponding row @@ -5555,7 +5556,7 @@ NULL #' If `input` is a matrix with `m` rows, `out` is an matrix of shape #' \eqn{(m \times \mbox{num\_samples})}. #' -#' If replacement is ``True``, samples are drawn with replacement. +#' If replacement is `TRUE`, samples are drawn with replacement. #' #' If not, they are drawn without replacement, which means that when a #' sample index is drawn for a row, it cannot be drawn again for that row. @@ -5566,11 +5567,11 @@ NULL #' elements in each row of `input` if it is a matrix). #' #' -#' @param input (Tensor) the input tensor containing probabilities +#' @param self (Tensor) the input tensor containing probabilities #' @param num_samples (int) number of samples to draw #' @param replacement (bool, optional) whether to draw with replacement or not #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_multinomial #' @@ -5580,7 +5581,7 @@ NULL #' Lgamma #' -#' @section lgamma(input, out=None) -> Tensor : +#' @section lgamma(input, out=NULL) -> Tensor : #' #' Computes the logarithm of the gamma function on `input`. #' @@ -5589,8 +5590,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_lgamma #' @@ -5600,7 +5601,7 @@ NULL #' Digamma #' -#' @section digamma(input, out=None) -> Tensor : +#' @section digamma(input, out=NULL) -> Tensor : #' #' Computes the logarithmic derivative of the gamma function on `input`. #' @@ -5609,7 +5610,7 @@ NULL #' } #' #' -#' @param input (Tensor) the tensor to compute the digamma function on +#' @param self (Tensor) the tensor to compute the digamma function on #' #' @name torch_digamma #' @@ -5619,7 +5620,7 @@ NULL #' Polygamma #' -#' @section polygamma(n, input, out=None) -> Tensor : +#' @section polygamma(n, input, out=NULL) -> Tensor : #' #' Computes the \eqn{n^{th}} derivative of the digamma function on `input`. #' \eqn{n \geq 0} is called the order of the polygamma function. @@ -5632,8 +5633,8 @@ NULL #' #' #' @param n (int) the order of the polygamma function -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_polygamma #' @@ -5643,7 +5644,7 @@ NULL #' Erfinv #' -#' @section erfinv(input, out=None) -> Tensor : +#' @section erfinv(input, out=NULL) -> Tensor : #' #' Computes the inverse error function of each element of `input`. #' The inverse error function is defined in the range \eqn{(-1, 1)} as: @@ -5653,8 +5654,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_erfinv #' @@ -5664,7 +5665,7 @@ NULL #' Sign #' -#' @section sign(input, out=None) -> Tensor : +#' @section sign(input, out=NULL) -> Tensor : #' #' Returns a new tensor with the signs of the elements of `input`. #' @@ -5673,8 +5674,8 @@ NULL #' } #' #' -#' @param input (Tensor) the input tensor. -#' @param out (Tensor, optional) the output tensor. +#' @param self (Tensor) the input tensor. +#' #' #' @name torch_sign #' @@ -5692,7 +5693,7 @@ NULL #' broadcastable . #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param other (Tensor) the Right-hand-side input tensor #' @param p (float, optional) the norm to be computed #' @@ -5704,7 +5705,7 @@ NULL #' Atan2 #' -#' @section atan2(input, other, out=None) -> Tensor : +#' @section atan2(input, other, out=NULL) -> Tensor : #' #' Element-wise arctangent of \eqn{\mbox{input}_{i} / \mbox{other}_{i}} #' with consideration of the quadrant. Returns a new tensor with the signed angles @@ -5713,13 +5714,13 @@ NULL #' parameter, is the x-coordinate, while \eqn{\mbox{input}_{i}}, the first #' parameter, is the y-coordinate.) #' -#' The shapes of ``input`` and ``other`` must be +#' The shapes of `input` and `other` must be #' broadcastable . #' #' -#' @param input (Tensor) the first input tensor +#' @param self (Tensor) the first input tensor #' @param other (Tensor) the second input tensor -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_atan2 #' @@ -5729,7 +5730,7 @@ NULL #' Lerp #' -#' @section lerp(input, end, weight, out=None) : +#' @section lerp(input, end, weight, out=NULL) : #' #' Does a linear interpolation of two tensors `start` (given by `input`) and `end` based #' on a scalar or tensor `weight` and returns the resulting `out` tensor. @@ -5742,10 +5743,10 @@ NULL #' the shapes of `weight`, `start`, and `end` must be broadcastable . #' #' -#' @param input (Tensor) the tensor with the starting points +#' @param self (Tensor) the tensor with the starting points #' @param end (Tensor) the tensor with the ending points #' @param weight (float or tensor) the weight for the interpolation formula -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_lerp #' @@ -5755,7 +5756,7 @@ NULL #' Histc #' -#' @section histc(input, bins=100, min=0, max=0, out=None) -> Tensor : +#' @section histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor : #' #' Computes the histogram of a tensor. #' @@ -5764,11 +5765,11 @@ NULL #' maximum values of the data are used. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param bins (int) number of histogram bins #' @param min (int) lower end of the range (inclusive) #' @param max (int) upper end of the range (inclusive) -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_histc #' @@ -5778,7 +5779,7 @@ NULL #' Fmod #' -#' @section fmod(input, other, out=None) -> Tensor : +#' @section fmod(input, other, out=NULL) -> Tensor : #' #' Computes the element-wise remainder of division. #' @@ -5789,9 +5790,9 @@ NULL #' `other` must be broadcastable . #' #' -#' @param input (Tensor) the dividend +#' @param self (Tensor) the dividend #' @param other (Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_fmod #' @@ -5801,7 +5802,7 @@ NULL #' Remainder #' -#' @section remainder(input, other, out=None) -> Tensor : +#' @section remainder(input, other, out=NULL) -> Tensor : #' #' Computes the element-wise remainder of division. #' @@ -5812,9 +5813,9 @@ NULL #' `other` must be broadcastable . #' #' -#' @param input (Tensor) the dividend +#' @param self (Tensor) the dividend #' @param other (Tensor or float) the divisor that may be either a number or a Tensor of the same shape as the dividend -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_remainder #' @@ -5824,14 +5825,14 @@ NULL #' Sort #' -#' @section sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor) : +#' @section sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor) : #' #' Sorts the elements of the `input` tensor along a given dimension #' in ascending order by value. #' #' If `dim` is not given, the last dimension of the `input` is chosen. #' -#' If `descending` is ``True`` then the elements are sorted in descending +#' If `descending` is `TRUE` then the elements are sorted in descending #' order by value. #' #' A namedtuple of (values, indices) is returned, where the `values` are the @@ -5839,10 +5840,9 @@ NULL #' `input` tensor. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int, optional) the dimension to sort along #' @param descending (bool, optional) controls the sorting order (ascending or descending) -#' @param out (tuple, optional) the output tuple of (`Tensor`, `LongTensor`) that can be optionally given to be used as output buffers #' #' @name torch_sort #' @@ -5861,7 +5861,7 @@ NULL #' for the exact semantics of this method. #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param dim (int, optional) the dimension to sort along #' @param descending (bool, optional) controls the sorting order (ascending or descending) #' @@ -5873,28 +5873,27 @@ NULL #' Topk #' -#' @section topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) : +#' @section topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) : #' #' Returns the `k` largest elements of the given `input` tensor along #' a given dimension. #' #' If `dim` is not given, the last dimension of the `input` is chosen. #' -#' If `largest` is ``False`` then the `k` smallest elements are returned. +#' If `largest` is `FALSE` then the `k` smallest elements are returned. #' #' A namedtuple of `(values, indices)` is returned, where the `indices` are the indices #' of the elements in the original `input` tensor. #' -#' The boolean option `sorted` if ``True``, will make sure that the returned +#' The boolean option `sorted` if `TRUE`, will make sure that the returned #' `k` elements are themselves sorted #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param k (int) the k in "top-k" #' @param dim (int, optional) the dimension to sort along #' @param largest (bool, optional) controls whether to return largest or smallest elements #' @param sorted (bool, optional) controls whether to return the elements in sorted order -#' @param out (tuple, optional) the output tuple of (Tensor, LongTensor) that can be optionally given to be used as output buffers #' #' @name torch_topk #' @@ -5904,7 +5903,7 @@ NULL #' Renorm #' -#' @section renorm(input, p, dim, maxnorm, out=None) -> Tensor : +#' @section renorm(input, p, dim, maxnorm, out=NULL) -> Tensor : #' #' Returns a tensor where each sub-tensor of `input` along dimension #' `dim` is normalized such that the `p`-norm of the sub-tensor is lower @@ -5913,11 +5912,11 @@ NULL #' @note If the norm of a row is lower than `maxnorm`, the row is unchanged #' #' -#' @param input (Tensor) the input tensor. +#' @param self (Tensor) the input tensor. #' @param p (float) the power for the norm computation #' @param dim (int) the dimension to slice over to get the sub-tensors #' @param maxnorm (float) the maximum norm to keep each sub-tensor under -#' @param out (Tensor, optional) the output tensor. +#' #' #' @name torch_renorm #' @@ -5929,8 +5928,10 @@ NULL #' #' @section equal(input, other) -> bool : #' -#' ``True`` if two tensors have the same size and elements, ``False`` otherwise. +#' `TRUE` if two tensors have the same size and elements, `FALSE` otherwise. #' +#' @param self the input tensor +#' @param other the other input tensor #' #' #' @@ -5942,7 +5943,7 @@ NULL #' Normal #' -#' @section normal(mean, std, *, generator=None, out=None) -> Tensor : +#' @section normal(mean, std, *, generator=NULL, out=NULL) -> Tensor : #' #' Returns a tensor of random numbers drawn from separate normal distributions #' whose mean and standard deviation are given. @@ -5959,17 +5960,17 @@ NULL #' @note When the shapes do not match, the shape of `mean` #' is used as the shape for the returned output tensor #' -#' @section normal(mean=0.0, std, out=None) -> Tensor : +#' @section normal(mean=0.0, std, out=NULL) -> Tensor : #' #' Similar to the function above, but the means are shared among all drawn #' elements. #' -#' @section normal(mean, std=1.0, out=None) -> Tensor : +#' @section normal(mean, std=1.0, out=NULL) -> Tensor : #' #' Similar to the function above, but the standard-deviations are shared among #' all drawn elements. #' -#' @section normal(mean, std, size, *, out=None) -> Tensor : +#' @section normal(mean, std, size, *, out=NULL) -> Tensor : #' #' Similar to the function above, but the means and standard deviations are shared #' among all drawn elements. The resulting tensor has size given by `size`. @@ -5978,7 +5979,7 @@ NULL #' @param mean (Tensor) the tensor of per-element means #' @param std (Tensor) the tensor of per-element standard deviations #' @param generator (`torch.Generator`, optional) a pseudorandom number generator for sampling -#' @param out (Tensor, optional) the output tensor. +#' #' @param size (int...) a sequence of integers defining the shape of the output tensor. #' #' @name torch_normal @@ -5994,7 +5995,7 @@ NULL #' Returns a new tensor with boolean elements representing if each element is `Finite` or not. #' #' -#' @param tensor (Tensor) A tensor to check +#' @param self (Tensor) A tensor to check #' #' @name torch_isfinite #' @@ -6009,7 +6010,7 @@ NULL #' Returns a new tensor with boolean elements representing if each element is `+/-INF` or not. #' #' -#' @param tensor (Tensor) A tensor to check +#' @param self (Tensor) A tensor to check #' #' @name torch_isinf #' diff --git a/R/gen-namespace-examples.R b/R/gen-namespace-examples.R index 5d400681ee46f2e27d8331688dc2cbe690708475..c026063fc799a67fea2319bfe0cd9f7cd880a4e9 100644 --- a/R/gen-namespace-examples.R +++ b/R/gen-namespace-examples.R @@ -641,6 +641,8 @@ NULL #' @name torch_einsum #' #' @examples +#' +#' if (FALSE) { #' #' x = torch_randn(c(5)) #' y = torch_randn(c(4)) @@ -658,6 +660,8 @@ NULL #' torch_einsum('...ii->...i', list(A)) # batch diagonal #' A = torch_randn(c(2, 3, 4, 5)) #' torch_einsum('...ij->...ji', list(A))$shape # batch permute +#' +#' } NULL # -> einsum <- @@ -1739,7 +1743,7 @@ NULL #' #' a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5)) #' b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2)) -#' torch_tensordot(a, b, dims_self=c(1, 0), dims_other = c(0, 1)) +#' torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2))) #' \dontrun{ #' a = torch_randn(3, 4, 5, device='cuda') #' b = torch_randn(4, 5, 6, device='cuda') @@ -2090,8 +2094,7 @@ NULL #' #' @examples #' -#' torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0) -#' # torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1)) +#' torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1) NULL # -> result_type <- @@ -2311,7 +2314,7 @@ NULL #' @examples #' #' src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE)) -#' torch_take(src, torch_tensor(c(0, 2, 5), dtype = torch_int64())) +#' torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64())) NULL # -> take <- diff --git a/R/gen-namespace.R b/R/gen-namespace.R index e8016f1071fd59467a31daf2cfa110ae480c7ce5..192e46cb9c9c9812cde0d3a365fbd9e40743a6aa 100644 --- a/R/gen-namespace.R +++ b/R/gen-namespace.R @@ -1,5 +1,6 @@ # This file is autogenerated. Do not modify it by hand. +#' @rdname torch___and__ torch___and__ <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -16,6 +17,7 @@ fun_type = 'namespace' } +#' @rdname torch___lshift__ torch___lshift__ <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -32,6 +34,7 @@ fun_type = 'namespace' } +#' @rdname torch___or__ torch___or__ <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -48,6 +51,7 @@ fun_type = 'namespace' } +#' @rdname torch___rshift__ torch___rshift__ <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -64,6 +68,7 @@ fun_type = 'namespace' } +#' @rdname torch___xor__ torch___xor__ <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -80,6 +85,7 @@ fun_type = 'namespace' } +#' @rdname torch__adaptive_avg_pool2d torch__adaptive_avg_pool2d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -96,6 +102,7 @@ fun_type = 'namespace' } +#' @rdname torch__adaptive_avg_pool2d_backward torch__adaptive_avg_pool2d_backward <- function(grad_output, self) { args <- mget(x = c("grad_output", "self")) expected_types <- list(grad_output = "Tensor", self = "Tensor") @@ -112,6 +119,7 @@ fun_type = 'namespace' } +#' @rdname torch__addr torch__addr <- function(self, vec1, vec2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "vec1", "vec2", "beta", "alpha")) expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar", @@ -129,6 +137,7 @@ fun_type = 'namespace' } +#' @rdname torch__addr_ torch__addr_ <- function(self, vec1, vec2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "vec1", "vec2", "beta", "alpha")) expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar", @@ -146,6 +155,7 @@ fun_type = 'namespace' } +#' @rdname torch__addr_out torch__addr_out <- function(out, self, vec1, vec2, beta = 1L, alpha = 1L) { args <- mget(x = c("out", "self", "vec1", "vec2", "beta", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", @@ -163,6 +173,7 @@ fun_type = 'namespace' } +#' @rdname torch__amp_non_finite_check_and_unscale_ torch__amp_non_finite_check_and_unscale_ <- function(self, found_inf, inv_scale) { args <- mget(x = c("self", "found_inf", "inv_scale")) expected_types <- list(self = "Tensor", found_inf = "Tensor", inv_scale = "Tensor") @@ -179,6 +190,7 @@ fun_type = 'namespace' } +#' @rdname torch__amp_update_scale torch__amp_update_scale <- function(growth_tracker, current_scale, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval) { args <- mget(x = c("growth_tracker", "current_scale", "found_inf", "scale_growth_factor", "scale_backoff_factor", "growth_interval")) expected_types <- list(growth_tracker = "Tensor", current_scale = "Tensor", found_inf = "Tensor", @@ -198,6 +210,7 @@ fun_type = 'namespace' } +#' @rdname torch__baddbmm_mkl_ torch__baddbmm_mkl_ <- function(self, batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "batch1", "batch2", "beta", "alpha")) expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar", @@ -215,6 +228,7 @@ fun_type = 'namespace' } +#' @rdname torch__batch_norm_impl_index torch__batch_norm_impl_index <- function(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled) { args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps", "cudnn_enabled")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", @@ -234,6 +248,7 @@ fun_type = 'namespace' } +#' @rdname torch__batch_norm_impl_index_backward torch__batch_norm_impl_index_backward <- function(impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace) { args <- mget(x = c("impl_index", "input", "grad_output", "weight", "running_mean", "running_var", "save_mean", "save_var_transform", "train", "eps", "output_mask", "reservedSpace")) expected_types <- list(impl_index = "int64_t", input = "Tensor", grad_output = "Tensor", @@ -255,6 +270,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Byte torch__cast_Byte <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -271,6 +287,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Char torch__cast_Char <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -287,6 +304,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Double torch__cast_Double <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -303,6 +321,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Float torch__cast_Float <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -319,6 +338,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Half torch__cast_Half <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -335,6 +355,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Int torch__cast_Int <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -351,6 +372,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Long torch__cast_Long <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -367,6 +389,7 @@ fun_type = 'namespace' } +#' @rdname torch__cast_Short torch__cast_Short <- function(self, non_blocking = FALSE) { args <- mget(x = c("self", "non_blocking")) expected_types <- list(self = "Tensor", non_blocking = "bool") @@ -383,6 +406,7 @@ fun_type = 'namespace' } +#' @rdname torch__cat torch__cat <- function(tensors, dim = 1L) { args <- mget(x = c("tensors", "dim")) expected_types <- list(tensors = "TensorList", dim = "int64_t") @@ -399,6 +423,7 @@ fun_type = 'namespace' } +#' @rdname torch__cat_out torch__cat_out <- function(out, tensors, dim = 1L) { args <- mget(x = c("out", "tensors", "dim")) expected_types <- list(out = "Tensor", tensors = "TensorList", dim = "int64_t") @@ -415,6 +440,7 @@ fun_type = 'namespace' } +#' @rdname torch__cdist_backward torch__cdist_backward <- function(grad, x1, x2, p, cdist) { args <- mget(x = c("grad", "x1", "x2", "p", "cdist")) expected_types <- list(grad = "Tensor", x1 = "Tensor", x2 = "Tensor", p = "double", @@ -432,6 +458,7 @@ fun_type = 'namespace' } +#' @rdname torch__cdist_forward torch__cdist_forward <- function(x1, x2, p, compute_mode) { args <- mget(x = c("x1", "x2", "p", "compute_mode")) expected_types <- list(x1 = "Tensor", x2 = "Tensor", p = "double", compute_mode = "int64_t") @@ -448,6 +475,7 @@ fun_type = 'namespace' } +#' @rdname torch__cholesky_helper torch__cholesky_helper <- function(self, upper) { args <- mget(x = c("self", "upper")) expected_types <- list(self = "Tensor", upper = "bool") @@ -464,6 +492,7 @@ fun_type = 'namespace' } +#' @rdname torch__cholesky_solve_helper torch__cholesky_solve_helper <- function(self, A, upper) { args <- mget(x = c("self", "A", "upper")) expected_types <- list(self = "Tensor", A = "Tensor", upper = "bool") @@ -480,6 +509,7 @@ fun_type = 'namespace' } +#' @rdname torch__convolution torch__convolution <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding", "groups", "benchmark", "deterministic", "cudnn_enabled")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -501,6 +531,7 @@ fun_type = 'namespace' } +#' @rdname torch__convolution_double_backward torch__convolution_double_backward <- function(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, output_mask) { args <- mget(x = c("ggI", "ggW", "ggb", "gO", "weight", "self", "stride", "padding", "dilation", "transposed", "output_padding", "groups", "benchmark", "deterministic", "cudnn_enabled", "output_mask")) expected_types <- list(ggI = "Tensor", ggW = "Tensor", ggb = "Tensor", gO = "Tensor", @@ -523,6 +554,7 @@ fun_type = 'namespace' } +#' @rdname torch__convolution_nogroup torch__convolution_nogroup <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -542,6 +574,7 @@ fun_type = 'namespace' } +#' @rdname torch__copy_from torch__copy_from <- function(self, dst, non_blocking = FALSE) { args <- mget(x = c("self", "dst", "non_blocking")) expected_types <- list(self = "Tensor", dst = "Tensor", non_blocking = "bool") @@ -558,6 +591,7 @@ fun_type = 'namespace' } +#' @rdname torch__ctc_loss torch__ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank = 0L, zero_infinity = FALSE) { args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank", "zero_infinity")) expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = "IntArrayRef", @@ -575,6 +609,7 @@ fun_type = 'namespace' } +#' @rdname torch__ctc_loss_backward torch__ctc_loss_backward <- function(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity = FALSE) { args <- mget(x = c("grad", "log_probs", "targets", "input_lengths", "target_lengths", "neg_log_likelihood", "log_alpha", "blank", "zero_infinity")) expected_types <- list(grad = "Tensor", log_probs = "Tensor", targets = "Tensor", @@ -595,6 +630,7 @@ fun_type = 'namespace' } +#' @rdname torch__cudnn_ctc_loss torch__cudnn_ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity) { args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank", "deterministic", "zero_infinity")) expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = "IntArrayRef", @@ -614,6 +650,7 @@ fun_type = 'namespace' } +#' @rdname torch__cudnn_init_dropout_state torch__cudnn_init_dropout_state <- function(dropout, train, dropout_seed, options) { args <- mget(x = c("dropout", "train", "dropout_seed", "options")) expected_types <- list(dropout = "double", train = "bool", dropout_seed = "int64_t", @@ -631,6 +668,7 @@ fun_type = 'namespace' } +#' @rdname torch__cudnn_rnn torch__cudnn_rnn <- function(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state) { args <- mget(x = c("input", "weight", "weight_stride0", "weight_buf", "hx", "cx", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state")) expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", @@ -653,6 +691,7 @@ fun_type = 'namespace' } +#' @rdname torch__cudnn_rnn_backward torch__cudnn_rnn_backward <- function(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask) { args <- mget(x = c("input", "weight", "weight_stride0", "weight_buf", "hx", "cx", "output", "grad_output", "grad_hy", "grad_cy", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state", "reserve", "output_mask")) expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", @@ -678,6 +717,7 @@ fun_type = 'namespace' } +#' @rdname torch__cudnn_rnn_flatten_weight torch__cudnn_rnn_flatten_weight <- function(weight_arr, weight_stride0, input_size, mode, hidden_size, num_layers, batch_first, bidirectional) { args <- mget(x = c("weight_arr", "weight_stride0", "input_size", "mode", "hidden_size", "num_layers", "batch_first", "bidirectional")) expected_types <- list(weight_arr = "TensorList", weight_stride0 = "int64_t", input_size = "int64_t", @@ -697,6 +737,7 @@ fun_type = 'namespace' } +#' @rdname torch__cufft_clear_plan_cache torch__cufft_clear_plan_cache <- function(device_index) { args <- mget(x = c("device_index")) expected_types <- list(device_index = "int64_t") @@ -713,6 +754,7 @@ fun_type = 'namespace' } +#' @rdname torch__cufft_get_plan_cache_max_size torch__cufft_get_plan_cache_max_size <- function(device_index) { args <- mget(x = c("device_index")) expected_types <- list(device_index = "int64_t") @@ -729,6 +771,7 @@ fun_type = 'namespace' } +#' @rdname torch__cufft_get_plan_cache_size torch__cufft_get_plan_cache_size <- function(device_index) { args <- mget(x = c("device_index")) expected_types <- list(device_index = "int64_t") @@ -745,6 +788,7 @@ fun_type = 'namespace' } +#' @rdname torch__cufft_set_plan_cache_max_size torch__cufft_set_plan_cache_max_size <- function(device_index, max_size) { args <- mget(x = c("device_index", "max_size")) expected_types <- list(device_index = "int64_t", max_size = "int64_t") @@ -761,6 +805,7 @@ fun_type = 'namespace' } +#' @rdname torch__cummax_helper torch__cummax_helper <- function(self, values, indices, dim) { args <- mget(x = c("self", "values", "indices", "dim")) expected_types <- list(self = "Tensor", values = "Tensor", indices = "Tensor", @@ -778,6 +823,7 @@ fun_type = 'namespace' } +#' @rdname torch__cummin_helper torch__cummin_helper <- function(self, values, indices, dim) { args <- mget(x = c("self", "values", "indices", "dim")) expected_types <- list(self = "Tensor", values = "Tensor", indices = "Tensor", @@ -795,6 +841,7 @@ fun_type = 'namespace' } +#' @rdname torch__cumprod torch__cumprod <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = "int64_t") @@ -811,6 +858,7 @@ fun_type = 'namespace' } +#' @rdname torch__cumprod_out torch__cumprod_out <- function(out, self, dim) { args <- mget(x = c("out", "self", "dim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = "int64_t") @@ -827,6 +875,7 @@ fun_type = 'namespace' } +#' @rdname torch__cumsum torch__cumsum <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = "int64_t") @@ -843,6 +892,7 @@ fun_type = 'namespace' } +#' @rdname torch__cumsum_out torch__cumsum_out <- function(out, self, dim) { args <- mget(x = c("out", "self", "dim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = "int64_t") @@ -859,6 +909,7 @@ fun_type = 'namespace' } +#' @rdname torch__debug_has_internal_overlap torch__debug_has_internal_overlap <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -875,6 +926,7 @@ fun_type = 'namespace' } +#' @rdname torch__dim_arange torch__dim_arange <- function(like, dim) { args <- mget(x = c("like", "dim")) expected_types <- list(like = "Tensor", dim = "int64_t") @@ -891,6 +943,7 @@ fun_type = 'namespace' } +#' @rdname torch__dirichlet_grad torch__dirichlet_grad <- function(x, alpha, total) { args <- mget(x = c("x", "alpha", "total")) expected_types <- list(x = "Tensor", alpha = "Tensor", total = "Tensor") @@ -907,6 +960,7 @@ fun_type = 'namespace' } +#' @rdname torch__embedding_bag torch__embedding_bag <- function(weight, indices, offsets, scale_grad_by_freq = FALSE, mode = 0L, sparse = FALSE, per_sample_weights = list(), include_last_offset = FALSE) { args <- mget(x = c("weight", "indices", "offsets", "scale_grad_by_freq", "mode", "sparse", "per_sample_weights", "include_last_offset")) expected_types <- list(weight = "Tensor", indices = "Tensor", offsets = "Tensor", @@ -925,6 +979,7 @@ fun_type = 'namespace' } +#' @rdname torch__embedding_bag_backward torch__embedding_bag_backward <- function(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights) { args <- mget(x = c("grad", "indices", "offsets", "offset2bag", "bag_size", "maximum_indices", "num_weights", "scale_grad_by_freq", "mode", "sparse", "per_sample_weights")) expected_types <- list(grad = "Tensor", indices = "Tensor", offsets = "Tensor", @@ -946,6 +1001,7 @@ fun_type = 'namespace' } +#' @rdname torch__embedding_bag_dense_backward torch__embedding_bag_dense_backward <- function(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights) { args <- mget(x = c("grad", "indices", "offsets", "offset2bag", "bag_size", "maximum_indices", "num_weights", "scale_grad_by_freq", "mode", "per_sample_weights")) expected_types <- list(grad = "Tensor", indices = "Tensor", offsets = "Tensor", @@ -967,6 +1023,7 @@ fun_type = 'namespace' } +#' @rdname torch__embedding_bag_per_sample_weights_backward torch__embedding_bag_per_sample_weights_backward <- function(grad, weight, indices, offsets, offset2bag, mode) { args <- mget(x = c("grad", "weight", "indices", "offsets", "offset2bag", "mode")) expected_types <- list(grad = "Tensor", weight = "Tensor", indices = "Tensor", @@ -985,6 +1042,7 @@ fun_type = 'namespace' } +#' @rdname torch__embedding_bag_sparse_backward torch__embedding_bag_sparse_backward <- function(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights) { args <- mget(x = c("grad", "indices", "offsets", "offset2bag", "bag_size", "num_weights", "scale_grad_by_freq", "mode", "per_sample_weights")) expected_types <- list(grad = "Tensor", indices = "Tensor", offsets = "Tensor", @@ -1004,6 +1062,7 @@ fun_type = 'namespace' } +#' @rdname torch__empty_affine_quantized torch__empty_affine_quantized <- function(size, options = list(), scale = 1L, zero_point = 0L, memory_format = torch_contiguous_format()) { args <- mget(x = c("size", "options", "scale", "zero_point", "memory_format")) expected_types <- list(size = "IntArrayRef", options = "TensorOptions", scale = "double", @@ -1021,6 +1080,7 @@ fun_type = 'namespace' } +#' @rdname torch__empty_per_channel_affine_quantized torch__empty_per_channel_affine_quantized <- function(size, scales, zero_points, axis, options = list(), memory_format = torch_contiguous_format()) { args <- mget(x = c("size", "scales", "zero_points", "axis", "options", "memory_format")) expected_types <- list(size = "IntArrayRef", scales = "Tensor", zero_points = "Tensor", @@ -1038,6 +1098,7 @@ fun_type = 'namespace' } +#' @rdname torch__fft_with_size torch__fft_with_size <- function(self, signal_ndim, complex_input, complex_output, inverse, checked_signal_sizes, normalized, onesided, output_sizes) { args <- mget(x = c("self", "signal_ndim", "complex_input", "complex_output", "inverse", "checked_signal_sizes", "normalized", "onesided", "output_sizes")) expected_types <- list(self = "Tensor", signal_ndim = "int64_t", complex_input = "bool", @@ -1058,6 +1119,7 @@ fun_type = 'namespace' } +#' @rdname torch__fused_dropout torch__fused_dropout <- function(self, p, generator = NULL) { args <- mget(x = c("self", "p", "generator")) expected_types <- list(self = "Tensor", p = "double", generator = "Generator *") @@ -1074,6 +1136,7 @@ fun_type = 'namespace' } +#' @rdname torch__gather_sparse_backward torch__gather_sparse_backward <- function(self, dim, index, grad) { args <- mget(x = c("self", "dim", "index", "grad")) expected_types <- list(self = "Tensor", dim = "int64_t", index = "Tensor", grad = "Tensor") @@ -1090,6 +1153,7 @@ fun_type = 'namespace' } +#' @rdname torch__has_compatible_shallow_copy_type torch__has_compatible_shallow_copy_type <- function(self, from) { args <- mget(x = c("self", "from")) expected_types <- list(self = "Tensor", from = "Tensor") @@ -1106,6 +1170,7 @@ fun_type = 'namespace' } +#' @rdname torch__index_copy_ torch__index_copy_ <- function(self, dim, index, source) { args <- mget(x = c("self", "dim", "index", "source")) expected_types <- list(self = "Tensor", dim = "int64_t", index = "Tensor", source = "Tensor") @@ -1122,6 +1187,7 @@ fun_type = 'namespace' } +#' @rdname torch__index_put_impl_ torch__index_put_impl_ <- function(self, indices, values, accumulate = FALSE, unsafe = FALSE) { args <- mget(x = c("self", "indices", "values", "accumulate", "unsafe")) expected_types <- list(self = "Tensor", indices = "TensorList", values = "Tensor", @@ -1139,6 +1205,7 @@ fun_type = 'namespace' } +#' @rdname torch__inverse_helper torch__inverse_helper <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -1155,6 +1222,7 @@ fun_type = 'namespace' } +#' @rdname torch__local_scalar_dense torch__local_scalar_dense <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -1171,6 +1239,7 @@ fun_type = 'namespace' } +#' @rdname torch__log_softmax torch__log_softmax <- function(self, dim, half_to_float) { args <- mget(x = c("self", "dim", "half_to_float")) expected_types <- list(self = "Tensor", dim = "int64_t", half_to_float = "bool") @@ -1187,6 +1256,7 @@ fun_type = 'namespace' } +#' @rdname torch__log_softmax_backward_data torch__log_softmax_backward_data <- function(grad_output, output, dim, self) { args <- mget(x = c("grad_output", "output", "dim", "self")) expected_types <- list(grad_output = "Tensor", output = "Tensor", dim = "int64_t", @@ -1204,6 +1274,7 @@ fun_type = 'namespace' } +#' @rdname torch__lu_solve_helper torch__lu_solve_helper <- function(self, LU_data, LU_pivots) { args <- mget(x = c("self", "LU_data", "LU_pivots")) expected_types <- list(self = "Tensor", LU_data = "Tensor", LU_pivots = "Tensor") @@ -1220,6 +1291,7 @@ fun_type = 'namespace' } +#' @rdname torch__lu_with_info torch__lu_with_info <- function(self, pivot = TRUE, check_errors = TRUE) { args <- mget(x = c("self", "pivot", "check_errors")) expected_types <- list(self = "Tensor", pivot = "bool", check_errors = "bool") @@ -1236,6 +1308,7 @@ fun_type = 'namespace' } +#' @rdname torch__make_per_channel_quantized_tensor torch__make_per_channel_quantized_tensor <- function(self, scale, zero_point, axis) { args <- mget(x = c("self", "scale", "zero_point", "axis")) expected_types <- list(self = "Tensor", scale = "Tensor", zero_point = "Tensor", @@ -1253,6 +1326,7 @@ fun_type = 'namespace' } +#' @rdname torch__make_per_tensor_quantized_tensor torch__make_per_tensor_quantized_tensor <- function(self, scale, zero_point) { args <- mget(x = c("self", "scale", "zero_point")) expected_types <- list(self = "Tensor", scale = "double", zero_point = "int64_t") @@ -1269,6 +1343,7 @@ fun_type = 'namespace' } +#' @rdname torch__masked_scale torch__masked_scale <- function(self, mask, scale) { args <- mget(x = c("self", "mask", "scale")) expected_types <- list(self = "Tensor", mask = "Tensor", scale = "double") @@ -1285,6 +1360,7 @@ fun_type = 'namespace' } +#' @rdname torch__max torch__max <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool") @@ -1301,6 +1377,7 @@ fun_type = 'namespace' } +#' @rdname torch__max_out torch__max_out <- function(max, max_indices, self, dim, keepdim = FALSE) { args <- mget(x = c("max", "max_indices", "self", "dim", "keepdim")) expected_types <- list(max = "Tensor", max_indices = "Tensor", self = "Tensor", @@ -1318,6 +1395,7 @@ fun_type = 'namespace' } +#' @rdname torch__min torch__min <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool") @@ -1334,6 +1412,7 @@ fun_type = 'namespace' } +#' @rdname torch__min_out torch__min_out <- function(min, min_indices, self, dim, keepdim = FALSE) { args <- mget(x = c("min", "min_indices", "self", "dim", "keepdim")) expected_types <- list(min = "Tensor", min_indices = "Tensor", self = "Tensor", @@ -1351,6 +1430,7 @@ fun_type = 'namespace' } +#' @rdname torch__mkldnn_reshape torch__mkldnn_reshape <- function(self, shape) { args <- mget(x = c("self", "shape")) expected_types <- list(self = "Tensor", shape = "IntArrayRef") @@ -1367,6 +1447,7 @@ fun_type = 'namespace' } +#' @rdname torch__mkldnn_transpose torch__mkldnn_transpose <- function(self, dim0, dim1) { args <- mget(x = c("self", "dim0", "dim1")) expected_types <- list(self = "Tensor", dim0 = "int64_t", dim1 = "int64_t") @@ -1383,6 +1464,7 @@ fun_type = 'namespace' } +#' @rdname torch__mkldnn_transpose_ torch__mkldnn_transpose_ <- function(self, dim0, dim1) { args <- mget(x = c("self", "dim0", "dim1")) expected_types <- list(self = "Tensor", dim0 = "int64_t", dim1 = "int64_t") @@ -1399,6 +1481,7 @@ fun_type = 'namespace' } +#' @rdname torch__mode torch__mode <- function(self, dim = -1L, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool") @@ -1415,6 +1498,7 @@ fun_type = 'namespace' } +#' @rdname torch__mode_out torch__mode_out <- function(values, indices, self, dim = -1L, keepdim = FALSE) { args <- mget(x = c("values", "indices", "self", "dim", "keepdim")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -1432,6 +1516,7 @@ fun_type = 'namespace' } +#' @rdname torch__multinomial_alias_draw torch__multinomial_alias_draw <- function(J, q, num_samples, generator = NULL) { args <- mget(x = c("J", "q", "num_samples", "generator")) expected_types <- list(J = "Tensor", q = "Tensor", num_samples = "int64_t", generator = "Generator *") @@ -1448,6 +1533,7 @@ fun_type = 'namespace' } +#' @rdname torch__multinomial_alias_setup torch__multinomial_alias_setup <- function(probs) { args <- mget(x = c("probs")) expected_types <- list(probs = "Tensor") @@ -1464,6 +1550,7 @@ fun_type = 'namespace' } +#' @rdname torch__nnpack_available torch__nnpack_available <- function() { args <- list() expected_types <- list() @@ -1480,6 +1567,7 @@ fun_type = 'namespace' } +#' @rdname torch__nnpack_spatial_convolution torch__nnpack_spatial_convolution <- function(input, weight, bias, padding, stride = 1L) { args <- mget(x = c("input", "weight", "bias", "padding", "stride")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", @@ -1497,6 +1585,7 @@ fun_type = 'namespace' } +#' @rdname torch__nnpack_spatial_convolution_backward torch__nnpack_spatial_convolution_backward <- function(input, grad_output, weight, padding, output_mask) { args <- mget(x = c("input", "grad_output", "weight", "padding", "output_mask")) expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -1514,6 +1603,7 @@ fun_type = 'namespace' } +#' @rdname torch__nnpack_spatial_convolution_backward_input torch__nnpack_spatial_convolution_backward_input <- function(input, grad_output, weight, padding) { args <- mget(x = c("input", "grad_output", "weight", "padding")) expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -1531,6 +1621,7 @@ fun_type = 'namespace' } +#' @rdname torch__nnpack_spatial_convolution_backward_weight torch__nnpack_spatial_convolution_backward_weight <- function(input, weightsize, grad_output, padding) { args <- mget(x = c("input", "weightsize", "grad_output", "padding")) expected_types <- list(input = "Tensor", weightsize = "IntArrayRef", grad_output = "Tensor", @@ -1548,6 +1639,7 @@ fun_type = 'namespace' } +#' @rdname torch__pack_padded_sequence torch__pack_padded_sequence <- function(input, lengths, batch_first) { args <- mget(x = c("input", "lengths", "batch_first")) expected_types <- list(input = "Tensor", lengths = "Tensor", batch_first = "bool") @@ -1564,6 +1656,7 @@ fun_type = 'namespace' } +#' @rdname torch__pack_padded_sequence_backward torch__pack_padded_sequence_backward <- function(grad, input_size, batch_sizes, batch_first) { args <- mget(x = c("grad", "input_size", "batch_sizes", "batch_first")) expected_types <- list(grad = "Tensor", input_size = "IntArrayRef", batch_sizes = "Tensor", @@ -1581,6 +1674,7 @@ fun_type = 'namespace' } +#' @rdname torch__pad_packed_sequence torch__pad_packed_sequence <- function(data, batch_sizes, batch_first, padding_value, total_length) { args <- mget(x = c("data", "batch_sizes", "batch_first", "padding_value", "total_length")) expected_types <- list(data = "Tensor", batch_sizes = "Tensor", batch_first = "bool", @@ -1599,6 +1693,7 @@ fun_type = 'namespace' } +#' @rdname torch__pdist_backward torch__pdist_backward <- function(grad, self, p, pdist) { args <- mget(x = c("grad", "self", "p", "pdist")) expected_types <- list(grad = "Tensor", self = "Tensor", p = "double", pdist = "Tensor") @@ -1615,6 +1710,7 @@ fun_type = 'namespace' } +#' @rdname torch__pdist_forward torch__pdist_forward <- function(self, p = 2L) { args <- mget(x = c("self", "p")) expected_types <- list(self = "Tensor", p = "double") @@ -1631,6 +1727,7 @@ fun_type = 'namespace' } +#' @rdname torch__qr_helper torch__qr_helper <- function(self, some) { args <- mget(x = c("self", "some")) expected_types <- list(self = "Tensor", some = "bool") @@ -1647,6 +1744,7 @@ fun_type = 'namespace' } +#' @rdname torch__reshape_from_tensor torch__reshape_from_tensor <- function(self, shape) { args <- mget(x = c("self", "shape")) expected_types <- list(self = "Tensor", shape = "Tensor") @@ -1663,6 +1761,7 @@ fun_type = 'namespace' } +#' @rdname torch__s_where torch__s_where <- function(condition, self, other) { args <- mget(x = c("condition", "self", "other")) expected_types <- list(condition = "Tensor", self = "Tensor", other = "Tensor") @@ -1679,6 +1778,7 @@ fun_type = 'namespace' } +#' @rdname torch__sample_dirichlet torch__sample_dirichlet <- function(self, generator = NULL) { args <- mget(x = c("self", "generator")) expected_types <- list(self = "Tensor", generator = "Generator *") @@ -1695,6 +1795,7 @@ fun_type = 'namespace' } +#' @rdname torch__shape_as_tensor torch__shape_as_tensor <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -1711,6 +1812,7 @@ fun_type = 'namespace' } +#' @rdname torch__sobol_engine_draw torch__sobol_engine_draw <- function(quasi, n, sobolstate, dimension, num_generated, dtype) { args <- mget(x = c("quasi", "n", "sobolstate", "dimension", "num_generated", "dtype")) expected_types <- list(quasi = "Tensor", n = "int64_t", sobolstate = "Tensor", @@ -1729,6 +1831,7 @@ fun_type = 'namespace' } +#' @rdname torch__sobol_engine_ff_ torch__sobol_engine_ff_ <- function(self, n, sobolstate, dimension, num_generated) { args <- mget(x = c("self", "n", "sobolstate", "dimension", "num_generated")) expected_types <- list(self = "Tensor", n = "int64_t", sobolstate = "Tensor", dimension = "int64_t", @@ -1746,6 +1849,7 @@ fun_type = 'namespace' } +#' @rdname torch__sobol_engine_initialize_state_ torch__sobol_engine_initialize_state_ <- function(self, dimension) { args <- mget(x = c("self", "dimension")) expected_types <- list(self = "Tensor", dimension = "int64_t") @@ -1762,6 +1866,7 @@ fun_type = 'namespace' } +#' @rdname torch__sobol_engine_scramble_ torch__sobol_engine_scramble_ <- function(self, ltm, dimension) { args <- mget(x = c("self", "ltm", "dimension")) expected_types <- list(self = "Tensor", ltm = "Tensor", dimension = "int64_t") @@ -1778,6 +1883,7 @@ fun_type = 'namespace' } +#' @rdname torch__softmax torch__softmax <- function(self, dim, half_to_float) { args <- mget(x = c("self", "dim", "half_to_float")) expected_types <- list(self = "Tensor", dim = "int64_t", half_to_float = "bool") @@ -1794,6 +1900,7 @@ fun_type = 'namespace' } +#' @rdname torch__softmax_backward_data torch__softmax_backward_data <- function(grad_output, output, dim, self) { args <- mget(x = c("grad_output", "output", "dim", "self")) expected_types <- list(grad_output = "Tensor", output = "Tensor", dim = "int64_t", @@ -1811,6 +1918,7 @@ fun_type = 'namespace' } +#' @rdname torch__solve_helper torch__solve_helper <- function(self, A) { args <- mget(x = c("self", "A")) expected_types <- list(self = "Tensor", A = "Tensor") @@ -1827,6 +1935,7 @@ fun_type = 'namespace' } +#' @rdname torch__sparse_addmm torch__sparse_addmm <- function(self, sparse, dense, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "sparse", "dense", "beta", "alpha")) expected_types <- list(self = "Tensor", sparse = "Tensor", dense = "Tensor", beta = "Scalar", @@ -1844,6 +1953,7 @@ fun_type = 'namespace' } +#' @rdname torch__sparse_coo_tensor_unsafe torch__sparse_coo_tensor_unsafe <- function(indices, values, size, options = list()) { args <- mget(x = c("indices", "values", "size", "options")) expected_types <- list(indices = "Tensor", values = "Tensor", size = "IntArrayRef", @@ -1861,6 +1971,7 @@ fun_type = 'namespace' } +#' @rdname torch__sparse_coo_tensor_with_dims torch__sparse_coo_tensor_with_dims <- function(sparse_dim, dense_dim, size, options) { args <- mget(x = c("sparse_dim", "dense_dim", "size", "options")) expected_types <- list(sparse_dim = "int64_t", dense_dim = "int64_t", size = "IntArrayRef", @@ -1878,6 +1989,7 @@ fun_type = 'namespace' } +#' @rdname torch__sparse_coo_tensor_with_dims_and_tensors torch__sparse_coo_tensor_with_dims_and_tensors <- function(sparse_dim, dense_dim, size, indices, values, options) { args <- mget(x = c("sparse_dim", "dense_dim", "size", "indices", "values", "options")) expected_types <- list(sparse_dim = "int64_t", dense_dim = "int64_t", size = "IntArrayRef", @@ -1896,6 +2008,7 @@ fun_type = 'namespace' } +#' @rdname torch__sparse_mm torch__sparse_mm <- function(sparse, dense) { args <- mget(x = c("sparse", "dense")) expected_types <- list(sparse = "Tensor", dense = "Tensor") @@ -1912,6 +2025,7 @@ fun_type = 'namespace' } +#' @rdname torch__sparse_sum torch__sparse_sum <- function(self, dim, dtype) { args <- mget(x = c("self", "dim", "dtype")) expected_types <- list(self = "Tensor", dim = "IntArrayRef", dtype = "ScalarType") @@ -1928,6 +2042,7 @@ fun_type = 'namespace' } +#' @rdname torch__sparse_sum_backward torch__sparse_sum_backward <- function(grad, self, dim) { args <- mget(x = c("grad", "self", "dim")) expected_types <- list(grad = "Tensor", self = "Tensor", dim = "IntArrayRef") @@ -1944,6 +2059,7 @@ fun_type = 'namespace' } +#' @rdname torch__standard_gamma torch__standard_gamma <- function(self, generator = NULL) { args <- mget(x = c("self", "generator")) expected_types <- list(self = "Tensor", generator = "Generator *") @@ -1960,6 +2076,7 @@ fun_type = 'namespace' } +#' @rdname torch__standard_gamma_grad torch__standard_gamma_grad <- function(self, output) { args <- mget(x = c("self", "output")) expected_types <- list(self = "Tensor", output = "Tensor") @@ -1976,6 +2093,7 @@ fun_type = 'namespace' } +#' @rdname torch__std torch__std <- function(self, unbiased = TRUE) { args <- mget(x = c("self", "unbiased")) expected_types <- list(self = "Tensor", unbiased = "bool") @@ -1992,6 +2110,7 @@ fun_type = 'namespace' } +#' @rdname torch__svd_helper torch__svd_helper <- function(self, some, compute_uv) { args <- mget(x = c("self", "some", "compute_uv")) expected_types <- list(self = "Tensor", some = "bool", compute_uv = "bool") @@ -2008,6 +2127,7 @@ fun_type = 'namespace' } +#' @rdname torch__symeig_helper torch__symeig_helper <- function(self, eigenvectors, upper) { args <- mget(x = c("self", "eigenvectors", "upper")) expected_types <- list(self = "Tensor", eigenvectors = "bool", upper = "bool") @@ -2024,6 +2144,7 @@ fun_type = 'namespace' } +#' @rdname torch__thnn_differentiable_gru_cell_backward torch__thnn_differentiable_gru_cell_backward <- function(grad_hy, input_gates, hidden_gates, hx, input_bias, hidden_bias) { args <- mget(x = c("grad_hy", "input_gates", "hidden_gates", "hx", "input_bias", "hidden_bias")) expected_types <- list(grad_hy = "Tensor", input_gates = "Tensor", hidden_gates = "Tensor", @@ -2042,6 +2163,7 @@ fun_type = 'namespace' } +#' @rdname torch__thnn_differentiable_lstm_cell_backward torch__thnn_differentiable_lstm_cell_backward <- function(grad_hy, grad_cy, input_gates, hidden_gates, input_bias, hidden_bias, cx, cy) { args <- mget(x = c("grad_hy", "grad_cy", "input_gates", "hidden_gates", "input_bias", "hidden_bias", "cx", "cy")) expected_types <- list(grad_hy = "Tensor", grad_cy = "Tensor", input_gates = "Tensor", @@ -2061,6 +2183,7 @@ fun_type = 'namespace' } +#' @rdname torch__thnn_fused_gru_cell torch__thnn_fused_gru_cell <- function(input_gates, hidden_gates, hx, input_bias = list(), hidden_bias = list()) { args <- mget(x = c("input_gates", "hidden_gates", "hx", "input_bias", "hidden_bias")) expected_types <- list(input_gates = "Tensor", hidden_gates = "Tensor", hx = "Tensor", @@ -2078,6 +2201,7 @@ fun_type = 'namespace' } +#' @rdname torch__thnn_fused_gru_cell_backward torch__thnn_fused_gru_cell_backward <- function(grad_hy, workspace, has_bias) { args <- mget(x = c("grad_hy", "workspace", "has_bias")) expected_types <- list(grad_hy = "Tensor", workspace = "Tensor", has_bias = "bool") @@ -2094,6 +2218,7 @@ fun_type = 'namespace' } +#' @rdname torch__thnn_fused_lstm_cell torch__thnn_fused_lstm_cell <- function(input_gates, hidden_gates, cx, input_bias = list(), hidden_bias = list()) { args <- mget(x = c("input_gates", "hidden_gates", "cx", "input_bias", "hidden_bias")) expected_types <- list(input_gates = "Tensor", hidden_gates = "Tensor", cx = "Tensor", @@ -2111,6 +2236,7 @@ fun_type = 'namespace' } +#' @rdname torch__thnn_fused_lstm_cell_backward torch__thnn_fused_lstm_cell_backward <- function(grad_hy, grad_cy, cx, cy, workspace, has_bias) { args <- mget(x = c("grad_hy", "grad_cy", "cx", "cy", "workspace", "has_bias")) expected_types <- list(grad_hy = "Tensor", grad_cy = "Tensor", cx = "Tensor", cy = "Tensor", @@ -2128,6 +2254,7 @@ fun_type = 'namespace' } +#' @rdname torch__triangular_solve_helper torch__triangular_solve_helper <- function(self, A, upper, transpose, unitriangular) { args <- mget(x = c("self", "A", "upper", "transpose", "unitriangular")) expected_types <- list(self = "Tensor", A = "Tensor", upper = "bool", transpose = "bool", @@ -2145,6 +2272,7 @@ fun_type = 'namespace' } +#' @rdname torch__trilinear torch__trilinear <- function(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim = 1L) { args <- mget(x = c("i1", "i2", "i3", "expand1", "expand2", "expand3", "sumdim", "unroll_dim")) expected_types <- list(i1 = "Tensor", i2 = "Tensor", i3 = "Tensor", expand1 = "IntArrayRef", @@ -2164,6 +2292,7 @@ fun_type = 'namespace' } +#' @rdname torch__unique torch__unique <- function(self, sorted = TRUE, return_inverse = FALSE) { args <- mget(x = c("self", "sorted", "return_inverse")) expected_types <- list(self = "Tensor", sorted = "bool", return_inverse = "bool") @@ -2180,6 +2309,7 @@ fun_type = 'namespace' } +#' @rdname torch__unique2 torch__unique2 <- function(self, sorted = TRUE, return_inverse = FALSE, return_counts = FALSE) { args <- mget(x = c("self", "sorted", "return_inverse", "return_counts")) expected_types <- list(self = "Tensor", sorted = "bool", return_inverse = "bool", @@ -2197,6 +2327,7 @@ fun_type = 'namespace' } +#' @rdname torch__unsafe_view torch__unsafe_view <- function(self, size) { args <- mget(x = c("self", "size")) expected_types <- list(self = "Tensor", size = "IntArrayRef") @@ -2213,6 +2344,7 @@ fun_type = 'namespace' } +#' @rdname torch__use_cudnn_ctc_loss torch__use_cudnn_ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank) { args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank")) expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = "IntArrayRef", @@ -2231,6 +2363,7 @@ fun_type = 'namespace' } +#' @rdname torch__use_cudnn_rnn_flatten_weight torch__use_cudnn_rnn_flatten_weight <- function() { args <- list() expected_types <- list() @@ -2247,6 +2380,7 @@ fun_type = 'namespace' } +#' @rdname torch__var torch__var <- function(self, unbiased = TRUE) { args <- mget(x = c("self", "unbiased")) expected_types <- list(self = "Tensor", unbiased = "bool") @@ -2263,6 +2397,7 @@ fun_type = 'namespace' } +#' @rdname torch__weight_norm torch__weight_norm <- function(v, g, dim = 1L) { args <- mget(x = c("v", "g", "dim")) expected_types <- list(v = "Tensor", g = "Tensor", dim = "int64_t") @@ -2279,6 +2414,7 @@ fun_type = 'namespace' } +#' @rdname torch__weight_norm_cuda_interface torch__weight_norm_cuda_interface <- function(v, g, dim = 1L) { args <- mget(x = c("v", "g", "dim")) expected_types <- list(v = "Tensor", g = "Tensor", dim = "int64_t") @@ -2295,6 +2431,7 @@ fun_type = 'namespace' } +#' @rdname torch__weight_norm_cuda_interface_backward torch__weight_norm_cuda_interface_backward <- function(grad_w, saved_v, saved_g, saved_norms, dim) { args <- mget(x = c("grad_w", "saved_v", "saved_g", "saved_norms", "dim")) expected_types <- list(grad_w = "Tensor", saved_v = "Tensor", saved_g = "Tensor", @@ -2312,6 +2449,7 @@ fun_type = 'namespace' } +#' @rdname torch__weight_norm_differentiable_backward torch__weight_norm_differentiable_backward <- function(grad_w, saved_v, saved_g, saved_norms, dim) { args <- mget(x = c("grad_w", "saved_v", "saved_g", "saved_norms", "dim")) expected_types <- list(grad_w = "Tensor", saved_v = "Tensor", saved_g = "Tensor", @@ -2329,6 +2467,7 @@ fun_type = 'namespace' } +#' @rdname torch_abs torch_abs <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -2345,6 +2484,7 @@ fun_type = 'namespace' } +#' @rdname torch_abs_ torch_abs_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -2361,6 +2501,7 @@ fun_type = 'namespace' } +#' @rdname torch_abs_out torch_abs_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -2377,6 +2518,7 @@ fun_type = 'namespace' } +#' @rdname torch_acos torch_acos <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -2393,6 +2535,7 @@ fun_type = 'namespace' } +#' @rdname torch_acos_ torch_acos_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -2409,6 +2552,7 @@ fun_type = 'namespace' } +#' @rdname torch_acos_out torch_acos_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -2425,6 +2569,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_avg_pool1d torch_adaptive_avg_pool1d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -2441,6 +2586,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_avg_pool2d torch_adaptive_avg_pool2d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -2457,6 +2603,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_avg_pool2d_out torch_adaptive_avg_pool2d_out <- function(out, self, output_size) { args <- mget(x = c("out", "self", "output_size")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef") @@ -2473,6 +2620,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_avg_pool3d torch_adaptive_avg_pool3d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -2489,6 +2637,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_avg_pool3d_backward torch_adaptive_avg_pool3d_backward <- function(grad_output, self) { args <- mget(x = c("grad_output", "self")) expected_types <- list(grad_output = "Tensor", self = "Tensor") @@ -2505,6 +2654,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_avg_pool3d_backward_out torch_adaptive_avg_pool3d_backward_out <- function(grad_input, grad_output, self) { args <- mget(x = c("grad_input", "grad_output", "self")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor") @@ -2521,6 +2671,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_avg_pool3d_out torch_adaptive_avg_pool3d_out <- function(out, self, output_size) { args <- mget(x = c("out", "self", "output_size")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef") @@ -2537,6 +2688,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool1d torch_adaptive_max_pool1d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -2553,6 +2705,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool2d torch_adaptive_max_pool2d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -2569,6 +2722,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool2d_backward torch_adaptive_max_pool2d_backward <- function(grad_output, self, indices) { args <- mget(x = c("grad_output", "self", "indices")) expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor") @@ -2585,6 +2739,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool2d_backward_out torch_adaptive_max_pool2d_backward_out <- function(grad_input, grad_output, self, indices) { args <- mget(x = c("grad_input", "grad_output", "self", "indices")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -2602,6 +2757,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool2d_out torch_adaptive_max_pool2d_out <- function(out, indices, self, output_size) { args <- mget(x = c("out", "indices", "self", "output_size")) expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", output_size = "IntArrayRef") @@ -2618,6 +2774,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool3d torch_adaptive_max_pool3d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -2634,6 +2791,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool3d_backward torch_adaptive_max_pool3d_backward <- function(grad_output, self, indices) { args <- mget(x = c("grad_output", "self", "indices")) expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor") @@ -2650,6 +2808,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool3d_backward_out torch_adaptive_max_pool3d_backward_out <- function(grad_input, grad_output, self, indices) { args <- mget(x = c("grad_input", "grad_output", "self", "indices")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -2667,6 +2826,7 @@ fun_type = 'namespace' } +#' @rdname torch_adaptive_max_pool3d_out torch_adaptive_max_pool3d_out <- function(out, indices, self, output_size) { args <- mget(x = c("out", "indices", "self", "output_size")) expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", output_size = "IntArrayRef") @@ -2683,6 +2843,7 @@ fun_type = 'namespace' } +#' @rdname torch_add torch_add <- function(self, other, alpha = 1L) { args <- mget(x = c("self", "other", "alpha")) expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar") @@ -2699,6 +2860,7 @@ fun_type = 'namespace' } +#' @rdname torch_add_out torch_add_out <- function(out, self, other, alpha = 1L) { args <- mget(x = c("out", "self", "other", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor", alpha = "Scalar") @@ -2715,6 +2877,7 @@ fun_type = 'namespace' } +#' @rdname torch_addbmm torch_addbmm <- function(self, batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "batch1", "batch2", "beta", "alpha")) expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar", @@ -2732,6 +2895,7 @@ fun_type = 'namespace' } +#' @rdname torch_addbmm_out torch_addbmm_out <- function(out, self, batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("out", "self", "batch1", "batch2", "beta", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", @@ -2749,6 +2913,7 @@ fun_type = 'namespace' } +#' @rdname torch_addcdiv torch_addcdiv <- function(self, tensor1, tensor2, value = 1L) { args <- mget(x = c("self", "tensor1", "tensor2", "value")) expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", @@ -2766,6 +2931,7 @@ fun_type = 'namespace' } +#' @rdname torch_addcdiv_out torch_addcdiv_out <- function(out, self, tensor1, tensor2, value = 1L) { args <- mget(x = c("out", "self", "tensor1", "tensor2", "value")) expected_types <- list(out = "Tensor", self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", @@ -2783,6 +2949,7 @@ fun_type = 'namespace' } +#' @rdname torch_addcmul torch_addcmul <- function(self, tensor1, tensor2, value = 1L) { args <- mget(x = c("self", "tensor1", "tensor2", "value")) expected_types <- list(self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", @@ -2800,6 +2967,7 @@ fun_type = 'namespace' } +#' @rdname torch_addcmul_out torch_addcmul_out <- function(out, self, tensor1, tensor2, value = 1L) { args <- mget(x = c("out", "self", "tensor1", "tensor2", "value")) expected_types <- list(out = "Tensor", self = "Tensor", tensor1 = "Tensor", tensor2 = "Tensor", @@ -2817,6 +2985,7 @@ fun_type = 'namespace' } +#' @rdname torch_addmm torch_addmm <- function(self, mat1, mat2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "mat1", "mat2", "beta", "alpha")) expected_types <- list(self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", beta = "Scalar", @@ -2834,6 +3003,7 @@ fun_type = 'namespace' } +#' @rdname torch_addmm_out torch_addmm_out <- function(out, self, mat1, mat2, beta = 1L, alpha = 1L) { args <- mget(x = c("out", "self", "mat1", "mat2", "beta", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", @@ -2851,6 +3021,7 @@ fun_type = 'namespace' } +#' @rdname torch_addmv torch_addmv <- function(self, mat, vec, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "mat", "vec", "beta", "alpha")) expected_types <- list(self = "Tensor", mat = "Tensor", vec = "Tensor", beta = "Scalar", @@ -2868,6 +3039,7 @@ fun_type = 'namespace' } +#' @rdname torch_addmv_ torch_addmv_ <- function(self, mat, vec, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "mat", "vec", "beta", "alpha")) expected_types <- list(self = "Tensor", mat = "Tensor", vec = "Tensor", beta = "Scalar", @@ -2885,6 +3057,7 @@ fun_type = 'namespace' } +#' @rdname torch_addmv_out torch_addmv_out <- function(out, self, mat, vec, beta = 1L, alpha = 1L) { args <- mget(x = c("out", "self", "mat", "vec", "beta", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", mat = "Tensor", vec = "Tensor", @@ -2902,6 +3075,7 @@ fun_type = 'namespace' } +#' @rdname torch_addr torch_addr <- function(self, vec1, vec2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "vec1", "vec2", "beta", "alpha")) expected_types <- list(self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", beta = "Scalar", @@ -2919,6 +3093,7 @@ fun_type = 'namespace' } +#' @rdname torch_addr_out torch_addr_out <- function(out, self, vec1, vec2, beta = 1L, alpha = 1L) { args <- mget(x = c("out", "self", "vec1", "vec2", "beta", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", vec1 = "Tensor", vec2 = "Tensor", @@ -2936,6 +3111,7 @@ fun_type = 'namespace' } +#' @rdname torch_affine_grid_generator torch_affine_grid_generator <- function(theta, size, align_corners) { args <- mget(x = c("theta", "size", "align_corners")) expected_types <- list(theta = "Tensor", size = "IntArrayRef", align_corners = "bool") @@ -2952,6 +3128,7 @@ fun_type = 'namespace' } +#' @rdname torch_affine_grid_generator_backward torch_affine_grid_generator_backward <- function(grad, size, align_corners) { args <- mget(x = c("grad", "size", "align_corners")) expected_types <- list(grad = "Tensor", size = "IntArrayRef", align_corners = "bool") @@ -2968,6 +3145,7 @@ fun_type = 'namespace' } +#' @rdname torch_alias torch_alias <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -2984,6 +3162,7 @@ fun_type = 'namespace' } +#' @rdname torch_align_tensors torch_align_tensors <- function(tensors) { args <- mget(x = c("tensors")) expected_types <- list(tensors = "TensorList") @@ -3000,6 +3179,7 @@ fun_type = 'namespace' } +#' @rdname torch_all torch_all <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool") @@ -3016,6 +3196,7 @@ fun_type = 'namespace' } +#' @rdname torch_all_out torch_all_out <- function(out, self, dim, keepdim = FALSE) { args <- mget(x = c("out", "self", "dim", "keepdim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname" @@ -3033,6 +3214,7 @@ fun_type = 'namespace' } +#' @rdname torch_allclose torch_allclose <- function(self, other, rtol = 0.000010, atol = 0.000000, equal_nan = FALSE) { args <- mget(x = c("self", "other", "rtol", "atol", "equal_nan")) expected_types <- list(self = "Tensor", other = "Tensor", rtol = "double", atol = "double", @@ -3050,6 +3232,7 @@ fun_type = 'namespace' } +#' @rdname torch_alpha_dropout torch_alpha_dropout <- function(input, p, train) { args <- mget(x = c("input", "p", "train")) expected_types <- list(input = "Tensor", p = "double", train = "bool") @@ -3066,6 +3249,7 @@ fun_type = 'namespace' } +#' @rdname torch_alpha_dropout_ torch_alpha_dropout_ <- function(self, p, train) { args <- mget(x = c("self", "p", "train")) expected_types <- list(self = "Tensor", p = "double", train = "bool") @@ -3082,6 +3266,7 @@ fun_type = 'namespace' } +#' @rdname torch_angle torch_angle <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -3098,6 +3283,7 @@ fun_type = 'namespace' } +#' @rdname torch_angle_out torch_angle_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -3114,6 +3300,7 @@ fun_type = 'namespace' } +#' @rdname torch_any torch_any <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool") @@ -3130,6 +3317,7 @@ fun_type = 'namespace' } +#' @rdname torch_any_out torch_any_out <- function(out, self, dim, keepdim = FALSE) { args <- mget(x = c("out", "self", "dim", "keepdim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname" @@ -3147,6 +3335,7 @@ fun_type = 'namespace' } +#' @rdname .torch_arange .torch_arange <- function(start, end, step, options = list()) { args <- mget(x = c("start", "end", "step", "options")) expected_types <- list(start = "Scalar", end = "Scalar", step = "Scalar", options = "TensorOptions") @@ -3163,6 +3352,7 @@ fun_type = 'namespace' } +#' @rdname torch_arange_out torch_arange_out <- function(out, start, end, step = 1L) { args <- mget(x = c("out", "start", "end", "step")) expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", step = "Scalar") @@ -3179,6 +3369,7 @@ fun_type = 'namespace' } +#' @rdname torch_argmax torch_argmax <- function(self, dim = NULL, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool") @@ -3195,6 +3386,7 @@ fun_type = 'namespace' } +#' @rdname torch_argmin torch_argmin <- function(self, dim = NULL, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = "int64_t", keepdim = "bool") @@ -3211,6 +3403,7 @@ fun_type = 'namespace' } +#' @rdname torch_argsort torch_argsort <- function(self, dim = -1L, descending = FALSE) { args <- mget(x = c("self", "dim", "descending")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), descending = "bool") @@ -3227,6 +3420,7 @@ fun_type = 'namespace' } +#' @rdname torch_as_strided torch_as_strided <- function(self, size, stride, storage_offset = NULL) { args <- mget(x = c("self", "size", "stride", "storage_offset")) expected_types <- list(self = "Tensor", size = "IntArrayRef", stride = "IntArrayRef", @@ -3244,6 +3438,7 @@ fun_type = 'namespace' } +#' @rdname torch_as_strided_ torch_as_strided_ <- function(self, size, stride, storage_offset = NULL) { args <- mget(x = c("self", "size", "stride", "storage_offset")) expected_types <- list(self = "Tensor", size = "IntArrayRef", stride = "IntArrayRef", @@ -3261,6 +3456,7 @@ fun_type = 'namespace' } +#' @rdname torch_asin torch_asin <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -3277,6 +3473,7 @@ fun_type = 'namespace' } +#' @rdname torch_asin_ torch_asin_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -3293,6 +3490,7 @@ fun_type = 'namespace' } +#' @rdname torch_asin_out torch_asin_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -3309,6 +3507,7 @@ fun_type = 'namespace' } +#' @rdname torch_atan torch_atan <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -3325,6 +3524,7 @@ fun_type = 'namespace' } +#' @rdname torch_atan_ torch_atan_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -3341,6 +3541,7 @@ fun_type = 'namespace' } +#' @rdname torch_atan_out torch_atan_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -3357,6 +3558,7 @@ fun_type = 'namespace' } +#' @rdname torch_atan2 torch_atan2 <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = "Tensor") @@ -3373,6 +3575,7 @@ fun_type = 'namespace' } +#' @rdname torch_atan2_out torch_atan2_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -3389,6 +3592,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool1d torch_avg_pool1d <- function(self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -3406,6 +3610,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool2d torch_avg_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -3424,6 +3629,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool2d_backward torch_avg_pool2d_backward <- function(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) { args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -3443,6 +3649,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool2d_backward_out torch_avg_pool2d_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) { args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -3463,6 +3670,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool2d_out torch_avg_pool2d_out <- function(out, self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) { args <- mget(x = c("out", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(out = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -3481,6 +3689,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool3d torch_avg_pool3d <- function(self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -3499,6 +3708,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool3d_backward torch_avg_pool3d_backward <- function(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) { args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -3518,6 +3728,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool3d_backward_out torch_avg_pool3d_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) { args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -3538,6 +3749,7 @@ fun_type = 'namespace' } +#' @rdname torch_avg_pool3d_out torch_avg_pool3d_out <- function(out, self, kernel_size, stride = list(), padding = 0L, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL) { args <- mget(x = c("out", "self", "kernel_size", "stride", "padding", "ceil_mode", "count_include_pad", "divisor_override")) expected_types <- list(out = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -3556,6 +3768,7 @@ fun_type = 'namespace' } +#' @rdname torch_baddbmm torch_baddbmm <- function(self, batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "batch1", "batch2", "beta", "alpha")) expected_types <- list(self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", beta = "Scalar", @@ -3573,6 +3786,7 @@ fun_type = 'namespace' } +#' @rdname torch_baddbmm_out torch_baddbmm_out <- function(out, self, batch1, batch2, beta = 1L, alpha = 1L) { args <- mget(x = c("out", "self", "batch1", "batch2", "beta", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", batch1 = "Tensor", batch2 = "Tensor", @@ -3590,7 +3804,8 @@ fun_type = 'namespace' } -torch_bartlett_window <- function(window_length, periodic, options = list()) { +#' @rdname .torch_bartlett_window +.torch_bartlett_window <- function(window_length, periodic, options = list()) { args <- mget(x = c("window_length", "periodic", "options")) expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions") nd_args <- c("window_length", "periodic") @@ -3606,6 +3821,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm torch_batch_norm <- function(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled) { args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps", "cudnn_enabled")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", @@ -3625,6 +3841,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_backward_elemt torch_batch_norm_backward_elemt <- function(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu) { args <- mget(x = c("grad_out", "input", "mean", "invstd", "weight", "mean_dy", "mean_dy_xmu")) expected_types <- list(grad_out = "Tensor", input = "Tensor", mean = "Tensor", @@ -3644,6 +3861,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_backward_reduce torch_batch_norm_backward_reduce <- function(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g) { args <- mget(x = c("grad_out", "input", "mean", "invstd", "weight", "input_g", "weight_g", "bias_g")) expected_types <- list(grad_out = "Tensor", input = "Tensor", mean = "Tensor", @@ -3663,6 +3881,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_elemt torch_batch_norm_elemt <- function(input, weight, bias, mean, invstd, eps) { args <- mget(x = c("input", "weight", "bias", "mean", "invstd", "eps")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", mean = "Tensor", @@ -3680,6 +3899,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_elemt_out torch_batch_norm_elemt_out <- function(out, input, weight, bias, mean, invstd, eps) { args <- mget(x = c("out", "input", "weight", "bias", "mean", "invstd", "eps")) expected_types <- list(out = "Tensor", input = "Tensor", weight = "Tensor", bias = "Tensor", @@ -3697,6 +3917,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_gather_stats torch_batch_norm_gather_stats <- function(input, mean, invstd, running_mean, running_var, momentum, eps, count) { args <- mget(x = c("input", "mean", "invstd", "running_mean", "running_var", "momentum", "eps", "count")) expected_types <- list(input = "Tensor", mean = "Tensor", invstd = "Tensor", running_mean = "Tensor", @@ -3716,6 +3937,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_gather_stats_with_counts torch_batch_norm_gather_stats_with_counts <- function(input, mean, invstd, running_mean, running_var, momentum, eps, counts) { args <- mget(x = c("input", "mean", "invstd", "running_mean", "running_var", "momentum", "eps", "counts")) expected_types <- list(input = "Tensor", mean = "Tensor", invstd = "Tensor", running_mean = "Tensor", @@ -3735,6 +3957,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_stats torch_batch_norm_stats <- function(input, eps) { args <- mget(x = c("input", "eps")) expected_types <- list(input = "Tensor", eps = "double") @@ -3751,6 +3974,7 @@ fun_type = 'namespace' } +#' @rdname torch_batch_norm_update_stats torch_batch_norm_update_stats <- function(input, running_mean, running_var, momentum) { args <- mget(x = c("input", "running_mean", "running_var", "momentum")) expected_types <- list(input = "Tensor", running_mean = "Tensor", running_var = "Tensor", @@ -3768,6 +3992,7 @@ fun_type = 'namespace' } +#' @rdname torch_bernoulli torch_bernoulli <- function(self, p, generator = NULL) { args <- mget(x = c("self", "p", "generator")) expected_types <- list(self = "Tensor", p = "double", generator = "Generator *") @@ -3784,6 +4009,7 @@ fun_type = 'namespace' } +#' @rdname torch_bernoulli_out torch_bernoulli_out <- function(out, self, generator = NULL) { args <- mget(x = c("out", "self", "generator")) expected_types <- list(out = "Tensor", self = "Tensor", generator = "Generator *") @@ -3800,6 +4026,7 @@ fun_type = 'namespace' } +#' @rdname torch_bilinear torch_bilinear <- function(input1, input2, weight, bias) { args <- mget(x = c("input1", "input2", "weight", "bias")) expected_types <- list(input1 = "Tensor", input2 = "Tensor", weight = "Tensor", @@ -3817,6 +4044,7 @@ fun_type = 'namespace' } +#' @rdname torch_binary_cross_entropy torch_binary_cross_entropy <- function(self, target, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "weight", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t") @@ -3833,6 +4061,7 @@ fun_type = 'namespace' } +#' @rdname torch_binary_cross_entropy_backward torch_binary_cross_entropy_backward <- function(grad_output, self, target, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("grad_output", "self", "target", "weight", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -3850,6 +4079,7 @@ fun_type = 'namespace' } +#' @rdname torch_binary_cross_entropy_backward_out torch_binary_cross_entropy_backward_out <- function(grad_input, grad_output, self, target, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "weight", "reduction")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -3867,6 +4097,7 @@ fun_type = 'namespace' } +#' @rdname torch_binary_cross_entropy_out torch_binary_cross_entropy_out <- function(out, self, target, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("out", "self", "target", "weight", "reduction")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", weight = "Tensor", @@ -3884,6 +4115,7 @@ fun_type = 'namespace' } +#' @rdname torch_binary_cross_entropy_with_logits torch_binary_cross_entropy_with_logits <- function(self, target, weight = list(), pos_weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "weight", "pos_weight", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", pos_weight = "Tensor", @@ -3901,6 +4133,7 @@ fun_type = 'namespace' } +#' @rdname torch_binary_cross_entropy_with_logits_backward torch_binary_cross_entropy_with_logits_backward <- function(grad_output, self, target, weight = list(), pos_weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("grad_output", "self", "target", "weight", "pos_weight", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -3918,6 +4151,7 @@ fun_type = 'namespace' } +#' @rdname torch_bincount torch_bincount <- function(self, weights = list(), minlength = 0L) { args <- mget(x = c("self", "weights", "minlength")) expected_types <- list(self = "Tensor", weights = "Tensor", minlength = "int64_t") @@ -3934,6 +4168,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_and torch_bitwise_and <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -3950,6 +4185,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_and_out torch_bitwise_and_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Tensor", "Scalar" @@ -3967,6 +4203,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_not torch_bitwise_not <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -3983,6 +4220,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_not_out torch_bitwise_not_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -3999,6 +4237,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_or torch_bitwise_or <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -4015,6 +4254,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_or_out torch_bitwise_or_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Tensor", "Scalar" @@ -4032,6 +4272,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_xor torch_bitwise_xor <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -4048,6 +4289,7 @@ fun_type = 'namespace' } +#' @rdname torch_bitwise_xor_out torch_bitwise_xor_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Tensor", "Scalar" @@ -4065,7 +4307,8 @@ fun_type = 'namespace' } -torch_blackman_window <- function(window_length, periodic, options = list()) { +#' @rdname .torch_blackman_window +.torch_blackman_window <- function(window_length, periodic, options = list()) { args <- mget(x = c("window_length", "periodic", "options")) expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions") nd_args <- c("window_length", "periodic") @@ -4081,6 +4324,7 @@ fun_type = 'namespace' } +#' @rdname torch_bmm torch_bmm <- function(self, mat2) { args <- mget(x = c("self", "mat2")) expected_types <- list(self = "Tensor", mat2 = "Tensor") @@ -4097,6 +4341,7 @@ fun_type = 'namespace' } +#' @rdname torch_bmm_out torch_bmm_out <- function(out, self, mat2) { args <- mget(x = c("out", "self", "mat2")) expected_types <- list(out = "Tensor", self = "Tensor", mat2 = "Tensor") @@ -4113,6 +4358,7 @@ fun_type = 'namespace' } +#' @rdname torch_broadcast_tensors torch_broadcast_tensors <- function(tensors) { args <- mget(x = c("tensors")) expected_types <- list(tensors = "TensorList") @@ -4129,6 +4375,7 @@ fun_type = 'namespace' } +#' @rdname torch_can_cast torch_can_cast <- function(from, to) { args <- mget(x = c("from", "to")) expected_types <- list(from = "ScalarType", to = "ScalarType") @@ -4145,6 +4392,7 @@ fun_type = 'namespace' } +#' @rdname torch_cartesian_prod torch_cartesian_prod <- function(tensors) { args <- mget(x = c("tensors")) expected_types <- list(tensors = "TensorList") @@ -4161,6 +4409,7 @@ fun_type = 'namespace' } +#' @rdname torch_cat torch_cat <- function(tensors, dim = 1L) { args <- mget(x = c("tensors", "dim")) expected_types <- list(tensors = "TensorList", dim = c("int64_t", "Dimname")) @@ -4177,6 +4426,7 @@ fun_type = 'namespace' } +#' @rdname torch_cat_out torch_cat_out <- function(out, tensors, dim = 1L) { args <- mget(x = c("out", "tensors", "dim")) expected_types <- list(out = "Tensor", tensors = "TensorList", dim = c("int64_t", @@ -4194,6 +4444,7 @@ fun_type = 'namespace' } +#' @rdname torch_cdist torch_cdist <- function(x1, x2, p = 2L, compute_mode = NULL) { args <- mget(x = c("x1", "x2", "p", "compute_mode")) expected_types <- list(x1 = "Tensor", x2 = "Tensor", p = "double", compute_mode = "int64_t") @@ -4210,6 +4461,7 @@ fun_type = 'namespace' } +#' @rdname torch_ceil torch_ceil <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -4226,6 +4478,7 @@ fun_type = 'namespace' } +#' @rdname torch_ceil_ torch_ceil_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -4242,6 +4495,7 @@ fun_type = 'namespace' } +#' @rdname torch_ceil_out torch_ceil_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -4258,6 +4512,7 @@ fun_type = 'namespace' } +#' @rdname torch_celu torch_celu <- function(self, alpha = 1.000000) { args <- mget(x = c("self", "alpha")) expected_types <- list(self = "Tensor", alpha = "Scalar") @@ -4274,6 +4529,7 @@ fun_type = 'namespace' } +#' @rdname torch_celu_ torch_celu_ <- function(self, alpha = 1.000000) { args <- mget(x = c("self", "alpha")) expected_types <- list(self = "Tensor", alpha = "Scalar") @@ -4290,6 +4546,7 @@ fun_type = 'namespace' } +#' @rdname torch_chain_matmul torch_chain_matmul <- function(matrices) { args <- mget(x = c("matrices")) expected_types <- list(matrices = "TensorList") @@ -4306,6 +4563,7 @@ fun_type = 'namespace' } +#' @rdname torch_cholesky torch_cholesky <- function(self, upper = FALSE) { args <- mget(x = c("self", "upper")) expected_types <- list(self = "Tensor", upper = "bool") @@ -4322,6 +4580,7 @@ fun_type = 'namespace' } +#' @rdname torch_cholesky_inverse torch_cholesky_inverse <- function(self, upper = FALSE) { args <- mget(x = c("self", "upper")) expected_types <- list(self = "Tensor", upper = "bool") @@ -4338,6 +4597,7 @@ fun_type = 'namespace' } +#' @rdname torch_cholesky_inverse_out torch_cholesky_inverse_out <- function(out, self, upper = FALSE) { args <- mget(x = c("out", "self", "upper")) expected_types <- list(out = "Tensor", self = "Tensor", upper = "bool") @@ -4354,6 +4614,7 @@ fun_type = 'namespace' } +#' @rdname torch_cholesky_out torch_cholesky_out <- function(out, self, upper = FALSE) { args <- mget(x = c("out", "self", "upper")) expected_types <- list(out = "Tensor", self = "Tensor", upper = "bool") @@ -4370,6 +4631,7 @@ fun_type = 'namespace' } +#' @rdname torch_cholesky_solve torch_cholesky_solve <- function(self, input2, upper = FALSE) { args <- mget(x = c("self", "input2", "upper")) expected_types <- list(self = "Tensor", input2 = "Tensor", upper = "bool") @@ -4386,6 +4648,7 @@ fun_type = 'namespace' } +#' @rdname torch_cholesky_solve_out torch_cholesky_solve_out <- function(out, self, input2, upper = FALSE) { args <- mget(x = c("out", "self", "input2", "upper")) expected_types <- list(out = "Tensor", self = "Tensor", input2 = "Tensor", upper = "bool") @@ -4402,6 +4665,7 @@ fun_type = 'namespace' } +#' @rdname torch_chunk torch_chunk <- function(self, chunks, dim = 1L) { args <- mget(x = c("self", "chunks", "dim")) expected_types <- list(self = "Tensor", chunks = "int64_t", dim = "int64_t") @@ -4418,6 +4682,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp torch_clamp <- function(self, min = NULL, max = NULL) { args <- mget(x = c("self", "min", "max")) expected_types <- list(self = "Tensor", min = "Scalar", max = "Scalar") @@ -4434,6 +4699,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_ torch_clamp_ <- function(self, min = NULL, max = NULL) { args <- mget(x = c("self", "min", "max")) expected_types <- list(self = "Tensor", min = "Scalar", max = "Scalar") @@ -4450,6 +4716,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_max torch_clamp_max <- function(self, max) { args <- mget(x = c("self", "max")) expected_types <- list(self = "Tensor", max = "Scalar") @@ -4466,6 +4733,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_max_ torch_clamp_max_ <- function(self, max) { args <- mget(x = c("self", "max")) expected_types <- list(self = "Tensor", max = "Scalar") @@ -4482,6 +4750,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_max_out torch_clamp_max_out <- function(out, self, max) { args <- mget(x = c("out", "self", "max")) expected_types <- list(out = "Tensor", self = "Tensor", max = "Scalar") @@ -4498,6 +4767,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_min torch_clamp_min <- function(self, min) { args <- mget(x = c("self", "min")) expected_types <- list(self = "Tensor", min = "Scalar") @@ -4514,6 +4784,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_min_ torch_clamp_min_ <- function(self, min) { args <- mget(x = c("self", "min")) expected_types <- list(self = "Tensor", min = "Scalar") @@ -4530,6 +4801,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_min_out torch_clamp_min_out <- function(out, self, min) { args <- mget(x = c("out", "self", "min")) expected_types <- list(out = "Tensor", self = "Tensor", min = "Scalar") @@ -4546,6 +4818,7 @@ fun_type = 'namespace' } +#' @rdname torch_clamp_out torch_clamp_out <- function(out, self, min = NULL, max = NULL) { args <- mget(x = c("out", "self", "min", "max")) expected_types <- list(out = "Tensor", self = "Tensor", min = "Scalar", max = "Scalar") @@ -4562,6 +4835,7 @@ fun_type = 'namespace' } +#' @rdname torch_clone torch_clone <- function(self, memory_format = NULL) { args <- mget(x = c("self", "memory_format")) expected_types <- list(self = "Tensor", memory_format = "MemoryFormat") @@ -4578,6 +4852,7 @@ fun_type = 'namespace' } +#' @rdname torch_col2im torch_col2im <- function(self, output_size, kernel_size, dilation, padding, stride) { args <- mget(x = c("self", "output_size", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", kernel_size = "IntArrayRef", @@ -4596,6 +4871,7 @@ fun_type = 'namespace' } +#' @rdname torch_col2im_backward torch_col2im_backward <- function(grad_output, kernel_size, dilation, padding, stride) { args <- mget(x = c("grad_output", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(grad_output = "Tensor", kernel_size = "IntArrayRef", dilation = "IntArrayRef", @@ -4614,6 +4890,7 @@ fun_type = 'namespace' } +#' @rdname torch_col2im_backward_out torch_col2im_backward_out <- function(grad_input, grad_output, kernel_size, dilation, padding, stride) { args <- mget(x = c("grad_input", "grad_output", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", kernel_size = "IntArrayRef", @@ -4632,6 +4909,7 @@ fun_type = 'namespace' } +#' @rdname torch_col2im_out torch_col2im_out <- function(out, self, output_size, kernel_size, dilation, padding, stride) { args <- mget(x = c("out", "self", "output_size", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -4651,6 +4929,7 @@ fun_type = 'namespace' } +#' @rdname torch_combinations torch_combinations <- function(self, r = 2L, with_replacement = FALSE) { args <- mget(x = c("self", "r", "with_replacement")) expected_types <- list(self = "Tensor", r = "int64_t", with_replacement = "bool") @@ -4667,6 +4946,7 @@ fun_type = 'namespace' } +#' @rdname torch_conj torch_conj <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -4683,6 +4963,7 @@ fun_type = 'namespace' } +#' @rdname torch_conj_out torch_conj_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -4699,6 +4980,7 @@ fun_type = 'namespace' } +#' @rdname torch_constant_pad_nd torch_constant_pad_nd <- function(self, pad, value = 0L) { args <- mget(x = c("self", "pad", "value")) expected_types <- list(self = "Tensor", pad = "IntArrayRef", value = "Scalar") @@ -4715,6 +4997,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv_tbc torch_conv_tbc <- function(self, weight, bias, pad = 0L) { args <- mget(x = c("self", "weight", "bias", "pad")) expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", pad = "int64_t") @@ -4731,6 +5014,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv_tbc_backward torch_conv_tbc_backward <- function(self, input, weight, bias, pad) { args <- mget(x = c("self", "input", "weight", "bias", "pad")) expected_types <- list(self = "Tensor", input = "Tensor", weight = "Tensor", bias = "Tensor", @@ -4748,6 +5032,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv_transpose1d torch_conv_transpose1d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, groups = 1L, dilation = 1L) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "output_padding", "groups", "dilation")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4766,6 +5051,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv_transpose2d torch_conv_transpose2d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, groups = 1L, dilation = 1L) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "output_padding", "groups", "dilation")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4784,6 +5070,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv_transpose3d torch_conv_transpose3d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, groups = 1L, dilation = 1L) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "output_padding", "groups", "dilation")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4802,6 +5089,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv1d torch_conv1d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, dilation = 1L, groups = 1L) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "groups")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4819,6 +5107,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv2d torch_conv2d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, dilation = 1L, groups = 1L) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "groups")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4836,6 +5125,7 @@ fun_type = 'namespace' } +#' @rdname torch_conv3d torch_conv3d <- function(input, weight, bias = list(), stride = 1L, padding = 0L, dilation = 1L, groups = 1L) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "groups")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4853,6 +5143,7 @@ fun_type = 'namespace' } +#' @rdname torch_convolution torch_convolution <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding", "groups")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4872,6 +5163,7 @@ fun_type = 'namespace' } +#' @rdname torch_convolution_backward_overrideable torch_convolution_backward_overrideable <- function(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask) { args <- mget(x = c("grad_output", "input", "weight", "stride", "padding", "dilation", "transposed", "output_padding", "groups", "output_mask")) expected_types <- list(grad_output = "Tensor", input = "Tensor", weight = "Tensor", @@ -4892,6 +5184,7 @@ fun_type = 'namespace' } +#' @rdname torch_convolution_overrideable torch_convolution_overrideable <- function(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups) { args <- mget(x = c("input", "weight", "bias", "stride", "padding", "dilation", "transposed", "output_padding", "groups")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", stride = "IntArrayRef", @@ -4911,6 +5204,7 @@ fun_type = 'namespace' } +#' @rdname torch_copy_sparse_to_sparse_ torch_copy_sparse_to_sparse_ <- function(self, src, non_blocking = FALSE) { args <- mget(x = c("self", "src", "non_blocking")) expected_types <- list(self = "Tensor", src = "Tensor", non_blocking = "bool") @@ -4927,6 +5221,7 @@ fun_type = 'namespace' } +#' @rdname torch_cos torch_cos <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -4943,6 +5238,7 @@ fun_type = 'namespace' } +#' @rdname torch_cos_ torch_cos_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -4959,6 +5255,7 @@ fun_type = 'namespace' } +#' @rdname torch_cos_out torch_cos_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -4975,6 +5272,7 @@ fun_type = 'namespace' } +#' @rdname torch_cosh torch_cosh <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -4991,6 +5289,7 @@ fun_type = 'namespace' } +#' @rdname torch_cosh_ torch_cosh_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -5007,6 +5306,7 @@ fun_type = 'namespace' } +#' @rdname torch_cosh_out torch_cosh_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -5023,6 +5323,7 @@ fun_type = 'namespace' } +#' @rdname torch_cosine_embedding_loss torch_cosine_embedding_loss <- function(input1, input2, target, margin = 0.000000, reduction = torch_reduction_mean()) { args <- mget(x = c("input1", "input2", "target", "margin", "reduction")) expected_types <- list(input1 = "Tensor", input2 = "Tensor", target = "Tensor", @@ -5040,6 +5341,7 @@ fun_type = 'namespace' } +#' @rdname torch_cosine_similarity torch_cosine_similarity <- function(x1, x2, dim = 2L, eps = 0.000000) { args <- mget(x = c("x1", "x2", "dim", "eps")) expected_types <- list(x1 = "Tensor", x2 = "Tensor", dim = "int64_t", eps = "double") @@ -5056,6 +5358,7 @@ fun_type = 'namespace' } +#' @rdname torch_cross torch_cross <- function(self, other, dim = NULL) { args <- mget(x = c("self", "other", "dim")) expected_types <- list(self = "Tensor", other = "Tensor", dim = "int64_t") @@ -5072,6 +5375,7 @@ fun_type = 'namespace' } +#' @rdname torch_cross_out torch_cross_out <- function(out, self, other, dim = NULL) { args <- mget(x = c("out", "self", "other", "dim")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor", dim = "int64_t") @@ -5088,6 +5392,7 @@ fun_type = 'namespace' } +#' @rdname torch_ctc_loss torch_ctc_loss <- function(log_probs, targets, input_lengths, target_lengths, blank = 0L, reduction = torch_reduction_mean(), zero_infinity = FALSE) { args <- mget(x = c("log_probs", "targets", "input_lengths", "target_lengths", "blank", "reduction", "zero_infinity")) expected_types <- list(log_probs = "Tensor", targets = "Tensor", input_lengths = c("IntArrayRef", @@ -5106,6 +5411,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_affine_grid_generator torch_cudnn_affine_grid_generator <- function(theta, False, C, H, W) { args <- mget(x = c("theta", "False", "C", "H", "W")) expected_types <- list(theta = "Tensor", False = "int64_t", C = "int64_t", H = "int64_t", @@ -5123,6 +5429,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_affine_grid_generator_backward torch_cudnn_affine_grid_generator_backward <- function(grad, False, C, H, W) { args <- mget(x = c("grad", "False", "C", "H", "W")) expected_types <- list(grad = "Tensor", False = "int64_t", C = "int64_t", H = "int64_t", @@ -5140,6 +5447,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_batch_norm torch_cudnn_batch_norm <- function(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon) { args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "exponential_average_factor", "epsilon")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", @@ -5159,6 +5467,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_batch_norm_backward torch_cudnn_batch_norm_backward <- function(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace) { args <- mget(x = c("input", "grad_output", "weight", "running_mean", "running_var", "save_mean", "save_var", "epsilon", "reserveSpace")) expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -5178,6 +5487,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution torch_cudnn_convolution <- function(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", @@ -5197,6 +5507,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution_backward torch_cudnn_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask) { args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask")) expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -5217,6 +5528,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution_backward_input torch_cudnn_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", @@ -5236,6 +5548,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution_backward_weight torch_cudnn_convolution_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", @@ -5255,6 +5568,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution_transpose torch_cudnn_convolution_transpose <- function(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self", "weight", "bias", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", @@ -5274,6 +5588,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution_transpose_backward torch_cudnn_convolution_transpose_backward <- function(self, grad_output, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, output_mask) { args <- mget(x = c("self", "grad_output", "weight", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask")) expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -5295,6 +5610,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution_transpose_backward_input torch_cudnn_convolution_transpose_backward_input <- function(grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(grad_output = "Tensor", weight = "Tensor", padding = "IntArrayRef", @@ -5314,6 +5630,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_convolution_transpose_backward_weight torch_cudnn_convolution_transpose_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", @@ -5333,6 +5650,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_grid_sampler torch_cudnn_grid_sampler <- function(self, grid) { args <- mget(x = c("self", "grid")) expected_types <- list(self = "Tensor", grid = "Tensor") @@ -5349,6 +5667,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_grid_sampler_backward torch_cudnn_grid_sampler_backward <- function(self, grid, grad_output) { args <- mget(x = c("self", "grid", "grad_output")) expected_types <- list(self = "Tensor", grid = "Tensor", grad_output = "Tensor") @@ -5365,6 +5684,7 @@ fun_type = 'namespace' } +#' @rdname torch_cudnn_is_acceptable torch_cudnn_is_acceptable <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -5381,6 +5701,7 @@ fun_type = 'namespace' } +#' @rdname torch_cummax torch_cummax <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname")) @@ -5397,6 +5718,7 @@ fun_type = 'namespace' } +#' @rdname torch_cummax_out torch_cummax_out <- function(values, indices, self, dim) { args <- mget(x = c("values", "indices", "self", "dim")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -5414,6 +5736,7 @@ fun_type = 'namespace' } +#' @rdname torch_cummin torch_cummin <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname")) @@ -5430,6 +5753,7 @@ fun_type = 'namespace' } +#' @rdname torch_cummin_out torch_cummin_out <- function(values, indices, self, dim) { args <- mget(x = c("values", "indices", "self", "dim")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -5447,6 +5771,7 @@ fun_type = 'namespace' } +#' @rdname torch_cumprod torch_cumprod <- function(self, dim, dtype = NULL) { args <- mget(x = c("self", "dim", "dtype")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType") @@ -5463,6 +5788,7 @@ fun_type = 'namespace' } +#' @rdname torch_cumprod_out torch_cumprod_out <- function(out, self, dim, dtype = NULL) { args <- mget(x = c("out", "self", "dim", "dtype")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname" @@ -5480,6 +5806,7 @@ fun_type = 'namespace' } +#' @rdname torch_cumsum torch_cumsum <- function(self, dim, dtype = NULL) { args <- mget(x = c("self", "dim", "dtype")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType") @@ -5496,6 +5823,7 @@ fun_type = 'namespace' } +#' @rdname torch_cumsum_out torch_cumsum_out <- function(out, self, dim, dtype = NULL) { args <- mget(x = c("out", "self", "dim", "dtype")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname" @@ -5513,6 +5841,7 @@ fun_type = 'namespace' } +#' @rdname torch_dequantize torch_dequantize <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -5529,6 +5858,7 @@ fun_type = 'namespace' } +#' @rdname torch_det torch_det <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -5545,6 +5875,7 @@ fun_type = 'namespace' } +#' @rdname torch_detach torch_detach <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -5561,6 +5892,7 @@ fun_type = 'namespace' } +#' @rdname torch_detach_ torch_detach_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -5577,6 +5909,7 @@ fun_type = 'namespace' } +#' @rdname torch_diag torch_diag <- function(self, diagonal = 0L) { args <- mget(x = c("self", "diagonal")) expected_types <- list(self = "Tensor", diagonal = "int64_t") @@ -5593,6 +5926,7 @@ fun_type = 'namespace' } +#' @rdname torch_diag_embed torch_diag_embed <- function(self, offset = 0L, dim1 = -2L, dim2 = -1L) { args <- mget(x = c("self", "offset", "dim1", "dim2")) expected_types <- list(self = "Tensor", offset = "int64_t", dim1 = "int64_t", dim2 = "int64_t") @@ -5609,6 +5943,7 @@ fun_type = 'namespace' } +#' @rdname torch_diag_out torch_diag_out <- function(out, self, diagonal = 0L) { args <- mget(x = c("out", "self", "diagonal")) expected_types <- list(out = "Tensor", self = "Tensor", diagonal = "int64_t") @@ -5625,6 +5960,7 @@ fun_type = 'namespace' } +#' @rdname torch_diagflat torch_diagflat <- function(self, offset = 0L) { args <- mget(x = c("self", "offset")) expected_types <- list(self = "Tensor", offset = "int64_t") @@ -5641,6 +5977,7 @@ fun_type = 'namespace' } +#' @rdname torch_diagonal torch_diagonal <- function(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L) { args <- mget(x = c("self", "outdim", "dim1", "dim2", "offset")) expected_types <- list(self = "Tensor", outdim = "Dimname", dim1 = c("int64_t", @@ -5658,6 +5995,7 @@ fun_type = 'namespace' } +#' @rdname torch_digamma torch_digamma <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -5674,6 +6012,7 @@ fun_type = 'namespace' } +#' @rdname torch_digamma_out torch_digamma_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -5690,6 +6029,7 @@ fun_type = 'namespace' } +#' @rdname torch_dist torch_dist <- function(self, other, p = 2L) { args <- mget(x = c("self", "other", "p")) expected_types <- list(self = "Tensor", other = "Tensor", p = "Scalar") @@ -5706,6 +6046,7 @@ fun_type = 'namespace' } +#' @rdname torch_div torch_div <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar")) @@ -5722,6 +6063,7 @@ fun_type = 'namespace' } +#' @rdname torch_div_out torch_div_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -5738,6 +6080,7 @@ fun_type = 'namespace' } +#' @rdname torch_dot torch_dot <- function(self, tensor) { args <- mget(x = c("self", "tensor")) expected_types <- list(self = "Tensor", tensor = "Tensor") @@ -5754,6 +6097,7 @@ fun_type = 'namespace' } +#' @rdname torch_dot_out torch_dot_out <- function(out, self, tensor) { args <- mget(x = c("out", "self", "tensor")) expected_types <- list(out = "Tensor", self = "Tensor", tensor = "Tensor") @@ -5770,6 +6114,7 @@ fun_type = 'namespace' } +#' @rdname torch_dropout torch_dropout <- function(input, p, train) { args <- mget(x = c("input", "p", "train")) expected_types <- list(input = "Tensor", p = "double", train = "bool") @@ -5786,6 +6131,7 @@ fun_type = 'namespace' } +#' @rdname torch_dropout_ torch_dropout_ <- function(self, p, train) { args <- mget(x = c("self", "p", "train")) expected_types <- list(self = "Tensor", p = "double", train = "bool") @@ -5802,6 +6148,7 @@ fun_type = 'namespace' } +#' @rdname torch_eig torch_eig <- function(self, eigenvectors = FALSE) { args <- mget(x = c("self", "eigenvectors")) expected_types <- list(self = "Tensor", eigenvectors = "bool") @@ -5818,6 +6165,7 @@ fun_type = 'namespace' } +#' @rdname torch_eig_out torch_eig_out <- function(e, v, self, eigenvectors = FALSE) { args <- mget(x = c("e", "v", "self", "eigenvectors")) expected_types <- list(e = "Tensor", v = "Tensor", self = "Tensor", eigenvectors = "bool") @@ -5834,6 +6182,7 @@ fun_type = 'namespace' } +#' @rdname torch_einsum torch_einsum <- function(equation, tensors) { args <- mget(x = c("equation", "tensors")) expected_types <- list(equation = "std::string", tensors = "TensorList") @@ -5850,6 +6199,7 @@ fun_type = 'namespace' } +#' @rdname torch_elu torch_elu <- function(self, alpha = 1L, scale = 1L, input_scale = 1L) { args <- mget(x = c("self", "alpha", "scale", "input_scale")) expected_types <- list(self = "Tensor", alpha = "Scalar", scale = "Scalar", input_scale = "Scalar") @@ -5866,6 +6216,7 @@ fun_type = 'namespace' } +#' @rdname torch_elu_ torch_elu_ <- function(self, alpha = 1L, scale = 1L, input_scale = 1L) { args <- mget(x = c("self", "alpha", "scale", "input_scale")) expected_types <- list(self = "Tensor", alpha = "Scalar", scale = "Scalar", input_scale = "Scalar") @@ -5882,6 +6233,7 @@ fun_type = 'namespace' } +#' @rdname torch_elu_backward torch_elu_backward <- function(grad_output, alpha, scale, input_scale, output) { args <- mget(x = c("grad_output", "alpha", "scale", "input_scale", "output")) expected_types <- list(grad_output = "Tensor", alpha = "Scalar", scale = "Scalar", @@ -5899,6 +6251,7 @@ fun_type = 'namespace' } +#' @rdname torch_elu_backward_out torch_elu_backward_out <- function(grad_input, grad_output, alpha, scale, input_scale, output) { args <- mget(x = c("grad_input", "grad_output", "alpha", "scale", "input_scale", "output")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", alpha = "Scalar", @@ -5917,6 +6270,7 @@ fun_type = 'namespace' } +#' @rdname torch_elu_out torch_elu_out <- function(out, self, alpha = 1L, scale = 1L, input_scale = 1L) { args <- mget(x = c("out", "self", "alpha", "scale", "input_scale")) expected_types <- list(out = "Tensor", self = "Tensor", alpha = "Scalar", scale = "Scalar", @@ -5934,6 +6288,7 @@ fun_type = 'namespace' } +#' @rdname torch_embedding torch_embedding <- function(weight, indices, padding_idx = -1L, scale_grad_by_freq = FALSE, sparse = FALSE) { args <- mget(x = c("weight", "indices", "padding_idx", "scale_grad_by_freq", "sparse")) expected_types <- list(weight = "Tensor", indices = "Tensor", padding_idx = "int64_t", @@ -5951,6 +6306,7 @@ fun_type = 'namespace' } +#' @rdname torch_embedding_backward torch_embedding_backward <- function(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse) { args <- mget(x = c("grad", "indices", "num_weights", "padding_idx", "scale_grad_by_freq", "sparse")) expected_types <- list(grad = "Tensor", indices = "Tensor", num_weights = "int64_t", @@ -5969,6 +6325,7 @@ fun_type = 'namespace' } +#' @rdname torch_embedding_bag torch_embedding_bag <- function(weight, indices, offsets, scale_grad_by_freq = FALSE, mode = 0L, sparse = FALSE, per_sample_weights = list(), include_last_offset = FALSE) { args <- mget(x = c("weight", "indices", "offsets", "scale_grad_by_freq", "mode", "sparse", "per_sample_weights", "include_last_offset")) expected_types <- list(weight = "Tensor", indices = "Tensor", offsets = "Tensor", @@ -5987,6 +6344,7 @@ fun_type = 'namespace' } +#' @rdname torch_embedding_dense_backward torch_embedding_dense_backward <- function(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq) { args <- mget(x = c("grad_output", "indices", "num_weights", "padding_idx", "scale_grad_by_freq")) expected_types <- list(grad_output = "Tensor", indices = "Tensor", num_weights = "int64_t", @@ -6005,6 +6363,7 @@ fun_type = 'namespace' } +#' @rdname torch_embedding_renorm_ torch_embedding_renorm_ <- function(self, indices, max_norm, norm_type) { args <- mget(x = c("self", "indices", "max_norm", "norm_type")) expected_types <- list(self = "Tensor", indices = "Tensor", max_norm = "double", @@ -6022,6 +6381,7 @@ fun_type = 'namespace' } +#' @rdname torch_embedding_sparse_backward torch_embedding_sparse_backward <- function(grad, indices, num_weights, padding_idx, scale_grad_by_freq) { args <- mget(x = c("grad", "indices", "num_weights", "padding_idx", "scale_grad_by_freq")) expected_types <- list(grad = "Tensor", indices = "Tensor", num_weights = "int64_t", @@ -6040,6 +6400,7 @@ fun_type = 'namespace' } +#' @rdname .torch_empty .torch_empty <- function(size, names, options = list(), memory_format = NULL) { args <- mget(x = c("size", "names", "options", "memory_format")) expected_types <- list(size = "IntArrayRef", names = "DimnameList", options = "TensorOptions", @@ -6057,6 +6418,7 @@ fun_type = 'namespace' } +#' @rdname .torch_empty_like .torch_empty_like <- function(self, options = list(), memory_format = NULL) { args <- mget(x = c("self", "options", "memory_format")) expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat") @@ -6073,6 +6435,7 @@ fun_type = 'namespace' } +#' @rdname torch_empty_out torch_empty_out <- function(out, size, memory_format = NULL) { args <- mget(x = c("out", "size", "memory_format")) expected_types <- list(out = "Tensor", size = "IntArrayRef", memory_format = "MemoryFormat") @@ -6089,6 +6452,7 @@ fun_type = 'namespace' } +#' @rdname .torch_empty_strided .torch_empty_strided <- function(size, stride, options = list()) { args <- mget(x = c("size", "stride", "options")) expected_types <- list(size = "IntArrayRef", stride = "IntArrayRef", options = "TensorOptions") @@ -6105,6 +6469,7 @@ fun_type = 'namespace' } +#' @rdname torch_eq torch_eq <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -6121,6 +6486,7 @@ fun_type = 'namespace' } +#' @rdname torch_eq_out torch_eq_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -6138,6 +6504,7 @@ fun_type = 'namespace' } +#' @rdname torch_equal torch_equal <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = "Tensor") @@ -6154,6 +6521,7 @@ fun_type = 'namespace' } +#' @rdname torch_erf torch_erf <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6170,6 +6538,7 @@ fun_type = 'namespace' } +#' @rdname torch_erf_ torch_erf_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6186,6 +6555,7 @@ fun_type = 'namespace' } +#' @rdname torch_erf_out torch_erf_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -6202,6 +6572,7 @@ fun_type = 'namespace' } +#' @rdname torch_erfc torch_erfc <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6218,6 +6589,7 @@ fun_type = 'namespace' } +#' @rdname torch_erfc_ torch_erfc_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6234,6 +6606,7 @@ fun_type = 'namespace' } +#' @rdname torch_erfc_out torch_erfc_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -6250,6 +6623,7 @@ fun_type = 'namespace' } +#' @rdname torch_erfinv torch_erfinv <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6266,6 +6640,7 @@ fun_type = 'namespace' } +#' @rdname torch_erfinv_out torch_erfinv_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -6282,6 +6657,7 @@ fun_type = 'namespace' } +#' @rdname torch_exp torch_exp <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6298,6 +6674,7 @@ fun_type = 'namespace' } +#' @rdname torch_exp_ torch_exp_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6314,6 +6691,7 @@ fun_type = 'namespace' } +#' @rdname torch_exp_out torch_exp_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -6330,6 +6708,7 @@ fun_type = 'namespace' } +#' @rdname torch_expm1 torch_expm1 <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6346,6 +6725,7 @@ fun_type = 'namespace' } +#' @rdname torch_expm1_ torch_expm1_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6362,6 +6742,7 @@ fun_type = 'namespace' } +#' @rdname torch_expm1_out torch_expm1_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -6378,6 +6759,7 @@ fun_type = 'namespace' } +#' @rdname .torch_eye .torch_eye <- function(n, m, options = list()) { args <- mget(x = c("n", "m", "options")) expected_types <- list(n = "int64_t", m = "int64_t", options = "TensorOptions") @@ -6394,6 +6776,7 @@ fun_type = 'namespace' } +#' @rdname torch_eye_out torch_eye_out <- function(out, n, m) { args <- mget(x = c("out", "n", "m")) expected_types <- list(out = "Tensor", n = "int64_t", m = "int64_t") @@ -6410,6 +6793,7 @@ fun_type = 'namespace' } +#' @rdname torch_fake_quantize_per_channel_affine torch_fake_quantize_per_channel_affine <- function(self, scale, zero_point, axis, quant_min, quant_max) { args <- mget(x = c("self", "scale", "zero_point", "axis", "quant_min", "quant_max")) expected_types <- list(self = "Tensor", scale = "Tensor", zero_point = "Tensor", @@ -6428,6 +6812,7 @@ fun_type = 'namespace' } +#' @rdname torch_fake_quantize_per_channel_affine_backward torch_fake_quantize_per_channel_affine_backward <- function(grad, self, scale, zero_point, axis, quant_min, quant_max) { args <- mget(x = c("grad", "self", "scale", "zero_point", "axis", "quant_min", "quant_max")) expected_types <- list(grad = "Tensor", self = "Tensor", scale = "Tensor", zero_point = "Tensor", @@ -6446,6 +6831,7 @@ fun_type = 'namespace' } +#' @rdname torch_fake_quantize_per_tensor_affine torch_fake_quantize_per_tensor_affine <- function(self, scale, zero_point, quant_min, quant_max) { args <- mget(x = c("self", "scale", "zero_point", "quant_min", "quant_max")) expected_types <- list(self = "Tensor", scale = "double", zero_point = "int64_t", @@ -6463,6 +6849,7 @@ fun_type = 'namespace' } +#' @rdname torch_fake_quantize_per_tensor_affine_backward torch_fake_quantize_per_tensor_affine_backward <- function(grad, self, scale, zero_point, quant_min, quant_max) { args <- mget(x = c("grad", "self", "scale", "zero_point", "quant_min", "quant_max")) expected_types <- list(grad = "Tensor", self = "Tensor", scale = "double", zero_point = "int64_t", @@ -6481,6 +6868,7 @@ fun_type = 'namespace' } +#' @rdname torch_fbgemm_linear_fp16_weight torch_fbgemm_linear_fp16_weight <- function(input, packed_weight, bias) { args <- mget(x = c("input", "packed_weight", "bias")) expected_types <- list(input = "Tensor", packed_weight = "Tensor", bias = "Tensor") @@ -6497,6 +6885,7 @@ fun_type = 'namespace' } +#' @rdname torch_fbgemm_linear_fp16_weight_fp32_activation torch_fbgemm_linear_fp16_weight_fp32_activation <- function(input, packed_weight, bias) { args <- mget(x = c("input", "packed_weight", "bias")) expected_types <- list(input = "Tensor", packed_weight = "Tensor", bias = "Tensor") @@ -6513,6 +6902,7 @@ fun_type = 'namespace' } +#' @rdname torch_fbgemm_linear_int8_weight torch_fbgemm_linear_int8_weight <- function(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias) { args <- mget(x = c("input", "weight", "packed", "col_offsets", "weight_scale", "weight_zero_point", "bias")) expected_types <- list(input = "Tensor", weight = "Tensor", packed = "Tensor", @@ -6532,6 +6922,7 @@ fun_type = 'namespace' } +#' @rdname torch_fbgemm_linear_int8_weight_fp32_activation torch_fbgemm_linear_int8_weight_fp32_activation <- function(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias) { args <- mget(x = c("input", "weight", "packed", "col_offsets", "weight_scale", "weight_zero_point", "bias")) expected_types <- list(input = "Tensor", weight = "Tensor", packed = "Tensor", @@ -6551,6 +6942,7 @@ fun_type = 'namespace' } +#' @rdname torch_fbgemm_linear_quantize_weight torch_fbgemm_linear_quantize_weight <- function(input) { args <- mget(x = c("input")) expected_types <- list(input = "Tensor") @@ -6567,6 +6959,7 @@ fun_type = 'namespace' } +#' @rdname torch_fbgemm_pack_gemm_matrix_fp16 torch_fbgemm_pack_gemm_matrix_fp16 <- function(input) { args <- mget(x = c("input")) expected_types <- list(input = "Tensor") @@ -6583,6 +6976,7 @@ fun_type = 'namespace' } +#' @rdname torch_fbgemm_pack_quantized_matrix torch_fbgemm_pack_quantized_matrix <- function(input, K, False) { args <- mget(x = c("input", "K", "False")) expected_types <- list(input = "Tensor", K = "int64_t", False = "int64_t") @@ -6599,6 +6993,7 @@ fun_type = 'namespace' } +#' @rdname torch_feature_alpha_dropout torch_feature_alpha_dropout <- function(input, p, train) { args <- mget(x = c("input", "p", "train")) expected_types <- list(input = "Tensor", p = "double", train = "bool") @@ -6615,6 +7010,7 @@ fun_type = 'namespace' } +#' @rdname torch_feature_alpha_dropout_ torch_feature_alpha_dropout_ <- function(self, p, train) { args <- mget(x = c("self", "p", "train")) expected_types <- list(self = "Tensor", p = "double", train = "bool") @@ -6631,6 +7027,7 @@ fun_type = 'namespace' } +#' @rdname torch_feature_dropout torch_feature_dropout <- function(input, p, train) { args <- mget(x = c("input", "p", "train")) expected_types <- list(input = "Tensor", p = "double", train = "bool") @@ -6647,6 +7044,7 @@ fun_type = 'namespace' } +#' @rdname torch_feature_dropout_ torch_feature_dropout_ <- function(self, p, train) { args <- mget(x = c("self", "p", "train")) expected_types <- list(self = "Tensor", p = "double", train = "bool") @@ -6663,6 +7061,7 @@ fun_type = 'namespace' } +#' @rdname torch_fft torch_fft <- function(self, signal_ndim, normalized = FALSE) { args <- mget(x = c("self", "signal_ndim", "normalized")) expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool") @@ -6679,6 +7078,7 @@ fun_type = 'namespace' } +#' @rdname torch_fill_ torch_fill_ <- function(self, value) { args <- mget(x = c("self", "value")) expected_types <- list(self = "Tensor", value = c("Scalar", "Tensor")) @@ -6695,6 +7095,7 @@ fun_type = 'namespace' } +#' @rdname torch_flatten torch_flatten <- function(self, dims, start_dim = 1L, end_dim = -1L, out_dim) { args <- mget(x = c("self", "dims", "start_dim", "end_dim", "out_dim")) expected_types <- list(self = "Tensor", dims = "DimnameList", start_dim = c("int64_t", @@ -6712,6 +7113,7 @@ fun_type = 'namespace' } +#' @rdname torch_flip torch_flip <- function(self, dims) { args <- mget(x = c("self", "dims")) expected_types <- list(self = "Tensor", dims = "IntArrayRef") @@ -6728,6 +7130,7 @@ fun_type = 'namespace' } +#' @rdname torch_floor torch_floor <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6744,6 +7147,7 @@ fun_type = 'namespace' } +#' @rdname torch_floor_ torch_floor_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6760,6 +7164,7 @@ fun_type = 'namespace' } +#' @rdname torch_floor_divide torch_floor_divide <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar")) @@ -6776,6 +7181,7 @@ fun_type = 'namespace' } +#' @rdname torch_floor_divide_out torch_floor_divide_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -6792,6 +7198,7 @@ fun_type = 'namespace' } +#' @rdname torch_floor_out torch_floor_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -6808,6 +7215,7 @@ fun_type = 'namespace' } +#' @rdname torch_fmod torch_fmod <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -6824,6 +7232,7 @@ fun_type = 'namespace' } +#' @rdname torch_fmod_out torch_fmod_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -6841,6 +7250,7 @@ fun_type = 'namespace' } +#' @rdname torch_frac torch_frac <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6857,6 +7267,7 @@ fun_type = 'namespace' } +#' @rdname torch_frac_ torch_frac_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -6873,6 +7284,7 @@ fun_type = 'namespace' } +#' @rdname torch_frac_out torch_frac_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -6889,6 +7301,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool2d torch_fractional_max_pool2d <- function(self, kernel_size, output_size, random_samples) { args <- mget(x = c("self", "kernel_size", "output_size", "random_samples")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", output_size = "IntArrayRef", @@ -6906,6 +7319,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool2d_backward torch_fractional_max_pool2d_backward <- function(grad_output, self, kernel_size, output_size, indices) { args <- mget(x = c("grad_output", "self", "kernel_size", "output_size", "indices")) expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -6924,6 +7338,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool2d_backward_out torch_fractional_max_pool2d_backward_out <- function(grad_input, grad_output, self, kernel_size, output_size, indices) { args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "output_size", "indices")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -6943,6 +7358,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool2d_out torch_fractional_max_pool2d_out <- function(output, indices, self, kernel_size, output_size, random_samples) { args <- mget(x = c("output", "indices", "self", "kernel_size", "output_size", "random_samples")) expected_types <- list(output = "Tensor", indices = "Tensor", self = "Tensor", @@ -6962,6 +7378,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool3d torch_fractional_max_pool3d <- function(self, kernel_size, output_size, random_samples) { args <- mget(x = c("self", "kernel_size", "output_size", "random_samples")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", output_size = "IntArrayRef", @@ -6979,6 +7396,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool3d_backward torch_fractional_max_pool3d_backward <- function(grad_output, self, kernel_size, output_size, indices) { args <- mget(x = c("grad_output", "self", "kernel_size", "output_size", "indices")) expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -6997,6 +7415,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool3d_backward_out torch_fractional_max_pool3d_backward_out <- function(grad_input, grad_output, self, kernel_size, output_size, indices) { args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "output_size", "indices")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -7016,6 +7435,7 @@ fun_type = 'namespace' } +#' @rdname torch_fractional_max_pool3d_out torch_fractional_max_pool3d_out <- function(output, indices, self, kernel_size, output_size, random_samples) { args <- mget(x = c("output", "indices", "self", "kernel_size", "output_size", "random_samples")) expected_types <- list(output = "Tensor", indices = "Tensor", self = "Tensor", @@ -7035,6 +7455,7 @@ fun_type = 'namespace' } +#' @rdname torch_frobenius_norm torch_frobenius_norm <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = "IntArrayRef", keepdim = "bool") @@ -7051,6 +7472,7 @@ fun_type = 'namespace' } +#' @rdname torch_frobenius_norm_out torch_frobenius_norm_out <- function(out, self, dim, keepdim = FALSE) { args <- mget(x = c("out", "self", "dim", "keepdim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = "IntArrayRef", keepdim = "bool") @@ -7067,6 +7489,7 @@ fun_type = 'namespace' } +#' @rdname torch_from_file torch_from_file <- function(filename, shared = NULL, size = 0L, options = list()) { args <- mget(x = c("filename", "shared", "size", "options")) expected_types <- list(filename = "std::string", shared = "bool", size = "int64_t", @@ -7084,6 +7507,7 @@ fun_type = 'namespace' } +#' @rdname .torch_full .torch_full <- function(size, fill_value, names, options = list()) { args <- mget(x = c("size", "fill_value", "names", "options")) expected_types <- list(size = "IntArrayRef", fill_value = "Scalar", names = "DimnameList", @@ -7101,6 +7525,7 @@ fun_type = 'namespace' } +#' @rdname .torch_full_like .torch_full_like <- function(self, fill_value, options = list(), memory_format = NULL) { args <- mget(x = c("self", "fill_value", "options", "memory_format")) expected_types <- list(self = "Tensor", fill_value = "Scalar", options = "TensorOptions", @@ -7118,6 +7543,7 @@ fun_type = 'namespace' } +#' @rdname torch_full_out torch_full_out <- function(out, size, fill_value) { args <- mget(x = c("out", "size", "fill_value")) expected_types <- list(out = "Tensor", size = "IntArrayRef", fill_value = "Scalar") @@ -7134,6 +7560,7 @@ fun_type = 'namespace' } +#' @rdname torch_gather torch_gather <- function(self, dim, index, sparse_grad = FALSE) { args <- mget(x = c("self", "dim", "index", "sparse_grad")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", @@ -7151,6 +7578,7 @@ fun_type = 'namespace' } +#' @rdname torch_gather_out torch_gather_out <- function(out, self, dim, index, sparse_grad = FALSE) { args <- mget(x = c("out", "self", "dim", "index", "sparse_grad")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname" @@ -7168,6 +7596,7 @@ fun_type = 'namespace' } +#' @rdname torch_ge torch_ge <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -7184,6 +7613,7 @@ fun_type = 'namespace' } +#' @rdname torch_ge_out torch_ge_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -7201,6 +7631,7 @@ fun_type = 'namespace' } +#' @rdname torch_gelu torch_gelu <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -7217,6 +7648,7 @@ fun_type = 'namespace' } +#' @rdname torch_gelu_backward torch_gelu_backward <- function(grad, self) { args <- mget(x = c("grad", "self")) expected_types <- list(grad = "Tensor", self = "Tensor") @@ -7233,6 +7665,7 @@ fun_type = 'namespace' } +#' @rdname torch_geqrf torch_geqrf <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -7249,6 +7682,7 @@ fun_type = 'namespace' } +#' @rdname torch_geqrf_out torch_geqrf_out <- function(a, tau, self) { args <- mget(x = c("a", "tau", "self")) expected_types <- list(a = "Tensor", tau = "Tensor", self = "Tensor") @@ -7265,6 +7699,7 @@ fun_type = 'namespace' } +#' @rdname torch_ger torch_ger <- function(self, vec2) { args <- mget(x = c("self", "vec2")) expected_types <- list(self = "Tensor", vec2 = "Tensor") @@ -7281,6 +7716,7 @@ fun_type = 'namespace' } +#' @rdname torch_ger_out torch_ger_out <- function(out, self, vec2) { args <- mget(x = c("out", "self", "vec2")) expected_types <- list(out = "Tensor", self = "Tensor", vec2 = "Tensor") @@ -7297,6 +7733,7 @@ fun_type = 'namespace' } +#' @rdname torch_glu torch_glu <- function(self, dim = -1L) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = "int64_t") @@ -7313,6 +7750,7 @@ fun_type = 'namespace' } +#' @rdname torch_glu_backward torch_glu_backward <- function(grad_output, self, dim) { args <- mget(x = c("grad_output", "self", "dim")) expected_types <- list(grad_output = "Tensor", self = "Tensor", dim = "int64_t") @@ -7329,6 +7767,7 @@ fun_type = 'namespace' } +#' @rdname torch_glu_backward_out torch_glu_backward_out <- function(grad_input, grad_output, self, dim) { args <- mget(x = c("grad_input", "grad_output", "self", "dim")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -7346,6 +7785,7 @@ fun_type = 'namespace' } +#' @rdname torch_glu_out torch_glu_out <- function(out, self, dim = -1L) { args <- mget(x = c("out", "self", "dim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = "int64_t") @@ -7362,6 +7802,7 @@ fun_type = 'namespace' } +#' @rdname torch_grid_sampler torch_grid_sampler <- function(input, grid, interpolation_mode, padding_mode, align_corners) { args <- mget(x = c("input", "grid", "interpolation_mode", "padding_mode", "align_corners")) expected_types <- list(input = "Tensor", grid = "Tensor", interpolation_mode = "int64_t", @@ -7380,6 +7821,7 @@ fun_type = 'namespace' } +#' @rdname torch_grid_sampler_2d torch_grid_sampler_2d <- function(input, grid, interpolation_mode, padding_mode, align_corners) { args <- mget(x = c("input", "grid", "interpolation_mode", "padding_mode", "align_corners")) expected_types <- list(input = "Tensor", grid = "Tensor", interpolation_mode = "int64_t", @@ -7398,6 +7840,7 @@ fun_type = 'namespace' } +#' @rdname torch_grid_sampler_2d_backward torch_grid_sampler_2d_backward <- function(grad_output, input, grid, interpolation_mode, padding_mode, align_corners) { args <- mget(x = c("grad_output", "input", "grid", "interpolation_mode", "padding_mode", "align_corners")) expected_types <- list(grad_output = "Tensor", input = "Tensor", grid = "Tensor", @@ -7417,6 +7860,7 @@ fun_type = 'namespace' } +#' @rdname torch_grid_sampler_3d torch_grid_sampler_3d <- function(input, grid, interpolation_mode, padding_mode, align_corners) { args <- mget(x = c("input", "grid", "interpolation_mode", "padding_mode", "align_corners")) expected_types <- list(input = "Tensor", grid = "Tensor", interpolation_mode = "int64_t", @@ -7435,6 +7879,7 @@ fun_type = 'namespace' } +#' @rdname torch_grid_sampler_3d_backward torch_grid_sampler_3d_backward <- function(grad_output, input, grid, interpolation_mode, padding_mode, align_corners) { args <- mget(x = c("grad_output", "input", "grid", "interpolation_mode", "padding_mode", "align_corners")) expected_types <- list(grad_output = "Tensor", input = "Tensor", grid = "Tensor", @@ -7454,6 +7899,7 @@ fun_type = 'namespace' } +#' @rdname torch_group_norm torch_group_norm <- function(input, num_groups, weight = list(), bias = list(), eps = 0.000010, cudnn_enabled = TRUE) { args <- mget(x = c("input", "num_groups", "weight", "bias", "eps", "cudnn_enabled")) expected_types <- list(input = "Tensor", num_groups = "int64_t", weight = "Tensor", @@ -7471,6 +7917,7 @@ fun_type = 'namespace' } +#' @rdname torch_gru torch_gru <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) { args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional")) expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", @@ -7492,6 +7939,7 @@ fun_type = 'namespace' } +#' @rdname torch_gru_cell torch_gru_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh")) expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", @@ -7509,6 +7957,7 @@ fun_type = 'namespace' } +#' @rdname torch_gt torch_gt <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -7525,6 +7974,7 @@ fun_type = 'namespace' } +#' @rdname torch_gt_out torch_gt_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -7542,7 +7992,8 @@ fun_type = 'namespace' } -torch_hamming_window <- function(window_length, periodic, alpha, beta, options = list()) { +#' @rdname .torch_hamming_window +.torch_hamming_window <- function(window_length, periodic, alpha, beta, options = list()) { args <- mget(x = c("window_length", "periodic", "alpha", "beta", "options")) expected_types <- list(window_length = "int64_t", periodic = "bool", alpha = "double", beta = "double", options = "TensorOptions") @@ -7559,7 +8010,8 @@ fun_type = 'namespace' } -torch_hann_window <- function(window_length, periodic, options = list()) { +#' @rdname .torch_hann_window +.torch_hann_window <- function(window_length, periodic, options = list()) { args <- mget(x = c("window_length", "periodic", "options")) expected_types <- list(window_length = "int64_t", periodic = "bool", options = "TensorOptions") nd_args <- c("window_length", "periodic") @@ -7575,6 +8027,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardshrink torch_hardshrink <- function(self, lambd = 0.500000) { args <- mget(x = c("self", "lambd")) expected_types <- list(self = "Tensor", lambd = "Scalar") @@ -7591,6 +8044,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardshrink_backward torch_hardshrink_backward <- function(grad_out, self, lambd) { args <- mget(x = c("grad_out", "self", "lambd")) expected_types <- list(grad_out = "Tensor", self = "Tensor", lambd = "Scalar") @@ -7607,6 +8061,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardsigmoid torch_hardsigmoid <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -7623,6 +8078,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardsigmoid_ torch_hardsigmoid_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -7639,6 +8095,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardsigmoid_backward torch_hardsigmoid_backward <- function(grad_output, self) { args <- mget(x = c("grad_output", "self")) expected_types <- list(grad_output = "Tensor", self = "Tensor") @@ -7655,6 +8112,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardsigmoid_out torch_hardsigmoid_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -7671,6 +8129,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardtanh torch_hardtanh <- function(self, min_val = -1L, max_val = 1L) { args <- mget(x = c("self", "min_val", "max_val")) expected_types <- list(self = "Tensor", min_val = "Scalar", max_val = "Scalar") @@ -7687,6 +8146,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardtanh_ torch_hardtanh_ <- function(self, min_val = -1L, max_val = 1L) { args <- mget(x = c("self", "min_val", "max_val")) expected_types <- list(self = "Tensor", min_val = "Scalar", max_val = "Scalar") @@ -7703,6 +8163,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardtanh_backward torch_hardtanh_backward <- function(grad_output, self, min_val, max_val) { args <- mget(x = c("grad_output", "self", "min_val", "max_val")) expected_types <- list(grad_output = "Tensor", self = "Tensor", min_val = "Scalar", @@ -7720,6 +8181,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardtanh_backward_out torch_hardtanh_backward_out <- function(grad_input, grad_output, self, min_val, max_val) { args <- mget(x = c("grad_input", "grad_output", "self", "min_val", "max_val")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -7737,6 +8199,7 @@ fun_type = 'namespace' } +#' @rdname torch_hardtanh_out torch_hardtanh_out <- function(out, self, min_val = -1L, max_val = 1L) { args <- mget(x = c("out", "self", "min_val", "max_val")) expected_types <- list(out = "Tensor", self = "Tensor", min_val = "Scalar", max_val = "Scalar") @@ -7753,6 +8216,7 @@ fun_type = 'namespace' } +#' @rdname torch_hinge_embedding_loss torch_hinge_embedding_loss <- function(self, target, margin = 1.000000, reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "margin", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", margin = "double", reduction = "int64_t") @@ -7769,6 +8233,7 @@ fun_type = 'namespace' } +#' @rdname torch_histc torch_histc <- function(self, bins = 100L, min = 0L, max = 0L) { args <- mget(x = c("self", "bins", "min", "max")) expected_types <- list(self = "Tensor", bins = "int64_t", min = "Scalar", max = "Scalar") @@ -7785,6 +8250,7 @@ fun_type = 'namespace' } +#' @rdname torch_histc_out torch_histc_out <- function(out, self, bins = 100L, min = 0L, max = 0L) { args <- mget(x = c("out", "self", "bins", "min", "max")) expected_types <- list(out = "Tensor", self = "Tensor", bins = "int64_t", min = "Scalar", @@ -7802,6 +8268,7 @@ fun_type = 'namespace' } +#' @rdname torch_hspmm torch_hspmm <- function(mat1, mat2) { args <- mget(x = c("mat1", "mat2")) expected_types <- list(mat1 = "Tensor", mat2 = "Tensor") @@ -7818,6 +8285,7 @@ fun_type = 'namespace' } +#' @rdname torch_hspmm_out torch_hspmm_out <- function(out, mat1, mat2) { args <- mget(x = c("out", "mat1", "mat2")) expected_types <- list(out = "Tensor", mat1 = "Tensor", mat2 = "Tensor") @@ -7834,6 +8302,7 @@ fun_type = 'namespace' } +#' @rdname torch_ifft torch_ifft <- function(self, signal_ndim, normalized = FALSE) { args <- mget(x = c("self", "signal_ndim", "normalized")) expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool") @@ -7850,6 +8319,7 @@ fun_type = 'namespace' } +#' @rdname torch_im2col torch_im2col <- function(self, kernel_size, dilation, padding, stride) { args <- mget(x = c("self", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", dilation = "IntArrayRef", @@ -7867,6 +8337,7 @@ fun_type = 'namespace' } +#' @rdname torch_im2col_backward torch_im2col_backward <- function(grad_output, input_size, kernel_size, dilation, padding, stride) { args <- mget(x = c("grad_output", "input_size", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(grad_output = "Tensor", input_size = "IntArrayRef", kernel_size = "IntArrayRef", @@ -7885,6 +8356,7 @@ fun_type = 'namespace' } +#' @rdname torch_im2col_backward_out torch_im2col_backward_out <- function(grad_input, grad_output, input_size, kernel_size, dilation, padding, stride) { args <- mget(x = c("grad_input", "grad_output", "input_size", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", input_size = "IntArrayRef", @@ -7904,6 +8376,7 @@ fun_type = 'namespace' } +#' @rdname torch_im2col_out torch_im2col_out <- function(out, self, kernel_size, dilation, padding, stride) { args <- mget(x = c("out", "self", "kernel_size", "dilation", "padding", "stride")) expected_types <- list(out = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -7922,6 +8395,7 @@ fun_type = 'namespace' } +#' @rdname torch_imag torch_imag <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -7938,6 +8412,7 @@ fun_type = 'namespace' } +#' @rdname torch_index torch_index <- function(self, indices) { args <- mget(x = c("self", "indices")) expected_types <- list(self = "Tensor", indices = "TensorList") @@ -7954,6 +8429,7 @@ fun_type = 'namespace' } +#' @rdname torch_index_add torch_index_add <- function(self, dim, index, source) { args <- mget(x = c("self", "dim", "index", "source")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", @@ -7971,6 +8447,7 @@ fun_type = 'namespace' } +#' @rdname torch_index_copy torch_index_copy <- function(self, dim, index, source) { args <- mget(x = c("self", "dim", "index", "source")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", @@ -7988,6 +8465,7 @@ fun_type = 'namespace' } +#' @rdname torch_index_fill torch_index_fill <- function(self, dim, index, value) { args <- mget(x = c("self", "dim", "index", "value")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", @@ -8005,6 +8483,7 @@ fun_type = 'namespace' } +#' @rdname torch_index_put torch_index_put <- function(self, indices, values, accumulate = FALSE) { args <- mget(x = c("self", "indices", "values", "accumulate")) expected_types <- list(self = "Tensor", indices = "TensorList", values = "Tensor", @@ -8022,6 +8501,7 @@ fun_type = 'namespace' } +#' @rdname torch_index_put_ torch_index_put_ <- function(self, indices, values, accumulate = FALSE) { args <- mget(x = c("self", "indices", "values", "accumulate")) expected_types <- list(self = "Tensor", indices = "TensorList", values = "Tensor", @@ -8039,6 +8519,7 @@ fun_type = 'namespace' } +#' @rdname torch_index_select torch_index_select <- function(self, dim, index) { args <- mget(x = c("self", "dim", "index")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor") @@ -8055,6 +8536,7 @@ fun_type = 'namespace' } +#' @rdname torch_index_select_out torch_index_select_out <- function(out, self, dim, index) { args <- mget(x = c("out", "self", "dim", "index")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname" @@ -8072,6 +8554,7 @@ fun_type = 'namespace' } +#' @rdname torch_instance_norm torch_instance_norm <- function(input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled) { args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "use_input_stats", "momentum", "eps", "cudnn_enabled")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", @@ -8091,6 +8574,7 @@ fun_type = 'namespace' } +#' @rdname torch_int_repr torch_int_repr <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8107,6 +8591,7 @@ fun_type = 'namespace' } +#' @rdname torch_inverse torch_inverse <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8123,6 +8608,7 @@ fun_type = 'namespace' } +#' @rdname torch_inverse_out torch_inverse_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -8139,6 +8625,7 @@ fun_type = 'namespace' } +#' @rdname torch_irfft torch_irfft <- function(self, signal_ndim, normalized = FALSE, onesided = TRUE, signal_sizes = list()) { args <- mget(x = c("self", "signal_ndim", "normalized", "onesided", "signal_sizes")) expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool", @@ -8156,6 +8643,7 @@ fun_type = 'namespace' } +#' @rdname torch_is_complex torch_is_complex <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8172,6 +8660,7 @@ fun_type = 'namespace' } +#' @rdname torch_is_distributed torch_is_distributed <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8188,6 +8677,7 @@ fun_type = 'namespace' } +#' @rdname torch_is_floating_point torch_is_floating_point <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8204,6 +8694,7 @@ fun_type = 'namespace' } +#' @rdname torch_is_nonzero torch_is_nonzero <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8220,6 +8711,7 @@ fun_type = 'namespace' } +#' @rdname torch_is_same_size torch_is_same_size <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = "Tensor") @@ -8236,6 +8728,7 @@ fun_type = 'namespace' } +#' @rdname torch_is_signed torch_is_signed <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8252,6 +8745,7 @@ fun_type = 'namespace' } +#' @rdname torch_isclose torch_isclose <- function(self, other, rtol = 0.000010, atol = 0.000000, equal_nan = FALSE) { args <- mget(x = c("self", "other", "rtol", "atol", "equal_nan")) expected_types <- list(self = "Tensor", other = "Tensor", rtol = "double", atol = "double", @@ -8269,6 +8763,7 @@ fun_type = 'namespace' } +#' @rdname torch_isfinite torch_isfinite <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8285,6 +8780,7 @@ fun_type = 'namespace' } +#' @rdname torch_isinf torch_isinf <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8301,6 +8797,7 @@ fun_type = 'namespace' } +#' @rdname torch_isnan torch_isnan <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8317,6 +8814,7 @@ fun_type = 'namespace' } +#' @rdname torch_kl_div torch_kl_div <- function(self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -8333,6 +8831,7 @@ fun_type = 'namespace' } +#' @rdname torch_kl_div_backward torch_kl_div_backward <- function(grad_output, self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("grad_output", "self", "target", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -8350,6 +8849,7 @@ fun_type = 'namespace' } +#' @rdname torch_kthvalue torch_kthvalue <- function(self, k, dim = -1L, keepdim = FALSE) { args <- mget(x = c("self", "k", "dim", "keepdim")) expected_types <- list(self = "Tensor", k = "int64_t", dim = c("int64_t", "Dimname" @@ -8367,6 +8867,7 @@ fun_type = 'namespace' } +#' @rdname torch_kthvalue_out torch_kthvalue_out <- function(values, indices, self, k, dim = -1L, keepdim = FALSE) { args <- mget(x = c("values", "indices", "self", "k", "dim", "keepdim")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -8384,6 +8885,7 @@ fun_type = 'namespace' } +#' @rdname torch_l1_loss torch_l1_loss <- function(self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -8400,6 +8902,7 @@ fun_type = 'namespace' } +#' @rdname torch_l1_loss_backward torch_l1_loss_backward <- function(grad_output, self, target, reduction) { args <- mget(x = c("grad_output", "self", "target", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -8417,6 +8920,7 @@ fun_type = 'namespace' } +#' @rdname torch_l1_loss_backward_out torch_l1_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -8434,6 +8938,7 @@ fun_type = 'namespace' } +#' @rdname torch_l1_loss_out torch_l1_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("out", "self", "target", "reduction")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -8450,6 +8955,7 @@ fun_type = 'namespace' } +#' @rdname torch_layer_norm torch_layer_norm <- function(input, normalized_shape, weight = list(), bias = list(), eps = 0.000010, cudnn_enable = TRUE) { args <- mget(x = c("input", "normalized_shape", "weight", "bias", "eps", "cudnn_enable")) expected_types <- list(input = "Tensor", normalized_shape = "IntArrayRef", weight = "Tensor", @@ -8467,6 +8973,7 @@ fun_type = 'namespace' } +#' @rdname torch_le torch_le <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -8483,6 +8990,7 @@ fun_type = 'namespace' } +#' @rdname torch_le_out torch_le_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -8500,6 +9008,7 @@ fun_type = 'namespace' } +#' @rdname torch_leaky_relu torch_leaky_relu <- function(self, negative_slope = 0.010000) { args <- mget(x = c("self", "negative_slope")) expected_types <- list(self = "Tensor", negative_slope = "Scalar") @@ -8516,6 +9025,7 @@ fun_type = 'namespace' } +#' @rdname torch_leaky_relu_ torch_leaky_relu_ <- function(self, negative_slope = 0.010000) { args <- mget(x = c("self", "negative_slope")) expected_types <- list(self = "Tensor", negative_slope = "Scalar") @@ -8532,6 +9042,7 @@ fun_type = 'namespace' } +#' @rdname torch_leaky_relu_backward torch_leaky_relu_backward <- function(grad_output, self, negative_slope, self_is_result) { args <- mget(x = c("grad_output", "self", "negative_slope", "self_is_result")) expected_types <- list(grad_output = "Tensor", self = "Tensor", negative_slope = "Scalar", @@ -8549,6 +9060,7 @@ fun_type = 'namespace' } +#' @rdname torch_leaky_relu_out torch_leaky_relu_out <- function(out, self, negative_slope = 0.010000) { args <- mget(x = c("out", "self", "negative_slope")) expected_types <- list(out = "Tensor", self = "Tensor", negative_slope = "Scalar") @@ -8565,6 +9077,7 @@ fun_type = 'namespace' } +#' @rdname torch_lerp torch_lerp <- function(self, end, weight) { args <- mget(x = c("self", "end", "weight")) expected_types <- list(self = "Tensor", end = "Tensor", weight = c("Scalar", "Tensor" @@ -8582,6 +9095,7 @@ fun_type = 'namespace' } +#' @rdname torch_lerp_out torch_lerp_out <- function(out, self, end, weight) { args <- mget(x = c("out", "self", "end", "weight")) expected_types <- list(out = "Tensor", self = "Tensor", end = "Tensor", weight = c("Scalar", @@ -8599,6 +9113,7 @@ fun_type = 'namespace' } +#' @rdname torch_lgamma torch_lgamma <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8615,6 +9130,7 @@ fun_type = 'namespace' } +#' @rdname torch_lgamma_out torch_lgamma_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -8631,6 +9147,7 @@ fun_type = 'namespace' } +#' @rdname torch_linear torch_linear <- function(input, weight, bias = list()) { args <- mget(x = c("input", "weight", "bias")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor") @@ -8647,6 +9164,7 @@ fun_type = 'namespace' } +#' @rdname .torch_linspace .torch_linspace <- function(start, end, steps = 100L, options = list()) { args <- mget(x = c("start", "end", "steps", "options")) expected_types <- list(start = "Scalar", end = "Scalar", steps = "int64_t", options = "TensorOptions") @@ -8663,6 +9181,7 @@ fun_type = 'namespace' } +#' @rdname torch_linspace_out torch_linspace_out <- function(out, start, end, steps = 100L) { args <- mget(x = c("out", "start", "end", "steps")) expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", steps = "int64_t") @@ -8679,6 +9198,7 @@ fun_type = 'namespace' } +#' @rdname torch_log torch_log <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8695,6 +9215,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_ torch_log_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8711,6 +9232,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_out torch_log_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -8727,6 +9249,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_sigmoid torch_log_sigmoid <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8743,6 +9266,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_sigmoid_backward torch_log_sigmoid_backward <- function(grad_output, self, buffer) { args <- mget(x = c("grad_output", "self", "buffer")) expected_types <- list(grad_output = "Tensor", self = "Tensor", buffer = "Tensor") @@ -8759,6 +9283,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_sigmoid_backward_out torch_log_sigmoid_backward_out <- function(grad_input, grad_output, self, buffer) { args <- mget(x = c("grad_input", "grad_output", "self", "buffer")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -8776,6 +9301,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_sigmoid_forward torch_log_sigmoid_forward <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8792,6 +9318,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_sigmoid_forward_out torch_log_sigmoid_forward_out <- function(output, buffer, self) { args <- mget(x = c("output", "buffer", "self")) expected_types <- list(output = "Tensor", buffer = "Tensor", self = "Tensor") @@ -8808,6 +9335,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_sigmoid_out torch_log_sigmoid_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -8824,6 +9352,7 @@ fun_type = 'namespace' } +#' @rdname torch_log_softmax torch_log_softmax <- function(self, dim, dtype = NULL) { args <- mget(x = c("self", "dim", "dtype")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType") @@ -8840,6 +9369,7 @@ fun_type = 'namespace' } +#' @rdname torch_log10 torch_log10 <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8856,6 +9386,7 @@ fun_type = 'namespace' } +#' @rdname torch_log10_ torch_log10_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8872,6 +9403,7 @@ fun_type = 'namespace' } +#' @rdname torch_log10_out torch_log10_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -8888,6 +9420,7 @@ fun_type = 'namespace' } +#' @rdname torch_log1p torch_log1p <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8904,6 +9437,7 @@ fun_type = 'namespace' } +#' @rdname torch_log1p_ torch_log1p_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8920,6 +9454,7 @@ fun_type = 'namespace' } +#' @rdname torch_log1p_out torch_log1p_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -8936,6 +9471,7 @@ fun_type = 'namespace' } +#' @rdname torch_log2 torch_log2 <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8952,6 +9488,7 @@ fun_type = 'namespace' } +#' @rdname torch_log2_ torch_log2_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -8968,6 +9505,7 @@ fun_type = 'namespace' } +#' @rdname torch_log2_out torch_log2_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -8984,6 +9522,7 @@ fun_type = 'namespace' } +#' @rdname torch_logdet torch_logdet <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -9000,6 +9539,7 @@ fun_type = 'namespace' } +#' @rdname torch_logical_and torch_logical_and <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = "Tensor") @@ -9016,6 +9556,7 @@ fun_type = 'namespace' } +#' @rdname torch_logical_and_out torch_logical_and_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -9032,6 +9573,7 @@ fun_type = 'namespace' } +#' @rdname .torch_logical_not .torch_logical_not <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -9048,6 +9590,7 @@ fun_type = 'namespace' } +#' @rdname torch_logical_not_out torch_logical_not_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -9064,6 +9607,7 @@ fun_type = 'namespace' } +#' @rdname torch_logical_or torch_logical_or <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = "Tensor") @@ -9080,6 +9624,7 @@ fun_type = 'namespace' } +#' @rdname torch_logical_or_out torch_logical_or_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -9096,6 +9641,7 @@ fun_type = 'namespace' } +#' @rdname torch_logical_xor torch_logical_xor <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = "Tensor") @@ -9112,6 +9658,7 @@ fun_type = 'namespace' } +#' @rdname torch_logical_xor_out torch_logical_xor_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -9128,6 +9675,7 @@ fun_type = 'namespace' } +#' @rdname .torch_logspace .torch_logspace <- function(start, end, steps = 100L, base = 10.000000, options = list()) { args <- mget(x = c("start", "end", "steps", "base", "options")) expected_types <- list(start = "Scalar", end = "Scalar", steps = "int64_t", base = "double", @@ -9145,6 +9693,7 @@ fun_type = 'namespace' } +#' @rdname torch_logspace_out torch_logspace_out <- function(out, start, end, steps = 100L, base = 10.000000) { args <- mget(x = c("out", "start", "end", "steps", "base")) expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", steps = "int64_t", @@ -9162,6 +9711,7 @@ fun_type = 'namespace' } +#' @rdname torch_logsumexp torch_logsumexp <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -9179,6 +9729,7 @@ fun_type = 'namespace' } +#' @rdname torch_logsumexp_out torch_logsumexp_out <- function(out, self, dim, keepdim = FALSE) { args <- mget(x = c("out", "self", "dim", "keepdim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", @@ -9196,6 +9747,7 @@ fun_type = 'namespace' } +#' @rdname torch_lstm torch_lstm <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) { args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional")) expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", @@ -9217,6 +9769,7 @@ fun_type = 'namespace' } +#' @rdname torch_lstm_cell torch_lstm_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh")) expected_types <- list(input = "Tensor", hx = "TensorList", w_ih = "Tensor", w_hh = "Tensor", @@ -9234,6 +9787,7 @@ fun_type = 'namespace' } +#' @rdname torch_lstsq torch_lstsq <- function(self, A) { args <- mget(x = c("self", "A")) expected_types <- list(self = "Tensor", A = "Tensor") @@ -9250,6 +9804,7 @@ fun_type = 'namespace' } +#' @rdname torch_lstsq_out torch_lstsq_out <- function(X, qr, self, A) { args <- mget(x = c("X", "qr", "self", "A")) expected_types <- list(X = "Tensor", qr = "Tensor", self = "Tensor", A = "Tensor") @@ -9266,6 +9821,7 @@ fun_type = 'namespace' } +#' @rdname torch_lt torch_lt <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -9282,6 +9838,7 @@ fun_type = 'namespace' } +#' @rdname torch_lt_out torch_lt_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -9299,6 +9856,7 @@ fun_type = 'namespace' } +#' @rdname torch_lu_solve torch_lu_solve <- function(self, LU_data, LU_pivots) { args <- mget(x = c("self", "LU_data", "LU_pivots")) expected_types <- list(self = "Tensor", LU_data = "Tensor", LU_pivots = "Tensor") @@ -9315,6 +9873,7 @@ fun_type = 'namespace' } +#' @rdname torch_lu_solve_out torch_lu_solve_out <- function(out, self, LU_data, LU_pivots) { args <- mget(x = c("out", "self", "LU_data", "LU_pivots")) expected_types <- list(out = "Tensor", self = "Tensor", LU_data = "Tensor", LU_pivots = "Tensor") @@ -9331,6 +9890,7 @@ fun_type = 'namespace' } +#' @rdname torch_margin_ranking_loss torch_margin_ranking_loss <- function(input1, input2, target, margin = 0.000000, reduction = torch_reduction_mean()) { args <- mget(x = c("input1", "input2", "target", "margin", "reduction")) expected_types <- list(input1 = "Tensor", input2 = "Tensor", target = "Tensor", @@ -9348,6 +9908,7 @@ fun_type = 'namespace' } +#' @rdname torch_masked_fill torch_masked_fill <- function(self, mask, value) { args <- mget(x = c("self", "mask", "value")) expected_types <- list(self = "Tensor", mask = "Tensor", value = c("Scalar", "Tensor" @@ -9365,6 +9926,7 @@ fun_type = 'namespace' } +#' @rdname torch_masked_scatter torch_masked_scatter <- function(self, mask, source) { args <- mget(x = c("self", "mask", "source")) expected_types <- list(self = "Tensor", mask = "Tensor", source = "Tensor") @@ -9381,6 +9943,7 @@ fun_type = 'namespace' } +#' @rdname torch_masked_select torch_masked_select <- function(self, mask) { args <- mget(x = c("self", "mask")) expected_types <- list(self = "Tensor", mask = "Tensor") @@ -9397,6 +9960,7 @@ fun_type = 'namespace' } +#' @rdname torch_masked_select_out torch_masked_select_out <- function(out, self, mask) { args <- mget(x = c("out", "self", "mask")) expected_types <- list(out = "Tensor", self = "Tensor", mask = "Tensor") @@ -9413,6 +9977,7 @@ fun_type = 'namespace' } +#' @rdname torch_matmul torch_matmul <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = "Tensor") @@ -9429,6 +9994,7 @@ fun_type = 'namespace' } +#' @rdname torch_matmul_out torch_matmul_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -9445,6 +10011,7 @@ fun_type = 'namespace' } +#' @rdname torch_matrix_power torch_matrix_power <- function(self, n) { args <- mget(x = c("self", "n")) expected_types <- list(self = "Tensor", n = "int64_t") @@ -9461,6 +10028,7 @@ fun_type = 'namespace' } +#' @rdname torch_matrix_rank torch_matrix_rank <- function(self, tol, symmetric = FALSE) { args <- mget(x = c("self", "tol", "symmetric")) expected_types <- list(self = "Tensor", tol = "double", symmetric = "bool") @@ -9477,6 +10045,7 @@ fun_type = 'namespace' } +#' @rdname .torch_max .torch_max <- function(self, dim, other, keepdim = FALSE) { args <- mget(x = c("self", "dim", "other", "keepdim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), other = "Tensor", @@ -9494,6 +10063,7 @@ fun_type = 'namespace' } +#' @rdname .torch_max_out .torch_max_out <- function(max, out, max_values, other, self, dim, keepdim = FALSE) { args <- mget(x = c("max", "out", "max_values", "other", "self", "dim", "keepdim")) expected_types <- list(max = "Tensor", out = "Tensor", max_values = "Tensor", other = "Tensor", @@ -9511,6 +10081,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_pool1d torch_max_pool1d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -9528,6 +10099,7 @@ fun_type = 'namespace' } +#' @rdname .torch_max_pool1d_with_indices .torch_max_pool1d_with_indices <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -9545,6 +10117,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_pool2d torch_max_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -9562,6 +10135,7 @@ fun_type = 'namespace' } +#' @rdname .torch_max_pool2d_with_indices .torch_max_pool2d_with_indices <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -9579,6 +10153,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_pool2d_with_indices_backward torch_max_pool2d_with_indices_backward <- function(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) { args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices")) expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -9598,6 +10173,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_pool2d_with_indices_backward_out torch_max_pool2d_with_indices_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) { args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -9617,6 +10193,7 @@ fun_type = 'namespace' } +#' @rdname .torch_max_pool2d_with_indices_out .torch_max_pool2d_with_indices_out <- function(out, indices, self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("out", "indices", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -9635,6 +10212,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_pool3d torch_max_pool3d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -9652,6 +10230,7 @@ fun_type = 'namespace' } +#' @rdname .torch_max_pool3d_with_indices .torch_max_pool3d_with_indices <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -9669,6 +10248,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_pool3d_with_indices_backward torch_max_pool3d_with_indices_backward <- function(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) { args <- mget(x = c("grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices")) expected_types <- list(grad_output = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -9688,6 +10268,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_pool3d_with_indices_backward_out torch_max_pool3d_with_indices_backward_out <- function(grad_input, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices) { args <- mget(x = c("grad_input", "grad_output", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode", "indices")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -9707,6 +10288,7 @@ fun_type = 'namespace' } +#' @rdname .torch_max_pool3d_with_indices_out .torch_max_pool3d_with_indices_out <- function(out, indices, self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("out", "indices", "self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(out = "Tensor", indices = "Tensor", self = "Tensor", kernel_size = "IntArrayRef", @@ -9725,6 +10307,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool2d torch_max_unpool2d <- function(self, indices, output_size) { args <- mget(x = c("self", "indices", "output_size")) expected_types <- list(self = "Tensor", indices = "Tensor", output_size = "IntArrayRef") @@ -9741,6 +10324,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool2d_backward torch_max_unpool2d_backward <- function(grad_output, self, indices, output_size) { args <- mget(x = c("grad_output", "self", "indices", "output_size")) expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor", @@ -9758,6 +10342,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool2d_backward_out torch_max_unpool2d_backward_out <- function(grad_input, grad_output, self, indices, output_size) { args <- mget(x = c("grad_input", "grad_output", "self", "indices", "output_size")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -9776,6 +10361,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool2d_out torch_max_unpool2d_out <- function(out, self, indices, output_size) { args <- mget(x = c("out", "self", "indices", "output_size")) expected_types <- list(out = "Tensor", self = "Tensor", indices = "Tensor", output_size = "IntArrayRef") @@ -9792,6 +10378,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool3d torch_max_unpool3d <- function(self, indices, output_size, stride, padding) { args <- mget(x = c("self", "indices", "output_size", "stride", "padding")) expected_types <- list(self = "Tensor", indices = "Tensor", output_size = "IntArrayRef", @@ -9809,6 +10396,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool3d_backward torch_max_unpool3d_backward <- function(grad_output, self, indices, output_size, stride, padding) { args <- mget(x = c("grad_output", "self", "indices", "output_size", "stride", "padding")) expected_types <- list(grad_output = "Tensor", self = "Tensor", indices = "Tensor", @@ -9827,6 +10415,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool3d_backward_out torch_max_unpool3d_backward_out <- function(grad_input, grad_output, self, indices, output_size, stride, padding) { args <- mget(x = c("grad_input", "grad_output", "self", "indices", "output_size", "stride", "padding")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -9846,6 +10435,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_unpool3d_out torch_max_unpool3d_out <- function(out, self, indices, output_size, stride, padding) { args <- mget(x = c("out", "self", "indices", "output_size", "stride", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", indices = "Tensor", output_size = "IntArrayRef", @@ -9864,6 +10454,7 @@ fun_type = 'namespace' } +#' @rdname torch_max_values torch_max_values <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -9881,6 +10472,7 @@ fun_type = 'namespace' } +#' @rdname torch_mean torch_mean <- function(self, dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("self", "dim", "keepdim", "dtype")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -9898,6 +10490,7 @@ fun_type = 'namespace' } +#' @rdname torch_mean_out torch_mean_out <- function(out, self, dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("out", "self", "dim", "keepdim", "dtype")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", @@ -9915,6 +10508,7 @@ fun_type = 'namespace' } +#' @rdname torch_median torch_median <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool") @@ -9931,6 +10525,7 @@ fun_type = 'namespace' } +#' @rdname torch_median_out torch_median_out <- function(values, indices, self, dim, keepdim = FALSE) { args <- mget(x = c("values", "indices", "self", "dim", "keepdim")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -9948,6 +10543,7 @@ fun_type = 'namespace' } +#' @rdname torch_meshgrid torch_meshgrid <- function(tensors) { args <- mget(x = c("tensors")) expected_types <- list(tensors = "TensorList") @@ -9964,6 +10560,7 @@ fun_type = 'namespace' } +#' @rdname .torch_min .torch_min <- function(self, dim, other, keepdim = FALSE) { args <- mget(x = c("self", "dim", "other", "keepdim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), other = "Tensor", @@ -9981,6 +10578,7 @@ fun_type = 'namespace' } +#' @rdname .torch_min_out .torch_min_out <- function(min, out, min_indices, other, self, dim, keepdim = FALSE) { args <- mget(x = c("min", "out", "min_indices", "other", "self", "dim", "keepdim")) expected_types <- list(min = "Tensor", out = "Tensor", min_indices = "Tensor", @@ -9999,6 +10597,7 @@ fun_type = 'namespace' } +#' @rdname torch_min_values torch_min_values <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -10016,6 +10615,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_batch_norm torch_miopen_batch_norm <- function(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon) { args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "exponential_average_factor", "epsilon")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", @@ -10035,6 +10635,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_batch_norm_backward torch_miopen_batch_norm_backward <- function(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon) { args <- mget(x = c("input", "grad_output", "weight", "running_mean", "running_var", "save_mean", "save_var", "epsilon")) expected_types <- list(input = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -10054,6 +10655,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution torch_miopen_convolution <- function(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", @@ -10073,6 +10675,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_backward torch_miopen_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask) { args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask")) expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -10093,6 +10696,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_backward_bias torch_miopen_convolution_backward_bias <- function(grad_output) { args <- mget(x = c("grad_output")) expected_types <- list(grad_output = "Tensor") @@ -10109,6 +10713,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_backward_input torch_miopen_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", @@ -10128,6 +10733,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_backward_weight torch_miopen_convolution_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", @@ -10147,6 +10753,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_transpose torch_miopen_convolution_transpose <- function(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self", "weight", "bias", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", @@ -10166,6 +10773,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_transpose_backward torch_miopen_convolution_transpose_backward <- function(self, grad_output, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, output_mask) { args <- mget(x = c("self", "grad_output", "weight", "padding", "output_padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask")) expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -10187,6 +10795,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_transpose_backward_input torch_miopen_convolution_transpose_backward_input <- function(grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(grad_output = "Tensor", weight = "Tensor", padding = "IntArrayRef", @@ -10206,6 +10815,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_convolution_transpose_backward_weight torch_miopen_convolution_transpose_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", @@ -10225,6 +10835,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_depthwise_convolution torch_miopen_depthwise_convolution <- function(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", @@ -10244,6 +10855,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_depthwise_convolution_backward torch_miopen_depthwise_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask) { args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic", "output_mask")) expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -10264,6 +10876,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_depthwise_convolution_backward_input torch_miopen_depthwise_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", @@ -10283,6 +10896,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_depthwise_convolution_backward_weight torch_miopen_depthwise_convolution_backward_weight <- function(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic) { args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "benchmark", "deterministic")) expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", @@ -10302,6 +10916,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_rnn torch_miopen_rnn <- function(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state) { args <- mget(x = c("input", "weight", "weight_stride0", "hx", "cx", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state")) expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", @@ -10324,6 +10939,7 @@ fun_type = 'namespace' } +#' @rdname torch_miopen_rnn_backward torch_miopen_rnn_backward <- function(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask) { args <- mget(x = c("input", "weight", "weight_stride0", "weight_buf", "hx", "cx", "output", "grad_output", "grad_hy", "grad_cy", "mode", "hidden_size", "num_layers", "batch_first", "dropout", "train", "bidirectional", "batch_sizes", "dropout_state", "reserve", "output_mask")) expected_types <- list(input = "Tensor", weight = "TensorList", weight_stride0 = "int64_t", @@ -10349,6 +10965,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_adaptive_avg_pool2d torch_mkldnn_adaptive_avg_pool2d <- function(self, output_size) { args <- mget(x = c("self", "output_size")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef") @@ -10365,6 +10982,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_convolution torch_mkldnn_convolution <- function(self, weight, bias, padding, stride, dilation, groups) { args <- mget(x = c("self", "weight", "bias", "padding", "stride", "dilation", "groups")) expected_types <- list(self = "Tensor", weight = "Tensor", bias = "Tensor", padding = "IntArrayRef", @@ -10383,6 +11001,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_convolution_backward torch_mkldnn_convolution_backward <- function(self, grad_output, weight, padding, stride, dilation, groups, output_mask) { args <- mget(x = c("self", "grad_output", "weight", "padding", "stride", "dilation", "groups", "output_mask")) expected_types <- list(self = "Tensor", grad_output = "Tensor", weight = "Tensor", @@ -10402,6 +11021,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_convolution_backward_input torch_mkldnn_convolution_backward_input <- function(self_size, grad_output, weight, padding, stride, dilation, groups, bias_defined) { args <- mget(x = c("self_size", "grad_output", "weight", "padding", "stride", "dilation", "groups", "bias_defined")) expected_types <- list(self_size = "IntArrayRef", grad_output = "Tensor", weight = "Tensor", @@ -10421,6 +11041,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_convolution_backward_weights torch_mkldnn_convolution_backward_weights <- function(weight_size, grad_output, self, padding, stride, dilation, groups, bias_defined) { args <- mget(x = c("weight_size", "grad_output", "self", "padding", "stride", "dilation", "groups", "bias_defined")) expected_types <- list(weight_size = "IntArrayRef", grad_output = "Tensor", self = "Tensor", @@ -10440,6 +11061,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_linear torch_mkldnn_linear <- function(input, weight, bias = list()) { args <- mget(x = c("input", "weight", "bias")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor") @@ -10456,6 +11078,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_max_pool2d torch_mkldnn_max_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -10473,6 +11096,7 @@ fun_type = 'namespace' } +#' @rdname torch_mkldnn_reorder_conv2d_weight torch_mkldnn_reorder_conv2d_weight <- function(self, padding = 0L, stride = 1L, dilation = 1L, groups = 1L) { args <- mget(x = c("self", "padding", "stride", "dilation", "groups")) expected_types <- list(self = "Tensor", padding = "IntArrayRef", stride = "IntArrayRef", @@ -10490,6 +11114,7 @@ fun_type = 'namespace' } +#' @rdname torch_mm torch_mm <- function(self, mat2) { args <- mget(x = c("self", "mat2")) expected_types <- list(self = "Tensor", mat2 = "Tensor") @@ -10506,6 +11131,7 @@ fun_type = 'namespace' } +#' @rdname torch_mm_out torch_mm_out <- function(out, self, mat2) { args <- mget(x = c("out", "self", "mat2")) expected_types <- list(out = "Tensor", self = "Tensor", mat2 = "Tensor") @@ -10522,6 +11148,7 @@ fun_type = 'namespace' } +#' @rdname torch_mode torch_mode <- function(self, dim = -1L, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool") @@ -10538,6 +11165,7 @@ fun_type = 'namespace' } +#' @rdname torch_mode_out torch_mode_out <- function(values, indices, self, dim = -1L, keepdim = FALSE) { args <- mget(x = c("values", "indices", "self", "dim", "keepdim")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -10555,6 +11183,7 @@ fun_type = 'namespace' } +#' @rdname torch_mse_loss torch_mse_loss <- function(self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -10571,6 +11200,7 @@ fun_type = 'namespace' } +#' @rdname torch_mse_loss_backward torch_mse_loss_backward <- function(grad_output, self, target, reduction) { args <- mget(x = c("grad_output", "self", "target", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -10588,6 +11218,7 @@ fun_type = 'namespace' } +#' @rdname torch_mse_loss_backward_out torch_mse_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -10605,6 +11236,7 @@ fun_type = 'namespace' } +#' @rdname torch_mse_loss_out torch_mse_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("out", "self", "target", "reduction")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -10621,6 +11253,7 @@ fun_type = 'namespace' } +#' @rdname torch_mul torch_mul <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar")) @@ -10637,6 +11270,7 @@ fun_type = 'namespace' } +#' @rdname torch_mul_out torch_mul_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -10653,7 +11287,8 @@ fun_type = 'namespace' } -torch_multi_margin_loss <- function(self, target, p = 1L, margin = 1L, weight = list(), reduction = torch_reduction_mean()) { +#' @rdname .torch_multi_margin_loss +.torch_multi_margin_loss <- function(self, target, p = 1L, margin = 1L, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "p", "margin", "weight", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", p = "Scalar", margin = "Scalar", weight = "Tensor", reduction = "int64_t") @@ -10670,6 +11305,7 @@ fun_type = 'namespace' } +#' @rdname torch_multi_margin_loss_backward torch_multi_margin_loss_backward <- function(grad_output, self, target, p, margin, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("grad_output", "self", "target", "p", "margin", "weight", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -10687,6 +11323,7 @@ fun_type = 'namespace' } +#' @rdname torch_multi_margin_loss_backward_out torch_multi_margin_loss_backward_out <- function(grad_input, grad_output, self, target, p, margin, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "p", "margin", "weight", "reduction")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -10706,6 +11343,7 @@ fun_type = 'namespace' } +#' @rdname torch_multi_margin_loss_out torch_multi_margin_loss_out <- function(out, self, target, p = 1L, margin = 1L, weight = list(), reduction = torch_reduction_mean()) { args <- mget(x = c("out", "self", "target", "p", "margin", "weight", "reduction")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", p = "Scalar", @@ -10723,7 +11361,8 @@ fun_type = 'namespace' } -torch_multilabel_margin_loss <- function(self, target, reduction = torch_reduction_mean()) { +#' @rdname .torch_multilabel_margin_loss +.torch_multilabel_margin_loss <- function(self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t") nd_args <- c("self", "target") @@ -10739,6 +11378,7 @@ fun_type = 'namespace' } +#' @rdname torch_multilabel_margin_loss_backward torch_multilabel_margin_loss_backward <- function(grad_output, self, target, reduction, is_target) { args <- mget(x = c("grad_output", "self", "target", "reduction", "is_target")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -10756,6 +11396,7 @@ fun_type = 'namespace' } +#' @rdname torch_multilabel_margin_loss_backward_out torch_multilabel_margin_loss_backward_out <- function(grad_input, grad_output, self, target, reduction, is_target) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction", "is_target")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -10774,6 +11415,7 @@ fun_type = 'namespace' } +#' @rdname torch_multilabel_margin_loss_forward torch_multilabel_margin_loss_forward <- function(self, target, reduction) { args <- mget(x = c("self", "target", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -10790,6 +11432,7 @@ fun_type = 'namespace' } +#' @rdname torch_multilabel_margin_loss_forward_out torch_multilabel_margin_loss_forward_out <- function(output, is_target, self, target, reduction) { args <- mget(x = c("output", "is_target", "self", "target", "reduction")) expected_types <- list(output = "Tensor", is_target = "Tensor", self = "Tensor", @@ -10807,6 +11450,7 @@ fun_type = 'namespace' } +#' @rdname torch_multilabel_margin_loss_out torch_multilabel_margin_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("out", "self", "target", "reduction")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -10823,6 +11467,7 @@ fun_type = 'namespace' } +#' @rdname torch_multinomial torch_multinomial <- function(self, num_samples, replacement = FALSE, generator = NULL) { args <- mget(x = c("self", "num_samples", "replacement", "generator")) expected_types <- list(self = "Tensor", num_samples = "int64_t", replacement = "bool", @@ -10840,6 +11485,7 @@ fun_type = 'namespace' } +#' @rdname torch_multinomial_out torch_multinomial_out <- function(out, self, num_samples, replacement = FALSE, generator = NULL) { args <- mget(x = c("out", "self", "num_samples", "replacement", "generator")) expected_types <- list(out = "Tensor", self = "Tensor", num_samples = "int64_t", @@ -10857,6 +11503,7 @@ fun_type = 'namespace' } +#' @rdname torch_mv torch_mv <- function(self, vec) { args <- mget(x = c("self", "vec")) expected_types <- list(self = "Tensor", vec = "Tensor") @@ -10873,6 +11520,7 @@ fun_type = 'namespace' } +#' @rdname torch_mv_out torch_mv_out <- function(out, self, vec) { args <- mget(x = c("out", "self", "vec")) expected_types <- list(out = "Tensor", self = "Tensor", vec = "Tensor") @@ -10889,6 +11537,7 @@ fun_type = 'namespace' } +#' @rdname torch_mvlgamma torch_mvlgamma <- function(self, p) { args <- mget(x = c("self", "p")) expected_types <- list(self = "Tensor", p = "int64_t") @@ -10905,6 +11554,7 @@ fun_type = 'namespace' } +#' @rdname torch_narrow torch_narrow <- function(self, dim, start, length) { args <- mget(x = c("self", "dim", "start", "length")) expected_types <- list(self = "Tensor", dim = "int64_t", start = c("int64_t", "Tensor" @@ -10922,6 +11572,7 @@ fun_type = 'namespace' } +#' @rdname torch_native_batch_norm torch_native_batch_norm <- function(input, weight, bias, running_mean, running_var, training, momentum, eps) { args <- mget(x = c("input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", running_mean = "Tensor", @@ -10941,6 +11592,7 @@ fun_type = 'namespace' } +#' @rdname torch_native_batch_norm_backward torch_native_batch_norm_backward <- function(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask) { args <- mget(x = c("grad_out", "input", "weight", "running_mean", "running_var", "save_mean", "save_invstd", "train", "eps", "output_mask")) expected_types <- list(grad_out = "Tensor", input = "Tensor", weight = "Tensor", @@ -10960,6 +11612,7 @@ fun_type = 'namespace' } +#' @rdname torch_native_batch_norm_out torch_native_batch_norm_out <- function(out, save_mean, save_invstd, input, weight, bias, running_mean, running_var, training, momentum, eps) { args <- mget(x = c("out", "save_mean", "save_invstd", "input", "weight", "bias", "running_mean", "running_var", "training", "momentum", "eps")) expected_types <- list(out = "Tensor", save_mean = "Tensor", save_invstd = "Tensor", @@ -10980,6 +11633,7 @@ fun_type = 'namespace' } +#' @rdname torch_native_layer_norm torch_native_layer_norm <- function(input, weight, bias, M, False, eps) { args <- mget(x = c("input", "weight", "bias", "M", "False", "eps")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", M = "int64_t", @@ -10997,6 +11651,7 @@ fun_type = 'namespace' } +#' @rdname torch_native_layer_norm_backward torch_native_layer_norm_backward <- function(grad_out, input, mean, rstd, weight, M, False, output_mask) { args <- mget(x = c("grad_out", "input", "mean", "rstd", "weight", "M", "False", "output_mask")) expected_types <- list(grad_out = "Tensor", input = "Tensor", mean = "Tensor", @@ -11016,6 +11671,7 @@ fun_type = 'namespace' } +#' @rdname torch_native_norm torch_native_norm <- function(self, p = 2L) { args <- mget(x = c("self", "p")) expected_types <- list(self = "Tensor", p = "Scalar") @@ -11032,6 +11688,7 @@ fun_type = 'namespace' } +#' @rdname torch_ne torch_ne <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -11048,6 +11705,7 @@ fun_type = 'namespace' } +#' @rdname torch_ne_out torch_ne_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -11065,6 +11723,7 @@ fun_type = 'namespace' } +#' @rdname torch_neg torch_neg <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11081,6 +11740,7 @@ fun_type = 'namespace' } +#' @rdname torch_neg_ torch_neg_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11097,6 +11757,7 @@ fun_type = 'namespace' } +#' @rdname torch_neg_out torch_neg_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -11113,6 +11774,7 @@ fun_type = 'namespace' } +#' @rdname .torch_nll_loss .torch_nll_loss <- function(self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) { args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", @@ -11130,6 +11792,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss_backward torch_nll_loss_backward <- function(grad_output, self, target, weight, reduction, ignore_index, total_weight) { args <- mget(x = c("grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -11149,6 +11812,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss_backward_out torch_nll_loss_backward_out <- function(grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -11168,6 +11832,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss_forward torch_nll_loss_forward <- function(self, target, weight, reduction, ignore_index) { args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", @@ -11185,6 +11850,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss_forward_out torch_nll_loss_forward_out <- function(output, total_weight, self, target, weight, reduction, ignore_index) { args <- mget(x = c("output", "total_weight", "self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(output = "Tensor", total_weight = "Tensor", self = "Tensor", @@ -11204,6 +11870,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss_out torch_nll_loss_out <- function(out, self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) { args <- mget(x = c("out", "self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", weight = "Tensor", @@ -11221,6 +11888,7 @@ fun_type = 'namespace' } +#' @rdname .torch_nll_loss2d .torch_nll_loss2d <- function(self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) { args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", @@ -11238,6 +11906,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss2d_backward torch_nll_loss2d_backward <- function(grad_output, self, target, weight, reduction, ignore_index, total_weight) { args <- mget(x = c("grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -11257,6 +11926,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss2d_backward_out torch_nll_loss2d_backward_out <- function(grad_input, grad_output, self, target, weight, reduction, ignore_index, total_weight) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "weight", "reduction", "ignore_index", "total_weight")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -11276,6 +11946,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss2d_forward torch_nll_loss2d_forward <- function(self, target, weight, reduction, ignore_index) { args <- mget(x = c("self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(self = "Tensor", target = "Tensor", weight = "Tensor", reduction = "int64_t", @@ -11293,6 +11964,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss2d_forward_out torch_nll_loss2d_forward_out <- function(output, total_weight, self, target, weight, reduction, ignore_index) { args <- mget(x = c("output", "total_weight", "self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(output = "Tensor", total_weight = "Tensor", self = "Tensor", @@ -11312,6 +11984,7 @@ fun_type = 'namespace' } +#' @rdname torch_nll_loss2d_out torch_nll_loss2d_out <- function(out, self, target, weight = list(), reduction = torch_reduction_mean(), ignore_index = -100L) { args <- mget(x = c("out", "self", "target", "weight", "reduction", "ignore_index")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", weight = "Tensor", @@ -11329,6 +12002,7 @@ fun_type = 'namespace' } +#' @rdname torch_nonzero torch_nonzero <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11345,6 +12019,7 @@ fun_type = 'namespace' } +#' @rdname torch_nonzero_numpy torch_nonzero_numpy <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11361,6 +12036,7 @@ fun_type = 'namespace' } +#' @rdname torch_nonzero_out torch_nonzero_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -11377,6 +12053,7 @@ fun_type = 'namespace' } +#' @rdname torch_norm torch_norm <- function(self, p = 2L, dim, keepdim = FALSE, dtype) { args <- mget(x = c("self", "p", "dim", "keepdim", "dtype")) expected_types <- list(self = "Tensor", p = "Scalar", dim = c("IntArrayRef", "DimnameList" @@ -11394,6 +12071,7 @@ fun_type = 'namespace' } +#' @rdname torch_norm_except_dim torch_norm_except_dim <- function(v, pow = 2L, dim = 1L) { args <- mget(x = c("v", "pow", "dim")) expected_types <- list(v = "Tensor", pow = "int64_t", dim = "int64_t") @@ -11410,6 +12088,7 @@ fun_type = 'namespace' } +#' @rdname torch_norm_out torch_norm_out <- function(out, self, p, dim, keepdim = FALSE, dtype) { args <- mget(x = c("out", "self", "p", "dim", "keepdim", "dtype")) expected_types <- list(out = "Tensor", self = "Tensor", p = "Scalar", dim = c("IntArrayRef", @@ -11427,7 +12106,8 @@ fun_type = 'namespace' } -torch_normal <- function(mean, std = 1L, size, generator = NULL, options = list()) { +#' @rdname .torch_normal +.torch_normal <- function(mean, std = 1L, size, generator = NULL, options = list()) { args <- mget(x = c("mean", "std", "size", "generator", "options")) expected_types <- list(mean = c("Tensor", "double"), std = c("double", "Tensor" ), size = "IntArrayRef", generator = "Generator *", options = "TensorOptions") @@ -11444,6 +12124,7 @@ fun_type = 'namespace' } +#' @rdname torch_normal_out torch_normal_out <- function(out, mean, std = 1L, size, generator = NULL) { args <- mget(x = c("out", "mean", "std", "size", "generator")) expected_types <- list(out = "Tensor", mean = c("Tensor", "double"), std = c("double", @@ -11461,6 +12142,7 @@ fun_type = 'namespace' } +#' @rdname torch_nuclear_norm torch_nuclear_norm <- function(self, dim, keepdim = FALSE) { args <- mget(x = c("self", "dim", "keepdim")) expected_types <- list(self = "Tensor", dim = "IntArrayRef", keepdim = "bool") @@ -11477,6 +12159,7 @@ fun_type = 'namespace' } +#' @rdname torch_nuclear_norm_out torch_nuclear_norm_out <- function(out, self, dim, keepdim = FALSE) { args <- mget(x = c("out", "self", "dim", "keepdim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = "IntArrayRef", keepdim = "bool") @@ -11493,6 +12176,7 @@ fun_type = 'namespace' } +#' @rdname torch_one_hot torch_one_hot <- function(self, num_classes = -1L) { args <- mget(x = c("self", "num_classes")) expected_types <- list(self = "Tensor", num_classes = "int64_t") @@ -11509,6 +12193,7 @@ fun_type = 'namespace' } +#' @rdname .torch_ones .torch_ones <- function(size, names, options = list()) { args <- mget(x = c("size", "names", "options")) expected_types <- list(size = "IntArrayRef", names = "DimnameList", options = "TensorOptions") @@ -11525,6 +12210,7 @@ fun_type = 'namespace' } +#' @rdname .torch_ones_like .torch_ones_like <- function(self, options = list(), memory_format = NULL) { args <- mget(x = c("self", "options", "memory_format")) expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat") @@ -11541,6 +12227,7 @@ fun_type = 'namespace' } +#' @rdname torch_ones_out torch_ones_out <- function(out, size) { args <- mget(x = c("out", "size")) expected_types <- list(out = "Tensor", size = "IntArrayRef") @@ -11557,6 +12244,7 @@ fun_type = 'namespace' } +#' @rdname torch_orgqr torch_orgqr <- function(self, input2) { args <- mget(x = c("self", "input2")) expected_types <- list(self = "Tensor", input2 = "Tensor") @@ -11573,6 +12261,7 @@ fun_type = 'namespace' } +#' @rdname torch_orgqr_out torch_orgqr_out <- function(out, self, input2) { args <- mget(x = c("out", "self", "input2")) expected_types <- list(out = "Tensor", self = "Tensor", input2 = "Tensor") @@ -11589,6 +12278,7 @@ fun_type = 'namespace' } +#' @rdname torch_ormqr torch_ormqr <- function(self, input2, input3, left = TRUE, transpose = FALSE) { args <- mget(x = c("self", "input2", "input3", "left", "transpose")) expected_types <- list(self = "Tensor", input2 = "Tensor", input3 = "Tensor", left = "bool", @@ -11606,6 +12296,7 @@ fun_type = 'namespace' } +#' @rdname torch_ormqr_out torch_ormqr_out <- function(out, self, input2, input3, left = TRUE, transpose = FALSE) { args <- mget(x = c("out", "self", "input2", "input3", "left", "transpose")) expected_types <- list(out = "Tensor", self = "Tensor", input2 = "Tensor", input3 = "Tensor", @@ -11623,6 +12314,7 @@ fun_type = 'namespace' } +#' @rdname torch_pairwise_distance torch_pairwise_distance <- function(x1, x2, p = 2L, eps = 0.000001, keepdim = FALSE) { args <- mget(x = c("x1", "x2", "p", "eps", "keepdim")) expected_types <- list(x1 = "Tensor", x2 = "Tensor", p = "double", eps = "double", @@ -11640,6 +12332,7 @@ fun_type = 'namespace' } +#' @rdname torch_pdist torch_pdist <- function(self, p = 2L) { args <- mget(x = c("self", "p")) expected_types <- list(self = "Tensor", p = "double") @@ -11656,6 +12349,7 @@ fun_type = 'namespace' } +#' @rdname torch_pinverse torch_pinverse <- function(self, rcond = 0.000000) { args <- mget(x = c("self", "rcond")) expected_types <- list(self = "Tensor", rcond = "double") @@ -11672,6 +12366,7 @@ fun_type = 'namespace' } +#' @rdname torch_pixel_shuffle torch_pixel_shuffle <- function(self, upscale_factor) { args <- mget(x = c("self", "upscale_factor")) expected_types <- list(self = "Tensor", upscale_factor = "int64_t") @@ -11688,6 +12383,7 @@ fun_type = 'namespace' } +#' @rdname torch_poisson torch_poisson <- function(self, generator = NULL) { args <- mget(x = c("self", "generator")) expected_types <- list(self = "Tensor", generator = "Generator *") @@ -11704,6 +12400,7 @@ fun_type = 'namespace' } +#' @rdname torch_poisson_nll_loss torch_poisson_nll_loss <- function(input, target, log_input, full, eps, reduction) { args <- mget(x = c("input", "target", "log_input", "full", "eps", "reduction")) expected_types <- list(input = "Tensor", target = "Tensor", log_input = "bool", @@ -11721,6 +12418,7 @@ fun_type = 'namespace' } +#' @rdname torch_polygamma torch_polygamma <- function(n, self) { args <- mget(x = c("n", "self")) expected_types <- list(n = "int64_t", self = "Tensor") @@ -11737,6 +12435,7 @@ fun_type = 'namespace' } +#' @rdname torch_polygamma_out torch_polygamma_out <- function(out, n, self) { args <- mget(x = c("out", "n", "self")) expected_types <- list(out = "Tensor", n = "int64_t", self = "Tensor") @@ -11753,6 +12452,7 @@ fun_type = 'namespace' } +#' @rdname torch_pow torch_pow <- function(self, exponent) { args <- mget(x = c("self", "exponent")) expected_types <- list(self = c("Tensor", "Scalar"), exponent = c("Scalar", "Tensor" @@ -11770,6 +12470,7 @@ fun_type = 'namespace' } +#' @rdname torch_pow_out torch_pow_out <- function(out, self, exponent) { args <- mget(x = c("out", "self", "exponent")) expected_types <- list(out = "Tensor", self = c("Tensor", "Scalar"), exponent = c("Scalar", @@ -11787,6 +12488,7 @@ fun_type = 'namespace' } +#' @rdname torch_prelu torch_prelu <- function(self, weight) { args <- mget(x = c("self", "weight")) expected_types <- list(self = "Tensor", weight = "Tensor") @@ -11803,6 +12505,7 @@ fun_type = 'namespace' } +#' @rdname torch_prelu_backward torch_prelu_backward <- function(grad_output, self, weight) { args <- mget(x = c("grad_output", "self", "weight")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor") @@ -11819,6 +12522,7 @@ fun_type = 'namespace' } +#' @rdname torch_prod torch_prod <- function(self, dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("self", "dim", "keepdim", "dtype")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), keepdim = "bool", @@ -11836,6 +12540,7 @@ fun_type = 'namespace' } +#' @rdname torch_prod_out torch_prod_out <- function(out, self, dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("out", "self", "dim", "keepdim", "dtype")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("int64_t", "Dimname" @@ -11853,6 +12558,7 @@ fun_type = 'namespace' } +#' @rdname torch_promote_types torch_promote_types <- function(type1, type2) { args <- mget(x = c("type1", "type2")) expected_types <- list(type1 = "ScalarType", type2 = "ScalarType") @@ -11869,6 +12575,7 @@ fun_type = 'namespace' } +#' @rdname torch_q_per_channel_axis torch_q_per_channel_axis <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11885,6 +12592,7 @@ fun_type = 'namespace' } +#' @rdname torch_q_per_channel_scales torch_q_per_channel_scales <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11901,6 +12609,7 @@ fun_type = 'namespace' } +#' @rdname torch_q_per_channel_zero_points torch_q_per_channel_zero_points <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11917,6 +12626,7 @@ fun_type = 'namespace' } +#' @rdname torch_q_scale torch_q_scale <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11933,6 +12643,7 @@ fun_type = 'namespace' } +#' @rdname torch_q_zero_point torch_q_zero_point <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -11949,6 +12660,7 @@ fun_type = 'namespace' } +#' @rdname torch_qr torch_qr <- function(self, some = TRUE) { args <- mget(x = c("self", "some")) expected_types <- list(self = "Tensor", some = "bool") @@ -11965,6 +12677,7 @@ fun_type = 'namespace' } +#' @rdname torch_qr_out torch_qr_out <- function(Q, R, self, some = TRUE) { args <- mget(x = c("Q", "R", "self", "some")) expected_types <- list(Q = "Tensor", R = "Tensor", self = "Tensor", some = "bool") @@ -11981,6 +12694,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantize_per_channel torch_quantize_per_channel <- function(self, scales, zero_points, axis, dtype) { args <- mget(x = c("self", "scales", "zero_points", "axis", "dtype")) expected_types <- list(self = "Tensor", scales = "Tensor", zero_points = "Tensor", @@ -11998,6 +12712,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantize_per_tensor torch_quantize_per_tensor <- function(self, scale, zero_point, dtype) { args <- mget(x = c("self", "scale", "zero_point", "dtype")) expected_types <- list(self = "Tensor", scale = "double", zero_point = "int64_t", @@ -12015,6 +12730,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_batch_norm torch_quantized_batch_norm <- function(input, weight, bias, mean, var, eps, output_scale, output_zero_point) { args <- mget(x = c("input", "weight", "bias", "mean", "var", "eps", "output_scale", "output_zero_point")) expected_types <- list(input = "Tensor", weight = "Tensor", bias = "Tensor", mean = "Tensor", @@ -12034,6 +12750,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_gru torch_quantized_gru <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) { args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional")) expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", @@ -12055,6 +12772,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_gru_cell torch_quantized_gru_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh")) expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", @@ -12076,6 +12794,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_lstm torch_quantized_lstm <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional, dtype = NULL, use_dynamic = FALSE) { args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional", "dtype", "use_dynamic")) expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", @@ -12098,6 +12817,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_lstm_cell torch_quantized_lstm_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh")) expected_types <- list(input = "Tensor", hx = "TensorList", w_ih = "Tensor", w_hh = "Tensor", @@ -12119,6 +12839,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_max_pool2d torch_quantized_max_pool2d <- function(self, kernel_size, stride = list(), padding = 0L, dilation = 1L, ceil_mode = FALSE) { args <- mget(x = c("self", "kernel_size", "stride", "padding", "dilation", "ceil_mode")) expected_types <- list(self = "Tensor", kernel_size = "IntArrayRef", stride = "IntArrayRef", @@ -12136,6 +12857,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_rnn_relu_cell torch_quantized_rnn_relu_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh")) expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", @@ -12157,6 +12879,7 @@ fun_type = 'namespace' } +#' @rdname torch_quantized_rnn_tanh_cell torch_quantized_rnn_tanh_cell <- function(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh", "packed_ih", "packed_hh", "col_offsets_ih", "col_offsets_hh", "scale_ih", "scale_hh", "zero_point_ih", "zero_point_hh")) expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", @@ -12178,6 +12901,7 @@ fun_type = 'namespace' } +#' @rdname .torch_rand .torch_rand <- function(size, generator, names, options = list()) { args <- mget(x = c("size", "generator", "names", "options")) expected_types <- list(size = "IntArrayRef", generator = "Generator *", names = "DimnameList", @@ -12195,6 +12919,7 @@ fun_type = 'namespace' } +#' @rdname .torch_rand_like .torch_rand_like <- function(self, options = list(), memory_format = NULL) { args <- mget(x = c("self", "options", "memory_format")) expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat") @@ -12211,6 +12936,7 @@ fun_type = 'namespace' } +#' @rdname torch_rand_out torch_rand_out <- function(out, size, generator) { args <- mget(x = c("out", "size", "generator")) expected_types <- list(out = "Tensor", size = "IntArrayRef", generator = "Generator *") @@ -12227,6 +12953,7 @@ fun_type = 'namespace' } +#' @rdname .torch_randint .torch_randint <- function(low, high, size, generator, options = list()) { args <- mget(x = c("low", "high", "size", "generator", "options")) expected_types <- list(low = "int64_t", high = "int64_t", size = "IntArrayRef", @@ -12244,6 +12971,7 @@ fun_type = 'namespace' } +#' @rdname .torch_randint_like .torch_randint_like <- function(self, low, high, options = list(), memory_format = NULL) { args <- mget(x = c("self", "low", "high", "options", "memory_format")) expected_types <- list(self = "Tensor", low = "int64_t", high = "int64_t", options = "TensorOptions", @@ -12261,6 +12989,7 @@ fun_type = 'namespace' } +#' @rdname torch_randint_out torch_randint_out <- function(out, low, high, size, generator) { args <- mget(x = c("out", "low", "high", "size", "generator")) expected_types <- list(out = "Tensor", low = "int64_t", high = "int64_t", size = "IntArrayRef", @@ -12278,6 +13007,7 @@ fun_type = 'namespace' } +#' @rdname .torch_randn .torch_randn <- function(size, generator, names, options = list()) { args <- mget(x = c("size", "generator", "names", "options")) expected_types <- list(size = "IntArrayRef", generator = "Generator *", names = "DimnameList", @@ -12295,6 +13025,7 @@ fun_type = 'namespace' } +#' @rdname .torch_randn_like .torch_randn_like <- function(self, options = list(), memory_format = NULL) { args <- mget(x = c("self", "options", "memory_format")) expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat") @@ -12311,6 +13042,7 @@ fun_type = 'namespace' } +#' @rdname torch_randn_out torch_randn_out <- function(out, size, generator) { args <- mget(x = c("out", "size", "generator")) expected_types <- list(out = "Tensor", size = "IntArrayRef", generator = "Generator *") @@ -12327,6 +13059,7 @@ fun_type = 'namespace' } +#' @rdname .torch_randperm .torch_randperm <- function(n, generator, options = list()) { args <- mget(x = c("n", "generator", "options")) expected_types <- list(n = "int64_t", generator = "Generator *", options = "TensorOptions") @@ -12343,6 +13076,7 @@ fun_type = 'namespace' } +#' @rdname torch_randperm_out torch_randperm_out <- function(out, n, generator) { args <- mget(x = c("out", "n", "generator")) expected_types <- list(out = "Tensor", n = "int64_t", generator = "Generator *") @@ -12359,6 +13093,7 @@ fun_type = 'namespace' } +#' @rdname .torch_range .torch_range <- function(start, end, step = 1L, options = list()) { args <- mget(x = c("start", "end", "step", "options")) expected_types <- list(start = "Scalar", end = "Scalar", step = "Scalar", options = "TensorOptions") @@ -12375,6 +13110,7 @@ fun_type = 'namespace' } +#' @rdname torch_range_out torch_range_out <- function(out, start, end, step = 1L) { args <- mget(x = c("out", "start", "end", "step")) expected_types <- list(out = "Tensor", start = "Scalar", end = "Scalar", step = "Scalar") @@ -12391,6 +13127,7 @@ fun_type = 'namespace' } +#' @rdname torch_real torch_real <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -12407,6 +13144,7 @@ fun_type = 'namespace' } +#' @rdname torch_reciprocal torch_reciprocal <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -12423,6 +13161,7 @@ fun_type = 'namespace' } +#' @rdname torch_reciprocal_ torch_reciprocal_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -12439,6 +13178,7 @@ fun_type = 'namespace' } +#' @rdname torch_reciprocal_out torch_reciprocal_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -12455,6 +13195,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad1d torch_reflection_pad1d <- function(self, padding) { args <- mget(x = c("self", "padding")) expected_types <- list(self = "Tensor", padding = "IntArrayRef") @@ -12471,6 +13212,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad1d_backward torch_reflection_pad1d_backward <- function(grad_output, self, padding) { args <- mget(x = c("grad_output", "self", "padding")) expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12487,6 +13229,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad1d_backward_out torch_reflection_pad1d_backward_out <- function(grad_input, grad_output, self, padding) { args <- mget(x = c("grad_input", "grad_output", "self", "padding")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -12504,6 +13247,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad1d_out torch_reflection_pad1d_out <- function(out, self, padding) { args <- mget(x = c("out", "self", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12520,6 +13264,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad2d torch_reflection_pad2d <- function(self, padding) { args <- mget(x = c("self", "padding")) expected_types <- list(self = "Tensor", padding = "IntArrayRef") @@ -12536,6 +13281,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad2d_backward torch_reflection_pad2d_backward <- function(grad_output, self, padding) { args <- mget(x = c("grad_output", "self", "padding")) expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12552,6 +13298,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad2d_backward_out torch_reflection_pad2d_backward_out <- function(grad_input, grad_output, self, padding) { args <- mget(x = c("grad_input", "grad_output", "self", "padding")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -12569,6 +13316,7 @@ fun_type = 'namespace' } +#' @rdname torch_reflection_pad2d_out torch_reflection_pad2d_out <- function(out, self, padding) { args <- mget(x = c("out", "self", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12585,6 +13333,7 @@ fun_type = 'namespace' } +#' @rdname torch_relu torch_relu <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -12601,6 +13350,7 @@ fun_type = 'namespace' } +#' @rdname torch_relu_ torch_relu_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -12617,6 +13367,7 @@ fun_type = 'namespace' } +#' @rdname torch_remainder torch_remainder <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Scalar", "Tensor")) @@ -12633,6 +13384,7 @@ fun_type = 'namespace' } +#' @rdname torch_remainder_out torch_remainder_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = c("Scalar", "Tensor" @@ -12650,6 +13402,7 @@ fun_type = 'namespace' } +#' @rdname torch_renorm torch_renorm <- function(self, p, dim, maxnorm) { args <- mget(x = c("self", "p", "dim", "maxnorm")) expected_types <- list(self = "Tensor", p = "Scalar", dim = "int64_t", maxnorm = "Scalar") @@ -12666,6 +13419,7 @@ fun_type = 'namespace' } +#' @rdname torch_renorm_out torch_renorm_out <- function(out, self, p, dim, maxnorm) { args <- mget(x = c("out", "self", "p", "dim", "maxnorm")) expected_types <- list(out = "Tensor", self = "Tensor", p = "Scalar", dim = "int64_t", @@ -12683,6 +13437,7 @@ fun_type = 'namespace' } +#' @rdname torch_repeat_interleave torch_repeat_interleave <- function(self, repeats, dim = NULL) { args <- mget(x = c("self", "repeats", "dim")) expected_types <- list(self = "Tensor", repeats = c("Tensor", "int64_t"), dim = "int64_t") @@ -12699,6 +13454,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad1d torch_replication_pad1d <- function(self, padding) { args <- mget(x = c("self", "padding")) expected_types <- list(self = "Tensor", padding = "IntArrayRef") @@ -12715,6 +13471,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad1d_backward torch_replication_pad1d_backward <- function(grad_output, self, padding) { args <- mget(x = c("grad_output", "self", "padding")) expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12731,6 +13488,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad1d_backward_out torch_replication_pad1d_backward_out <- function(grad_input, grad_output, self, padding) { args <- mget(x = c("grad_input", "grad_output", "self", "padding")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -12748,6 +13506,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad1d_out torch_replication_pad1d_out <- function(out, self, padding) { args <- mget(x = c("out", "self", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12764,6 +13523,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad2d torch_replication_pad2d <- function(self, padding) { args <- mget(x = c("self", "padding")) expected_types <- list(self = "Tensor", padding = "IntArrayRef") @@ -12780,6 +13540,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad2d_backward torch_replication_pad2d_backward <- function(grad_output, self, padding) { args <- mget(x = c("grad_output", "self", "padding")) expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12796,6 +13557,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad2d_backward_out torch_replication_pad2d_backward_out <- function(grad_input, grad_output, self, padding) { args <- mget(x = c("grad_input", "grad_output", "self", "padding")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -12813,6 +13575,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad2d_out torch_replication_pad2d_out <- function(out, self, padding) { args <- mget(x = c("out", "self", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12829,6 +13592,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad3d torch_replication_pad3d <- function(self, padding) { args <- mget(x = c("self", "padding")) expected_types <- list(self = "Tensor", padding = "IntArrayRef") @@ -12845,6 +13609,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad3d_backward torch_replication_pad3d_backward <- function(grad_output, self, padding) { args <- mget(x = c("grad_output", "self", "padding")) expected_types <- list(grad_output = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12861,6 +13626,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad3d_backward_out torch_replication_pad3d_backward_out <- function(grad_input, grad_output, self, padding) { args <- mget(x = c("grad_input", "grad_output", "self", "padding")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -12878,6 +13644,7 @@ fun_type = 'namespace' } +#' @rdname torch_replication_pad3d_out torch_replication_pad3d_out <- function(out, self, padding) { args <- mget(x = c("out", "self", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", padding = "IntArrayRef") @@ -12894,6 +13661,7 @@ fun_type = 'namespace' } +#' @rdname torch_reshape torch_reshape <- function(self, shape) { args <- mget(x = c("self", "shape")) expected_types <- list(self = "Tensor", shape = "IntArrayRef") @@ -12910,6 +13678,7 @@ fun_type = 'namespace' } +#' @rdname torch_resize_as_ torch_resize_as_ <- function(self, the_template, memory_format = NULL) { args <- mget(x = c("self", "the_template", "memory_format")) expected_types <- list(self = "Tensor", the_template = "Tensor", memory_format = "MemoryFormat") @@ -12926,7 +13695,8 @@ fun_type = 'namespace' } -torch_result_type <- function(scalar, scalar1, other, scalar2, tensor) { +#' @rdname .torch_result_type +.torch_result_type <- function(scalar, scalar1, other, scalar2, tensor) { args <- mget(x = c("scalar", "scalar1", "other", "scalar2", "tensor")) expected_types <- list(scalar = "Scalar", scalar1 = "Scalar", other = c("Tensor", "Scalar"), scalar2 = "Scalar", tensor = "Tensor") @@ -12943,6 +13713,7 @@ fun_type = 'namespace' } +#' @rdname torch_rfft torch_rfft <- function(self, signal_ndim, normalized = FALSE, onesided = TRUE) { args <- mget(x = c("self", "signal_ndim", "normalized", "onesided")) expected_types <- list(self = "Tensor", signal_ndim = "int64_t", normalized = "bool", @@ -12960,6 +13731,7 @@ fun_type = 'namespace' } +#' @rdname torch_rnn_relu torch_rnn_relu <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) { args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional")) expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", @@ -12981,6 +13753,7 @@ fun_type = 'namespace' } +#' @rdname torch_rnn_relu_cell torch_rnn_relu_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh")) expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", @@ -12998,6 +13771,7 @@ fun_type = 'namespace' } +#' @rdname torch_rnn_tanh torch_rnn_tanh <- function(data, input, batch_sizes, hx, params, has_biases, num_layers, dropout, train, batch_first, bidirectional) { args <- mget(x = c("data", "input", "batch_sizes", "hx", "params", "has_biases", "num_layers", "dropout", "train", "batch_first", "bidirectional")) expected_types <- list(data = "Tensor", input = "Tensor", batch_sizes = "Tensor", @@ -13019,6 +13793,7 @@ fun_type = 'namespace' } +#' @rdname torch_rnn_tanh_cell torch_rnn_tanh_cell <- function(input, hx, w_ih, w_hh, b_ih = list(), b_hh = list()) { args <- mget(x = c("input", "hx", "w_ih", "w_hh", "b_ih", "b_hh")) expected_types <- list(input = "Tensor", hx = "Tensor", w_ih = "Tensor", w_hh = "Tensor", @@ -13036,6 +13811,7 @@ fun_type = 'namespace' } +#' @rdname torch_roll torch_roll <- function(self, shifts, dims = list()) { args <- mget(x = c("self", "shifts", "dims")) expected_types <- list(self = "Tensor", shifts = "IntArrayRef", dims = "IntArrayRef") @@ -13052,6 +13828,7 @@ fun_type = 'namespace' } +#' @rdname torch_rot90 torch_rot90 <- function(self, k = 1L, dims = c(0,1)) { args <- mget(x = c("self", "k", "dims")) expected_types <- list(self = "Tensor", k = "int64_t", dims = "IntArrayRef") @@ -13068,6 +13845,7 @@ fun_type = 'namespace' } +#' @rdname torch_round torch_round <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13084,6 +13862,7 @@ fun_type = 'namespace' } +#' @rdname torch_round_ torch_round_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13100,6 +13879,7 @@ fun_type = 'namespace' } +#' @rdname torch_round_out torch_round_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -13116,6 +13896,7 @@ fun_type = 'namespace' } +#' @rdname torch_rrelu torch_rrelu <- function(self, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) { args <- mget(x = c("self", "lower", "upper", "training", "generator")) expected_types <- list(self = "Tensor", lower = "Scalar", upper = "Scalar", training = "bool", @@ -13133,6 +13914,7 @@ fun_type = 'namespace' } +#' @rdname torch_rrelu_ torch_rrelu_ <- function(self, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) { args <- mget(x = c("self", "lower", "upper", "training", "generator")) expected_types <- list(self = "Tensor", lower = "Scalar", upper = "Scalar", training = "bool", @@ -13150,6 +13932,7 @@ fun_type = 'namespace' } +#' @rdname torch_rrelu_with_noise torch_rrelu_with_noise <- function(self, noise, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) { args <- mget(x = c("self", "noise", "lower", "upper", "training", "generator")) expected_types <- list(self = "Tensor", noise = "Tensor", lower = "Scalar", upper = "Scalar", @@ -13167,6 +13950,7 @@ fun_type = 'namespace' } +#' @rdname torch_rrelu_with_noise_ torch_rrelu_with_noise_ <- function(self, noise, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) { args <- mget(x = c("self", "noise", "lower", "upper", "training", "generator")) expected_types <- list(self = "Tensor", noise = "Tensor", lower = "Scalar", upper = "Scalar", @@ -13184,6 +13968,7 @@ fun_type = 'namespace' } +#' @rdname torch_rrelu_with_noise_backward torch_rrelu_with_noise_backward <- function(grad_output, self, noise, lower, upper, training, self_is_result) { args <- mget(x = c("grad_output", "self", "noise", "lower", "upper", "training", "self_is_result")) expected_types <- list(grad_output = "Tensor", self = "Tensor", noise = "Tensor", @@ -13202,6 +13987,7 @@ fun_type = 'namespace' } +#' @rdname torch_rrelu_with_noise_out torch_rrelu_with_noise_out <- function(out, self, noise, lower = 0.125000, upper = 0.333333, training = FALSE, generator = NULL) { args <- mget(x = c("out", "self", "noise", "lower", "upper", "training", "generator")) expected_types <- list(out = "Tensor", self = "Tensor", noise = "Tensor", lower = "Scalar", @@ -13219,6 +14005,7 @@ fun_type = 'namespace' } +#' @rdname torch_rsqrt torch_rsqrt <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13235,6 +14022,7 @@ fun_type = 'namespace' } +#' @rdname torch_rsqrt_ torch_rsqrt_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13251,6 +14039,7 @@ fun_type = 'namespace' } +#' @rdname torch_rsqrt_out torch_rsqrt_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -13267,6 +14056,7 @@ fun_type = 'namespace' } +#' @rdname torch_rsub torch_rsub <- function(self, other, alpha = 1L) { args <- mget(x = c("self", "other", "alpha")) expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar") @@ -13283,6 +14073,7 @@ fun_type = 'namespace' } +#' @rdname torch_scalar_tensor torch_scalar_tensor <- function(s, options = list()) { args <- mget(x = c("s", "options")) expected_types <- list(s = "Scalar", options = "TensorOptions") @@ -13299,6 +14090,7 @@ fun_type = 'namespace' } +#' @rdname torch_scatter torch_scatter <- function(self, dim, index, src, value) { args <- mget(x = c("self", "dim", "index", "src", "value")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", @@ -13316,6 +14108,7 @@ fun_type = 'namespace' } +#' @rdname torch_scatter_add torch_scatter_add <- function(self, dim, index, src) { args <- mget(x = c("self", "dim", "index", "src")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), index = "Tensor", @@ -13333,6 +14126,7 @@ fun_type = 'namespace' } +#' @rdname torch_select torch_select <- function(self, dim, index) { args <- mget(x = c("self", "dim", "index")) expected_types <- list(self = "Tensor", dim = c("Dimname", "int64_t"), index = "int64_t") @@ -13349,6 +14143,7 @@ fun_type = 'namespace' } +#' @rdname torch_selu torch_selu <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13365,6 +14160,7 @@ fun_type = 'namespace' } +#' @rdname torch_selu_ torch_selu_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13381,6 +14177,7 @@ fun_type = 'namespace' } +#' @rdname torch_sigmoid torch_sigmoid <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13397,6 +14194,7 @@ fun_type = 'namespace' } +#' @rdname torch_sigmoid_ torch_sigmoid_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13413,6 +14211,7 @@ fun_type = 'namespace' } +#' @rdname torch_sigmoid_backward torch_sigmoid_backward <- function(grad_output, output) { args <- mget(x = c("grad_output", "output")) expected_types <- list(grad_output = "Tensor", output = "Tensor") @@ -13429,6 +14228,7 @@ fun_type = 'namespace' } +#' @rdname torch_sigmoid_backward_out torch_sigmoid_backward_out <- function(grad_input, grad_output, output) { args <- mget(x = c("grad_input", "grad_output", "output")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output = "Tensor") @@ -13445,6 +14245,7 @@ fun_type = 'namespace' } +#' @rdname torch_sigmoid_out torch_sigmoid_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -13461,6 +14262,7 @@ fun_type = 'namespace' } +#' @rdname torch_sign torch_sign <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13477,6 +14279,7 @@ fun_type = 'namespace' } +#' @rdname torch_sign_out torch_sign_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -13493,6 +14296,7 @@ fun_type = 'namespace' } +#' @rdname torch_sin torch_sin <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13509,6 +14313,7 @@ fun_type = 'namespace' } +#' @rdname torch_sin_ torch_sin_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13525,6 +14330,7 @@ fun_type = 'namespace' } +#' @rdname torch_sin_out torch_sin_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -13541,6 +14347,7 @@ fun_type = 'namespace' } +#' @rdname torch_sinh torch_sinh <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13557,6 +14364,7 @@ fun_type = 'namespace' } +#' @rdname torch_sinh_ torch_sinh_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13573,6 +14381,7 @@ fun_type = 'namespace' } +#' @rdname torch_sinh_out torch_sinh_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -13589,6 +14398,7 @@ fun_type = 'namespace' } +#' @rdname torch_size torch_size <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname")) @@ -13605,6 +14415,7 @@ fun_type = 'namespace' } +#' @rdname torch_slice torch_slice <- function(self, dim = 1L, start = 0L, end = 9223372036854775807, step = 1L) { args <- mget(x = c("self", "dim", "start", "end", "step")) expected_types <- list(self = "Tensor", dim = "int64_t", start = "int64_t", end = "int64_t", @@ -13622,6 +14433,7 @@ fun_type = 'namespace' } +#' @rdname torch_slogdet torch_slogdet <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -13638,6 +14450,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_dilated2d torch_slow_conv_dilated2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13656,6 +14469,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_dilated2d_backward torch_slow_conv_dilated2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask) { args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation", "output_mask")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", @@ -13675,6 +14489,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_dilated3d torch_slow_conv_dilated3d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13693,6 +14508,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_dilated3d_backward torch_slow_conv_dilated3d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask) { args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation", "output_mask")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", @@ -13712,6 +14528,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose2d torch_slow_conv_transpose2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13730,6 +14547,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose2d_backward torch_slow_conv_transpose2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones, output_mask) { args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "columns", "ones", "output_mask")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", @@ -13751,6 +14569,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose2d_backward_out torch_slow_conv_transpose2d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, columns, ones) { args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "columns", "ones")) expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", @@ -13773,6 +14592,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose2d_out torch_slow_conv_transpose2d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) { args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation")) expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13791,6 +14611,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose3d torch_slow_conv_transpose3d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13809,6 +14630,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose3d_backward torch_slow_conv_transpose3d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input, output_mask) { args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "finput", "fgrad_input", "output_mask")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", @@ -13830,6 +14652,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose3d_backward_out torch_slow_conv_transpose3d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, output_padding, dilation, finput, fgrad_input) { args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "output_padding", "dilation", "finput", "fgrad_input")) expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", @@ -13852,6 +14675,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv_transpose3d_out torch_slow_conv_transpose3d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, output_padding = 0L, dilation = 1L) { args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "output_padding", "dilation")) expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13870,6 +14694,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv3d torch_slow_conv3d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13887,6 +14712,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv3d_backward torch_slow_conv3d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask) { args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input", "output_mask")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", @@ -13906,6 +14732,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv3d_backward_out torch_slow_conv3d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input) { args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input")) expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", @@ -13927,6 +14754,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv3d_forward torch_slow_conv3d_forward <- function(self, weight, kernel_size, bias, stride, padding) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13945,6 +14773,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv3d_forward_out torch_slow_conv3d_forward_out <- function(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding) { args <- mget(x = c("output", "finput", "fgrad_input", "self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(output = "Tensor", finput = "Tensor", fgrad_input = "Tensor", @@ -13964,6 +14793,7 @@ fun_type = 'namespace' } +#' @rdname torch_slow_conv3d_out torch_slow_conv3d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) { args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -13981,6 +14811,7 @@ fun_type = 'namespace' } +#' @rdname torch_smm torch_smm <- function(self, mat2) { args <- mget(x = c("self", "mat2")) expected_types <- list(self = "Tensor", mat2 = "Tensor") @@ -13997,6 +14828,7 @@ fun_type = 'namespace' } +#' @rdname torch_smooth_l1_loss torch_smooth_l1_loss <- function(self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -14013,6 +14845,7 @@ fun_type = 'namespace' } +#' @rdname torch_smooth_l1_loss_backward torch_smooth_l1_loss_backward <- function(grad_output, self, target, reduction) { args <- mget(x = c("grad_output", "self", "target", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -14030,6 +14863,7 @@ fun_type = 'namespace' } +#' @rdname torch_smooth_l1_loss_backward_out torch_smooth_l1_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -14047,6 +14881,7 @@ fun_type = 'namespace' } +#' @rdname torch_smooth_l1_loss_out torch_smooth_l1_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("out", "self", "target", "reduction")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -14063,6 +14898,7 @@ fun_type = 'namespace' } +#' @rdname torch_soft_margin_loss torch_soft_margin_loss <- function(self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("self", "target", "reduction")) expected_types <- list(self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -14079,6 +14915,7 @@ fun_type = 'namespace' } +#' @rdname torch_soft_margin_loss_backward torch_soft_margin_loss_backward <- function(grad_output, self, target, reduction) { args <- mget(x = c("grad_output", "self", "target", "reduction")) expected_types <- list(grad_output = "Tensor", self = "Tensor", target = "Tensor", @@ -14096,6 +14933,7 @@ fun_type = 'namespace' } +#' @rdname torch_soft_margin_loss_backward_out torch_soft_margin_loss_backward_out <- function(grad_input, grad_output, self, target, reduction) { args <- mget(x = c("grad_input", "grad_output", "self", "target", "reduction")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -14113,6 +14951,7 @@ fun_type = 'namespace' } +#' @rdname torch_soft_margin_loss_out torch_soft_margin_loss_out <- function(out, self, target, reduction = torch_reduction_mean()) { args <- mget(x = c("out", "self", "target", "reduction")) expected_types <- list(out = "Tensor", self = "Tensor", target = "Tensor", reduction = "int64_t") @@ -14129,6 +14968,7 @@ fun_type = 'namespace' } +#' @rdname torch_softmax torch_softmax <- function(self, dim, dtype = NULL) { args <- mget(x = c("self", "dim", "dtype")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), dtype = "ScalarType") @@ -14145,6 +14985,7 @@ fun_type = 'namespace' } +#' @rdname torch_softplus torch_softplus <- function(self, beta = 1L, threshold = 20L) { args <- mget(x = c("self", "beta", "threshold")) expected_types <- list(self = "Tensor", beta = "Scalar", threshold = "Scalar") @@ -14161,6 +15002,7 @@ fun_type = 'namespace' } +#' @rdname torch_softplus_backward torch_softplus_backward <- function(grad_output, self, beta, threshold, output) { args <- mget(x = c("grad_output", "self", "beta", "threshold", "output")) expected_types <- list(grad_output = "Tensor", self = "Tensor", beta = "Scalar", @@ -14178,6 +15020,7 @@ fun_type = 'namespace' } +#' @rdname torch_softplus_backward_out torch_softplus_backward_out <- function(grad_input, grad_output, self, beta, threshold, output) { args <- mget(x = c("grad_input", "grad_output", "self", "beta", "threshold", "output")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -14196,6 +15039,7 @@ fun_type = 'namespace' } +#' @rdname torch_softplus_out torch_softplus_out <- function(out, self, beta = 1L, threshold = 20L) { args <- mget(x = c("out", "self", "beta", "threshold")) expected_types <- list(out = "Tensor", self = "Tensor", beta = "Scalar", threshold = "Scalar") @@ -14212,6 +15056,7 @@ fun_type = 'namespace' } +#' @rdname torch_softshrink torch_softshrink <- function(self, lambd = 0.500000) { args <- mget(x = c("self", "lambd")) expected_types <- list(self = "Tensor", lambd = "Scalar") @@ -14228,6 +15073,7 @@ fun_type = 'namespace' } +#' @rdname torch_softshrink_backward torch_softshrink_backward <- function(grad_output, self, lambd) { args <- mget(x = c("grad_output", "self", "lambd")) expected_types <- list(grad_output = "Tensor", self = "Tensor", lambd = "Scalar") @@ -14244,6 +15090,7 @@ fun_type = 'namespace' } +#' @rdname torch_softshrink_backward_out torch_softshrink_backward_out <- function(grad_input, grad_output, self, lambd) { args <- mget(x = c("grad_input", "grad_output", "self", "lambd")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", self = "Tensor", @@ -14261,6 +15108,7 @@ fun_type = 'namespace' } +#' @rdname torch_softshrink_out torch_softshrink_out <- function(out, self, lambd = 0.500000) { args <- mget(x = c("out", "self", "lambd")) expected_types <- list(out = "Tensor", self = "Tensor", lambd = "Scalar") @@ -14277,6 +15125,7 @@ fun_type = 'namespace' } +#' @rdname torch_solve torch_solve <- function(self, A) { args <- mget(x = c("self", "A")) expected_types <- list(self = "Tensor", A = "Tensor") @@ -14293,6 +15142,7 @@ fun_type = 'namespace' } +#' @rdname torch_solve_out torch_solve_out <- function(solution, lu, self, A) { args <- mget(x = c("solution", "lu", "self", "A")) expected_types <- list(solution = "Tensor", lu = "Tensor", self = "Tensor", A = "Tensor") @@ -14309,6 +15159,7 @@ fun_type = 'namespace' } +#' @rdname torch_sort torch_sort <- function(self, dim = -1L, descending = FALSE) { args <- mget(x = c("self", "dim", "descending")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname"), descending = "bool") @@ -14325,6 +15176,7 @@ fun_type = 'namespace' } +#' @rdname torch_sort_out torch_sort_out <- function(values, indices, self, dim = -1L, descending = FALSE) { args <- mget(x = c("values", "indices", "self", "dim", "descending")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -14342,7 +15194,8 @@ fun_type = 'namespace' } -torch_sparse_coo_tensor <- function(indices, values, size, options = list()) { +#' @rdname .torch_sparse_coo_tensor +.torch_sparse_coo_tensor <- function(indices, values, size, options = list()) { args <- mget(x = c("indices", "values", "size", "options")) expected_types <- list(indices = "Tensor", values = "Tensor", size = "IntArrayRef", options = "TensorOptions") @@ -14359,6 +15212,7 @@ fun_type = 'namespace' } +#' @rdname torch_split torch_split <- function(self, split_size, dim = 1L) { args <- mget(x = c("self", "split_size", "dim")) expected_types <- list(self = "Tensor", split_size = "int64_t", dim = "int64_t") @@ -14375,6 +15229,7 @@ fun_type = 'namespace' } +#' @rdname torch_split_with_sizes torch_split_with_sizes <- function(self, split_sizes, dim = 1L) { args <- mget(x = c("self", "split_sizes", "dim")) expected_types <- list(self = "Tensor", split_sizes = "IntArrayRef", dim = "int64_t") @@ -14391,6 +15246,7 @@ fun_type = 'namespace' } +#' @rdname torch_sqrt torch_sqrt <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14407,6 +15263,7 @@ fun_type = 'namespace' } +#' @rdname torch_sqrt_ torch_sqrt_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14423,6 +15280,7 @@ fun_type = 'namespace' } +#' @rdname torch_sqrt_out torch_sqrt_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -14439,6 +15297,7 @@ fun_type = 'namespace' } +#' @rdname torch_square torch_square <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14455,6 +15314,7 @@ fun_type = 'namespace' } +#' @rdname torch_square_ torch_square_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14471,6 +15331,7 @@ fun_type = 'namespace' } +#' @rdname torch_squeeze torch_squeeze <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname")) @@ -14487,6 +15348,7 @@ fun_type = 'namespace' } +#' @rdname torch_sspaddmm torch_sspaddmm <- function(self, mat1, mat2, beta = 1L, alpha = 1L) { args <- mget(x = c("self", "mat1", "mat2", "beta", "alpha")) expected_types <- list(self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", beta = "Scalar", @@ -14504,6 +15366,7 @@ fun_type = 'namespace' } +#' @rdname torch_sspaddmm_out torch_sspaddmm_out <- function(out, self, mat1, mat2, beta = 1L, alpha = 1L) { args <- mget(x = c("out", "self", "mat1", "mat2", "beta", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", mat1 = "Tensor", mat2 = "Tensor", @@ -14521,6 +15384,7 @@ fun_type = 'namespace' } +#' @rdname torch_stack torch_stack <- function(tensors, dim = 1L) { args <- mget(x = c("tensors", "dim")) expected_types <- list(tensors = "TensorList", dim = "int64_t") @@ -14537,6 +15401,7 @@ fun_type = 'namespace' } +#' @rdname torch_stack_out torch_stack_out <- function(out, tensors, dim = 1L) { args <- mget(x = c("out", "tensors", "dim")) expected_types <- list(out = "Tensor", tensors = "TensorList", dim = "int64_t") @@ -14553,6 +15418,7 @@ fun_type = 'namespace' } +#' @rdname torch_std torch_std <- function(self, dim, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("self", "dim", "unbiased", "keepdim")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -14570,6 +15436,7 @@ fun_type = 'namespace' } +#' @rdname torch_std_mean torch_std_mean <- function(self, dim, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("self", "dim", "unbiased", "keepdim")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -14587,6 +15454,7 @@ fun_type = 'namespace' } +#' @rdname torch_std_out torch_std_out <- function(out, self, dim, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("out", "self", "dim", "unbiased", "keepdim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", @@ -14604,7 +15472,8 @@ fun_type = 'namespace' } -torch_stft <- function(self, n_fft, hop_length = NULL, win_length = NULL, window = list(), normalized = FALSE, onesided = TRUE) { +#' @rdname .torch_stft +.torch_stft <- function(self, n_fft, hop_length = NULL, win_length = NULL, window = list(), normalized = FALSE, onesided = TRUE) { args <- mget(x = c("self", "n_fft", "hop_length", "win_length", "window", "normalized", "onesided")) expected_types <- list(self = "Tensor", n_fft = "int64_t", hop_length = "int64_t", win_length = "int64_t", window = "Tensor", normalized = "bool", @@ -14622,6 +15491,7 @@ fun_type = 'namespace' } +#' @rdname torch_stride torch_stride <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname")) @@ -14638,6 +15508,7 @@ fun_type = 'namespace' } +#' @rdname torch_sub torch_sub <- function(self, other, alpha = 1L) { args <- mget(x = c("self", "other", "alpha")) expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar"), alpha = "Scalar") @@ -14654,6 +15525,7 @@ fun_type = 'namespace' } +#' @rdname torch_sub_out torch_sub_out <- function(out, self, other, alpha = 1L) { args <- mget(x = c("out", "self", "other", "alpha")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor", alpha = "Scalar") @@ -14670,6 +15542,7 @@ fun_type = 'namespace' } +#' @rdname torch_sum torch_sum <- function(self, dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("self", "dim", "keepdim", "dtype")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -14687,6 +15560,7 @@ fun_type = 'namespace' } +#' @rdname torch_sum_out torch_sum_out <- function(out, self, dim, keepdim = FALSE, dtype = NULL) { args <- mget(x = c("out", "self", "dim", "keepdim", "dtype")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", @@ -14704,6 +15578,7 @@ fun_type = 'namespace' } +#' @rdname torch_svd torch_svd <- function(self, some = TRUE, compute_uv = TRUE) { args <- mget(x = c("self", "some", "compute_uv")) expected_types <- list(self = "Tensor", some = "bool", compute_uv = "bool") @@ -14720,6 +15595,7 @@ fun_type = 'namespace' } +#' @rdname torch_svd_out torch_svd_out <- function(U, S, V, self, some = TRUE, compute_uv = TRUE) { args <- mget(x = c("U", "S", "V", "self", "some", "compute_uv")) expected_types <- list(U = "Tensor", S = "Tensor", V = "Tensor", self = "Tensor", @@ -14737,6 +15613,7 @@ fun_type = 'namespace' } +#' @rdname torch_symeig torch_symeig <- function(self, eigenvectors = FALSE, upper = TRUE) { args <- mget(x = c("self", "eigenvectors", "upper")) expected_types <- list(self = "Tensor", eigenvectors = "bool", upper = "bool") @@ -14753,6 +15630,7 @@ fun_type = 'namespace' } +#' @rdname torch_symeig_out torch_symeig_out <- function(e, V, self, eigenvectors = FALSE, upper = TRUE) { args <- mget(x = c("e", "V", "self", "eigenvectors", "upper")) expected_types <- list(e = "Tensor", V = "Tensor", self = "Tensor", eigenvectors = "bool", @@ -14770,6 +15648,7 @@ fun_type = 'namespace' } +#' @rdname torch_t torch_t <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14786,6 +15665,7 @@ fun_type = 'namespace' } +#' @rdname torch_take torch_take <- function(self, index) { args <- mget(x = c("self", "index")) expected_types <- list(self = "Tensor", index = "Tensor") @@ -14802,6 +15682,7 @@ fun_type = 'namespace' } +#' @rdname torch_take_out torch_take_out <- function(out, self, index) { args <- mget(x = c("out", "self", "index")) expected_types <- list(out = "Tensor", self = "Tensor", index = "Tensor") @@ -14818,6 +15699,7 @@ fun_type = 'namespace' } +#' @rdname torch_tan torch_tan <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14834,6 +15716,7 @@ fun_type = 'namespace' } +#' @rdname torch_tan_ torch_tan_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14850,6 +15733,7 @@ fun_type = 'namespace' } +#' @rdname torch_tan_out torch_tan_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -14866,6 +15750,7 @@ fun_type = 'namespace' } +#' @rdname torch_tanh torch_tanh <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14882,6 +15767,7 @@ fun_type = 'namespace' } +#' @rdname torch_tanh_ torch_tanh_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -14898,6 +15784,7 @@ fun_type = 'namespace' } +#' @rdname torch_tanh_backward torch_tanh_backward <- function(grad_output, output) { args <- mget(x = c("grad_output", "output")) expected_types <- list(grad_output = "Tensor", output = "Tensor") @@ -14914,6 +15801,7 @@ fun_type = 'namespace' } +#' @rdname torch_tanh_backward_out torch_tanh_backward_out <- function(grad_input, grad_output, output) { args <- mget(x = c("grad_input", "grad_output", "output")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output = "Tensor") @@ -14930,6 +15818,7 @@ fun_type = 'namespace' } +#' @rdname torch_tanh_out torch_tanh_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -14946,7 +15835,8 @@ fun_type = 'namespace' } -torch_tensordot <- function(self, other, dims_self, dims_other) { +#' @rdname .torch_tensordot +.torch_tensordot <- function(self, other, dims_self, dims_other) { args <- mget(x = c("self", "other", "dims_self", "dims_other")) expected_types <- list(self = "Tensor", other = "Tensor", dims_self = "IntArrayRef", dims_other = "IntArrayRef") @@ -14963,6 +15853,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv_depthwise2d torch_thnn_conv_depthwise2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -14981,6 +15872,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv_depthwise2d_backward torch_thnn_conv_depthwise2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, dilation, output_mask) { args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation", "output_mask")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", @@ -15000,6 +15892,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv_depthwise2d_backward_out torch_thnn_conv_depthwise2d_backward_out <- function(grad_input, grad_weight, grad_output, self, weight, kernel_size, stride, padding, dilation) { args <- mget(x = c("grad_input", "grad_weight", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "dilation")) expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_output = "Tensor", @@ -15019,6 +15912,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv_depthwise2d_forward torch_thnn_conv_depthwise2d_forward <- function(self, weight, kernel_size, bias, stride, padding, dilation) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding", "dilation")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -15038,6 +15932,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv_depthwise2d_forward_out torch_thnn_conv_depthwise2d_forward_out <- function(out, self, weight, kernel_size, bias, stride, padding, dilation) { args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "dilation")) expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -15057,6 +15952,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv_depthwise2d_out torch_thnn_conv_depthwise2d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L, dilation = 1L) { args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding", "dilation")) expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -15075,6 +15971,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv2d torch_thnn_conv2d <- function(self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -15092,6 +15989,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv2d_backward torch_thnn_conv2d_backward <- function(grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input, output_mask) { args <- mget(x = c("grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input", "output_mask")) expected_types <- list(grad_output = "Tensor", self = "Tensor", weight = "Tensor", @@ -15111,6 +16009,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv2d_backward_out torch_thnn_conv2d_backward_out <- function(grad_input, grad_weight, grad_bias, grad_output, self, weight, kernel_size, stride, padding, finput, fgrad_input) { args <- mget(x = c("grad_input", "grad_weight", "grad_bias", "grad_output", "self", "weight", "kernel_size", "stride", "padding", "finput", "fgrad_input")) expected_types <- list(grad_input = "Tensor", grad_weight = "Tensor", grad_bias = "Tensor", @@ -15132,6 +16031,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv2d_forward torch_thnn_conv2d_forward <- function(self, weight, kernel_size, bias, stride, padding) { args <- mget(x = c("self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -15150,6 +16050,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv2d_forward_out torch_thnn_conv2d_forward_out <- function(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding) { args <- mget(x = c("output", "finput", "fgrad_input", "self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(output = "Tensor", finput = "Tensor", fgrad_input = "Tensor", @@ -15169,6 +16070,7 @@ fun_type = 'namespace' } +#' @rdname torch_thnn_conv2d_out torch_thnn_conv2d_out <- function(out, self, weight, kernel_size, bias = list(), stride = 1L, padding = 0L) { args <- mget(x = c("out", "self", "weight", "kernel_size", "bias", "stride", "padding")) expected_types <- list(out = "Tensor", self = "Tensor", weight = "Tensor", kernel_size = "IntArrayRef", @@ -15186,6 +16088,7 @@ fun_type = 'namespace' } +#' @rdname torch_threshold torch_threshold <- function(self, threshold, value) { args <- mget(x = c("self", "threshold", "value")) expected_types <- list(self = "Tensor", threshold = "Scalar", value = "Scalar") @@ -15202,6 +16105,7 @@ fun_type = 'namespace' } +#' @rdname torch_threshold_ torch_threshold_ <- function(self, threshold, value) { args <- mget(x = c("self", "threshold", "value")) expected_types <- list(self = "Tensor", threshold = "Scalar", value = "Scalar") @@ -15218,6 +16122,7 @@ fun_type = 'namespace' } +#' @rdname torch_threshold_backward torch_threshold_backward <- function(grad_output, self, threshold) { args <- mget(x = c("grad_output", "self", "threshold")) expected_types <- list(grad_output = "Tensor", self = "Tensor", threshold = "Scalar") @@ -15234,6 +16139,7 @@ fun_type = 'namespace' } +#' @rdname torch_threshold_out torch_threshold_out <- function(out, self, threshold, value) { args <- mget(x = c("out", "self", "threshold", "value")) expected_types <- list(out = "Tensor", self = "Tensor", threshold = "Scalar", value = "Scalar") @@ -15250,6 +16156,7 @@ fun_type = 'namespace' } +#' @rdname torch_to_dense_backward torch_to_dense_backward <- function(grad, input) { args <- mget(x = c("grad", "input")) expected_types <- list(grad = "Tensor", input = "Tensor") @@ -15266,6 +16173,7 @@ fun_type = 'namespace' } +#' @rdname torch_to_mkldnn_backward torch_to_mkldnn_backward <- function(grad, input) { args <- mget(x = c("grad", "input")) expected_types <- list(grad = "Tensor", input = "Tensor") @@ -15282,6 +16190,7 @@ fun_type = 'namespace' } +#' @rdname torch_topk torch_topk <- function(self, k, dim = -1L, largest = TRUE, sorted = TRUE) { args <- mget(x = c("self", "k", "dim", "largest", "sorted")) expected_types <- list(self = "Tensor", k = "int64_t", dim = "int64_t", largest = "bool", @@ -15299,6 +16208,7 @@ fun_type = 'namespace' } +#' @rdname torch_topk_out torch_topk_out <- function(values, indices, self, k, dim = -1L, largest = TRUE, sorted = TRUE) { args <- mget(x = c("values", "indices", "self", "k", "dim", "largest", "sorted")) expected_types <- list(values = "Tensor", indices = "Tensor", self = "Tensor", @@ -15316,6 +16226,7 @@ fun_type = 'namespace' } +#' @rdname torch_trace torch_trace <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -15332,6 +16243,7 @@ fun_type = 'namespace' } +#' @rdname torch_transpose torch_transpose <- function(self, dim0, dim1) { args <- mget(x = c("self", "dim0", "dim1")) expected_types <- list(self = "Tensor", dim0 = c("int64_t", "Dimname"), dim1 = c("int64_t", @@ -15349,6 +16261,7 @@ fun_type = 'namespace' } +#' @rdname torch_trapz torch_trapz <- function(y, dx = 1L, x, dim = -1L) { args <- mget(x = c("y", "dx", "x", "dim")) expected_types <- list(y = "Tensor", dx = "double", x = "Tensor", dim = "int64_t") @@ -15365,6 +16278,7 @@ fun_type = 'namespace' } +#' @rdname torch_triangular_solve torch_triangular_solve <- function(self, A, upper = TRUE, transpose = FALSE, unitriangular = FALSE) { args <- mget(x = c("self", "A", "upper", "transpose", "unitriangular")) expected_types <- list(self = "Tensor", A = "Tensor", upper = "bool", transpose = "bool", @@ -15382,6 +16296,7 @@ fun_type = 'namespace' } +#' @rdname torch_triangular_solve_out torch_triangular_solve_out <- function(X, M, self, A, upper = TRUE, transpose = FALSE, unitriangular = FALSE) { args <- mget(x = c("X", "M", "self", "A", "upper", "transpose", "unitriangular")) expected_types <- list(X = "Tensor", M = "Tensor", self = "Tensor", A = "Tensor", @@ -15399,6 +16314,7 @@ fun_type = 'namespace' } +#' @rdname torch_tril torch_tril <- function(self, diagonal = 0L) { args <- mget(x = c("self", "diagonal")) expected_types <- list(self = "Tensor", diagonal = "int64_t") @@ -15415,7 +16331,8 @@ fun_type = 'namespace' } -torch_tril_indices <- function(row, col, offset = 0L, options = torch_long()) { +#' @rdname .torch_tril_indices +.torch_tril_indices <- function(row, col, offset = 0L, options = torch_long()) { args <- mget(x = c("row", "col", "offset", "options")) expected_types <- list(row = "int64_t", col = "int64_t", offset = "int64_t", options = "TensorOptions") nd_args <- c("row", "col") @@ -15431,6 +16348,7 @@ fun_type = 'namespace' } +#' @rdname torch_tril_out torch_tril_out <- function(out, self, diagonal = 0L) { args <- mget(x = c("out", "self", "diagonal")) expected_types <- list(out = "Tensor", self = "Tensor", diagonal = "int64_t") @@ -15447,6 +16365,7 @@ fun_type = 'namespace' } +#' @rdname torch_triplet_margin_loss torch_triplet_margin_loss <- function(anchor, positive, negative, margin = 1.000000, p = 2L, eps = 0.000001, swap = FALSE, reduction = torch_reduction_mean()) { args <- mget(x = c("anchor", "positive", "negative", "margin", "p", "eps", "swap", "reduction")) expected_types <- list(anchor = "Tensor", positive = "Tensor", negative = "Tensor", @@ -15465,6 +16384,7 @@ fun_type = 'namespace' } +#' @rdname torch_triu torch_triu <- function(self, diagonal = 0L) { args <- mget(x = c("self", "diagonal")) expected_types <- list(self = "Tensor", diagonal = "int64_t") @@ -15481,7 +16401,8 @@ fun_type = 'namespace' } -torch_triu_indices <- function(row, col, offset = 0L, options = torch_long()) { +#' @rdname .torch_triu_indices +.torch_triu_indices <- function(row, col, offset = 0L, options = torch_long()) { args <- mget(x = c("row", "col", "offset", "options")) expected_types <- list(row = "int64_t", col = "int64_t", offset = "int64_t", options = "TensorOptions") nd_args <- c("row", "col") @@ -15497,6 +16418,7 @@ fun_type = 'namespace' } +#' @rdname torch_triu_out torch_triu_out <- function(out, self, diagonal = 0L) { args <- mget(x = c("out", "self", "diagonal")) expected_types <- list(out = "Tensor", self = "Tensor", diagonal = "int64_t") @@ -15513,6 +16435,7 @@ fun_type = 'namespace' } +#' @rdname torch_true_divide torch_true_divide <- function(self, other) { args <- mget(x = c("self", "other")) expected_types <- list(self = "Tensor", other = c("Tensor", "Scalar")) @@ -15529,6 +16452,7 @@ fun_type = 'namespace' } +#' @rdname torch_true_divide_out torch_true_divide_out <- function(out, self, other) { args <- mget(x = c("out", "self", "other")) expected_types <- list(out = "Tensor", self = "Tensor", other = "Tensor") @@ -15545,6 +16469,7 @@ fun_type = 'namespace' } +#' @rdname torch_trunc torch_trunc <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -15561,6 +16486,7 @@ fun_type = 'namespace' } +#' @rdname torch_trunc_ torch_trunc_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -15577,6 +16503,7 @@ fun_type = 'namespace' } +#' @rdname torch_trunc_out torch_trunc_out <- function(out, self) { args <- mget(x = c("out", "self")) expected_types <- list(out = "Tensor", self = "Tensor") @@ -15593,6 +16520,7 @@ fun_type = 'namespace' } +#' @rdname torch_unbind torch_unbind <- function(self, dim = 1L) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = c("int64_t", "Dimname")) @@ -15609,6 +16537,7 @@ fun_type = 'namespace' } +#' @rdname torch_unique_consecutive torch_unique_consecutive <- function(self, return_inverse = FALSE, return_counts = FALSE, dim = NULL) { args <- mget(x = c("self", "return_inverse", "return_counts", "dim")) expected_types <- list(self = "Tensor", return_inverse = "bool", return_counts = "bool", @@ -15626,6 +16555,7 @@ fun_type = 'namespace' } +#' @rdname torch_unique_dim torch_unique_dim <- function(self, dim, sorted = TRUE, return_inverse = FALSE, return_counts = FALSE) { args <- mget(x = c("self", "dim", "sorted", "return_inverse", "return_counts")) expected_types <- list(self = "Tensor", dim = "int64_t", sorted = "bool", return_inverse = "bool", @@ -15643,6 +16573,7 @@ fun_type = 'namespace' } +#' @rdname torch_unique_dim_consecutive torch_unique_dim_consecutive <- function(self, dim, return_inverse = FALSE, return_counts = FALSE) { args <- mget(x = c("self", "dim", "return_inverse", "return_counts")) expected_types <- list(self = "Tensor", dim = "int64_t", return_inverse = "bool", @@ -15660,6 +16591,7 @@ fun_type = 'namespace' } +#' @rdname torch_unsqueeze torch_unsqueeze <- function(self, dim) { args <- mget(x = c("self", "dim")) expected_types <- list(self = "Tensor", dim = "int64_t") @@ -15676,6 +16608,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bicubic2d torch_upsample_bicubic2d <- function(self, output_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("self", "output_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", @@ -15693,6 +16626,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bicubic2d_backward torch_upsample_bicubic2d_backward <- function(grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", @@ -15711,6 +16645,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bicubic2d_backward_out torch_upsample_bicubic2d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", @@ -15730,6 +16665,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bicubic2d_out torch_upsample_bicubic2d_out <- function(out, self, output_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("out", "self", "output_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -15747,6 +16683,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bilinear2d torch_upsample_bilinear2d <- function(self, output_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("self", "output_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", @@ -15764,6 +16701,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bilinear2d_backward torch_upsample_bilinear2d_backward <- function(grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", @@ -15782,6 +16720,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bilinear2d_backward_out torch_upsample_bilinear2d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", @@ -15801,6 +16740,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_bilinear2d_out torch_upsample_bilinear2d_out <- function(out, self, output_size, align_corners, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("out", "self", "output_size", "align_corners", "scales_h", "scales_w")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -15818,6 +16758,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_linear1d torch_upsample_linear1d <- function(self, output_size, align_corners, scales = NULL) { args <- mget(x = c("self", "output_size", "align_corners", "scales")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", @@ -15835,6 +16776,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_linear1d_backward torch_upsample_linear1d_backward <- function(grad_output, output_size, input_size, align_corners, scales = NULL) { args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales")) expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", @@ -15853,6 +16795,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_linear1d_backward_out torch_upsample_linear1d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales = NULL) { args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", @@ -15871,6 +16814,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_linear1d_out torch_upsample_linear1d_out <- function(out, self, output_size, align_corners, scales = NULL) { args <- mget(x = c("out", "self", "output_size", "align_corners", "scales")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -15888,6 +16832,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest1d torch_upsample_nearest1d <- function(self, output_size, scales = NULL) { args <- mget(x = c("self", "output_size", "scales")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", scales = "double") @@ -15904,6 +16849,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest1d_backward torch_upsample_nearest1d_backward <- function(grad_output, output_size, input_size, scales = NULL) { args <- mget(x = c("grad_output", "output_size", "input_size", "scales")) expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", @@ -15921,6 +16867,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest1d_backward_out torch_upsample_nearest1d_backward_out <- function(grad_input, grad_output, output_size, input_size, scales = NULL) { args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "scales")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", @@ -15938,6 +16885,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest1d_out torch_upsample_nearest1d_out <- function(out, self, output_size, scales = NULL) { args <- mget(x = c("out", "self", "output_size", "scales")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -15955,6 +16903,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest2d torch_upsample_nearest2d <- function(self, output_size, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("self", "output_size", "scales_h", "scales_w")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", scales_h = "double", @@ -15972,6 +16921,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest2d_backward torch_upsample_nearest2d_backward <- function(grad_output, output_size, input_size, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_output", "output_size", "input_size", "scales_h", "scales_w")) expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", @@ -15989,6 +16939,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest2d_backward_out torch_upsample_nearest2d_backward_out <- function(grad_input, grad_output, output_size, input_size, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "scales_h", "scales_w")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", @@ -16006,6 +16957,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest2d_out torch_upsample_nearest2d_out <- function(out, self, output_size, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("out", "self", "output_size", "scales_h", "scales_w")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -16023,6 +16975,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest3d torch_upsample_nearest3d <- function(self, output_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("self", "output_size", "scales_d", "scales_h", "scales_w")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", scales_d = "double", @@ -16040,6 +16993,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest3d_backward torch_upsample_nearest3d_backward <- function(grad_output, output_size, input_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_output", "output_size", "input_size", "scales_d", "scales_h", "scales_w")) expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", @@ -16057,6 +17011,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest3d_backward_out torch_upsample_nearest3d_backward_out <- function(grad_input, grad_output, output_size, input_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "scales_d", "scales_h", "scales_w")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", @@ -16075,6 +17030,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_nearest3d_out torch_upsample_nearest3d_out <- function(out, self, output_size, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("out", "self", "output_size", "scales_d", "scales_h", "scales_w")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -16092,6 +17048,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_trilinear3d torch_upsample_trilinear3d <- function(self, output_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("self", "output_size", "align_corners", "scales_d", "scales_h", "scales_w")) expected_types <- list(self = "Tensor", output_size = "IntArrayRef", align_corners = "bool", @@ -16109,6 +17066,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_trilinear3d_backward torch_upsample_trilinear3d_backward <- function(grad_output, output_size, input_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_output", "output_size", "input_size", "align_corners", "scales_d", "scales_h", "scales_w")) expected_types <- list(grad_output = "Tensor", output_size = "IntArrayRef", input_size = "IntArrayRef", @@ -16128,6 +17086,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_trilinear3d_backward_out torch_upsample_trilinear3d_backward_out <- function(grad_input, grad_output, output_size, input_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("grad_input", "grad_output", "output_size", "input_size", "align_corners", "scales_d", "scales_h", "scales_w")) expected_types <- list(grad_input = "Tensor", grad_output = "Tensor", output_size = "IntArrayRef", @@ -16147,6 +17106,7 @@ fun_type = 'namespace' } +#' @rdname torch_upsample_trilinear3d_out torch_upsample_trilinear3d_out <- function(out, self, output_size, align_corners, scales_d = NULL, scales_h = NULL, scales_w = NULL) { args <- mget(x = c("out", "self", "output_size", "align_corners", "scales_d", "scales_h", "scales_w")) expected_types <- list(out = "Tensor", self = "Tensor", output_size = "IntArrayRef", @@ -16165,6 +17125,7 @@ fun_type = 'namespace' } +#' @rdname torch_var torch_var <- function(self, dim, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("self", "dim", "unbiased", "keepdim")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -16182,6 +17143,7 @@ fun_type = 'namespace' } +#' @rdname torch_var_mean torch_var_mean <- function(self, dim, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("self", "dim", "unbiased", "keepdim")) expected_types <- list(self = "Tensor", dim = c("IntArrayRef", "DimnameList"), @@ -16199,6 +17161,7 @@ fun_type = 'namespace' } +#' @rdname torch_var_out torch_var_out <- function(out, self, dim, unbiased = TRUE, keepdim = FALSE) { args <- mget(x = c("out", "self", "dim", "unbiased", "keepdim")) expected_types <- list(out = "Tensor", self = "Tensor", dim = c("IntArrayRef", @@ -16216,6 +17179,7 @@ fun_type = 'namespace' } +#' @rdname torch_where torch_where <- function(condition, self, other) { args <- mget(x = c("condition", "self", "other")) expected_types <- list(condition = "Tensor", self = "Tensor", other = "Tensor") @@ -16232,6 +17196,7 @@ fun_type = 'namespace' } +#' @rdname torch_zero_ torch_zero_ <- function(self) { args <- mget(x = c("self")) expected_types <- list(self = "Tensor") @@ -16248,6 +17213,7 @@ fun_type = 'namespace' } +#' @rdname .torch_zeros .torch_zeros <- function(size, names, options = list()) { args <- mget(x = c("size", "names", "options")) expected_types <- list(size = "IntArrayRef", names = "DimnameList", options = "TensorOptions") @@ -16264,6 +17230,7 @@ fun_type = 'namespace' } +#' @rdname .torch_zeros_like .torch_zeros_like <- function(self, options = list(), memory_format = NULL) { args <- mget(x = c("self", "options", "memory_format")) expected_types <- list(self = "Tensor", options = "TensorOptions", memory_format = "MemoryFormat") @@ -16280,6 +17247,7 @@ fun_type = 'namespace' } +#' @rdname torch_zeros_out torch_zeros_out <- function(out, size) { args <- mget(x = c("out", "size")) expected_types <- list(out = "Tensor", size = "IntArrayRef") diff --git a/R/generator.R b/R/generator.R index 0734f5efecb65e0658aca8c8d03d013318d13fbd..2a631d6bd552752c944ddc25b666384bebf82b76 100644 --- a/R/generator.R +++ b/R/generator.R @@ -19,15 +19,14 @@ Generator <- R6::R6Class( if (!requireNamespace("bit64")) warning("bit64 is required to correctly show the seed.") - cpp_generator_current_seed(self$ptr) + bit64::as.integer64(cpp_generator_current_seed(self$ptr)) }, set_current_seed = function(seed) { if ((!is.integer(seed)) && (!inherits(seed, "integer64"))) stop("Seed must an integer or integer64.") - if (is.integer(seed)) - seed <- as.numeric(seed) + seed <- as.character(seed) cpp_generator_set_current_seed(self$ptr, seed) } @@ -58,3 +57,12 @@ is_torch_generator <- function(x) { inherits(x, "torch_generator") } +#' Sets the seed for generating random numbers. +#' +#' @param seed integer seed. +#' +#' @export +torch_manual_seed <- function(seed) { + cpp_torch_manual_seed(as.character(seed)) +} + diff --git a/R/install.R b/R/install.R index cb5217ff7194f1e9a7bdd26cfda43bbf08af0518..8b10459d85e9e44b28f3c9213b371b0e5de144d5 100644 --- a/R/install.R +++ b/R/install.R @@ -1,5 +1,6 @@ branch <- "master" + install_config <- list( "1.5.0" = list( "cpu" = list( @@ -88,6 +89,13 @@ install_exists <- function() { dir.exists(install_path()) } +#' Verifies if torch is installed +#' +#' @export +torch_is_installed <- function() { + install_exists() +} + lib_installed <- function(library_name, install_path) { x <- list.files(install_path) @@ -216,6 +224,7 @@ install_type <- function(version) { #' @export install_torch <- function(version = "1.5.0", type = install_type(version = version), reinstall = FALSE, path = install_path(), ...) { + if (reinstall) { unlink(path, recursive = TRUE) } diff --git a/R/nn-batchnorm.R b/R/nn-batchnorm.R index 5bd2e4324eaef743ac677ac65bc60e92b0897868..90ad87fc6be3d10f9da67f5eeaca5ead79fd28ad 100644 --- a/R/nn-batchnorm.R +++ b/R/nn-batchnorm.R @@ -48,6 +48,14 @@ nn_norm_base_ <- nn_module( nn_init_ones_(self$weight) nn_init_zeros_(self$bias) } + }, + .load_from_state_dict = function(state_dict, prefix) { + + num_batches_tracked_key <- paste0(prefix, "num_batches_tracked") + if (!num_batches_tracked_key %in% names(state_dict)) + state_dict[[num_batches_tracked_key]] <- torch_tensor(0, dtype = torch_long()) + + super$.load_from_state_dict(state_dict, prefix) } ) diff --git a/R/nn-distance.R b/R/nn-distance.R new file mode 100644 index 0000000000000000000000000000000000000000..a53ee597500695069883748594d89fd48061727b --- /dev/null +++ b/R/nn-distance.R @@ -0,0 +1,39 @@ +#' Pairwise distance +#' +#' Computes the batchwise pairwise distance between vectors \eqn{v_1}, \eqn{v_2} +#' using the p-norm: +#' +#' \deqn{ +#' \Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}. +#' } +#' +#' @param p (real): the norm degree. Default: 2 +#' @param eps (float, optional): Small value to avoid division by zero. +#' Default: 1e-6 +#' @param keepdim (bool, optional): Determines whether or not to keep the vector dimension. +#' Default: FALSE +#' +#' @section Shape: +#' - Input1: \eqn{(N, D)} where `D = vector dimension` +#' - Input2: \eqn{(N, D)}, same shape as the Input1 +#' - Output: \eqn{(N)}. If `keepdim` is `TRUE`, then \eqn{(N, 1)}. +#' +#' @examples +#' pdist <- nn_pairwise_distance(p=2) +#' input1 <- torch_randn(100, 128) +#' input2 <- torch_randn(100, 128) +#' output <- pdist(input1, input2) +#' +#' @export +nn_pairwise_distance <- nn_module( + "nn_pairwise_distance", + initialize = function(p = 2, eps = 1e-6, keepdim = FALSE) { + self$norm <- p + self$eps <- eps + self$keepdim <- keepdim + }, + forward = function(x1, x2) { + nnf_pairwise_distance(x1, x2, p = self$norm, eps = self$eps, + keepdim = self$keepdim) + } +) \ No newline at end of file diff --git a/R/nn-dropout.R b/R/nn-dropout.R index b09eda328fde4f9e357cfdc438b4575f734a231d..702afc920ea7870658b0dbfb6f2a44b5f90a0c33 100644 --- a/R/nn-dropout.R +++ b/R/nn-dropout.R @@ -62,7 +62,7 @@ nn_dropout <- nn_module( #' Usually the input comes from [nn_conv2d] modules. #' #' As described in the paper -#' [Efficient Object Localization Using Convolutional Networks](http://arxiv.org/abs/1411.4280) , +#' [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) , #' if adjacent pixels within feature maps are strongly correlated #' (as is normally the case in early convolution layers) then i.i.d. dropout #' will not regularize the activations and will otherwise just result @@ -104,7 +104,7 @@ nn_dropout2d <- nn_module( #' Usually the input comes from [nn_conv2d] modules. #' #' As described in the paper -#' [Efficient Object Localization Using Convolutional Networks](http://arxiv.org/abs/1411.4280) , +#' [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) , #' if adjacent pixels within feature maps are strongly correlated #' (as is normally the case in early convolution layers) then i.i.d. dropout #' will not regularize the activations and will otherwise just result diff --git a/R/nn-loss.R b/R/nn-loss.R index 283120b55bd9610984181f016d5081f4febe66e9..f64d85450d3541b44f8896700d48a79b840aa1cc 100644 --- a/R/nn-loss.R +++ b/R/nn-loss.R @@ -17,6 +17,377 @@ nn_weighted_loss <- nn_module( } ) +#' L1 loss +#' +#' Creates a criterion that measures the mean absolute error (MAE) between each +#' element in the input \eqn{x} and target \eqn{y}. +#' +#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described +#' as: +#' +#' \deqn{ +#' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad +#' l_n = \left| x_n - y_n \right|, +#' } +#' +#' where \eqn{N} is the batch size. If `reduction` is not `'none'` +#' (default `'mean'`), then: +#' +#' \deqn{ +#' \ell(x, y) = +#' \begin{array}{ll} +#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ +#' \mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +#' \end{array} +#' } +#' +#' \eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total +#' of \eqn{n} elements each. +#' +#' The sum operation still operates over all the elements, and divides by \eqn{n}. +#' The division by \eqn{n} can be avoided if one sets `reduction = 'sum'`. +#' +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional +#' dimensions +#' - Target: \eqn{(N, *)}, same shape as the input +#' - Output: scalar. If `reduction` is `'none'`, then +#' \eqn{(N, *)}, same shape as the input +#' +#' @examples +#' loss <- nn_l1_loss() +#' input <- torch_randn(3, 5, requires_grad=TRUE) +#' target <- torch_randn(3, 5) +#' output <- loss(input, target) +#' output$backward() +#' +#' @export +nn_l1_loss <- nn_module( + "nn_l1_loss", + inherit = nn_loss, + forward = function(input, target) { + nnf_l1_loss(input, target, reduction = self$reduction) + } +) + +#' Nll loss +#' +#' The negative log likelihood loss. It is useful to train a classification +#' problem with `C` classes. +#' +#' If provided, the optional argument `weight` should be a 1D Tensor assigning +#' weight to each of the classes. This is particularly useful when you have an +#' unbalanced training set. +#' +#' The `input` given through a forward call is expected to contain +#' log-probabilities of each class. `input` has to be a Tensor of size either +#' \eqn{(minibatch, C)} or \eqn{(minibatch, C, d_1, d_2, ..., d_K)} +#' with \eqn{K \geq 1} for the `K`-dimensional case (described later). +#' +#' Obtaining log-probabilities in a neural network is easily achieved by +#' adding a `LogSoftmax` layer in the last layer of your network. +#' +#' You may use `CrossEntropyLoss` instead, if you prefer not to add an extra +#' layer. +#' +#' The `target` that this loss expects should be a class index in the range \eqn{[0, C-1]} +#' where `C = number of classes`; if `ignore_index` is specified, this loss also accepts +#' this class index (this index may not necessarily be in the class range). +#' +#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described as: +#' +#' \deqn{ +#' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad +#' l_n = - w_{y_n} x_{n,y_n}, \quad +#' w_{c} = \mbox{weight}[c] \cdot \mbox{1}\{c \not= \mbox{ignore\_index}\}, +#' } +#' +#' where \eqn{x} is the input, \eqn{y} is the target, \eqn{w} is the weight, and +#' \eqn{N} is the batch size. If `reduction` is not `'none'` +#' (default `'mean'`), then +#' +#' \deqn{ +#' \ell(x, y) = \begin{array}{ll} +#' \sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, & +#' \mbox{if reduction} = \mbox{'mean';}\\ +#' \sum_{n=1}^N l_n, & +#' \mbox{if reduction} = \mbox{'sum'.} +#' \end{array} +#' } +#' +#' Can also be used for higher dimension inputs, such as 2D images, by providing +#' an input of size \eqn{(minibatch, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1}, +#' where \eqn{K} is the number of dimensions, and a target of appropriate shape +#' (see below). In the case of images, it computes NLL loss per-pixel. +#' +#' +#' @param weight (Tensor, optional): a manual rescaling weight given to each +#' class. If given, it has to be a Tensor of size `C`. Otherwise, it is +#' treated as if having all ones. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will +#' be applied, `'mean'`: the weighted mean of the output is taken, +#' `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in +#' the meantime, specifying either of those two args will override +#' `reduction`. Default: `'mean'` +#' @param ignore_index (int, optional): Specifies a target value that is ignored +#' and does not contribute to the input gradient. +#' +#' @section Shape: +#' - Input: \eqn{(N, C)} where `C = number of classes`, or +#' \eqn{(N, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} +#' in the case of `K`-dimensional loss. +#' - Target: \eqn{(N)} where each value is \eqn{0 \leq \mbox{targets}[i] \leq C-1}, or +#' \eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case of +#' K-dimensional loss. +#' - Output: scalar. +#' +#' If `reduction` is `'none'`, then the same size as the target: \eqn{(N)}, or +#' \eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case +#' of K-dimensional loss. +#' +#' @examples +#' m <- nn_log_softmax(dim=2) +#' loss <- nn_nll_loss() +#' # input is of size N x C = 3 x 5 +#' input <- torch_randn(3, 5, requires_grad=TRUE) +#' # each element in target has to have 0 <= value < C +#' target <- torch_tensor(c(2, 1, 5), dtype = torch_long()) +#' output <- loss(m(input), target) +#' output$backward() +#' +#' # 2D loss example (used, for example, with image inputs) +#' N <- 5 +#' C <- 4 +#' loss <- nn_nll_loss() +#' # input is of size N x C x height x width +#' data <- torch_randn(N, 16, 10, 10) +#' conv <- nn_conv2d(16, C, c(3, 3)) +#' m <- nn_log_softmax(dim=1) +#' # each element in target has to have 0 <= value < C +#' target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(1, C) +#' output <- loss(m(conv(data)), target) +#' output$backward() +#' +#' @export +nn_nll_loss <- nn_module( + "nn_nll_loss", + inherit = nn_weighted_loss, + initialize = function(weight = NULL, ignore_index = -100, reduction = "mean") { + super$initialize(weight, reduction) + self$ignore_index <- ignore_index + }, + forward = function(input, target) { + nnf_nll_loss(input, target, weight = self$weight, + ignore_index = self$ignore_index, reduction = self$reduction) + } +) + +#' Poisson NLL loss +#' +#' Negative log likelihood loss with Poisson distribution of target. +#' The loss can be described as: +#' +#' \deqn{ +#' \mbox{target} \sim \mathrm{Poisson}(\mbox{input}) +#' \mbox{loss}(\mbox{input}, \mbox{target}) = \mbox{input} - \mbox{target} * \log(\mbox{input}) +#' + \log(\mbox{target!}) +#' } +#' +#' The last term can be omitted or approximated with Stirling formula. The +#' approximation is used for target values more than 1. For targets less or +#' equal to 1 zeros are added to the loss. +#' +#' @param log_input (bool, optional): if `TRUE` the loss is computed as +#' \eqn{\exp(\mbox{input}) - \mbox{target}*\mbox{input}}, if `FALSE` the loss is +#' \eqn{\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})}. +#' @param full (bool, optional): whether to compute full loss, i. e. to add the +#' Stirling approximation term +#' \eqn{\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})}. +#' @param eps (float, optional): Small value to avoid evaluation of \eqn{\log(0)} when +#' `log_input = FALSE`. Default: 1e-8 +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @examples +#' loss <- nn_poisson_nll_loss() +#' log_input <- torch_randn(5, 2, requires_grad=TRUE) +#' target <- torch_randn(5, 2) +#' output <- loss(log_input, target) +#' output$backward() +#' +#' @section Shape: +#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional +#' dimensions +#' - Target: \eqn{(N, *)}, same shape as the input +#' - Output: scalar by default. If `reduction` is `'none'`, then \eqn{(N, *)}, +#' the same shape as the input +#' +#' @export +nn_poisson_nll_loss <- nn_module( + "nn_poisson_nll_loss", + inherit = nn_loss, + initialize = function(log_input = TRUE, full = FALSE, eps = 1e-8, + reduction = 'mean') { + super$initialize(reduction = reduction) + self$log_input <- log_input + self$full <- full + self$eps <- eps + }, + forward = function(log_input, target) { + nnf_poisson_nll_loss(log_input, target, log_input = self$log_input, + full = self$full, eps = self$eps, + reduction = self$reduction) + } +) + +#' Kullback-Leibler divergence loss +#' +#' The Kullback-Leibler divergence loss measure +#' [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback-Leibler_divergence) +#' is a useful distance measure for continuous distributions and is often +#' useful when performing direct regression over the space of (discretely sampled) +#' continuous output distributions. +#' +#' As with [nn_nll_loss()], the `input` given is expected to contain +#' *log-probabilities* and is not restricted to a 2D Tensor. +#' +#' The targets are interpreted as *probabilities* by default, but could be considered +#' as *log-probabilities* with `log_target` set to `TRUE`. +#' +#' This criterion expects a `target` `Tensor` of the same size as the +#' `input` `Tensor`. +#' +#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described +#' as: +#' +#' \deqn{ +#' l(x,y) = L = \{ l_1,\dots,l_N \}, \quad +#' l_n = y_n \cdot \left( \log y_n - x_n \right) +#' } +#' +#' where the index \eqn{N} spans all dimensions of `input` and \eqn{L} has the same +#' shape as `input`. If `reduction` is not `'none'` (default `'mean'`), then: +#' +#' \deqn{ +#' \ell(x, y) = \begin{array}{ll} +#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';} \\ +#' \mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +#' \end{array} +#' } +#' +#' In default `reduction` mode `'mean'`, the losses are averaged for each minibatch +#' over observations **as well as** over dimensions. `'batchmean'` mode gives the +#' correct KL divergence where losses are averaged over batch dimension only. +#' `'mean'` mode's behavior will be changed to the same as `'batchmean'` in the next +#' major release. +#' +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'batchmean'` | `'sum'` | `'mean'`. +#' `'none'`: no reduction will be applied. +#' `'batchmean'`: the sum of the output will be divided by batchsize. +#' `'sum'`: the output will be summed. +#' `'mean'`: the output will be divided by the number of elements in the output. +#' Default: `'mean'` +#' +#' @note +#' `reduction` = `'mean'` doesn't return the true kl divergence value, +#' please use `reduction` = `'batchmean'` which aligns with KL math +#' definition. +#' In the next major release, `'mean'` will be changed to be the same as +#' `'batchmean'`. +#' +#' @section Shape: +#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional +#' dimensions +#' - Target: \eqn{(N, *)}, same shape as the input +#' - Output: scalar by default. If `reduction` is `'none'`, then \eqn{(N, *)}, +#' the same shape as the input +#' +#' @export +nn_kl_div_loss <- nn_module( + "nn_kl_div_loss", + inherit = nn_loss, + initialize = function(reduction = 'mean') { + super$initialize(reduction = reduction) + }, + forward = function(input, target) { + nnf_kl_div(input, target, reduction=self$reduction) + } +) + +#' MSE loss +#' +#' Creates a criterion that measures the mean squared error (squared L2 norm) between +#' each element in the input \eqn{x} and target \eqn{y}. +#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described +#' as: +#' +#' \deqn{ +#' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad +#' l_n = \left( x_n - y_n \right)^2, +#' } +#' +#' where \eqn{N} is the batch size. If `reduction` is not `'none'` +#' (default `'mean'`), then: +#' +#' \deqn{ +#' \ell(x, y) = +#' \begin{array}{ll} +#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ +#' \mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +#' \end{array} +#' } +#' +#' \eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total +#' of \eqn{n} elements each. +#' +#' The mean operation still operates over all the elements, and divides by \eqn{n}. +#' The division by \eqn{n} can be avoided if one sets `reduction = 'sum'`. +#' +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional +#' dimensions +#' - Target: \eqn{(N, *)}, same shape as the input +#' +#' @examples +#' loss <- nn_mse_loss() +#' input <- torch_randn(3, 5, requires_grad=TRUE) +#' target <- torch_randn(3, 5) +#' output <- loss(input, target) +#' output$backward() +#' +#' @export +nn_mse_loss <- nn_module( + "nn_mse_loss", + inherit = nn_loss, + initialize = function(reduction = 'mean') { + super$initialize(reduction = reduction) + }, + forward = function(input, target) { + nnf_mse_loss(input, target, reduction=self$reduction) + } +) + #' Binary cross entropy loss #' #' Creates a criterion that measures the Binary Cross Entropy @@ -27,7 +398,7 @@ nn_weighted_loss <- nn_module( #' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad #' l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right] #' } -#' where \eqn{N} is the batch size. If `reduction` is not ``'none'`` +#' where \eqn{N} is the batch size. If `reduction` is not `'none'` #' (default `'mean'`), then #' #' \deqn{ @@ -62,17 +433,17 @@ nn_weighted_loss <- nn_module( #' @param weight (Tensor, optional): a manual rescaling weight given to the loss #' of each batch element. If given, has to be a Tensor of size `nbatch`. #' @param reduction (string, optional): Specifies the reduction to apply to the output: -#' ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, -#' ``'mean'``: the sum of the output will be divided by the number of -#' elements in the output, ``'sum'``: the output will be summed. Note: `size_average` +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` #' and `reduce` are in the process of being deprecated, and in the meantime, -#' specifying either of those two args will override `reduction`. Default: ``'mean'`` +#' specifying either of those two args will override `reduction`. Default: `'mean'` #' #' @section Shape: #' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional #' dimensions #' - Target: \eqn{(N, *)}, same shape as the input -#' - Output: scalar. If `reduction` is ``'none'``, then \eqn{(N, *)}, same +#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N, *)}, same #' shape as input. #' #' @examples @@ -95,6 +466,296 @@ nn_bce_loss <- nn_module( } ) +#' BCE with logits loss +#' +#' This loss combines a `Sigmoid` layer and the `BCELoss` in one single +#' class. This version is more numerically stable than using a plain `Sigmoid` +#' followed by a `BCELoss` as, by combining the operations into one layer, +#' we take advantage of the log-sum-exp trick for numerical stability. +#' +#' The unreduced (i.e. with `reduction` set to `'none'`) loss can be described as: +#' +#' \deqn{ +#' \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad +#' l_n = - w_n \left[ y_n \cdot \log \sigma(x_n) +#' + (1 - y_n) \cdot \log (1 - \sigma(x_n)) \right], +#' } +#' +#' where \eqn{N} is the batch size. If `reduction` is not `'none'` +#' (default `'mean'`), then +#' +#' \deqn{ +#' \ell(x, y) = \begin{array}{ll} +#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ +#' \mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +#' \end{array} +#' } +#' +#' This is used for measuring the error of a reconstruction in for example +#' an auto-encoder. Note that the targets `t[i]` should be numbers +#' between 0 and 1. +#' It's possible to trade off recall and precision by adding weights to positive examples. +#' In the case of multi-label classification the loss can be described as: +#' +#' \deqn{ +#' \ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad +#' l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log \sigma(x_{n,c}) +#' + (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c})) \right], +#' } +#' where \eqn{c} is the class number (\eqn{c > 1} for multi-label binary +#' classification, +#' +#' \eqn{c = 1} for single-label binary classification), +#' \eqn{n} is the number of the sample in the batch and +#' \eqn{p_c} is the weight of the positive answer for the class \eqn{c}. +#' \eqn{p_c > 1} increases the recall, \eqn{p_c < 1} increases the precision. +#' For example, if a dataset contains 100 positive and 300 negative examples of a single class, +#' then `pos_weight` for the class should be equal to \eqn{\frac{300}{100}=3}. +#' The loss would act as if the dataset contains \eqn{3\times 100=300} positive examples. +#' +#' @param weight (Tensor, optional): a manual rescaling weight given to the loss +#' of each batch element. If given, has to be a Tensor of size `nbatch`. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' @param pos_weight (Tensor, optional): a weight of positive examples. +#' Must be a vector with length equal to the number of classes. +#' +#' @section Shape: +#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional dimensions +#' - Target: \eqn{(N, *)}, same shape as the input +#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N, *)}, same +#' shape as input. +#' +#' @examples +#' loss <- nn_bce_with_logits_loss() +#' input <- torch_randn(3, requires_grad=TRUE) +#' target <- torch_empty(3)$random_(1, 2) +#' output <- loss(input, target) +#' output$backward() +#' +#' target <- torch_ones(10, 64, dtype=torch_float32()) # 64 classes, batch size = 10 +#' output <- torch_full(c(10, 64), 1.5) # A prediction (logit) +#' pos_weight <- torch_ones(64) # All weights are equal to 1 +#' criterion <- nn_bce_with_logits_loss(pos_weight=pos_weight) +#' criterion(output, target) # -log(sigmoid(1.5)) +#' +#' @export +nn_bce_with_logits_loss <- nn_module( + "nn_bce_with_logits_loss", + inherit = nn_loss, + initialize = function(weight = NULL, reduction = 'mean', pos_weight = NULL) { + super$initialize(reduction = reduction) + self$register_buffer('weight', weight) + self$register_buffer('pos_weight', pos_weight) + }, + forward = function(input, target) { + nnf_binary_cross_entropy_with_logits( + input, target, self$weight, + pos_weight=self$pos_weight, + reduction=self$reduction) + } +) + +#' Hinge embedding loss +#' +#' Measures the loss given an input tensor \eqn{x} and a labels tensor \eqn{y} +#' (containing 1 or -1). +#' +#' This is usually used for measuring whether two inputs are similar or +#' dissimilar, e.g. using the L1 pairwise distance as \eqn{x}, and is typically +#' used for learning nonlinear embeddings or semi-supervised learning. +#' The loss function for \eqn{n}-th sample in the mini-batch is +#' +#' \deqn{ +#' l_n = \begin{array}{ll} +#' x_n, & \mbox{if}\; y_n = 1,\\ +#' \max \{0, \Delta - x_n\}, & \mbox{if}\; y_n = -1, +#' \end{array} +#' } +#' +#' and the total loss functions is +#' +#' \deqn{ +#' \ell(x, y) = \begin{array}{ll} +#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ +#' \mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} +#' \end{array} +#' } +#' +#' where \eqn{L = \{l_1,\dots,l_N\}^\top}. +#' +#' @param margin (float, optional): Has a default value of `1`. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(*)} where \eqn{*} means, any number of dimensions. The sum operation +#' operates over all the elements. +#' - Target: \eqn{(*)}, same shape as the input +#' - Output: scalar. If `reduction` is `'none'`, then same shape as the input +#' +#' @export +nn_hinge_embedding_loss <- nn_module( + "nn_hinge_embedding_loss", + inherit = nn_loss, + initialize = function(margin = 1.0, reduction = 'mean') { + super$initialize(reduction = reduction) + self$margin <- margin + }, + forward = function(input, target) { + nnf_hinge_embedding_loss(input, target, margin=self$margin, + reduction=self$reduction) + } +) + +#' Multilabel margin loss +#' +#' Creates a criterion that optimizes a multi-class multi-classification +#' hinge loss (margin-based loss) between input \eqn{x} (a 2D mini-batch `Tensor`) +#' and output \eqn{y} (which is a 2D `Tensor` of target class indices). +#' For each sample in the mini-batch: +#' +#' \deqn{ +#' \mbox{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\mbox{x.size}(0)} +#' } +#' +#' where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}}, \ +#' \eqn{y \in \left\{0, \; \cdots , \; \mbox{y.size}(0) - 1\right\}}, \ +#' \eqn{0 \leq y[j] \leq \mbox{x.size}(0)-1}, \ +#' and \eqn{i \neq y[j]} for all \eqn{i} and \eqn{j}. +#' \eqn{y} and \eqn{x} must have the same size. +#' +#' The criterion only considers a contiguous block of non-negative targets that +#' starts at the front. +#' This allows for different samples to have variable amounts of target classes. +#' +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(C)} or \eqn{(N, C)} where `N` is the batch size and `C` +#' is the number of classes. +#' - Target: \eqn{(C)} or \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input. +#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N)}. +#' +#' @examples +#' loss <- nn_multilabel_margin_loss() +#' x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1,4)) +#' # for target y, only consider labels 4 and 1, not after label -1 +#' y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1,4)) +#' loss(x, y) +#' +#' @export +nn_multilabel_margin_loss <- nn_module( + "nn_multilabel_margin_loss", + inherit = nn_loss, + initialize = function(reduction = "mean") { + super$initialize(reduction = reduction) + }, + forward = function(input, target) { + nnf_multilabel_margin_loss(input, target, reduction = self$reduction) + } +) + +#' Smooth L1 loss +#' +#' Creates a criterion that uses a squared term if the absolute +#' element-wise error falls below 1 and an L1 term otherwise. +#' It is less sensitive to outliers than the `MSELoss` and in some cases +#' prevents exploding gradients (e.g. see `Fast R-CNN` paper by Ross Girshick). +#' Also known as the Huber loss: +#' +#' \deqn{ +#' \mbox{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i} +#' } +#' +#' where \eqn{z_{i}} is given by: +#' +#' \deqn{ +#' z_{i} = +#' \begin{array}{ll} +#' 0.5 (x_i - y_i)^2, & \mbox{if } |x_i - y_i| < 1 \\ +#' |x_i - y_i| - 0.5, & \mbox{otherwise } +#' \end{array} +#' } +#' +#' \eqn{x} and \eqn{y} arbitrary shapes with a total of \eqn{n} elements each +#' the sum operation still operates over all the elements, and divides by \eqn{n}. +#' The division by \eqn{n} can be avoided if sets `reduction = 'sum'`. +#' +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(N, *)} where \eqn{*} means, any number of additional +#' dimensions +#' - Target: \eqn{(N, *)}, same shape as the input +#' - Output: scalar. If `reduction` is `'none'`, then +#' \eqn{(N, *)}, same shape as the input +#' +#' @export +nn_smooth_l1_loss <- nn_module( + "nn_smooth_l1_loss", + inherit = nn_loss, + initialize = function(reduction = "mean") { + super$initialize(reduction = reduction) + }, + forward = function(input, target) { + nnf_smooth_l1_loss(input, target, reduction=self$reduction) + } +) + +#' Soft margin loss +#' +#' Creates a criterion that optimizes a two-class classification +#' logistic loss between input tensor \eqn{x} and target tensor \eqn{y} +#' (containing 1 or -1). +#' +#' \deqn{ +#' \mbox{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\mbox{x.nelement}()} +#' } +#' +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(*)} where \eqn{*} means, any number of additional +#' dimensions +#' - Target: \eqn{(*)}, same shape as the input +#' - Output: scalar. If `reduction` is `'none'`, then same shape as the input +#' +#' @export +nn_soft_margin_loss <- nn_module( + "nn_soft_margin_loss", + inherit = nn_loss, + initialize = function(reduction = "mean") { + super$initialize(reduction = reduction) + }, + forward = function(input, target) { + nnf_soft_margin_loss(input, target, reduction = self$reduction) + } +) + #' CrossEntropyLoss module #' #' This criterion combines [nn_log_softmax()] and `nn_nll_loss()` in one single class. @@ -175,3 +836,514 @@ nn_cross_entropy_loss <- nn_module( ignore_index = self$ignore_index, reduction = self$reduction) } ) + +#' Multi label soft margin loss +#' +#' Creates a criterion that optimizes a multi-label one-versus-all +#' loss based on max-entropy, between input \eqn{x} and target \eqn{y} of size +#' \eqn{(N, C)}. +#' +#' For each sample in the minibatch: +#' +#' \deqn{ +#' loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1}) +#' + (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right) +#' } +#' +#' where \eqn{i \in \left\{0, \; \cdots , \; \mbox{x.nElement}() - 1\right\}}, +#' \eqn{y[i] \in \left\{0, \; 1\right\}}. +#' +#' @param weight (Tensor, optional): a manual rescaling weight given to each +#' class. If given, it has to be a Tensor of size `C`. Otherwise, it is +#' treated as if having all ones. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(N, C)} where `N` is the batch size and `C` is the number of classes. +#' - Target: \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input. +#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N)}. +#' +#' @export +nn_multilabel_soft_margin_loss <- nn_module( + "nn_multilabel_soft_margin_loss", + inherit = nn_weighted_loss, + initialize = function(weight = NULL, reduction = "mean") { + super$initialize(weight = weight, reduction = reduction) + }, + forward = function(input, target) { + nnf_multilabel_soft_margin_loss(input, target, weight=self$weight, + reduction=self$reduction) + } +) + +#' Cosine embedding loss +#' +#' Creates a criterion that measures the loss given input tensors +#' \eqn{x_1}, \eqn{x_2} and a `Tensor` label \eqn{y} with values 1 or -1. +#' This is used for measuring whether two inputs are similar or dissimilar, +#' using the cosine distance, and is typically used for learning nonlinear +#' embeddings or semi-supervised learning. +#' The loss function for each sample is: +#' +#' \deqn{ +#' \mbox{loss}(x, y) = +#' \begin{array}{ll} +#' 1 - \cos(x_1, x_2), & \mbox{if } y = 1 \\ +#' \max(0, \cos(x_1, x_2) - \mbox{margin}), & \mbox{if } y = -1 +#' \end{array} +#' } +#' +#' @param margin (float, optional): Should be a number from \eqn{-1} to \eqn{1}, +#' \eqn{0} to \eqn{0.5} is suggested. If `margin` is missing, the +#' default value is \eqn{0}. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @export +nn_cosine_embedding_loss <- nn_module( + "nn_cosine_embedding_loss", + inherit = nn_loss, + initialize = function(margin = 0, reduction = "mean") { + super$initialize(reduction = reduction) + self$margin <- margin + }, + forward = function(input1, input2, target) { + nnf_cosine_embedding_loss(input1, input2, target, margin = self$margin, + reduction = self$reduction) + } +) + +#' Margin ranking loss +#' +#' Creates a criterion that measures the loss given +#' inputs \eqn{x1}, \eqn{x2}, two 1D mini-batch `Tensors`, +#' and a label 1D mini-batch tensor \eqn{y} (containing 1 or -1). +#' If \eqn{y = 1} then it assumed the first input should be ranked higher +#' (have a larger value) than the second input, and vice-versa for \eqn{y = -1}. +#' +#' The loss function for each pair of samples in the mini-batch is: +#' +#' \deqn{ +#' \mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{margin}) +#' } +#' +#' +#' @param margin (float, optional): Has a default value of \eqn{0}. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input1: \eqn{(N)} where `N` is the batch size. +#' - Input2: \eqn{(N)}, same shape as the Input1. +#' - Target: \eqn{(N)}, same shape as the inputs. +#' - Output: scalar. If `reduction` is `'none'`, then \eqn{(N)}. +#' +#' @examples +#' loss <- nn_margin_ranking_loss() +#' input1 <- torch_randn(3, requires_grad=TRUE) +#' input2 <- torch_randn(3, requires_grad=TRUE) +#' target <- torch_randn(3)$sign() +#' output <- loss(input1, input2, target) +#' output$backward() +#' +#' @export +nn_margin_ranking_loss <- nn_module( + "nn_margin_ranking_loss", + inherit = nn_loss, + initialize = function(margin = 0, reduction = "mean") { + super$initialize(reduction = reduction) + self$margin <- margin + }, + forward = function(input1, input2, target){ + nnf_margin_ranking_loss(input1, input2, target, margin = self$margin, + reduction = self$reduction) + } +) + +#' Multi margin loss +#' +#' Creates a criterion that optimizes a multi-class classification hinge +#' loss (margin-based loss) between input \eqn{x} (a 2D mini-batch `Tensor`) and +#' output \eqn{y} (which is a 1D tensor of target class indices, +#' \eqn{0 \leq y \leq \mbox{x.size}(1)-1}): +#' +#' For each mini-batch sample, the loss in terms of the 1D input \eqn{x} and scalar +#' output \eqn{y} is: +#' \deqn{ +#' \mbox{loss}(x, y) = \frac{\sum_i \max(0, \mbox{margin} - x[y] + x[i]))^p}{\mbox{x.size}(0)} +#' } +#' +#' where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}} +#' and \eqn{i \neq y}. +#' +#' Optionally, you can give non-equal weighting on the classes by passing +#' a 1D `weight` tensor into the constructor. +#' The loss function then becomes: +#' +#' \deqn{ +#' \mbox{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\mbox{margin} - x[y] + x[i]))^p)}{\mbox{x.size}(0)} +#' } +#' +#' @param p (int, optional): Has a default value of \eqn{1}. \eqn{1} and \eqn{2} +#' are the only supported values. +#' @param margin (float, optional): Has a default value of \eqn{1}. +#' @param weight (Tensor, optional): a manual rescaling weight given to each +#' class. If given, it has to be a Tensor of size `C`. Otherwise, it is +#' treated as if having all ones. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @export +nn_multi_margin_loss <- nn_module( + "nn_multi_margin_loss", + inherit = nn_weighted_loss, + initialize = function(p = 1, margin = 1, weight = NULL, reduction = "mean") { + super$initialize(weight = weight, reduction = reduction) + if (p != 1 && p != 2) { + value_error("only p == 1 or p == 2 are supported.") + } + if (!is.null(weight) && weight$dim() != 1) { + value_error("weight must be NULL or 1-dimensional") + } + self$p <- p + self$margin <- margin + }, + forward = function(input, target) { + nnf_multi_margin_loss(input, target, p = self$p, margin = self$margin, + weight = self$weight, reduction = self$reduction) + } +) + +#' Triplet margin loss +#' +#' Creates a criterion that measures the triplet loss given an input +#' tensors \eqn{x1}, \eqn{x2}, \eqn{x3} and a margin with a value greater than \eqn{0}. +#' This is used for measuring a relative similarity between samples. A triplet +#' is composed by `a`, `p` and `n` (i.e., `anchor`, `positive examples` and `negative +#' examples` respectively). The shapes of all input tensors should be +#' \eqn{(N, D)}. +#' +#' The distance swap is described in detail in the paper +#' [Learning shallow convolutional feature descriptors with triplet losses](http://www.bmva.org/bmvc/2016/papers/paper119/index.html) by +#' V. Balntas, E. Riba et al. +#' +#' The loss function for each sample in the mini-batch is: +#' +#' \deqn{ +#' L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} +#' } +#' +#' where +#' +#' \deqn{ +#' d(x_i, y_i) = | {\bf x}_i - {\bf y}_i |_p +#' } +#' +#' See also [nn_triplet_margin_with_distance_loss()], which computes the +#' triplet margin loss for input tensors using a custom distance function. +#' +#' @param margin (float, optional): Default: \eqn{1}. +#' @param p (int, optional): The norm degree for pairwise distance. Default: \eqn{2}. +#' @param swap (bool, optional): The distance swap is described in detail in the paper +#' [Learning shallow convolutional feature descriptors with triplet losses](http://www.bmva.org/bmvc/2016/papers/paper119/index.html) by +#' V. Balntas, E. Riba et al. Default: `FALSE`. +#' @param eps constant to avoid NaN's +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Note: `size_average` +#' and `reduce` are in the process of being deprecated, and in the meantime, +#' specifying either of those two args will override `reduction`. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(N, D)} where \eqn{D} is the vector dimension. +#' - Output: A Tensor of shape \eqn{(N)} if `reduction` is `'none'`, or a scalar +#' otherwise. +#' +#' @examples +#' triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2) +#' anchor <- torch_randn(100, 128, requires_grad=TRUE) +#' positive <- torch_randn(100, 128, requires_grad=TRUE) +#' negative <- torch_randn(100, 128, requires_grad=TRUE) +#' output <- triplet_loss(anchor, positive, negative) +#' output$backward() +#' +#' @export +nn_triplet_margin_loss <- nn_module( + "nn_triplet_margin_loss", + inherit = nn_loss, + initialize = function(margin = 1, p = 2, eps = 1e-6, swap = FALSE, + reduction = "mean") { + super$initialize(reduction = reduction) + self$margin <- margin + self$p <- p + self$eps <- eps + self$swap <- swap + }, + forward = function(anchor, positive, negative) { + nnf_triplet_margin_loss(anchor, positive, negative, margin = self$margin, + p = self$p, eps = self$eps, swap = self$swap) + } +) + +#' Triplet margin with distance loss +#' +#' Creates a criterion that measures the triplet loss given input +#' tensors \eqn{a}, \eqn{p}, and \eqn{n} (representing anchor, +#' positive, and negative examples, respectively), and a nonnegative, +#' real-valued function ("distance function") used to compute the relationship +#' between the anchor and positive example ("positive distance") and the +#' anchor and negative example ("negative distance"). +#' +#' The unreduced loss (i.e., with `reduction` set to `'none'`) +#' can be described as: +#' +#' \deqn{ +#' \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad +#' l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} +#' } +#' +#' where \eqn{N} is the batch size; \eqn{d} is a nonnegative, real-valued function +#' quantifying the closeness of two tensors, referred to as the `distance_function`; +#' and \eqn{margin} is a non-negative margin representing the minimum difference +#' between the positive and negative distances that is required for the loss to +#' be 0. The input tensors have \eqn{N} elements each and can be of any shape +#' that the distance function can handle. +#' If `reduction` is not `'none'` +#' (default `'mean'`), then: +#' +#' \deqn{ +#' \ell(x, y) = +#' \begin{array}{ll} +#' \mbox{mean}(L), & \mbox{if reduction} = \mbox{`mean';}\\ +#' \mbox{sum}(L), & \mbox{if reduction} = \mbox{`sum'.} +#' \end{array} +#' } +#' +#' See also [nn_triplet_margin_loss()], which computes the triplet +#' loss for input tensors using the \eqn{l_p} distance as the distance function. +#' +#' @param distance_function (callable, optional): A nonnegative, real-valued function that +#' quantifies the closeness of two tensors. If not specified, +#' [nn_pairwise_distance()] will be used. Default: `None` +#' @param margin (float, optional): A non-negative margin representing the minimum difference +#' between the positive and negative distances required for the loss to be 0. Larger +#' margins penalize cases where the negative examples are not distant enough from the +#' anchors, relative to the positives. Default: \eqn{1}. +#' @param swap (bool, optional): Whether to use the distance swap described in the paper +#' [Learning shallow convolutional feature descriptors with triplet losses](http://www.bmva.org/bmvc/2016/papers/paper119/index.html) by +#' V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the +#' negative example than the anchor is, swaps the positive example and the anchor in +#' the loss computation. Default: `FALSE`. +#' @param reduction (string, optional): Specifies the (optional) reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the sum of the output will be divided by the number of +#' elements in the output, `'sum'`: the output will be summed. Default: `'mean'` +#' +#' @section Shape: +#' - Input: \eqn{(N, *)} where \eqn{*} represents any number of additional dimensions +#' as supported by the distance function. +#' - Output: A Tensor of shape \eqn{(N)} if `reduction` is `'none'`, or a scalar +#' otherwise. +#' +#' @examples +#' # Initialize embeddings +#' embedding <- nn_embedding(1000, 128) +#' anchor_ids <- torch_randint(0, 1000, 1, dtype = torch_long()) +#' positive_ids <- torch_randint(0, 1000, 1, dtype = torch_long()) +#' negative_ids <- torch_randint(0, 1000, 1, dtype = torch_long()) +#' anchor <- embedding(anchor_ids) +#' positive <- embedding(positive_ids) +#' negative <- embedding(negative_ids) +#' +#' # Built-in Distance Function +#' triplet_loss <- nn_triplet_margin_with_distance_loss( +#' distance_function=nn_pairwise_distance() +#' ) +#' output <- triplet_loss(anchor, positive, negative) +#' +#' # Custom Distance Function +#' l_infinity <- function(x1, x2) { +#' torch_max(torch_abs(x1 - x2), dim = 1)[[1]] +#' } +#' +#' triplet_loss <- nn_triplet_margin_with_distance_loss( +#' distance_function=l_infinity, margin=1.5 +#' ) +#' output <- triplet_loss(anchor, positive, negative) +#' +#' # Custom Distance Function (Lambda) +#' triplet_loss <- nn_triplet_margin_with_distance_loss( +#' distance_function = function(x, y) { +#' 1 - nnf_cosine_similarity(x, y) +#' } +#' ) +#' +#' output <- triplet_loss(anchor, positive, negative) +#' +#' @export +nn_triplet_margin_with_distance_loss <- nn_module( + "nn_triplet_margin_with_distance_loss", + inherit = nn_loss, + initialize = function(distance_function = NULL, margin = 1, swap = FALSE, + reduction = "mean") { + super$initialize(reduction = reduction) + if (is.null(distance_function)) { + self$distance_function <- nn_pairwise_distance() + } else { + self$distance_function <- distance_function + } + self$margin <- margin + self$swap <- swap + }, + forward = function(anchor, positive, negative) { + nnf_triplet_margin_with_distance_loss( + anchor, positive, negative, + distance_function=self$distance_function, + margin=self$margin, swap=self$swap, reduction=self$reduction + ) + } +) + +#' The Connectionist Temporal Classification loss. +#' +#' Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the +#' probability of possible alignments of input to target, producing a loss value which is differentiable +#' with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which +#' limits the length of the target sequence such that it must be \eqn{\leq} the input length. +#' +#' +#' @param blank (int, optional): blank label. Default \eqn{0}. +#' @param reduction (string, optional): Specifies the reduction to apply to the output: +#' `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, +#' `'mean'`: the output losses will be divided by the target lengths and +#' then the mean over the batch is taken. Default: `'mean'` +#' @param zero_infinity (bool, optional): +#' Whether to zero infinite losses and the associated gradients. +#' Default: `FALSE` +#' Infinite losses mainly occur when the inputs are too short +#' to be aligned to the targets. +#' +#' @section Shape: +#' - Log_probs: Tensor of size \eqn{(T, N, C)}, +#' where \eqn{T = \mbox{input length}}, +#' \eqn{N = \mbox{batch size}}, and +#' \eqn{C = \mbox{number of classes (including blank)}}. +#' The logarithmized probabilities of the outputs (e.g. obtained with +#' [nnf)log_softmax()]). +#' - Targets: Tensor of size \eqn{(N, S)} or +#' \eqn{(\mbox{sum}(\mbox{target\_lengths}))}, +#' where \eqn{N = \mbox{batch size}} and +#' \eqn{S = \mbox{max target length, if shape is } (N, S)}. +#' It represent the target sequences. Each element in the target +#' sequence is a class index. And the target index cannot be blank (default=0). +#' In the \eqn{(N, S)} form, targets are padded to the +#' length of the longest sequence, and stacked. +#' In the \eqn{(\mbox{sum}(\mbox{target\_lengths}))} form, +#' the targets are assumed to be un-padded and +#' concatenated within 1 dimension. +#' - Input_lengths: Tuple or tensor of size \eqn{(N)}, +#' where \eqn{N = \mbox{batch size}}. It represent the lengths of the +#' inputs (must each be \eqn{\leq T}). And the lengths are specified +#' for each sequence to achieve masking under the assumption that sequences +#' are padded to equal lengths. +#' - Target_lengths: Tuple or tensor of size \eqn{(N)}, +#' where \eqn{N = \mbox{batch size}}. It represent lengths of the targets. +#' Lengths are specified for each sequence to achieve masking under the +#' assumption that sequences are padded to equal lengths. If target shape is +#' \eqn{(N,S)}, target_lengths are effectively the stop index +#' \eqn{s_n} for each target sequence, such that `target_n = targets[n,0:s_n]` for +#' each target in a batch. Lengths must each be \eqn{\leq S} +#' If the targets are given as a 1d tensor that is the concatenation of individual +#' targets, the target_lengths must add up to the total length of the tensor. +#' - Output: scalar. If `reduction` is `'none'`, then +#' \eqn{(N)}, where \eqn{N = \mbox{batch size}}. +#' +#' @examples +#' # Target are to be padded +#' T <- 50 # Input sequence length +#' C <- 20 # Number of classes (including blank) +#' N <- 16 # Batch size +#' S <- 30 # Target sequence length of longest target in batch (padding length) +#' S_min <- 10 # Minimum target length, for demonstration purposes +#' +#' # Initialize random batch of input vectors, for *size = (T,N,C) +#' input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_() +#' +#' # Initialize random batch of targets (0 = blank, 1:C = classes) +#' target <- torch_randint(low=1, high=C, size=c(N, S), dtype=torch_long()) +#' +#' input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long()) +#' target_lengths <- torch_randint(low=S_min, high=S, size=c(N), dtype=torch_long()) +#' ctc_loss <- nn_ctc_loss() +#' loss <- ctc_loss(input, target, input_lengths, target_lengths) +#' loss$backward() +#' +#' +#' # Target are to be un-padded +#' T <- 50 # Input sequence length +#' C <- 20 # Number of classes (including blank) +#' N <- 16 # Batch size +#' +#' # Initialize random batch of input vectors, for *size = (T,N,C) +#' input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_() +#' input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long()) +#' +#' # Initialize random batch of targets (0 = blank, 1:C = classes) +#' target_lengths <- torch_randint(low=1, high=T, size=c(N), dtype=torch_long()) +#' target <- torch_randint(low=1, high=C, size=as.integer(sum(target_lengths)), dtype=torch_long()) +#' ctc_loss <- nn_ctc_loss() +#' loss <- ctc_loss(input, target, input_lengths, target_lengths) +#' loss$backward() +#' +#' @references +#' A. Graves et al.: Connectionist Temporal Classification: +#' Labelling Unsegmented Sequence Data with Recurrent Neural Networks: +#' https://www.cs.toronto.edu/~graves/icml_2006.pdf +#' +#' @note +#' In order to use CuDNN, the following must be satisfied: `targets` must be +#' in concatenated format, all `input_lengths` must be `T`. \eqn{blank=0}, +#' `target_lengths` \eqn{\leq 256}, the integer arguments must be of +#' The regular implementation uses the (more common in PyTorch) `torch_long` dtype. +#' dtype `torch_int32`. +#' +#' @note +#' In some circumstances when using the CUDA backend with CuDNN, this operator +#' may select a nondeterministic algorithm to increase performance. If this is +#' undesirable, you can try to make the operation deterministic (potentially at +#' a performance cost) by setting `torch.backends.cudnn.deterministic = TRUE`. +#' +#' @export +nn_ctc_loss <- nn_module( + "nn_ctc_loss", + inherit = nn_loss, + initialize = function(blank = 0, reduction = 'mean', zero_infinity = FALSE) { + super$initialize(reduction = reduction) + self$blank <- blank + self$zero_infinity <- zero_infinity + }, + forward = function(log_probs, targets, input_lengths, target_lengths) { + nnf_ctc_loss( + log_probs, targets, input_lengths, target_lengths, blank = self$blank, + reduction = self$reduction, zero_infinity = self$zero_infinity + ) + } +) \ No newline at end of file diff --git a/R/nn-pooling.R b/R/nn-pooling.R index c2e68370773d0edfc0efebd9386d61e3f103fde2..cf910c6d36e7fb521e1e3c1d93456f80a1dc5df8 100644 --- a/R/nn-pooling.R +++ b/R/nn-pooling.R @@ -81,7 +81,7 @@ nn_max_pool1d <- nn_module( #' can be precisely described as: #' #' \deqn{ -#' \begin{array}{ll} +#' \begin{array}{ll} #' out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ #' & \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m, #' \mbox{stride[1]} \times w + n) @@ -139,3 +139,923 @@ nn_max_pool2d <- nn_module( } ) +#' Applies a 3D max pooling over an input signal composed of several input +#' planes. +#' +#' In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)}, +#' output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and `kernel_size` \eqn{(kD, kH, kW)} +#' can be precisely described as: +#' +#' \deqn{ +#' \begin{array}{ll} +#' \mbox{out}(N_i, C_j, d, h, w) = & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ +#' & \mbox{input}(N_i, C_j, \mbox{stride[0]} \times d + k, \mbox{stride[1]} \times h + m, \mbox{stride[2]} \times w + n) +#' \end{array} +#' } +#' +#' If `padding` is non-zero, then the input is implicitly zero-padded on both sides +#' for `padding` number of points. `dilation` controls the spacing between the kernel points. +#' It is harder to describe, but this `link`_ has a nice visualization of what `dilation` does. +#' The parameters `kernel_size`, `stride`, `padding`, `dilation` can either be: +#' - a single `int` -- in which case the same value is used for the depth, height and width dimension +#' - a `tuple` of three ints -- in which case, the first `int` is used for the depth dimension, +#' the second `int` for the height dimension and the third `int` for the width dimension +#' +#' @param kernel_size the size of the window to take a max over +#' @param stride the stride of the window. Default value is `kernel_size` +#' @param padding implicit zero padding to be added on all three sides +#' @param dilation a parameter that controls the stride of elements in the window +#' @param return_indices if `TRUE`, will return the max indices along with the outputs. +#' Useful for `torch_nn.MaxUnpool3d` later +#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape +#' +#' @section Shape: +#' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})} +#' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where +#' \deqn{ +#' D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times +#' (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor +#' } +#' +#' \deqn{ +#' H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times +#' (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor +#' } +#' +#' \deqn{ +#' W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times +#' (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor +#' } +#' +#' @examples +#' # pool of square window of size=3, stride=2 +#' m <- nn_max_pool3d(3, stride=2) +#' # pool of non-square window +#' m <- nn_max_pool3d(c(3, 2, 2), stride=c(2, 1, 2)) +#' input <- torch_randn(20, 16, 50,44, 31) +#' output <- m(input) +#' +#' @export +nn_max_pool3d <- nn_module( + "nn_max_pool3d", + inherit = nn_max_pool_nd, + forward = function(input) { + nnf_max_pool3d(input, self$kernel_size, self$stride, + self$padding, self$dilation, self$ceil_mode, + self$return_indices) + } +) + +#' Computes a partial inverse of `MaxPool1d`. +#' +#' `MaxPool1d` is not fully invertible, since the non-maximal values are lost. +#' `MaxUnpool1d` takes in as input the output of `MaxPool1d` +#' including the indices of the maximal values and computes a partial inverse +#' in which all non-maximal values are set to zero. +#' +#' @note `MaxPool1d` can map several input sizes to the same output +#' sizes. Hence, the inversion process can get ambiguous. +#' To accommodate this, you can provide the needed output size +#' as an additional argument `output_size` in the forward call. +#' See the Inputs and Example below. +#' +#' @param kernel_size (int or tuple): Size of the max pooling window. +#' @param stride (int or tuple): Stride of the max pooling window. +#' It is set to `kernel_size` by default. +#' @param padding (int or tuple): Padding that was added to the input +#' +#' @section Inputs: +#' +#' - `input`: the input Tensor to invert +#' - `indices`: the indices given out by [nn_max_pool1d()] +#' - `output_size` (optional): the targeted output size +#' +#' @section Shape: +#' - Input: \eqn{(N, C, H_{in})} +#' - Output: \eqn{(N, C, H_{out})}, where +#' \deqn{ +#' H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0] +#' } +#' or as given by `output_size` in the call operator +#' +#' @examples +#' pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE) +#' unpool <- nn_max_unpool1d(2, stride=2) +#' +#' input <- torch_tensor(array(1:8/1, dim = c(1,1,8))) +#' out <- pool(input) +#' unpool(out[[1]], out[[2]]) +#' +#' # Example showcasing the use of output_size +#' input <- torch_tensor(array(1:8/1, dim = c(1,1,8))) +#' out <- pool(input) +#' unpool(out[[1]], out[[2]], output_size=input$size()) +#' unpool(out[[1]], out[[2]]) +#' +#' @export +nn_max_unpool1d <- nn_module( + "nn_max_unpool1d", + initialize = function(kernel_size, stride = NULL, padding = 0) { + self$kernel_size = nn_util_single(kernel_size) + + if (is.null(stride)) + stride <- kernel_size + + self$stride = nn_util_single(stride) + self$padding = nn_util_single(padding) + }, + forward = function(input, indices, output_size = NULL) { + nnf_max_unpool1d(input, indices, self$kernel_size, self$stride, + self$padding, output_size) + } +) + + +#' Computes a partial inverse of `MaxPool2d`. +#' +#' `MaxPool2d` is not fully invertible, since the non-maximal values are lost. +#' `MaxUnpool2d` takes in as input the output of `MaxPool2d` +#' including the indices of the maximal values and computes a partial inverse +#' in which all non-maximal values are set to zero. +#' +#' @note `MaxPool2d` can map several input sizes to the same output +#' sizes. Hence, the inversion process can get ambiguous. +#' To accommodate this, you can provide the needed output size +#' as an additional argument `output_size` in the forward call. +#' See the Inputs and Example below. +#' +#' @param kernel_size (int or tuple): Size of the max pooling window. +#' @param stride (int or tuple): Stride of the max pooling window. +#' It is set to `kernel_size` by default. +#' @param padding (int or tuple): Padding that was added to the input +#' +#' @section Inputs: +#' - `input`: the input Tensor to invert +#' - `indices`: the indices given out by [nn_max_pool2d()] +#' - `output_size` (optional): the targeted output size +#' +#' @section Shape: +#' - Input: \eqn{(N, C, H_{in}, W_{in})} +#' - Output: \eqn{(N, C, H_{out}, W_{out})}, where +#' \deqn{ +#' H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]} +#' } +#' \deqn{ +#' W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]} +#' } +#' or as given by `output_size` in the call operator +#' +#' @examples +#' +#' pool <- nn_max_pool2d(2, stride=2, return_indices=TRUE) +#' unpool <- nn_max_unpool2d(2, stride=2) +#' input <- torch_randn(1,1,4,4) +#' out <- pool(input) +#' unpool(out[[1]], out[[2]]) +#' +#' # specify a different output size than input size +#' unpool(out[[1]], out[[2]], output_size=c(1, 1, 5, 5)) +#' +#' @export +nn_max_unpool2d <- nn_module( + "nn_max_unpool2d", + initialize = function(kernel_size, stride = NULL, padding = 0) { + self$kernel_size = nn_util_pair(kernel_size) + + if (is.null(stride)) + stride <- kernel_size + + self$stride = nn_util_pair(stride) + self$padding = nn_util_pair(padding) + }, + forward = function(input, indices, output_size = NULL) { + nnf_max_unpool2d(input, indices, self$kernel_size, self$stride, + self$padding, output_size) + } +) + + +#' Computes a partial inverse of `MaxPool3d`. +#' +#' `MaxPool3d` is not fully invertible, since the non-maximal values are lost. +#' `MaxUnpool3d` takes in as input the output of `MaxPool3d` +#' including the indices of the maximal values and computes a partial inverse +#' in which all non-maximal values are set to zero. +#' +#' @note `MaxPool3d` can map several input sizes to the same output +#' sizes. Hence, the inversion process can get ambiguous. +#' To accommodate this, you can provide the needed output size +#' as an additional argument `output_size` in the forward call. +#' See the Inputs section below. +#' +#' @param kernel_size (int or tuple): Size of the max pooling window. +#' @param stride (int or tuple): Stride of the max pooling window. +#' It is set to `kernel_size` by default. +#' @param padding (int or tuple): Padding that was added to the input +#' +#' @section Inputs: +#' - `input`: the input Tensor to invert +#' - `indices`: the indices given out by [nn_max_pool3d()] +#' - `output_size` (optional): the targeted output size +#' +#' @section Shape: +#' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})} +#' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where +#' +#' \deqn{ +#' D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]} +#' } +#' \deqn{ +#' H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]} +#' } +#' \deqn{ +#' W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]} +#' } +#' +#' or as given by `output_size` in the call operator +#' +#' @examples +#' +#' # pool of square window of size=3, stride=2 +#' pool <- nn_max_pool3d(3, stride=2, return_indices=TRUE) +#' unpool <- nn_max_unpool3d(3, stride=2) +#' out <- pool(torch_randn(20, 16, 51, 33, 15)) +#' unpooled_output <- unpool(out[[1]], out[[2]]) +#' unpooled_output$size() +#' +#' @export +nn_max_unpool3d <- nn_module( + "nn_max_unpool3d", + initialize = function(kernel_size, stride = NULL, padding = 0) { + self$kernel_size = nn_util_triple(kernel_size) + + if (is.null(stride)) + stride <- kernel_size + + self$stride = nn_util_triple(stride) + self$padding = nn_util_triple(padding) + }, + forward = function(input, indices, output_size = NULL) { + nnf_max_unpool3d(input, indices, self$kernel_size, self$stride, + self$padding, output_size) + } +) + +#' Applies a 1D average pooling over an input signal composed of several +#' input planes. +#' +#' In the simplest case, the output value of the layer with input size \eqn{(N, C, L)}, +#' output \eqn{(N, C, L_{out})} and `kernel_size` \eqn{k} +#' can be precisely described as: +#' +#' \deqn{ +#' \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1} +#' \mbox{input}(N_i, C_j, \mbox{stride} \times l + m) +#' } +#' +#' If `padding` is non-zero, then the input is implicitly zero-padded on both sides +#' for `padding` number of points. +#' +#' The parameters `kernel_size`, `stride`, `padding` can each be +#' an `int` or a one-element tuple. +#' +#' @param kernel_size the size of the window +#' @param stride the stride of the window. Default value is `kernel_size` +#' @param padding implicit zero padding to be added on both sides +#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape +#' @param count_include_pad when TRUE, will include the zero-padding in the averaging calculation +#' +#' @section Shape: +#' - Input: \eqn{(N, C, L_{in})} +#' - Output: \eqn{(N, C, L_{out})}, where +#' +#' \deqn{ +#' L_{out} = \left\lfloor \frac{L_{in} + +#' 2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor +#' } +#' +#' @examples +#' +#' # pool with window of size=3, stride=2 +#' m <- nn_avg_pool1d(3, stride=2) +#' m(torch_randn(1, 1, 8)) +#' +#' @export +nn_avg_pool1d <- nn_module( + "nn_avg_pool1d", + initialize = function(kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE, + count_include_pad= TRUE) { + + self$kernel_size <- nn_util_single(kernel_size) + + if (is.null(stride)) + stride <- kernel_size + + self$stride <- nn_util_single(stride) + + self$padding <- nn_util_single(padding) + self$ceil_mode <- ceil_mode + self$count_include_pad <- count_include_pad + + }, + forward = function(input) { + nnf_avg_pool1d( + input, self$kernel_size, self$stride, self$padding, self$ceil_mode, + self$count_include_pad) + } +) + +#' Applies a 2D average pooling over an input signal composed of several input +#' planes. +#' +#' In the simplest case, the output value of the layer with input size \eqn{(N, C, H, W)}, +#' output \eqn{(N, C, H_{out}, W_{out})} and `kernel_size` \eqn{(kH, kW)} +#' can be precisely described as: +#' +#' \deqn{ +#' out(N_i, C_j, h, w) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} +#' input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n) +#' } +#' +#' If `padding` is non-zero, then the input is implicitly zero-padded on both sides +#' for `padding` number of points. +#' +#' The parameters `kernel_size`, `stride`, `padding` can either be: +#' +#' - a single `int` -- in which case the same value is used for the height and width dimension +#' - a `tuple` of two ints -- in which case, the first `int` is used for the height dimension, +#' and the second `int` for the width dimension +#' +#' +#' @param kernel_size the size of the window +#' @param stride the stride of the window. Default value is `kernel_size` +#' @param padding implicit zero padding to be added on both sides +#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape +#' @param count_include_pad when TRUE, will include the zero-padding in the averaging calculation +#' @param divisor_override if specified, it will be used as divisor, otherwise `kernel_size` will be used +#' +#' @section Shape: +#' - Input: \eqn{(N, C, H_{in}, W_{in})} +#' - Output: \eqn{(N, C, H_{out}, W_{out})}, where +#' +#' \deqn{ +#' H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[0] - +#' \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor +#' } +#' \deqn{ +#' W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[1] - +#' \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor +#' } +#' +#' @examples +#' +#' # pool of square window of size=3, stride=2 +#' m <- nn_avg_pool2d(3, stride=2) +#' # pool of non-square window +#' m <- nn_avg_pool2d(c(3, 2), stride=c(2, 1)) +#' input <- torch_randn(20, 16, 50, 32) +#' output <- m(input) +#' +#' @export +nn_avg_pool2d <- nn_module( + "nn_avg_pool2d", + initialize = function(kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE, + count_include_pad= TRUE, divisor_override = NULL) { + + self$kernel_size <- kernel_size + + if (is.null(stride)) + stride <- kernel_size + + self$stride <- stride + + self$padding <- padding + self$ceil_mode <- ceil_mode + self$count_include_pad <- count_include_pad + self$divisor_override <- divisor_override + + }, + forward = function(input) { + nnf_avg_pool2d( + input, self$kernel_size, self$stride, self$padding, self$ceil_mode, + self$count_include_pad, self$divisor_override) + } +) + +#' Applies a 3D average pooling over an input signal composed of several input +#' planes. +#' +#' In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)}, +#' output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and `kernel_size` \eqn{(kD, kH, kW)} +#' can be precisely described as: +#' +#' \deqn{ +#' \begin{array}{ll} +#' \mbox{out}(N_i, C_j, d, h, w) = & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\ +#' & \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW} +#' \end{array} +#' } +#' +#' If `padding` is non-zero, then the input is implicitly zero-padded on all three sides +#' for `padding` number of points. +#' +#' The parameters `kernel_size`, `stride` can either be: +#' +#' - a single `int` -- in which case the same value is used for the depth, height and width dimension +#' - a `tuple` of three ints -- in which case, the first `int` is used for the depth dimension, +#' the second `int` for the height dimension and the third `int` for the width dimension +#' +#' @param kernel_size the size of the window +#' @param stride the stride of the window. Default value is `kernel_size` +#' @param padding implicit zero padding to be added on all three sides +#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape +#' @param count_include_pad when TRUE, will include the zero-padding in the averaging calculation +#' @param divisor_override if specified, it will be used as divisor, otherwise `kernel_size` will be used +#' +#' @section Shape: +#' - Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})} +#' - Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where +#' +#' \deqn{ +#' D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - +#' \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor +#' } +#' \deqn{ +#' H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - +#' \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor +#' } +#' \deqn{ +#' W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - +#' \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor +#' } +#' +#' @examples +#' +#' # pool of square window of size=3, stride=2 +#' m = nn_avg_pool3d(3, stride=2) +#' # pool of non-square window +#' m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2)) +#' input = torch_randn(20, 16, 50,44, 31) +#' output = m(input) +#' +#' @export +nn_avg_pool3d <- nn_module( + "nn_avg_pool3d", + initialize = function(kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE, + count_include_pad= TRUE, divisor_override = NULL) { + + self$kernel_size <- kernel_size + + if (is.null(stride)) + stride <- kernel_size + + self$stride <- stride + + self$padding <- padding + self$ceil_mode <- ceil_mode + self$count_include_pad <- count_include_pad + self$divisor_override <- divisor_override + + }, + forward = function(input) { + nnf_avg_pool3d( + input, self$kernel_size, self$stride, self$padding, self$ceil_mode, + self$count_include_pad, self$divisor_override) + } +) + +#' Applies a 2D fractional max pooling over an input signal composed of several input planes. +#' +#' Fractional MaxPooling is described in detail in the paper +#' [Fractional MaxPooling](https://arxiv.org/abs/1412.6071) by Ben Graham +#' +#' The max-pooling operation is applied in \eqn{kH \times kW} regions by a stochastic +#' step size determined by the target output size. +#' The number of output features is equal to the number of input planes. +#' +#' @param kernel_size the size of the window to take a max over. +#' Can be a single number k (for a square kernel of k x k) or a tuple `(kh, kw)` +#' @param output_size the target output size of the image of the form `oH x oW`. +#' Can be a tuple `(oH, oW)` or a single number oH for a square image `oH x oH` +#' @param output_ratio If one wants to have an output size as a ratio of the input size, this option can be given. +#' This has to be a number or tuple in the range (0, 1) +#' @param return_indices if `TRUE`, will return the indices along with the outputs. +#' Useful to pass to [nn_max_unpool2d()]. Default: `FALSE` +#' +#' @examples +# # pool of square window of size=3, and target output size 13x12 +# m = nn_fractional_max_pool2d(3, output_size=c(13, 12)) +# # pool of square window and target output size being half of input image size +# m = nn_fractional_max_pool2d(3, output_ratio=c(0.5, 0.5)) +# input = torch_randn(20, 16, 50, 32) +# output = m(input) +#' +#' @export +nn_fractional_max_pool2d <- nn_module( + "nn_fractional_max_pool2d", + initialize = function(kernel_size, output_size = NULL, + output_ratio = NULL, + return_indices = FALSE) { + + random_samples <- NULL + + self$kernel_size <- nn_util_pair(kernel_size) + self$return_indices <- return_indices + self$register_buffer('random_samples', random_samples) + + if (!is.null(output_size)) + output_size <- nn_util_pair(output_size) + + self$output_size <- output_size + + if (!is.null(output_ratio)) + output_ratio <- nn_util_pair(output_ratio) + + self$output_ratio <- output_ratio + + + if (is.null(output_ratio) && is.null(output_size)) + value_error("both output_size and output_ratio are NULL") + + if (!is.null(output_ratio) && !is.null(output_size)) + value_error("both output_size and oytput_ratio are not NULL") + + if (!is.null(output_ratio)) + if (any(output_ratio > 1 | output_ratio < 0)) + value_error("output_ratio must be between 0 and 1.") + + }, + forward = function(input) { + nnf_fractional_max_pool2d( + input, self$kernel_size, self$output_size, self$output_ratio, + self$return_indices, + random_samples=self$random_samples) + } +) + +#' Applies a 3D fractional max pooling over an input signal composed of several input planes. +#' +#' Fractional MaxPooling is described in detail in the paper +#' [Fractional MaxPooling](https://arxiv.org/abs/1412.6071) by Ben Graham +#' +#' The max-pooling operation is applied in \eqn{kTxkHxkW} regions by a stochastic +#' step size determined by the target output size. +#' The number of output features is equal to the number of input planes. +#' +#' @param kernel_size the size of the window to take a max over. +#' Can be a single number k (for a square kernel of k x k x k) or a tuple `(kt x kh x kw)` +#' @param output_size the target output size of the image of the form `oT x oH x oW`. +#' Can be a tuple `(oT, oH, oW)` or a single number oH for a square image `oH x oH x oH` +#' @param output_ratio If one wants to have an output size as a ratio of the input size, this option can be given. +#' This has to be a number or tuple in the range (0, 1) +#' @param return_indices if `TRUE`, will return the indices along with the outputs. +#' Useful to pass to [nn_max_unpool3d()]. Default: `FALSE` +#' +#' @examples +#' # pool of cubic window of size=3, and target output size 13x12x11 +#' m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11)) +#' # pool of cubic window and target output size being half of input size +#' m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5)) +#' input = torch_randn(20, 16, 50, 32, 16) +#' output = m(input) +#' +#' @export +nn_fractional_max_pool3d <- nn_module( + "nn_fractional_max_pool3d", + initialize = function(kernel_size, output_size = NULL, + output_ratio = NULL, + return_indices = FALSE) { + + random_samples <- NULL + + self$kernel_size <- nn_util_triple(kernel_size) + self$return_indices <- return_indices + self$register_buffer('random_samples', random_samples) + + if (!is.null(output_size)) + output_size <- nn_util_triple(output_size) + + self$output_size <- output_size + + if (!is.null(output_ratio)) + output_ratio <- nn_util_triple(output_ratio) + + self$output_ratio <- output_ratio + + + if (is.null(output_ratio) && is.null(output_size)) + value_error("both output_size and output_ratio are NULL") + + if (!is.null(output_ratio) && !is.null(output_size)) + value_error("both output_size and oytput_ratio are not NULL") + + if (!is.null(output_ratio)) + if (any(output_ratio > 1 | output_ratio < 0)) + value_error("output_ratio must be between 0 and 1.") + + }, + forward = function(input) { + nnf_fractional_max_pool3d( + input, self$kernel_size, self$output_size, self$output_ratio, + self$return_indices, + random_samples=self$random_samples) + } +) + +lp_pool_nd <- nn_module( + "lp_pool_nd", + initialize = function(norm_type, kernel_size, stride = NULL, + ceil_mode = FALSE) { + + self$norm_type <- norm_type + self$kernel_size <- kernel_size + self$stride <- stride + self$ceil_mode <- ceil_mode + + } +) + +#' Applies a 1D power-average pooling over an input signal composed of several input +#' planes. +#' +#' On each window, the function computed is: +#' +#' \deqn{ +#' f(X) = \sqrt[p]{\sum_{x \in X} x^{p}} +#' } +#' +#' - At p = \eqn{\infty}, one gets Max Pooling +#' - At p = 1, one gets Sum Pooling (which is proportional to Average Pooling) +#' +#' @note If the sum to the power of `p` is zero, the gradient of this function is +#' not defined. This implementation will set the gradient to zero in this case. +#' +#' @param norm_type if inf than one gets max pooling if 0 you get sum pooling ( +#' proportional to the avg pooling) +#' @param kernel_size a single int, the size of the window +#' @param stride a single int, the stride of the window. Default value is `kernel_size` +#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape +#' +#' @section Shape: +#' - Input: \eqn{(N, C, L_{in})} +#' - Output: \eqn{(N, C, L_{out})}, where +#' +#' \deqn{ +#' L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor +#' } +#' +#' @examples +#' # power-2 pool of window of length 3, with stride 2. +#' m <- nn_lp_pool1d(2, 3, stride=2) +#' input <- torch_randn(20, 16, 50) +#' output <- m(input) +#' +#' @export +nn_lp_pool1d <- nn_module( + "nn_lp_pool1d", + inherit = lp_pool_nd, + forward = function(input) { + nnf_lp_pool1d(input, self$norm_type, self$kernel_size, + self$stride, self$ceil_mode) + } +) + +#' Applies a 2D power-average pooling over an input signal composed of several input +#' planes. +#' +#' On each window, the function computed is: +#' +#' \deqn{ +#' f(X) = \sqrt[p]{\sum_{x \in X} x^{p}} +#' } +#' +#' - At p = \eqn{\infty}, one gets Max Pooling +#' - At p = 1, one gets Sum Pooling (which is proportional to average pooling) +#' +#' The parameters `kernel_size`, `stride` can either be: +#' +#' - a single `int` -- in which case the same value is used for the height and width dimension +#' - a `tuple` of two ints -- in which case, the first `int` is used for the height dimension, +#' and the second `int` for the width dimension +#' +#' @note If the sum to the power of `p` is zero, the gradient of this function is +#' not defined. This implementation will set the gradient to zero in this case. +#' +#' @param norm_type if inf than one gets max pooling if 0 you get sum pooling ( +#' proportional to the avg pooling) +#' @param kernel_size the size of the window +#' @param stride the stride of the window. Default value is `kernel_size` +#' @param ceil_mode when TRUE, will use `ceil` instead of `floor` to compute the output shape +#' +#' @section Shape: +#' +#' - Input: \eqn{(N, C, H_{in}, W_{in})} +#' - Output: \eqn{(N, C, H_{out}, W_{out})}, where +#' +#' \deqn{ +#' H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor +#' } +#' \deqn{ +#' W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor +#' } +#' +#' @examples +#' +#' # power-2 pool of square window of size=3, stride=2 +#' m <- nn_lp_pool2d(2, 3, stride=2) +#' # pool of non-square window of power 1.2 +#' m <- nn_lp_pool2d(1.2, c(3, 2), stride=c(2, 1)) +#' input <- torch_randn(20, 16, 50, 32) +#' output <- m(input) +#' +#' @export +nn_lp_pool2d <- nn_module( + "nn_lp_pool2d", + inherit = lp_pool_nd, + forward = function(input) { + nnf_lp_pool2d(input, self$norm_type, self$kernel_size, + self$stride, self$ceil_mode) + } +) + +#' Applies a 1D adaptive max pooling over an input signal composed of several input planes. +#' +#' The output size is H, for any input size. +#' The number of output features is equal to the number of input planes. +#' +#' @param output_size the target output size H +#' @param return_indices if `TRUE`, will return the indices along with the outputs. +#' Useful to pass to [nn_max_unpool1d()]. Default: `FALSE` +#' +#' @examples +#' # target output size of 5 +#' m <- nn_adaptive_max_pool1d(5) +#' input <- torch_randn(1, 64, 8) +#' output <- m(input) +#' +#' @export +nn_adaptive_max_pool1d <- nn_module( + "nn_adaptive_max_pool1d", + initialize = function(output_size, return_indices = FALSE) { + self$output_size <- output_size + self$return_indices <- return_indices + }, + forward = function(input) { + nnf_adaptive_max_pool1d(input, self$output_size, self$return_indices) + } +) + +#' Applies a 2D adaptive max pooling over an input signal composed of several input planes. +#' +#' The output is of size H x W, for any input size. +#' The number of output features is equal to the number of input planes. +#' +#' @param output_size the target output size of the image of the form H x W. +#' Can be a tuple `(H, W)` or a single H for a square image H x H. +#' H and W can be either a `int`, or `None` which means the size will +#' be the same as that of the input. +#' @param return_indices if `TRUE`, will return the indices along with the outputs. +#' Useful to pass to [nn_max_unpool2d()]. Default: `FALSE` +#' +#' @examples +#' # target output size of 5x7 +#' m <- nn_adaptive_max_pool2d(c(5,7)) +#' input <- torch_randn(1, 64, 8, 9) +#' output <- m(input) +#' # target output size of 7x7 (square) +#' m <- nn_adaptive_max_pool2d(7) +#' input <- torch_randn(1, 64, 10, 9) +#' output <- m(input) +#' +#' @export +nn_adaptive_max_pool2d <- nn_module( + "nn_adaptive_max_pool2d", + initialize = function(output_size, return_indices = FALSE) { + self$output_size <- nn_util_pair(output_size) + self$return_indices <- return_indices + }, + forward = function(input) { + nnf_adaptive_max_pool2d(input, self$output_size, self$return_indices) + } +) + +#' Applies a 3D adaptive max pooling over an input signal composed of several input planes. +#' +#' The output is of size D x H x W, for any input size. +#' The number of output features is equal to the number of input planes. +#' +#' +#' @param output_size the target output size of the image of the form D x H x W. +#' Can be a tuple (D, H, W) or a single D for a cube D x D x D. +#' D, H and W can be either a `int`, or `None` which means the size will +#' be the same as that of the input. +#' @param return_indices if `TRUE`, will return the indices along with the outputs. +#' Useful to pass to [nn_max_unpool3d()]. Default: `FALSE` +#' +#' @examples +#' # target output size of 5x7x9 +#' m <- nn_adaptive_max_pool3d(c(5,7,9)) +#' input <- torch_randn(1, 64, 8, 9, 10) +#' output <- m(input) +#' # target output size of 7x7x7 (cube) +#' m <- nn_adaptive_max_pool3d(7) +#' input <- torch_randn(1, 64, 10, 9, 8) +#' output <- m(input) +#' +#' @export +nn_adaptive_max_pool3d <- nn_module( + "nn_adaptive_max_pool3d", + initialize = function(output_size, return_indices = FALSE) { + self$output_size <- nn_util_triple(output_size) + self$return_indices <- return_indices + }, + forward = function(input) { + nnf_adaptive_max_pool3d(input, self$output_size, self$return_indices) + } +) + +#' Applies a 1D adaptive average pooling over an input signal composed of several input planes. +#' +#' The output size is H, for any input size. +#' The number of output features is equal to the number of input planes. +#' +#' @param output_size the target output size H +#' +#' @examples +#' # target output size of 5 +#' m = nn_adaptive_avg_pool1d(5) +#' input <- torch_randn(1, 64, 8) +#' output <- m(input) +#' +#' @export +nn_adaptive_avg_pool1d <- nn_module( + "nn_adaptive_avg_pool1d", + initialize = function(output_size) { + self$output_size <- output_size + }, + forward = function(input) { + nnf_adaptive_avg_pool1d(input, self$output_size) + } +) + +#' Applies a 2D adaptive average pooling over an input signal composed of several input planes. +#' +#' The output is of size H x W, for any input size. +#' The number of output features is equal to the number of input planes. +#' +#' @param output_size the target output size of the image of the form H x W. +#' Can be a tuple (H, W) or a single H for a square image H x H. +#' H and W can be either a `int`, or `NULL` which means the size will +#' be the same as that of the input. +#' +#' @examples +#' # target output size of 5x7 +#' m <- nn_adaptive_avg_pool2d(c(5,7)) +#' input <- torch_randn(1, 64, 8, 9) +#' output <- m(input) +#' # target output size of 7x7 (square) +#' m <- nn_adaptive_avg_pool2d(7) +#' input <- torch_randn(1, 64, 10, 9) +#' output <- m(input) +#' +#' @export +nn_adaptive_avg_pool2d <- nn_module( + "nn_adaptive_avg_pool2d", + initialize = function(output_size) { + self$output_size <- nn_util_pair(output_size) + }, + forward = function(input) { + nnf_adaptive_avg_pool2d(input, self$output_size) + } +) + +#' Applies a 3D adaptive average pooling over an input signal composed of several input planes. +#' +#' The output is of size D x H x W, for any input size. +#' The number of output features is equal to the number of input planes. +#' +#' @param output_size the target output size of the form D x H x W. +#' Can be a tuple (D, H, W) or a single number D for a cube D x D x D. +#' D, H and W can be either a `int`, or `None` which means the size will +#' be the same as that of the input. +#' +#' @examples +#' # target output size of 5x7x9 +#' m <- nn_adaptive_avg_pool3d(c(5,7,9)) +#' input <- torch_randn(1, 64, 8, 9, 10) +#' output <- m(input) +#' # target output size of 7x7x7 (cube) +#' m <- nn_adaptive_avg_pool3d(7) +#' input <- torch_randn(1, 64, 10, 9, 8) +#' output <- m(input) +#' +#' @export +nn_adaptive_avg_pool3d <- nn_module( + "nn_adaptive_avg_pool3d", + initialize = function(output_size) { + self$output_size <- nn_util_triple(output_size) + }, + forward = function(input) { + nnf_adaptive_avg_pool3d(input, self$output_size) + } +) \ No newline at end of file diff --git a/R/nn-rnn.R b/R/nn-rnn.R index 9c47af147d655c0e1bd67010141a73c9b4a7fd7d..744f4bc4cf629c26e9fa82aa8c88c1f3be64bce3 100644 --- a/R/nn-rnn.R +++ b/R/nn-rnn.R @@ -145,7 +145,7 @@ nn_rnn_base <- nn_module( num_directions <- ifelse(self$bidirectional, 2, 1) hx <- torch_zeros(self$num_layers * num_directions, max_batch_size, self$hidden_size, - dtype=input$dtype(), device=input$device()) + dtype=input$dtype, device=input$device) } else { hx <- self$permute_hidden(hx, sorted_indices) diff --git a/R/nn.R b/R/nn.R index 75c6e78d43084b672d478ced85661babc5d73017..1f5cebdd5a0ede8ce233e8b15d6726673cb29590 100644 --- a/R/nn.R +++ b/R/nn.R @@ -14,7 +14,7 @@ nn_Module <- R6::R6Class( add_module = function(name, module) { if (is.numeric(name)) - name <- paste0("m", name) + name <- as.character(name) private$modules_[[name]] <- module }, @@ -223,6 +223,15 @@ nn_Module <- R6::R6Class( ) ) +#' Creates an `nn_parameter` +#' +#' Indicates to nn_module that `x` is a parameter +#' +#' @param x the tensor that you want to indicate as parameter +#' @param requires_grad whether this parameter should have +#' `requires_grad = TRUE` +#' +#' @export nn_parameter <- function(x, requires_grad = TRUE) { if (!is_torch_tensor(x)) stop("`x` must be a tensor.") @@ -231,22 +240,45 @@ nn_parameter <- function(x, requires_grad = TRUE) { x } +#' Checks if an object is a nn_parameter +#' +#' @param x the object to check +#' +#' @export is_nn_parameter <- function(x) { inherits(x, "nn_parameter") } +#' Creates a nn_buffer +#' +#' Indicates that a tensor is a buffer in a nn_module +#' +#' @param x the tensor that will be converted to nn_buffer +#' @param persistent whether the buffer should be persistent or not. +#' +#' @export nn_buffer <- function(x, persistent = TRUE) { class(x) <- c(class(x), "nn_buffer") attr(x, "persistent") <- persistent x } +#' Checks if the object is a nn_buffer +#' +#' @param x object to check +#' +#' @export is_nn_buffer <- function(x) { inherits(x, "nn_buffer") } +#' Checks if the object is an nn_module +#' +#' @param x object to check +#' +#' @export is_nn_module <- function(x) { - inherits(x, "nn_module") + inherits(x, "nn_module") && !inherits(x, "nn_module_generator") } #' Base class for all neural network modules. @@ -259,6 +291,7 @@ is_nn_module <- function(x) { #' @param classname an optional name for the module #' @param inherit an optional module to inherit from #' @param ... methods implementation +#' @param parent_env passed to [R6::R6Class()]. #' #' @examples #' model <- nn_module( @@ -276,10 +309,14 @@ is_nn_module <- function(x) { #' ) #' #' @export -nn_module <- function(classname = NULL, inherit = nn_Module, ...) { +nn_module <- function(classname = NULL, inherit = nn_Module, ..., + parent_env = parent.frame()) { if (inherits(inherit, "nn_module")) inherit <- attr(inherit, "module") + + e <- new.env(parent = parent_env) + e$inherit <- inherit classes <- c(classname, "nn_module") @@ -290,7 +327,8 @@ nn_module <- function(classname = NULL, inherit = nn_Module, ...) { public = list( .classes = classes, ... - ) + ), + parent_env = e ) init <- get_init(Module) @@ -302,7 +340,7 @@ nn_module <- function(classname = NULL, inherit = nn_Module, ...) { create_nn_module_callable(instance) }) ) - attr(fun, "class") <- classes + attr(fun, "class") <- c(classes, "nn_module_generator") attr(fun, "module") <- Module fun } @@ -364,6 +402,7 @@ create_nn_module_callable <- function(instance) { #' @export `[[<-.nn_Module` <- function(x, name, value) { + if (inherits(value, "nn_parameter")) { x$register_parameter(name, value) } else if (inherits(value, "nn_buffer")) { @@ -383,6 +422,15 @@ create_nn_module_callable <- function(instance) { invisible(x) } +#' @export +`$<-.nn_module` <- function(x, name, value) { + attr(x, "module")[[name]] <- value + invisible(x) +} + +#' @export +`[[<-.nn_module` <- `$<-.nn_module` + #' @export names.nn_module <- function(x, ...) { x <- attr(x, "module") @@ -420,9 +468,9 @@ nn_sequential <- function(... , name = NULL) { module <- nn_module( classname = ifelse(is.null(name), "nn_sequential", name), initialize = function(...) { - modules <- list(...) + modules <- rlang::list2(...) for (i in seq_along(modules)) { - self$add_module(name = i, module = modules[[i]]) + self$add_module(name = i - 1, module = modules[[i]]) } }, forward = function(input) { diff --git a/R/nnf-activation.R b/R/nnf-activation.R index a9f0dcdbc000102c1a078fc24874ed94b8690287..5b2a62fa4fba5b2ac4113c44e3034c5845bd070b 100644 --- a/R/nnf-activation.R +++ b/R/nnf-activation.R @@ -727,3 +727,15 @@ nnf_multi_head_attention_forward <- function( return(list(attn_output, NULL)) } } + +#' Sigmoid +#' +#' Applies element-wise \eqn{Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}} +#' +#' @inheritParams nnf_elu +#' +#' @export +nnf_sigmoid <- function(input) { + torch_sigmoid(input) +} + diff --git a/R/nnf-loss.R b/R/nnf-loss.R index 7ded4615003503af9e66c534b11823f2951826d7..726710c3d52106765280dfcaa5c6fcb18545be9c 100644 --- a/R/nnf-loss.R +++ b/R/nnf-loss.R @@ -67,6 +67,16 @@ nnf_kl_div <- function(input, target, reduction = "mean") { #' @export nnf_mse_loss <- function(input, target, reduction = "mean") { + if (!all(target$shape == input$shape)) { + + target_shape <- paste0("(", paste(target$shape, collapse = ","), ")") + input_shape <- paste0("(", paste(input$shape, collapse = ","), ")") + + warn("Using a target size {target_shape} that is different to the input size {input_shape}. ", + "This will likely lead to incorrect results due to broadcasting. ", + "Please ensure they have the same size.") + } + if (target$requires_grad) { ret <- (input - target) ^ 2 if (!is.null(reduction)) { @@ -167,20 +177,8 @@ nnf_cosine_embedding_loss <- function(input1, input2, target, margin=0, #' #' @export nnf_smooth_l1_loss <- function(input, target, reduction = "mean") { - if (target$requires_grad) { - ret <- nnf_smooth_l1_loss(input, target) - if (reduction != "none") { - if (reduction == "mean") - ret <- torch_mean(ret) - else - ret <- torch_sum(ret) - } - } else { - expanded <- torch_broadcast_tensors(list(input, target)) - ret <- torch_smooth_l1_loss(expanded[[1]], expanded[[2]], reduction_enum(reduction)) - } - - ret + expanded <- torch_broadcast_tensors(list(input, target)) + torch_smooth_l1_loss(expanded[[1]], expanded[[2]], reduction_enum(reduction)) } #' Multilabel_margin_loss @@ -263,6 +261,41 @@ nnf_triplet_margin_loss <- function(anchor, positive, negative, margin = 1, p = reduction_enum(reduction)) } +#' Triplet margin with distance loss +#' +#' See [nn_triplet_margin_with_distance_loss()] +#' +#' @inheritParams nnf_triplet_margin_loss +#' @inheritParams nn_triplet_margin_with_distance_loss +#' +#' @export +nnf_triplet_margin_with_distance_loss <- function(anchor, positive, negative, + distance_function=NULL, + margin=1.0, swap=FALSE, + reduction="mean") { + if (is.null(distance_function)) + distance_function <- nnf_pairwise_distance + + positive_dist <- distance_function(anchor, positive) + negative_dist <- distance_function(anchor, negative) + + if (swap) { + swap_dist <- distance_function(positive, negative) + negative_dist <- torch_min(negative_dist, swap_dist) + } + + output <- torch_clamp(positive_dist - negative_dist + margin, min=0.0) + + reduction_enum <- reduction_enum(reduction) + + if (reduction_enum == 1) + return(output$mean()) + else if (reduction_enum == 2) + return(output$sum()) + else + return(output) +} + #' Ctc_loss #' #' The Connectionist Temporal Classification loss. @@ -406,6 +439,7 @@ nnf_nll_loss <- function(input, target, weight = NULL, ignore_index = -100, #' @export nnf_cross_entropy <- function(input, target, weight=NULL, ignore_index=-100, reduction=c("mean", "sum", "none")) { + reduction <- match.arg(reduction) torch_nll_loss(self = torch_log_softmax(input, 1), target = target, weight = weight, reduction = reduction_enum(reduction), ignore_index = ignore_index) } @@ -427,6 +461,7 @@ nnf_cross_entropy <- function(input, target, weight=NULL, ignore_index=-100, nnf_binary_cross_entropy_with_logits <- function(input, target, weight = NULL, reduction = c("mean", "sum", "none"), pos_weight = NULL) { + reduction <- match.arg(reduction) torch_binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum(reduction)) } diff --git a/R/nnf-pooling.R b/R/nnf-pooling.R index b2bf1f7f0fc5865e8094b3f5c5a060cde7282402..cec0805787bcab0a75de50712f60c84ea32bcc69 100644 --- a/R/nnf-pooling.R +++ b/R/nnf-pooling.R @@ -256,8 +256,8 @@ unpool_output_size <- function(input, kernel_size, stride, padding, output_size) } for (d in seq_along(kernel_size)) { - min_size <- default_size[d] - stride[d] - max_size <- default_size[d] + stride[d] + min_size <- default_size[[d]] - stride[d] + max_size <- default_size[[d]] + stride[d] } ret <- output_size diff --git a/R/nnf-upsampling.R b/R/nnf-upsampling.R index 28ce4830b73aee7b1139854fcc7a710c9b11da51..791566c7328e9d7837570dcaeaa485777e01652b 100644 --- a/R/nnf-upsampling.R +++ b/R/nnf-upsampling.R @@ -107,12 +107,20 @@ nnf_interpolate <- function(input, size = NULL, scale_factor = NULL, recompute_scale_factor = NULL) { scale_factor_len <- input$dim() - 2 - if (length(scale_factor) == 1) - scale_factor_repeated <- rep(scale_factor, scale_factor_len) - else - scale_factor_repeated <- scale_factor + scale_factor_list <- scale_factor - sfl <- scale_factor_repeated + if (!is.null(scale_factor) && ( + is.null(recompute_scale_factor) || !recompute_scale_factor)) { + + if (length(scale_factor) == 1) + scale_factor_repeated <- rep(scale_factor, scale_factor_len) + else + scale_factor_repeated <- scale_factor + + scale_factor_list <- scale_factor_repeated + } + + sfl <- scale_factor_list sze <- interp_output_size(input, size = size, scale_factor = scale_factor, recompute_scale_factor = recompute_scale_factor) diff --git a/R/optim.R b/R/optim.R index 12e0837506e04fc795055bc834cdcd78f1020511..9d6295a784cb1af47c900f214ae1dcdf3a1926c4 100644 --- a/R/optim.R +++ b/R/optim.R @@ -58,7 +58,7 @@ Optimizer <- R6::R6Class( if (is_optim_required(self$defaults[[nm]]) && !nm %in% names(param_group)) { value_error("parameter group didn't specify a value of required ", - "optimization parameter {name}") + "optimization parameter {nm}") } else if (!nm %in% names(param_group)) { param_group[[nm]] <- self$defaults[[nm]] } diff --git a/R/package.R b/R/package.R index 14694b0b929b2bca6f3bde67010f22580ed8baef..68dc2401e3e326e07e226395ae11e2b20f46c161 100644 --- a/R/package.R +++ b/R/package.R @@ -11,8 +11,12 @@ globalVariables(c("..", "self", "private", "N")) .onLoad <- function(libname, pkgname){ install_success <- TRUE + autoinstall <- interactive() || + "JPY_PARENT_PID" %in% names(Sys.getenv()) || + identical(getOption("jupyter.in_kernel"), TRUE) + if (!install_exists() && Sys.getenv("TORCH_INSTALL", unset = 2) != 0 && - (interactive() || Sys.getenv("TORCH_INSTALL", unset = 2) == "1")) { + (autoinstall || Sys.getenv("TORCH_INSTALL", unset = 2) == "1")) { install_success <- tryCatch({ install_torch() TRUE @@ -39,5 +43,3 @@ globalVariables(c("..", "self", "private", "N")) } - - diff --git a/R/qscheme.R b/R/qscheme.R index 1361bf4c5aed7ec737d1b4e3c0ec88e88be943ee..0fb46e76bc38538fd09b0460ff7d281cc4c11038 100644 --- a/R/qscheme.R +++ b/R/qscheme.R @@ -15,7 +15,7 @@ QScheme <- R6::R6Class( #' #' @rdname torch_qscheme #' @name torch_qscheme -#' +#' @concept tensor-attributes NULL #' @rdname torch_qscheme diff --git a/R/reduction.R b/R/reduction.R index a1f9f4aaa44df386fcc13e04995614b01ca68e2a..958ae8f5785e6eb44ce96ce81bbc40994644ebe8 100644 --- a/R/reduction.R +++ b/R/reduction.R @@ -2,17 +2,21 @@ #' #' @name torch_reduction #' @rdname torch_reduction +#' @concept tensor-attributes #' NULL #' @rdname torch_reduction +#' @concept tensor-attributes #' @export torch_reduction_sum <- function() cpp_torch_reduction_sum() +#' @concept tensor-attributes #' @rdname torch_reduction #' @export torch_reduction_mean <- function() cpp_torch_reduction_mean() +#' @concept tensor-attributes #' @rdname torch_reduction #' @export torch_reduction_none <- function() cpp_torch_reduction_none() diff --git a/R/save.R b/R/save.R index 69c86ef6dc1a155ce686998ba12c8f4eb41f9013..52fc4248cd647e0dd709d91a6e2db6c4d01db12e 100644 --- a/R/save.R +++ b/R/save.R @@ -8,12 +8,14 @@ #' @param ... not currently used. #' #' @family torch_save +#' @concept serialization #' #' @export torch_save <- function(obj, path, ...) { UseMethod("torch_save") } +#' @concept serialization #' @export torch_save.torch_tensor <- function(obj, path, ...) { values <- cpp_tensor_save(obj$ptr) @@ -29,6 +31,7 @@ tensor_to_raw_vector <- function(x) { r } +#' @concept serialization #' @export torch_save.nn_module <- function(obj, path, ...) { state_dict <- obj$state_dict() @@ -43,6 +46,7 @@ torch_save.nn_module <- function(obj, path, ...) { #' @family torch_save #' #' @export +#' @concept serialization torch_load <- function(path) { r <- readRDS(path) if (r$type == "tensor") @@ -65,3 +69,28 @@ torch_load_module <- function(obj) { obj$module$load_state_dict(obj$state_dict) obj$module } + +#' Load a state dict file +#' +#' This function should only be used to load models saved in python. +#' For it to work correctly you need to use `torch.save` with the flag: +#' `_use_new_zipfile_serialization=True` and also remove all `nn.Parameter` +#' classes from the tensors in the dict. +#' +#' The above might change with development of [this](https://github.com/pytorch/pytorch/issues/37213) +#' in pytorch's C++ api. +#' +#' @param path to the state dict file +#' +#' @return a named list of tensors. +#' +#' @export +#' @concept serialization +load_state_dict <- function(path) { + path <- normalizePath(path) + o <- cpp_load_state_dict(path) + + values <- TensorList$new(ptr = o$values)$to_r() + names(values) <- o$keys + values +} diff --git a/R/tensor.R b/R/tensor.R index 9b5e06f6bc821654cd8d4ea7939ab24e718e74f9..a623cda950ab4b4b8b1af6879532ab5fbbb6019d 100644 --- a/R/tensor.R +++ b/R/tensor.R @@ -15,12 +15,14 @@ Tensor <- R7Class( if (is.null(dtype)) { if (is.integer(data)) { - dtype <- torch_int() + dtype <- torch_long() + } else if (bit64::is.integer64(data)) { + dtype <- torch_long() } else if (is.double(data)) { dtype <- torch_float() # default to float } else if (is.logical(data)) { dtype <- torch_bool() - } + } } @@ -36,19 +38,13 @@ Tensor <- R7Class( self$ptr <- cpp_torch_tensor(data, rev(dimension), options$ptr, - requires_grad) + requires_grad, inherits(data, "integer64")) }, print = function() { cat(sprintf("torch_tensor \n")) cpp_torch_tensor_print(self$ptr) invisible(self) }, - dtype = function() { - torch_dtype$new(ptr = cpp_torch_tensor_dtype(self$ptr)) - }, - device = function() { - Device$new(ptr = cpp_tensor_device(self$ptr)) - }, dim = function() { length(self$size()) }, @@ -79,7 +75,7 @@ Tensor <- R7Class( args$memory_format <- memory_format if (is.null(args$dtype) && is.null(args$other)) - args$dtype <- self$dtype() + args$dtype <- self$dtype do.call(private$`_to`, args) }, @@ -118,6 +114,18 @@ Tensor <- R7Class( active = list( shape = function() { self$size() + }, + dtype = function() { + torch_dtype$new(ptr = cpp_torch_tensor_dtype(self$ptr)) + }, + device = function() { + Device$new(ptr = cpp_tensor_device(self$ptr)) + }, + is_cuda = function() { + self$device$type == "cuda" + }, + ndim = function() { + self$dim() } ) ) @@ -154,25 +162,38 @@ as.array.torch_tensor <- function(x, ...) { as_array(x) } +as_array_impl <- function(x) { + a <- cpp_as_array(x$ptr) + + if (length(a$dim) <= 1L) { + out <- a$vec + } else if (length(a$dim) == 2L) { + out <- t(matrix(a$vec, ncol = a$dim[1], nrow = a$dim[2])) + } else { + out <- aperm(array(a$vec, dim = rev(a$dim)), seq(length(a$dim), 1)) + } + + if (x$dtype == torch_long() && !inherits(out, "integer64")) + class(out) <- c(class(out), "integer64") + + out +} + #' @export as_array.torch_tensor <- function(x) { - if (x$device()$type == "cuda") + if (x$device$type == "cuda") runtime_error("Can't convert cuda tensor to R. Convert to cpu tensor before.") # dequantize before converting if (x$is_quantized()) x <- x$dequantize() - a <- cpp_as_array(x$ptr) + # auto convert to int32 if long. + if (x$dtype == torch_long()) + x <- x$to(dtype = torch_int32()) - if (length(a$dim) <= 1L) { - out <- a$vec - } else if (length(a$dim) == 2L) { - out <- t(matrix(a$vec, ncol = a$dim[1], nrow = a$dim[2])) - } else { - out <- aperm(array(a$vec, dim = rev(a$dim)), seq(length(a$dim), 1)) - } + out <- as_array_impl(x) out } @@ -181,7 +202,30 @@ is_torch_tensor <- function(x) { inherits(x, "torch_tensor") } +#' Checks if a tensor is undefined +#' +#' @param x tensor to check +#' +#' @export is_undefined_tensor <- function(x) { cpp_tensor_is_undefined(x$ptr) } +#' @importFrom bit64 as.integer64 +#' @export +as.integer64.torch_tensor <- function(x, keep.names = FALSE, ...) { + x <- x$to(dtype = torch_long()) + as_array_impl(x) +} + +#' @export +str.torch_tensor <- function(object, ...) { + dtype <- object$dtype$.type() + + dims <- dim(object) + dims <- paste(paste0("1:", dims), collapse = ", ") + + out <- paste0(dtype, " [", dims, "]") + cat(out) + cat("\n") +} diff --git a/R/type-info.R b/R/type-info.R new file mode 100644 index 0000000000000000000000000000000000000000..9348e34cddab61c01607a6cf51a97eca9789d16a --- /dev/null +++ b/R/type-info.R @@ -0,0 +1,89 @@ +#' Integer type info +#' +#' A list that represents the numerical properties of a integer +#' type. +#' +#' @param dtype dtype to get information from. +#' @concept tensor-attributes +#' +#' @export +torch_iinfo <- function(dtype) { + + if (dtype == torch_int32()) { + list( + bits = 32, + max = bit64::as.integer64("2147483647"), + min = bit64::as.integer64("-2147483648") + ) + } else if (dtype == torch_int64()) { + + list( + bits = 64, + max = bit64::as.integer64("9223372036854775807"), + min = bit64::as.integer64("-9223372036854775808") + ) + + } else if (dtype == torch_int16()) { + + list( + bits = 16, + max = 32767L, + min = -32768L + ) + + } else { + + value_error("dtype must be an integer type.") + + } + +} + +#' Floating point type info +#' +#' A list that represents the numerical properties of a +#' floating point torch.dtype +#' +#' @param dtype dtype to check information +#' @concept tensor-attributes +#' +#' @export +torch_finfo <- function(dtype) { + + if (dtype == torch_float32()) { + + list( + bits = 32, + max = 3.4028234663852886e+38, + min = -3.4028234663852886e+38, + eps = 1.1920928955078125e-07, + tiny = 1.1754943508222875e-38 + ) + + } else if (dtype == torch_float64()) { + + list( + bits = 64, + max = 1.7976931348623157e+308, + min = -1.7976931348623157e+308, + eps = 2.220446049250313e-16, + tiny = 2.2250738585072014e-308 + ) + + } else if (dtype == torch_float16()) { + + list( + bits = 16, + max = 65504.0, + min = -65504.0, + eps = 0.0009765625, + tiny = 6.103515625e-05 + ) + + } else { + + value_error("dtype must be a float type.") + } + +} + diff --git a/R/utils-data.R b/R/utils-data.R index abf76458db5b0b41853ec801753ba4c99f539ce0..e12ecf02fc838d1688f567da9a66ec4c1caeb63e 100644 --- a/R/utils-data.R +++ b/R/utils-data.R @@ -43,9 +43,11 @@ get_init <- function(x) { #' @param inherit you can optionally inherit from a dataset when creating a #' new dataset. #' @param ... public methods for the dataset class +#' @param parent_env An environment to use as the parent of newly-created +#' objects. #' #' @export -dataset <- function(name = NULL, inherit = Dataset, ...) { +dataset <- function(name = NULL, inherit = Dataset, ..., parent_env = parent.frame()) { args <- list(...) @@ -55,13 +57,17 @@ dataset <- function(name = NULL, inherit = Dataset, ...) { if (!is.null(attr(inherit, "Dataset"))) inherit <- attr(inherit, "Dataset") + + e <- new.env(parent = parent_env) + e$inherit <- inherit d <- R6::R6Class( classname = name, lock_objects = FALSE, inherit = inherit, public = args, - active = active + active = active, + parent_env = e ) init <- get_init(d) diff --git a/R/utils-pipe.R b/R/utils-pipe.R new file mode 100644 index 0000000000000000000000000000000000000000..e79f3d80856576d6db32c9fb05cdbc02b28f4ff8 --- /dev/null +++ b/R/utils-pipe.R @@ -0,0 +1,11 @@ +#' Pipe operator +#' +#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. +#' +#' @name %>% +#' @rdname pipe +#' @keywords internal +#' @export +#' @importFrom magrittr %>% +#' @usage lhs \%>\% rhs +NULL diff --git a/R/wrapers.R b/R/wrapers.R index 3693f6ec655804f15a55dbff819e03def60b37fb..3bd6b40ee5ba8efea7d97fe4f5357853fc9952da 100644 --- a/R/wrapers.R +++ b/R/wrapers.R @@ -47,3 +47,160 @@ torch_lu <- function(A, pivot=TRUE, get_infos=FALSE, out=NULL) { torch_logical_not <- function(self) { .torch_logical_not(self) } + +#' @rdname torch_bartlett_window +torch_bartlett_window <- function(window_length, periodic=TRUE, dtype=NULL, + layout=torch_strided(), device=NULL, + requires_grad=FALSE) { + opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device, + requires_grad = requires_grad) + .torch_bartlett_window(window_length = window_length, periodic = periodic, + options = opt) +} + +#' @rdname torch_blackman_window +torch_blackman_window <- function(window_length, periodic=TRUE, dtype=NULL, + layout=torch_strided(), device=NULL, + requires_grad=FALSE) { + opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device, + requires_grad = requires_grad) + .torch_blackman_window(window_length = window_length, periodic = periodic, + options = opt) +} + +#' @rdname torch_hamming_window +torch_hamming_window <- function(window_length, periodic=TRUE, alpha=0.54, + beta=0.46, dtype=NULL, layout=torch_strided(), + device=NULL, requires_grad=FALSE) { + opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device, + requires_grad = requires_grad) + .torch_hamming_window(window_length = window_length, periodic = periodic, + alpha = alpha, beta = beta, options = opt) +} + +#' @rdname torch_hann_window +torch_hann_window <- function(window_length, periodic=TRUE, dtype=NULL, + layout=torch_strided(), device=NULL, + requires_grad=FALSE) { + opt <- torch_tensor_options(dtype = dtype, layout = layout, device = device, + requires_grad = requires_grad) + .torch_hann_window(window_length = window_length, periodic = periodic, + options = opt) +} + +#' @rdname torch_normal +torch_normal <- function(mean, std = 1L, size, generator = NULL) { + .torch_normal(mean, std, size, generator) +} + +#' @rdname torch_result_type +torch_result_type <- function(tensor1, tensor2) { + + if (is_torch_tensor(tensor1) && is_torch_tensor(tensor2)) { + o <- cpp_torch_namespace_result_type_tensor_Tensor_other_Tensor( + tensor1$ptr, + tensor2$ptr + ) + } else if (is_torch_tensor(tensor1) && !is_torch_tensor(tensor2)) { + o <- cpp_torch_namespace_result_type_tensor_Tensor_other_Scalar( + tensor1$ptr, + torch_scalar(tensor2)$ptr + ) + } else if (!is_torch_tensor(tensor1) && is_torch_tensor(tensor2)) { + o <- cpp_torch_namespace_result_type_scalar_Scalar_tensor_Tensor( + torch_scalar(tensor1)$ptr, + tensor2$ptr + ) + } else if (!is_torch_tensor(tensor1) && !is_torch_tensor(tensor2)) { + o <- cpp_torch_namespace_result_type_scalar1_Scalar_scalar2_Scalar( + torch_scalar(tensor1)$ptr, + torch_scalar(tensor2)$ptr + ) + } + + torch_dtype$new(ptr = o) +} + +#' @rdname torch_sparse_coo_tensor +torch_sparse_coo_tensor <- function(indices, values, size=NULL, dtype=NULL, + device=NULL, requires_grad=FALSE) { + opt <- torch_tensor_options(dtype = dtype, device = device, + requires_grad = requires_grad) + + if (is.null(size)) + .torch_sparse_coo_tensor(indices, values, options = opt) + else + .torch_sparse_coo_tensor(indices, values, size = size, options = opt) +} + +#' @rdname torch_stft +torch_stft <- function(input, n_fft, hop_length=NULL, win_length=NULL, + window=NULL, center=TRUE, pad_mode='reflect', + normalized=FALSE, onesided=TRUE) { + if (center) { + signal_dim <- input$dim() + extended_shape <- c( + rep(2, 3 - signal_dim), + input$size() + ) + pad <- as.integer(n_fft %/% 2) + input <- nnf_pad(input$view(extended_shape), c(pad, pad), pad_mode) + input <- input$view(utils::tail(input$shape(), signal_dim)) + } + + .torch_stft(self = input, n_fft = n_fft, hop_length = hop_length, + win_length = win_length, window = window, + normalized = normalized, onesided = onesided) +} + +#' @rdname torch_tensordot +torch_tensordot <- function(a, b, dims = 2) { + + if (is.list(dims)) { + dims_a <- dims[[1]] + dims_b <- dims[[2]] + } else if (is_torch_tensor(dims) && dims$numel() > 1) { + dims_a <- as_array(dims[1]) + dims_b <- as_array(dims[2]) + } else { + + if (is_torch_tensor(dims)) + dims <- dims$item() + + if (dims < 1) + runtime_error("tensordot expects dims >= 1, but got {dims}") + + dims_a <- seq(from = -dims, to = 0) + dims_b <- seq(from = 1, to = dims) + } + + .torch_tensordot(a, b, dims_a, dims_b) +} + +#' @rdname torch_tril_indices +torch_tril_indices <- function(row, col, offset=0, dtype=torch_long(), + device='cpu', layout=torch_strided()) { + opt <- torch_tensor_options(dtype = dtype, device = device, layout = layout) + .torch_tril_indices(row, col, offset, options = opt) +} + +#' @rdname torch_triu_indices +torch_triu_indices <- function(row, col, offset=0, dtype=torch_long(), + device='cpu', layout=torch_strided()) { + opt <- torch_tensor_options(dtype = dtype, device = device, layout = layout) + .torch_triu_indices(row, col, offset, options = opt) +} + +#' @rdname torch_multilabel_margin_loss +torch_multilabel_margin_loss <- function(self, target, reduction = torch_reduction_mean()) { + .torch_multilabel_margin_loss(self, as_1_based_tensor(target), reduction) +} + +#' @rdname torch_multi_margin_loss +torch_multi_margin_loss <- function(self, target, p = 1L, margin = 1L, weight = list(), + reduction = torch_reduction_mean()) { + .torch_multi_margin_loss(self, as_1_based_tensor(target), p, margin, weight, + reduction) +} + + diff --git a/README.Rmd b/README.Rmd index a91c01a6dc065c0cff1c9bca56b2d8de6c99dacb..fdd1d8531e48e816925578b4cd275b1c69f30cd2 100644 --- a/README.Rmd +++ b/README.Rmd @@ -13,10 +13,12 @@ knitr::opts_chunk$set( ) ``` -# torch +# torch [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) ![R build status](https://github.com/mlverse/torch/workflows/Test/badge.svg) +[![CRAN status](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch) +[![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch) ## Installation diff --git a/README.md b/README.md index d8eedd868c6d47ac65940caec7c09e3cd16929f1..a5798e8ee7e51ebccd9ddaf970af0318d49f5ab7 100644 --- a/README.md +++ b/README.md @@ -1,71 +1,71 @@ -# torch +torch +============================================================================================================ [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) ![R build status](https://github.com/mlverse/torch/workflows/Test/badge.svg) +[![CRAN +status](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch) +[![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch) -## Installation +Installation +------------ Run: -``` r -remotes::install_github("mlverse/torch") -``` + remotes::install_github("mlverse/torch") At the first package load additional software will be installed. -## Example +Example +------- Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object. -``` r -library(torch) -x <- array(runif(8), dim = c(2, 2, 2)) -y <- torch_tensor(x, dtype = torch_float64()) -y -#> torch_tensor -#> (1,.,.) = -#> 0.8687 0.0157 -#> 0.4237 0.8971 -#> -#> (2,.,.) = -#> 0.4021 0.5509 -#> 0.3374 0.9034 -#> [ CPUDoubleType{2,2,2} ] -identical(x, as_array(y)) -#> [1] TRUE -``` + library(torch) + x <- array(runif(8), dim = c(2, 2, 2)) + y <- torch_tensor(x, dtype = torch_float64()) + y + #> torch_tensor + #> (1,.,.) = + #> 0.5406 0.8648 + #> 0.3097 0.9715 + #> + #> (2,.,.) = + #> 0.1309 0.8992 + #> 0.4849 0.1902 + #> [ CPUDoubleType{2,2,2} ] + identical(x, as_array(y)) + #> [1] TRUE ### Simple Autograd Example In the following snippet we let torch, using the autograd feature, calculate the derivatives: -``` r -x <- torch_tensor(1, requires_grad = TRUE) -w <- torch_tensor(2, requires_grad = TRUE) -b <- torch_tensor(3, requires_grad = TRUE) -y <- w * x + b -y$backward() -x$grad -#> torch_tensor -#> 2 -#> [ CPUFloatType{1} ] -w$grad -#> torch_tensor -#> 1 -#> [ CPUFloatType{1} ] -b$grad -#> torch_tensor -#> 1 -#> [ CPUFloatType{1} ] -``` + x <- torch_tensor(1, requires_grad = TRUE) + w <- torch_tensor(2, requires_grad = TRUE) + b <- torch_tensor(3, requires_grad = TRUE) + y <- w * x + b + y$backward() + x$grad + #> torch_tensor + #> 2 + #> [ CPUFloatType{1} ] + w$grad + #> torch_tensor + #> 1 + #> [ CPUFloatType{1} ] + b$grad + #> torch_tensor + #> 1 + #> [ CPUFloatType{1} ] ### Linear Regression @@ -75,41 +75,41 @@ scratch using torch’s Autograd. **Note** all methods that end with `_` (eg. `sub_`), will modify the tensors in place. -``` r -x <- torch_randn(100, 2) -y <- 0.1 + 0.5*x[,1] - 0.7*x[,2] - -w <- torch_randn(2, 1, requires_grad = TRUE) -b <- torch_zeros(1, requires_grad = TRUE) - -lr <- 0.5 -for (i in 1:100) { - y_hat <- torch_mm(x, w) + b - loss <- torch_mean((y - y_hat$squeeze(1))^2) - - loss$backward() - - with_no_grad({ - w$sub_(w$grad*lr) - b$sub_(b$grad*lr) - - w$grad$zero_() - b$grad$zero_() - }) -} -print(w) -#> torch_tensor -#> 0.5000 -#> -0.7000 -#> [ CPUFloatType{2,1} ] -print(b) -#> torch_tensor -#> 0.01 * -#> 10.0000 -#> [ CPUFloatType{1} ] -``` - -## Contributing + x <- torch_randn(100, 2) + y <- 0.1 + 0.5*x[,1] - 0.7*x[,2] + + w <- torch_randn(2, 1, requires_grad = TRUE) + b <- torch_zeros(1, requires_grad = TRUE) + + lr <- 0.5 + for (i in 1:100) { + y_hat <- torch_mm(x, w) + b + loss <- torch_mean((y - y_hat$squeeze(1))^2) + + loss$backward() + + with_no_grad({ + w$sub_(w$grad*lr) + b$sub_(b$grad*lr) + + w$grad$zero_() + b$grad$zero_() + }) + } + print(w) + #> torch_tensor + #> 1e-09 * + #> 5.2672 + #> -6.7969 + #> [ CPUFloatType{2,1} ] + print(b) + #> torch_tensor + #> 0.01 * + #> -9.6802 + #> [ CPUFloatType{1} ] + +Contributing +------------ No matter your current skills it’s possible to contribute to `torch` development. See the contributing guide for more information. diff --git a/_pkgdown.yml b/_pkgdown.yml index 4cc2b9c81a1a11c0806dcc609a1635a366d7f2dc..538bb8fd93780d130924a1bc05036d9e11265e59 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -2,12 +2,43 @@ destination: docs template: params: bootswatch: united + ganalytics: UA-178883486-1 + +development: + mode: auto navbar: structure: - left: [home, articles, examples, reference, news] + left: [home, getting-started, articles, examples, reference, news] right: [github] components: + getting-started: + text: Getting started + menu: + - text: Beginers guide + - text: Warm-up + href: articles/getting-started/warmup.html + - text: Tensors + href: articles/getting-started/tensors.html + - text: Tensors and autograd + href: articles/getting-started/tensors-and-autograd.html + - text: Defining new autograd functions + href: articles/getting-started/new-autograd-functions.html + - text: 'nn: neural networks with torch' + href: articles/getting-started/nn.html + - text: 'optim: optimizers in torch' + href: articles/getting-started/optim.html + - text: Custom nn modules + href: articles/getting-started/custom-nn.html + - text: Control flow & Weight sharing + href: articles/getting-started/control-flow-and-weight-sharing.html + - text: Torch Mechanics + - text: What's torch? + href: articles/getting-started/what-is-torch.html + - text: 'Autograd: automatic differentiation' + href: articles/getting-started/autograd.html + - text: Neural networks + href: articles/getting-started/neural-networks.html articles: text: Articles menu: @@ -16,6 +47,10 @@ navbar: href: articles/tensor-creation.html - text: Indexing href: articles/indexing.html + - text: Tensor class + href: articles/tensor/index.html + - text: Serialization + href: articles/serialization.html - text: Datasets - text: Loading Data href: articles/loading-data.html @@ -27,10 +62,86 @@ navbar: examples: text: Examples menu: - - text: Vision - - text: mnist-mlp - href: articles/examples/mnist-mlp.html - - text: mnist-cnn - href: articles/examples/mnist-cnn.html - - text: mnist-dcgan - href: articles/examples/mnist-dcgan.html + - text: basic-autograd + href: articles/examples/basic-autograd.html + - text: basic-nn-module + href: articles/examples/basic-nn-module.html + - text: dataset + href: articles/examples/dataset.html + +reference: + - title: Tensor creation utilities + contents: + - torch_empty + - torch_arange + - torch_eye + - torch_full + - torch_linspace + - torch_logspace + - torch_ones + - torch_rand + - torch_randint + - torch_randn + - torch_randperm + - torch_zeros + - matches("torch_.*_like") + - as_array + - title: Tensor attributes + contents: + - has_concept("tensor-attributes") + - is_torch_layout + - is_torch_memory_format + - is_torch_qscheme + - is_undefined_tensor + - title: Serialization + contents: + - has_concept("serialization") + - title: Mathematical operations on tensors + contents: + - starts_with("torch_") + - -torch_empty + - -torch_arange + - -torch_eye + - -torch_full + - -torch_linspace + - -torch_logspace + - -torch_ones + - -torch_rand + - -torch_randint + - -torch_randn + - -torch_randperm + - -torch_zeros + - -matches("torch_.*_like") + - -has_concept("tensor-attributes") + - -has_concept("serialization") + - title: Neural network modules + contents: + - starts_with("nn_") + - is_nn_module + - is_nn_parameter + - is_nn_buffer + - title: Neural networks functional module + contents: + - starts_with("nnf_") + - title: Optimizers + contents: + - starts_with("optim_") + - title: Datasets + contents: + - starts_with("dataset") + - starts_with("dataloader") + - starts_with("enumerate") + - tensor_dataset + - is_dataloader + - title: Autograd + contents: + - starts_with("autograd_") + - with_no_grad + - with_enable_grad + - AutogradContext + - title: Cuda utilities + contents: + - starts_with("cuda_") + - title: Installation + contents: + - install_torch diff --git a/cran-comments.md b/cran-comments.md new file mode 100644 index 0000000000000000000000000000000000000000..e9710d597f16c8c6377c5bd06ab2d33643e9b00f --- /dev/null +++ b/cran-comments.md @@ -0,0 +1,25 @@ + +We found a bug in the `Makevars` that could explain the errors with parallel +make that motivated removing this package from CRAN. +This release hopefully fixes the issue, however we are not able to reproduce +this outside of CRAN so we can further debug. + +## Test environments + +* local R installation, R 4.0.2 +* local mac OS install, R 4.0.0 +* ubuntu 16.04 (on github actions), R 4.0.0 +* mac OS 10.15.4 (on github actions) R 4.0.0 +* Microsoft Windows Server 2019 10.0.17763 (on github actions) R 4.0.0 +* win-builder (devel) + +## R CMD check results + +0 errors | 0 warnings | 1 note + +installed size is 23.1Mb +sub-directories of 1Mb or more: + R 3.1Mb + help 2.6Mb + libs 17.2Mb + diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index c39b2d5eee392e3d9f14cc771df4facbcbb84fc3..0000000000000000000000000000000000000000 --- a/docs/404.html +++ /dev/null @@ -1,191 +0,0 @@ - - - - - - - - -Page not found (404) • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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This outlines how to propose a change to torch. For more detailed info about contributing to this, and other tidyverse packages, please see the development contributing guide.

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mnist-cnn

- - - Source: vignettes/examples/mnist-cnn.Rmd - - -
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dir <- "~/Downloads/mnist"
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-ds <- mnist_dataset(
-  dir,
-  download = TRUE,
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
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-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$conv1 <- nn_conv2d(1, 32, 3, 1)
-    self$conv2 <- nn_conv2d(32, 64, 3, 1)
-    self$dropout1 <- nn_dropout2d(0.25)
-    self$dropout2 <- nn_dropout2d(0.5)
-    self$fc1 <- nn_linear(9216, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x <- self$conv1(x)
-    x <- nnf_relu(x)
-    x <- self$conv2(x)
-    x <- nnf_relu(x)
-    x <- nnf_max_pool2d(x, 2)
-    x <- self$dropout1(x)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$fc1(x)
-    x <- nnf_relu(x)
-    x <- self$dropout2(x)
-    x <- self$fc2(x)
-    output <- nnf_log_softmax(x, dim=1)
-    output
-  }
-)
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-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
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-epochs <- 10
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-for (epoch in 1:10) {
-
-  pb <- progress::progress_bar$new(
-    total = length(dl),
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/examples/mnist-cnn_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/examples/mnist-cnn_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-cnn_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-cnn_files/header-attrs-2.3/header-attrs.js b/docs/articles/examples/mnist-cnn_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-cnn_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-dcgan.html b/docs/articles/examples/mnist-dcgan.html deleted file mode 100644 index ca8f65769502c81540e85024836eada8ce46d013..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-dcgan.html +++ /dev/null @@ -1,296 +0,0 @@ - - - - - - - -mnist-dcgan • torch - - - - - - - - - - -
-
- - - - -
-
-
-

mnist-dcgan

- - - Source: vignettes/examples/mnist-dcgan.Rmd - - -
- - - -
library(torch)
-
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir,
-  download = TRUE,
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x <- 2*(x - 0.5)
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-generator <- nn_module(
-  "generator",
-  initialize = function(latent_dim, out_channels) {
-    self$main <- nn_sequential(
-      nn_conv_transpose2d(latent_dim, 512, kernel_size = 4,
-                          stride = 1, padding = 0, bias = FALSE),
-      nn_batch_norm2d(512),
-      nn_relu(),
-      nn_conv_transpose2d(512, 256, kernel_size = 4,
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(256),
-      nn_relu(),
-      nn_conv_transpose2d(256, 128, kernel_size = 4,
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(128),
-      nn_relu(),
-      nn_conv_transpose2d(128, out_channels, kernel_size = 4,
-                          stride = 2, padding = 3, bias = FALSE),
-      nn_tanh()
-    )
-  },
-  forward = function(input) {
-    self$main(input)
-  }
-)
-
-discriminator <- nn_module(
-  "discriminator",
-  initialize = function(in_channels) {
-    self$main <- nn_sequential(
-      nn_conv2d(in_channels, 16, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(16, 32, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(32),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(32, 64, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(64),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(64, 128, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE)
-    )
-    self$linear <- nn_linear(128, 1)
-    self$sigmoid <- nn_sigmoid()
-  },
-  forward = function(input) {
-    x <- self$main(input)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$linear(x)
-    self$sigmoid(x)
-  }
-)
-
-plot_gen <- function(noise) {
-  img <- G(noise)
-  img <- img$cpu()
-  img <- img[1,1,,,newaxis]/2 + 0.5
-  img <- torch_stack(list(img, img, img), dim = 2)[..,1]
-  img <- as.raster(as_array(img))
-  plot(img)
-}
-
-device <- torch_device(ifelse(cuda_is_available(),  "cuda", "cpu"))
-
-G <- generator(latent_dim = 100, out_channels = 1)
-D <- discriminator(in_channels = 1)
-
-init_weights <- function(m) {
-  if (grepl("conv", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 0.0, 0.02)
-  } else if (grepl("batch_norm", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 1.0, 0.02)
-    nn_init_constant_(m$bias$data(), 0)
-  }
-}
-
-G[[1]]$apply(init_weights)
-D[[1]]$apply(init_weights)
-
-G$to(device = device)
-D$to(device = device)
-
-G_optimizer <- optim_adam(G$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-D_optimizer <- optim_adam(D$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-
-fixed_noise <- torch_randn(1, 100, 1, 1, device = device)
-
-loss <- nn_bce_loss()
-
-for (epoch in 1:10) {
-
-  pb <- progress::progress_bar$new(
-    total = length(dl),
-    format = "[:bar] :eta Loss D: :lossd Loss G: :lossg"
-  )
-  lossg <- c()
-  lossd <- c()
-
-  for (b in enumerate(dl)) {
-
-    y_real <- torch_ones(32, device = device)
-    y_fake <- torch_zeros(32, device = device)
-
-    noise <- torch_randn(32, 100, 1, 1, device = device)
-    fake <- G(noise)
-
-    img <- b[[1]]$to(device = device)
-
-    # train the discriminator ---
-    D_loss <- loss(D(img), y_real) + loss(D(fake$detach()), y_fake)
-
-    D_optimizer$zero_grad()
-    D_loss$backward()
-    D_optimizer$step()
-
-    # train the generator ---
-
-    G_loss <- loss(D(fake), y_real)
-
-    G_optimizer$zero_grad()
-    G_loss$backward()
-    G_optimizer$step()
-
-    lossd <- c(lossd, D_loss$item())
-    lossg <- c(lossg, G_loss$item())
-    pb$tick(tokens = list(lossd = mean(lossd), lossg = mean(lossg)))
-  }
-  plot_gen(fixed_noise)
-
-  cat(sprintf("Epoch %d - Loss D: %3f Loss G: %3f\n", epoch, mean(lossd), mean(lossg)))
-}
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js b/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-mlp.html b/docs/articles/examples/mnist-mlp.html deleted file mode 100644 index db40b8ea67a827c698d268c466c47076dab8c7b5..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-mlp.html +++ /dev/null @@ -1,203 +0,0 @@ - - - - - - - -mnist-mlp • torch - - - - - - - - - - -
-
- - - - -
-
-
-

mnist-mlp

- - - Source: vignettes/examples/mnist-mlp.Rmd - - -
- - - -
dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir,
-  download = TRUE,
-  transform = function(x) {
-    x$to(dtype = torch_float())/256
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$fc1 <- nn_linear(784, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x %>%
-      torch_flatten(start_dim = 2) %>%
-      self$fc1() %>%
-      nnf_relu() %>%
-      self$fc2() %>%
-      nnf_log_softmax(dim = 1)
-  }
-)
-
-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
-epochs <- 10
-
-for (epoch in 1:10) {
-
-  pb <- progress::progress_bar$new(
-    total = length(dl),
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/examples/mnist-mlp_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/examples/mnist-mlp_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-mlp_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-mlp_files/header-attrs-2.3/header-attrs.js b/docs/articles/examples/mnist-mlp_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/examples/mnist-mlp_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/extending-autograd.html b/docs/articles/extending-autograd.html deleted file mode 100644 index 8c27bfa2e021ff15406cac0fe22ed6f3dce868d4..0000000000000000000000000000000000000000 --- a/docs/articles/extending-autograd.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - -Extending Autograd • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Extending Autograd

- - - Source: vignettes/extending-autograd.Rmd - - -
- - - -
library(torch)
-

Adding operations to autograd requires implementing a new autograd_function for each operation. Recall that autograd_functionss are what autograd uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:

-
    -

  • forward() - the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., with requires_grad=TRUE) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list of Tensors if there are multiple outputs. Also, please refer to the docs of autograd_function to find descriptions of useful methods that can be called only from forward().

    • -

  • backward() - gradient formula. It will be given as many Tensor arguments as there were outputs, with each of them representing gradient w.r.t. that output. It should return as many Tensors as there were Tensor's that required gradients in forward, with each of them containing the gradient w.r.t. its corresponding input.

    • -
-
-

-Note

-

It’s the user’s responsibility to use the special functions in the forward’s ctx properly in order to ensure that the new autograd_function works properly with the autograd engine.

-
    -

  • save_for_backward() must be used when saving input or ouput of the forward to be used later in the backward.

    • -

  • mark_dirty() must be used to mark any input that is modified inplace by the forward function.

    • -

  • mark_non_differentiable() must be used to tell the engine if an output is not differentiable.

    • -
-
-
-

-Examples

-

Below you can find code for a linear function:

-
linear <- autograd_function(
-  forward = function(ctx, input, weight, bias = NULL) {
-    ctx$save_for_backward(input = input, weight = weight, bias = bias)
-    output <- input$mm(weight$t())
-    if (!is.null(bias))
-      output <- output + bias$unsqueeze(0)$expand_as(output)
-
-    output
-  },
-  backward = function(ctx, grad_output) {
-
-    s <- ctx$saved_variables
-
-    grads <- list(
-      input = NULL,
-      weight = NULL,
-      bias = NULL
-    )
-
-    if (ctx$needs_input_grad$input)
-      grads$input <- grad_output$mm(s$weight)
-
-    if (ctx$needs_input_grad$weight)
-      grads$weight <- grad_output$t()$mm(s$input)
-
-    if (!is.null(s$bias) && ctx$needs_input_grad$bias)
-      grads$bias <- grad_output$sum(dim = 0)
-
-    grads
-  }
-)
-

Here, we give an additional example of a function that is parametrized by non-Tensor arguments:

-
mul_constant <- autograd_function(
-  forward = function(ctx, tensor, constant) {
-    ctx$save_for_backward(constant = constant)
-    tensor * constant
-  },
-  backward = function(ctx, grad_output) {
-    v <- ctx$saved_variables
-    list(
-      tensor = grad_output * v$constant
-    )
-  }
-)
-
x <- torch_tensor(1, requires_grad = TRUE)
-o <- mul_constant(x, 2)
-o$backward()
-x$grad
-
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/extending-autograd_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/extending-autograd_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/extending-autograd_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/extending-autograd_files/header-attrs-2.3/header-attrs.js b/docs/articles/extending-autograd_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/extending-autograd_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index 50afc9ac2fd9bfcf423c06aab30da3ce6a49ca59..0000000000000000000000000000000000000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,204 +0,0 @@ - - - - - - - - -Articles • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-
Extending Autograd
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Indexing tensors
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Loading data
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Creating tensors
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Using autograd
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Developed by Daniel Falbel, Javier Luraschi.

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Site built with pkgdown 1.5.1.

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- - - - - - - - diff --git a/docs/articles/indexing.html b/docs/articles/indexing.html deleted file mode 100644 index 0207c0554ad6c5f58fd2b3d8f823b693921165d4..0000000000000000000000000000000000000000 --- a/docs/articles/indexing.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - -Indexing tensors • torch - - - - - - - - - - -
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- - - - -
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Indexing tensors

- - - Source: vignettes/indexing.Rmd - - -
- - - -
library(torch)
-

In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.

-

Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the numpy indexing article available here.

-
-

-Single element indexing

-

Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)

-
x <- torch_tensor(1:10)
-x[1]
-x[-1]
-

You can also subset matrices and higher dimensions arrays using the same syntax:

-
x <- x$reshape(shape = c(2,5))
-x
-x[1,3]
-x[1,-1]
-

Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:

-
x[1]
-
-
-

-Slicing and striding

-

It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:

-
x <- torch_tensor(1:10)
-x
-x[2:5]
-x[1:(-7)]
-

You can also use the 1:10:2 syntax which means: In the range from 1 to 10, take every second item. For example:

-
x[1:5:2]
-

Another special syntax is the N, meaning the size of the specified dimension.

-
x[5:N]
-
-
-

-Getting the complete dimension

-

Like in R, you can take all elements in a dimension by leaving an index empty.

-

Consider a matrix:

-
x <- torch_randn(2, 3)
-x
-

The following syntax will give you the first row:

-
x[1,]
-

And this would give you the first 2 columns:

-
x[,1:2]
-
-
-

-Dropping dimensions

-

By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:

-
x <- torch_randn(2, 3)
-x[1,]$shape
-

You can optionally use the drop = FALSE argument to avoid dropping the dimension.

-
x[1,,drop = FALSE]$shape
-
-
-

-Adding a new dimension

-

It’s possible to add a new dimension to a tensor using index-like syntax:

-
x <- torch_tensor(c(10))
-x$shape
-x[, newaxis]$shape
-x[, newaxis, newaxis]$shape
-

You can also use NULL instead of newaxis:

-
x[,NULL]$shape
-
-
-

-Dealing with variable number of indices

-

Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use ..:

-
z <- torch_tensor(1:125)$reshape(c(5,5,5))
-z[1,..]
-z[..,1]
-
-
- - - -
- - - -
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Developed by Daniel Falbel, Javier Luraschi.

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Site built with pkgdown 1.5.1.

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- - - - - - diff --git a/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/indexing_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/indexing_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/indexing_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/indexing_files/header-attrs-2.3/header-attrs.js b/docs/articles/indexing_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/indexing_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/loading-data.html b/docs/articles/loading-data.html deleted file mode 100644 index 3fd2a41a3fbf466887de1aa02e43612b7e8cb2e3..0000000000000000000000000000000000000000 --- a/docs/articles/loading-data.html +++ /dev/null @@ -1,274 +0,0 @@ - - - - - - - -Loading data • torch - - - - - - - - - - -
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- - - - -
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-

Loading data

- - - Source: vignettes/loading-data.Rmd - - -
- - - -
library(torch)
-
-

-Datasets and data loaders

-

Central to data ingestion and preprocessing are datasets and data loaders.

-

torch comes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g., mnist_dataset()), which can be iterated over by means of dataloaders:

-
# ...
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x[newaxis,..]
-  }
-)
-
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-for (b in enumerate(dl)) {
-  # ...
-

Cf. vignettes/examples/mnist-cnn.R for a complete example.

-

What if you want to train on a different dataset? In these cases, you subclass Dataset, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement .getitem(), and say what should be returned when the data loader is asking for the next batch.

-

In .getitem(), you can implement whatever preprocessing you require. Additionally, you should implement .length(), so users can find out how many items there are in the dataset.

-

While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own dataset to train on Allison Horst's penguins.

-
-
-

-A custom dataset

-
library(palmerpenguins)
-library(magrittr)
-
-penguins
-

Datasets are R6 classes created using the dataset() constructor. You can pass a name and various member functions. Among those should be initialize(), to create instance variables, .getitem(), to indicate how the data should be returned, and .length(), to say how many items we have.

-

In addition, any number of helper functions can be defined.

-

Here, we assume the penguins have already been loaded, and all preprocessing consists in removing lines with NA values, transforming factors to numbers starting from 0, and converting from R data types to torch tensors.

-

In .getitem, we essentially decide how this data is going to be used: All variables besides species go into x, the predictor, and species will constitute y, the target. Predictor and target are returned in a list, to be accessed as batch[[1]] and batch[[2]] during training.

-
penguins_dataset <- dataset(
-
-  name = "penguins_dataset",
-
-  initialize = function() {
-    self$data <- self$prepare_penguin_data()
-  },
-
-  .getitem = function(index) {
-
-    x <- self$data[index, 2:-1]
-    y <- self$data[index, 1]$to(torch_long())
-
-    list(x, y)
-  },
-
-  .length = function() {
-    self$data$size()[[1]]
-  },
-
-  prepare_penguin_data = function() {
-
-    input <- na.omit(penguins)
-    # conveniently, the categorical data are already factors
-    input$species <- as.numeric(input$species)
-    input$island <- as.numeric(input$island)
-    input$sex <- as.numeric(input$sex)
-
-    input <- as.matrix(input)
-    torch_tensor(input)
-  }
-)
-

Let’s create the dataset , query for it’s length, and look at its first item:

-
tuxes <- penguins_dataset()
-tuxes$.length()
-tuxes$.getitem(1)
-

To be able to iterate over tuxes, we need a data loader (we override the default batch size of 1):

-
dl <-tuxes %>% dataloader(batch_size = 8)
-

Calling .length() on a data loader (as opposed to a dataset) will return the number of batches we have:

-
dl$.length()
-

And we can create an iterator to inspect the first batch:

-
iter <- dl$.iter()
-b <- iter$.next()
-b
-

To train a network, we can use enumerate to iterate over batches.

-
-
-

-Training with data loaders

-

Our example network is very simple. (In reality, we would want to treat island as the categorical variable it is, and either one-hot-encode or embed it.)

-
net <- nn_module(
-  "PenguinNet",
-  initialize = function() {
-    self$fc1 <- nn_linear(6, 32)
-    self$fc2 <- nn_linear(32, 3)
-  },
-  forward = function(x) {
-    x %>%
-      self$fc1() %>%
-      nnf_relu() %>%
-      self$fc2() %>%
-      nnf_log_softmax(dim = 1)
-  }
-)
-
-model <- net()
-

We still need an optimizer:

-
optimizer <- optim_sgd(model$parameters, lr = 0.01)
-

And we’re ready to train:

-
for (epoch in 1:10) {
-
-  l <- c()
-
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-  }
-
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
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- - - - - - diff --git a/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/loading-data_files/header-attrs-2.3/header-attrs.js b/docs/articles/loading-data_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/loading-data_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/tensor-creation.html b/docs/articles/tensor-creation.html deleted file mode 100644 index 96bc72ff20e6eee597bea4ad3f71d32889586277..0000000000000000000000000000000000000000 --- a/docs/articles/tensor-creation.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - -Creating tensors • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Creating tensors

- - - Source: vignettes/tensor-creation.Rmd - - -
- - - -
library(torch)
-

In this article we describe various ways of creating torch tensors in R.

-
-

-From R objects

-

You can create tensors from R objects using the torch_tensor function. The torch_tensor function takes an R vector, matrix or array and creates an equivalent torch_tensor.

-

You can see a few examples below:

-
torch_tensor(c(1,2,3))
-
-# conform to row-major indexing used in torch
-torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE))
-torch_tensor(array(runif(12), dim = c(2, 2, 3)))
-

By default, we will create tensors in the cpu device, converting their R datatype to the corresponding torch dtype.

-
-

Note currently, only numeric and boolean types are supported.

-
-

You can always modify dtype and device when converting an R object to a torch tensor. For example:

-
torch_tensor(1, dtype = torch_long())
-torch_tensor(1, device = "cpu", dtype = torch_float64())
-

Other options available when creating a tensor are:

-
    -
  • -requires_grad: boolean indicating if you want autograd to record operations on them for automatic differentiation.
    • -
  • -pin_memory: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.
    • -
-

These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.

-
-
-

-Using creation functions

-

You can also use the torch_* functions listed below to create torch tensors using some algorithm.

-

For example, the torch_randn function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the ... argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.

-
x <- torch_randn(5, 3)
-x
-

Another example is torch_ones, which creates a tensor filled with ones.

-
x <- torch_ones(2, 4, dtype = torch_int64(), device = "cpu")
-x
-

Here is the full list of functions that can be used to bulk-create tensors in torch:

-
    -
  • -torch_arange: Returns a tensor with a sequence of integers,
    • -
  • -torch_empty: Returns a tensor with uninitialized values,
    • -
  • -torch_eye: Returns an identity matrix,
    • -
  • -torch_full: Returns a tensor filled with a single value,
    • -
  • -torch_linspace: Returns a tensor with values linearly spaced in some interval,
    • -
  • -torch_logspace: Returns a tensor with values logarithmically spaced in some interval,
    • -
  • -torch_ones: Returns a tensor filled with all ones,
    • -
  • -torch_rand: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).
    • -
  • -torch_randint: Returns a tensor with integers randomly drawn from an interval,
    • -
  • -torch_randn: Returns a tensor filled with values drawn from a unit normal distribution,
    • -
  • -torch_randperm: Returns a tensor filled with a random permutation of integers in some interval,
    • -
  • -torch_zeros: Returns a tensor filled with all zeros.
    • -
-
-
-

-Conversion

-

Once a tensor exists you can convert between dtypes and move to a different device with to method. For example:

-
x <- torch_tensor(1)
-y <- x$to(dtype = torch_int32())
-x
-y
-

You can also copy a tensor to the GPU using:

-
x <- torch_tensor(1)
-y <- x$cuda())
-
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/tensor-creation_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/tensor-creation_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/tensor-creation_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/tensor-creation_files/header-attrs-2.3/header-attrs.js b/docs/articles/tensor-creation_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/tensor-creation_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/using-autograd.html b/docs/articles/using-autograd.html deleted file mode 100644 index 9cd634b0cef30e19ac25fa32a466f70c17818a5a..0000000000000000000000000000000000000000 --- a/docs/articles/using-autograd.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - -Using autograd • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Using autograd

- - - Source: vignettes/using-autograd.Rmd - - -
- - - -
library(torch)
-

So far, all we’ve been using from torch is tensors, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.

-

Before we see that in action, let’s get some more background.

-
-

-Automatic differentiation with autograd

-

Torch uses a module called autograd to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and propagating gradients back through the network. This is a form of reverse mode automatic differentiation.

-

As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with requires_grad = TRUE. E.g.

-
x <- torch_ones(2,2, requires_grad = TRUE)
-

To be clear, this is a tensor with respect to which gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data 1. If we now perform some operation on that tensor, assigning the result to y

-
y <- x$mean()
-

we find that y now has a non-empty grad_fn that tells torch how to compute the gradient of y with respect to x:

-
y$grad_fn
-

Actual computation of gradients is triggered by calling backward() on the output tensor.

-
y$backward()
-

That executed, x now has a non-empty field grad that stores the gradient of y with respect to x:

-
x$grad
-

With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.

-

Here is a slightly more complex example. We call retain_grad() on y and z just for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.

-
x1 <- torch_ones(2,2, requires_grad = TRUE)
-x2 <- torch_tensor(1.1, requires_grad = TRUE)
-y <- x1 * (x2 + 2)
-y$retain_grad()
-z <- y$pow(2) * 3
-z$retain_grad()
-out <- z$mean()
-

Starting from out$grad_fn, we can follow the graph all back to the leaf nodes:

-
# how to compute the gradient for mean, the last operation executed
-out$grad_fn
-# how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3
-out$grad_fn$next_functions
-# how to compute the gradient for pow in z = y.pow(2) * 3
-out$grad_fn$next_functions[[1]]$next_functions
-# how to compute the gradient for the multiplication in y = x * (x + 2)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions
-# how to compute the gradient for the two branches of y = x * (x + 2),
-# where the left branch is a leaf node (AccumulateGrad for x1)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions
-# here we arrive at the other leaf node (AccumulateGrad for x2)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions
-

After calling out$backward(), all tensors in the graph will have their respective gradients created. Without our calls to retain_grad above, z$grad and y$grad would be empty:

-
out$backward()
-z$grad
-y$grad
-x2$grad
-x1$grad
-

Thus acquainted with autograd, we’re ready to modify our example.

-
-
-

-The simple network, now using autograd

-

For a single new line calling loss$backward(), now a number of lines (that did manual backprop) are gone:

-
### generate training data -----------------------------------------------------
-# input dimensionality (number of input features)
-d_in <- 3
-# output dimensionality (number of predicted features)
-d_out <- 1
-# number of observations in training set
-n <- 100
-# create random data
-x <- torch_randn(n, d_in)
-y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n)
-y <- y$unsqueeze(dim = 1)
-### initialize weights ---------------------------------------------------------
-# dimensionality of hidden layer
-d_hidden <- 32
-# weights connecting input to hidden layer
-w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE)
-# weights connecting hidden to output layer
-w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE)
-# hidden layer bias
-b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE)
-# output layer bias
-b2 <- torch_zeros(1, d_out,requires_grad = TRUE)
-### network parameters ---------------------------------------------------------
-learning_rate <- 1e-4
-### training loop --------------------------------------------------------------
-for (t in 1:200) {
-
-    ### -------- Forward pass -------- 
-    y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2)
-    ### -------- compute loss -------- 
-    loss <- (y_pred - y)$pow(2)$mean()
-    if (t %% 10 == 0) cat(t, as_array(loss), "\n")
-    ### -------- Backpropagation -------- 
-    # compute the gradient of loss with respect to all tensors with requires_grad = True.
-    loss$backward()
-
-    ### -------- Update weights -------- 
-
-    # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation
-    with_no_grad({
-
-      w1$sub_(learning_rate * w1$grad)
-      w2$sub_(learning_rate * w2$grad)
-      b1$sub_(learning_rate * b1$grad)
-      b2$sub_(learning_rate * b2$grad)
-
-      # Zero the gradients after every pass, because they'd accumulate otherwise
-      w1$grad$zero_()
-      w2$grad$zero_()
-      b1$grad$zero_()
-      b2$grad$zero_()
-
-    })
-
-}
-

We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.

-
-
-
-
    -

  1. Unless we want to change the data, as in adversarial example generation↩︎

    2. -
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-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

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Site built with pkgdown 1.5.1.

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- - - - - - diff --git a/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a570108bde9d7daace534cd651c5f042..0000000000000000000000000000000000000000 --- a/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/using-autograd_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/using-autograd_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/using-autograd_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/using-autograd_files/header-attrs-2.3/header-attrs.js b/docs/articles/using-autograd_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e02028785163a821c31bca8743a8ab59a..0000000000000000000000000000000000000000 --- a/docs/articles/using-autograd_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 5d8ead1a4be11ba65ff26f9890e1fc052f175747..0000000000000000000000000000000000000000 --- a/docs/authors.html +++ /dev/null @@ -1,206 +0,0 @@ - - - - - - - - -Authors • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
- - - - -
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Authors

-
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    -
  • -

    Daniel Falbel. Author, maintainer. -

    -
    • -
  • -

    Javier Luraschi. Author. -

    -
    • -
  • -

    Dmitriy Selivanov. Contributor. -

    -
    • -
  • -

    Athos Damiani. Contributor. -

    -
    • -
  • -

    RStudio. Copyright holder. -

    -
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-

Developed by Daniel Falbel, Javier Luraschi.

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- -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; -} - -.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { - margin-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion { - padding: 0; - overflow: visible -} - -.algolia-autocomplete .algolia-docsearch-suggestion--category-header { - padding: .125rem 1rem; - margin-top: 0; - font-size: 1.3em; - font-weight: 500; - color: #00008B; - border-bottom: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { - float: none; - padding-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { - float: none; - width: auto; - padding: 0; - text-align: left -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content { - float: none; - width: auto; - padding: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content::before { - display: none -} - -.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { - padding-top: .75rem; - margin-top: .75rem; - border-top: 1px solid rgba(0, 0, 0, .1) -} - -.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { - display: block; - padding: .1rem 1rem; - margin-bottom: 0.1; - font-size: 1.0em; - font-weight: 400 - /* display: none */ -} - -.algolia-autocomplete .algolia-docsearch-suggestion--title { - display: block; - padding: .25rem 1rem; - margin-bottom: 0; - font-size: 0.9em; - font-weight: 400 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--text { - padding: 0 1rem .5rem; 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- // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index a36128e298c5f03de709e362febefee84db4729f..0000000000000000000000000000000000000000 --- a/docs/index.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - -Tensors and Neural Networks with GPU Acceleration • torch - - - - - - - - - - -
    -
    - - - - -
    -
    -
    -

    -torch -

    - -
    -

    -Installation

    -

    Run:

    -
    remotes::install_github("mlverse/torch")
    -

    At the first package load additional software will be installed.

    -
    -
    -

    -Example

    -

    Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.

    -
    library(torch)
-x <- array(runif(8), dim = c(2, 2, 2))
-y <- torch_tensor(x, dtype = torch_float64())
-y
-#> torch_tensor 
-#> (1,.,.) = 
-#>   0.8687  0.0157
-#>   0.4237  0.8971
-#> 
-#> (2,.,.) = 
-#>   0.4021  0.5509
-#>   0.3374  0.9034
-#> [ CPUDoubleType{2,2,2} ]
-identical(x, as_array(y))
-#> [1] TRUE
    -
    -

    -Simple Autograd Example

    -

    In the following snippet we let torch, using the autograd feature, calculate the derivatives:

    -
    x <- torch_tensor(1, requires_grad = TRUE)
-w <- torch_tensor(2, requires_grad = TRUE)
-b <- torch_tensor(3, requires_grad = TRUE)
-y <- w * x + b
-y$backward()
-x$grad
-#> torch_tensor 
-#>  2
-#> [ CPUFloatType{1} ]
-w$grad
-#> torch_tensor 
-#>  1
-#> [ CPUFloatType{1} ]
-b$grad
-#> torch_tensor 
-#>  1
-#> [ CPUFloatType{1} ]
    -
    -
    -

    -Linear Regression

    -

    In the following example we are going to fit a linear regression from scratch using torch’s Autograd.

    -

    Note all methods that end with _ (eg. sub_), will modify the tensors in place.

    -
    x <- torch_randn(100, 2)
-y <- 0.1 + 0.5*x[,1] - 0.7*x[,2]
-
-w <- torch_randn(2, 1, requires_grad = TRUE)
-b <- torch_zeros(1, requires_grad = TRUE)
-
-lr <- 0.5
-for (i in 1:100) {
-  y_hat <- torch_mm(x, w) + b
-  loss <- torch_mean((y - y_hat$squeeze(1))^2)
-
-  loss$backward()
-
-  with_no_grad({
-    w$sub_(w$grad*lr)
-    b$sub_(b$grad*lr)
-
-    w$grad$zero_()
-    b$grad$zero_()
-  })
-}
-print(w)
-#> torch_tensor 
-#>  0.5000
-#> -0.7000
-#> [ CPUFloatType{2,1} ]
-print(b)
-#> torch_tensor 
-#> 0.01 *
-#> 10.0000
-#> [ CPUFloatType{1} ]
    -
    -
    -
    -

    -Contributing

    -

    No matter your current skills it’s possible to contribute to torch development. See the contributing guide for more information.

    -
    -
    -
    - - -
    - - -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad82769b87f10725c57dca6fcf41b4bffe462c..0000000000000000000000000000000000000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index c01e5923be6ff1edbccf20f93be6bdf8dbd67bb3..0000000000000000000000000000000000000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,367 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; -} -footer .pkgdown { - text-align: right; -} -footer p { - margin-bottom: 0; -} - -img.icon { - float: right; -} - -img { - max-width: 100%; -} - -/* Fix bug in bootstrap (only seen in firefox) */ -summary { - display: list-item; -} - -/* Typographic tweaking ---------------------------------*/ - -.contents .page-header { - margin-top: calc(-60px + 1em); -} - -dd { - margin-left: 3em; -} - -/* Section anchors ---------------------------------*/ - -a.anchor { - margin-left: -30px; - display:inline-block; - width: 30px; - height: 30px; - visibility: hidden; - - background-image: url(./link.svg); - background-repeat: no-repeat; - background-size: 20px 20px; - background-position: center center; -} - -.hasAnchor:hover a.anchor { - visibility: visible; -} - -@media (max-width: 767px) { - .hasAnchor:hover a.anchor { - visibility: hidden; - } -} - - -/* Fixes for fixed navbar --------------------------*/ - -.contents h1, .contents h2, .contents h3, .contents h4 { - padding-top: 60px; - margin-top: -40px; -} - -/* Navbar submenu --------------------------*/ - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu>.dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover>.dropdown-menu { - display: block; -} - -.dropdown-submenu>a:after { - display: block; - content: " "; - float: right; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; - border-width: 5px 0 5px 5px; - border-left-color: #cccccc; - margin-top: 5px; - margin-right: -10px; -} - -.dropdown-submenu:hover>a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left>.dropdown-menu { - left: -100%; - margin-left: 10px; - border-radius: 6px 0 6px 6px; -} - -/* Sidebar --------------------------*/ - -#pkgdown-sidebar { - margin-top: 30px; - position: -webkit-sticky; - position: sticky; - top: 70px; -} - -#pkgdown-sidebar h2 { - font-size: 1.5em; - margin-top: 1em; -} - -#pkgdown-sidebar h2:first-child { - margin-top: 0; -} - -#pkgdown-sidebar .list-unstyled li { - margin-bottom: 0.5em; -} - -/* bootstrap-toc tweaks ------------------------------------------------------*/ - -/* All levels of nav */ - -nav[data-toggle='toc'] .nav > li > a { - padding: 4px 20px 4px 6px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; -} - -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 5px; - color: inherit; - border-left: 1px solid #878787; -} - -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 5px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; - border-left: 2px solid #878787; -} - -/* Nav: second level (shown on .active) */ - -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} - -nav[data-toggle='toc'] .nav .nav > li > a { - padding-left: 16px; - font-size: 1.35rem; -} - -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 15px; -} - -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 15px; - font-weight: 500; - font-size: 1.35rem; -} - -/* orcid ------------------------------------------------------------------- */ - -.orcid { - font-size: 16px; - color: #A6CE39; - /* margins are required by official ORCID trademark and display guidelines */ - margin-left:4px; - margin-right:4px; - vertical-align: middle; -} - -/* Reference index & topics ----------------------------------------------- */ - -.ref-index th {font-weight: normal;} - -.ref-index td {vertical-align: top;} -.ref-index .icon {width: 40px;} -.ref-index .alias {width: 40%;} -.ref-index-icons .alias {width: calc(40% - 40px);} -.ref-index .title {width: 60%;} - -.ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top;} -.ref-arguments .name {width: 20%;} -.ref-arguments .desc {width: 80%;} - -/* Nice scrolling for wide elements --------------------------------------- */ - -table { - display: block; - overflow: auto; -} - -/* Syntax highlighting ---------------------------------------------------- */ - -pre { - word-wrap: normal; - word-break: normal; - border: 1px solid #eee; -} - -pre, code { - background-color: #f8f8f8; - color: #333; -} - -pre code { - overflow: auto; - word-wrap: normal; - white-space: pre; -} - -pre .img { - margin: 5px 0; -} - -pre .img img { - background-color: #fff; - display: block; - height: auto; -} - -code a, pre a { - color: #375f84; -} - -a.sourceLine:hover { - text-decoration: none; -} - -.fl {color: #1514b5;} -.fu {color: #000000;} /* function */ -.ch,.st {color: #036a07;} /* string */ -.kw {color: #264D66;} /* keyword */ -.co {color: #888888;} /* comment */ - -.message { color: black; font-weight: bolder;} -.error { color: orange; font-weight: bolder;} -.warning { color: #6A0366; font-weight: bolder;} - -/* Clipboard --------------------------*/ - -.hasCopyButton { - position: relative; -} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - -/* mark.js ----------------------------*/ - -mark { - background-color: rgba(255, 255, 51, 0.5); - border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js deleted file mode 100644 index 7e7048faebb92b85ed06afddd1a8a4581241d6a4..0000000000000000000000000000000000000000 --- a/docs/pkgdown.js +++ /dev/null @@ -1,108 +0,0 @@ -/* http://gregfranko.com/blog/jquery-best-practices/ */ -(function($) { - $(function() { - - $('.navbar-fixed-top').headroom(); - - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $(".examples, div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index ffc2acea34c75c2a31440ddcee5eedb5de3c9431..0000000000000000000000000000000000000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,14 +0,0 @@ -pandoc: 2.9.2.1 -pkgdown: 1.5.1 -pkgdown_sha: ~ -articles: - mnist-cnn: examples/mnist-cnn.html - mnist-dcgan: examples/mnist-dcgan.html - mnist-mlp: examples/mnist-mlp.html - extending-autograd: extending-autograd.html - indexing: indexing.html - loading-data: loading-data.html - tensor-creation: tensor-creation.html - using-autograd: using-autograd.html -last_built: 2020-07-22T19:57Z - diff --git a/docs/reference/AutogradContext.html b/docs/reference/AutogradContext.html deleted file mode 100644 index b1fcfa13c646f16eb14583decd96d9d5e50e9ca6..0000000000000000000000000000000000000000 --- a/docs/reference/AutogradContext.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - - - -Class representing the context. — AutogradContext • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Class representing the context.

    - Source: R/autograd.R - -
    - -
    -

    Class representing the context.

    -

    Class representing the context.

    -
    - - - -

    Public fields

    - -

    -
    ptr

    (Dev related) pointer to the context c++ object.

    - -

    -

    Active bindings

    - -

    -
    needs_input_grad

    boolean listing arguments of forward and whether they require_grad.

    - -
    saved_variables

    list of objects that were saved for backward via save_for_backward.

    - -

    -

    Methods

    - - -

    Public methods

    - - -

    -

    Method new()

    -

    (Dev related) Initializes the context. Not user related.

    Usage

    -

    AutogradContext$new(
-  ptr,
-  env,
-  argument_names = NULL,
-  argument_needs_grad = NULL
-)

    - -

    Arguments

    -

    -
    ptr

    pointer to the c++ object

    - -
    env

    environment that encloses both forward and backward

    - -
    argument_names

    names of forward arguments

    - -
    argument_needs_grad

    whether each argument in forward needs grad.

    - -

    -

    -

    Method save_for_backward()

    -

    Saves given objects for a future call to backward().

    -

    This should be called at most once, and only from inside the forward() -method.

    -

    Later, saved objects can be accessed through the saved_variables attribute. -Before returning them to the user, a check is made to ensure they weren’t used -in any in-place operation that modified their content.

    -

    Arguments can also be any kind of R object.

    Usage

    -

    AutogradContext$save_for_backward(...)

    - -

    Arguments

    -

    -
    ...

    any kind of R object that will be saved for the backward pass. -It's common to pass named arguments.

    - -

    -

    -

    Method mark_non_differentiable()

    -

    Marks outputs as non-differentiable.

    -

    This should be called at most once, only from inside the forward() method, -and all arguments should be outputs.

    -

    This will mark outputs as not requiring gradients, increasing the efficiency -of backward computation. You still need to accept a gradient for each output -in backward(), but it’s always going to be a zero tensor with the same -shape as the shape of a corresponding output.

    -

    This is used e.g. for indices returned from a max Function.

    Usage

    -

    AutogradContext$mark_non_differentiable(...)

    - -

    Arguments

    -

    -
    ...

    non-differentiable outputs.

    - -

    -

    -

    Method mark_dirty()

    -

    Marks given tensors as modified in an in-place operation.

    -

    This should be called at most once, only from inside the forward() method, -and all arguments should be inputs.

    -

    Every tensor that’s been modified in-place in a call to forward() should -be given to this function, to ensure correctness of our checks. It doesn’t -matter whether the function is called before or after modification.

    Usage

    -

    AutogradContext$mark_dirty(...)

    - -

    Arguments

    -

    -
    ...

    tensors that are modified in-place.

    - -

    -

    -

    Method clone()

    -

    The objects of this class are cloneable with this method.

    Usage

    -

    AutogradContext$clone(deep = FALSE)

    - -

    Arguments

    -

    -
    deep

    Whether to make a deep clone.

    - -

    - - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/as_array.html b/docs/reference/as_array.html deleted file mode 100644 index 6e35287d8c32d6f26f6b45fbf6eed26d9a9f0ce2..0000000000000000000000000000000000000000 --- a/docs/reference/as_array.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Converts to array — as_array • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Converts to array

    - Source: R/tensor.R - -
    - -
    -

    Converts to array

    -
    - - 
    as_array(x)
    - -

    Arguments

    - - - - - - -
    x

    object to be converted into an array

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/autograd_backward.html b/docs/reference/autograd_backward.html deleted file mode 100644 index 087f1d74cc4d1f88f7762a851079897019537cd2..0000000000000000000000000000000000000000 --- a/docs/reference/autograd_backward.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Computes the sum of gradients of given tensors w.r.t. graph leaves.

    - Source: R/autograd.R - -
    - -
    -

    The graph is differentiated using the chain rule. If any of tensors are -non-scalar (i.e. their data has more than one element) and require gradient, -then the Jacobian-vector product would be computed, in this case the function -additionally requires specifying grad_tensors. It should be a sequence of -matching length, that contains the “vector” in the Jacobian-vector product, -usually the gradient of the differentiated function w.r.t. corresponding -tensors (None is an acceptable value for all tensors that don’t need gradient -tensors).

    -
    - - 
    autograd_backward(
-  tensors,
-  grad_tensors = NULL,
-  retain_graph = create_graph,
-  create_graph = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    tensors

    (list of Tensor) – Tensors of which the derivative will -be computed.

    grad_tensors

    (list of (Tensor or NULL)) – The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors. NULLvalues can be specified for scalar Tensors or ones that don’t require grad. If aNULL` value would be acceptable for all -grad_tensors, then this argument is optional.

    retain_graph

    (bool, optional) – If FALSE, the graph used to compute -the grad will be freed. Note that in nearly all cases setting this option to -TRUE is not needed and often can be worked around in a much more efficient -way. Defaults to the value of create_graph.

    create_graph

    (bool, optional) – If TRUE, graph of the derivative will -be constructed, allowing to compute higher order derivative products. -Defaults to FALSE.

    - -

    Details

    - -

    This function accumulates gradients in the leaves - you might need to zero -them before calling it.

    - -

    Examples

    - 
    # \dontrun{
-x <- torch_tensor(1, requires_grad = TRUE)
-y <- 2 * x
-
-a <- torch_tensor(1, requires_grad = TRUE)
-b <- 3 * a
-
-autograd_backward(list(y, b))
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/autograd_function.html b/docs/reference/autograd_function.html deleted file mode 100644 index f5ca06c73a4f9fd7071d2d73b68170f6ad245981..0000000000000000000000000000000000000000 --- a/docs/reference/autograd_function.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - - -Records operation history and defines formulas for differentiating ops. — autograd_function • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Records operation history and defines formulas for differentiating ops.

    - Source: R/autograd.R - -
    - -
    -

    Every operation performed on Tensor's creates a new function object, that -performs the computation, and records that it happened. The history is -retained in the form of a DAG of functions, with edges denoting data -dependencies (input <- output). Then, when backward is called, the graph is -processed in the topological ordering, by calling backward() methods of each -Function object, and passing returned gradients on to next Function's.

    -
    - - 
    autograd_function(forward, backward)
    - -

    Arguments

    - - - - - - - - - - -
    forward

    Performs the operation. It must accept a context ctx as the first argument, -followed by any number of arguments (tensors or other types). The context can be -used to store tensors that can be then retrieved during the backward pass. -See AutogradContext for more information about context methods.

    backward

    Defines a formula for differentiating the operation. It must accept -a context ctx as the first argument, followed by as many outputs did forward() -return, and it should return a named list. Each argument is the gradient w.r.t -the given output, and each element in the returned list should be the gradient -w.r.t. the corresponding input. The context can be used to retrieve tensors saved -during the forward pass. It also has an attribute ctx$needs_input_grad as a -named list of booleans representing whether each input needs gradient. -E.g., backward() will have ctx$needs_input_grad$input = TRUE if the input -argument to forward() needs gradient computated w.r.t. the output. -See AutogradContext for more information about context methods.

    - - -

    Examples

    - 
    # \dontrun{
-
-exp2 <- autograd_function(
-  forward = function(ctx, i) {
-    result <- i$exp()
-    ctx$save_for_backward(result = result)
-    result
-  },
-  backward = function(ctx, grad_output) {
-    list(i = grad_output * ctx$saved_variable$result)
-  }
-)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/autograd_grad.html b/docs/reference/autograd_grad.html deleted file mode 100644 index da8949015b9bdd4421f3af4af4fc240ef1163104..0000000000000000000000000000000000000000 --- a/docs/reference/autograd_grad.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - - -Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Computes and returns the sum of gradients of outputs w.r.t. the inputs.

    - Source: R/autograd.R - -
    - -
    -

    grad_outputs should be a list of length matching output containing the “vector” -in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of -the outputs. If an output doesn’t require_grad, then the gradient can be None).

    -
    - - 
    autograd_grad(
-  outputs,
-  inputs,
-  grad_outputs = NULL,
-  retain_graph = create_graph,
-  create_graph = FALSE,
-  allow_unused = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    outputs

    (sequence of Tensor) – outputs of the differentiated function.

    inputs

    (sequence of Tensor) – Inputs w.r.t. which the gradient will be -returned (and not accumulated into .grad).

    grad_outputs

    (sequence of Tensor) – The “vector” in the Jacobian-vector -product. Usually gradients w.r.t. each output. None values can be specified for -scalar Tensors or ones that don’t require grad. If a None value would be acceptable -for all grad_tensors, then this argument is optional. Default: None.

    retain_graph

    (bool, optional) – If FALSE, the graph used to compute the -grad will be freed. Note that in nearly all cases setting this option to TRUE is -not needed and often can be worked around in a much more efficient way. -Defaults to the value of create_graph.

    create_graph

    (bool, optional) – If TRUE, graph of the derivative will be constructed, allowing to compute higher order derivative products. Default: FALSE`.

    allow_unused

    (bool, optional) – If FALSE, specifying inputs that were -not used when computing outputs (and therefore their grad is always zero) is an -error. Defaults to FALSE

    - -

    Details

    - -

    If only_inputs is TRUE, the function will only return a list of gradients w.r.t -the specified inputs. If it’s FALSE, then gradient w.r.t. all remaining leaves -will still be computed, and will be accumulated into their .grad attribute.

    - -

    Examples

    - 
    # \dontrun{
-w <- torch_tensor(0.5, requires_grad = TRUE)
-b <- torch_tensor(0.9, requires_grad = TRUE)
-x <- torch_tensor(runif(100))
-y <- 2 * x + 1
-loss <- (y - (w*x + b))^2
-loss <- loss$mean()
-
-o <- autograd_grad(loss, list(w, b))
-o
    #> [[1]]
-#> torch_tensor 
-#> -0.9935
-#> [ CPUFloatType{1} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> -1.6206
-#> [ CPUFloatType{1} ]
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    - - - - - - - - diff --git a/docs/reference/autograd_set_grad_mode.html b/docs/reference/autograd_set_grad_mode.html deleted file mode 100644 index f91387ad10edcea706a0a14794aaf2db6d3b7d3e..0000000000000000000000000000000000000000 --- a/docs/reference/autograd_set_grad_mode.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Set grad mode — autograd_set_grad_mode • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Set grad mode

    - Source: R/autograd.R - -
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    Sets or disables gradient history.

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    Arguments

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    bool wether to enable or disable the gradient recording.

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    - - - - - - - - diff --git a/docs/reference/cuda_current_device.html b/docs/reference/cuda_current_device.html deleted file mode 100644 index 8a6145c0554b94a9ac39157bfc5f674d2809ac78..0000000000000000000000000000000000000000 --- a/docs/reference/cuda_current_device.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - - -Returns the index of a currently selected device. — cuda_current_device • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Returns the index of a currently selected device.

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    Returns the index of a currently selected device.

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    Returns the number of GPUs available.

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    Returns the number of GPUs available.

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    Returns a bool indicating if CUDA is currently available.

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    - - - - - - - - diff --git a/docs/reference/dataloader.html b/docs/reference/dataloader.html deleted file mode 100644 index 2e3cc18e42d233d6a2998e0ea287daf77fbef00a..0000000000000000000000000000000000000000 --- a/docs/reference/dataloader.html +++ /dev/null @@ -1,278 +0,0 @@ - - - - - - - - -Data loader. Combines a dataset and a sampler, and provides -single- or multi-process iterators over the dataset. — dataloader • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Data loader. Combines a dataset and a sampler, and provides -single- or multi-process iterators over the dataset.

    - Source: R/utils-data-dataloader.R - -
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    Data loader. Combines a dataset and a sampler, and provides -single- or multi-process iterators over the dataset.

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    dataloader(
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    Arguments

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    dataset

    (Dataset): dataset from which to load the data.

    batch_size

    (int, optional): how many samples per batch to load -(default: 1).

    shuffle

    (bool, optional): set to TRUE to have the data reshuffled -at every epoch (default: FALSE).

    sampler

    (Sampler, optional): defines the strategy to draw samples from -the dataset. If specified, shuffle must be False.

    batch_sampler

    (Sampler, optional): like sampler, but returns a batch of -indices at a time. Mutually exclusive with batch_size, -shuffle, sampler, and drop_last.

    num_workers

    (int, optional): how many subprocesses to use for data -loading. 0 means that the data will be loaded in the main process. -(default: 0)

    collate_fn

    (callable, optional): merges a list of samples to form a mini-batch.

    pin_memory

    (bool, optional): If TRUE, the data loader will copy tensors -into CUDA pinned memory before returning them. If your data elements -are a custom type, or your collate_fn returns a batch that is a custom type -see the example below.

    drop_last

    (bool, optional): set to TRUE to drop the last incomplete batch, -if the dataset size is not divisible by the batch size. If FALSE and -the size of dataset is not divisible by the batch size, then the last batch -will be smaller. (default: FALSE)

    timeout

    (numeric, optional): if positive, the timeout value for collecting a batch -from workers. Should always be non-negative. (default: 0)

    worker_init_fn

    (callable, optional): If not NULL, this will be called on each -worker subprocess with the worker id (an int in [0, num_workers - 1]) as -input, after seeding and before data loading. (default: NULL)

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    - - - - - - - - diff --git a/docs/reference/dataloader_make_iter.html b/docs/reference/dataloader_make_iter.html deleted file mode 100644 index 8e3cf682a4b5c76e057710a33908abe14926eb74..0000000000000000000000000000000000000000 --- a/docs/reference/dataloader_make_iter.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Creates an iterator from a DataLoader — dataloader_make_iter • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Creates an iterator from a DataLoader

    - Source: R/utils-data-dataloader.R - -
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    Creates an iterator from a DataLoader

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    dataloader

    a dataloader object.

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    - - - - - - - - diff --git a/docs/reference/dataloader_next.html b/docs/reference/dataloader_next.html deleted file mode 100644 index 0ab5eef50c660c10863d8d58c6ae173acaa48c24..0000000000000000000000000000000000000000 --- a/docs/reference/dataloader_next.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Get the next element of a dataloader iterator — dataloader_next • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Get the next element of a dataloader iterator

    - Source: R/utils-data-dataloader.R - -
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    Get the next element of a dataloader iterator

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    a DataLoader iter created with dataloader_make_iter.

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    - - - - - - - - diff --git a/docs/reference/dataset.html b/docs/reference/dataset.html deleted file mode 100644 index 35b359ea3800b1750ba4b0d9a4fa127cae3fb100..0000000000000000000000000000000000000000 --- a/docs/reference/dataset.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -An abstract class representing a <code>Dataset</code>. — dataset • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    An abstract class representing a Dataset.

    - Source: R/utils-data.R - -
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    All datasets that represent a map from keys to data samples should subclass -it. All subclasses should overwrite get_item, supporting fetching a -data sample for a given key. Subclasses could also optionally overwrite -lenght, which is expected to return the size of the dataset by many -~torch.utils.data.Sampler implementations and the default options -of ~torch.utils.data.DataLoader.

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    a name for the dataset. It it's also used as the class -for it.

    inherit

    you can optionally inherit from a dataset when creating a -new dataset.

    ...

    public methods for the dataset class

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    Note

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    ~torch.utils.data.DataLoader by default constructs a index -sampler that yields integral indices. To make it work with a map-style -dataset with non-integral indices/keys, a custom sampler must be provided.

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    - - - - - - - - diff --git a/docs/reference/default_dtype.html b/docs/reference/default_dtype.html deleted file mode 100644 index 5d27dca58158c960c8c978b76c2e3921c8a4e52c..0000000000000000000000000000000000000000 --- a/docs/reference/default_dtype.html +++ /dev/null @@ -1,208 +0,0 @@ - - - - - - - - -Gets and sets the default floating point dtype. — torch_set_default_dtype • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Gets and sets the default floating point dtype.

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    The default floating point dtype to set. Initially set to -torch_float().

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    - - - - - - - - diff --git a/docs/reference/enumerate.dataloader.html b/docs/reference/enumerate.dataloader.html deleted file mode 100644 index 9499c94fa6ddf21677c847ac187b34138f0f1351..0000000000000000000000000000000000000000 --- a/docs/reference/enumerate.dataloader.html +++ /dev/null @@ -1,214 +0,0 @@ - - - - - - - - -Enumerate an iterator — enumerate.dataloader • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Enumerate an iterator

    - Source: R/utils-data-enum.R - -
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    Enumerate an iterator

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    # S3 method for dataloader
-enumerate(x, max_len = 1e+06, ...)
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    x

    the generator to enumerate.

    max_len

    maximum number of iterations.

    ...

    passed to specific methods.

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    - Source: R/utils-data-enum.R - -
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    Enumerate an iterator

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    the generator to enumerate.

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    passed to specific methods.

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    All functions

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    AutogradContext

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    Class representing the context.

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    as_array()

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    Converts to array

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    autograd_backward()

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    Computes the sum of gradients of given tensors w.r.t. graph leaves.

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    autograd_function()

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    Records operation history and defines formulas for differentiating ops.

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    autograd_grad()

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    Computes and returns the sum of gradients of outputs w.r.t. the inputs.

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    autograd_set_grad_mode()

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    Set grad mode

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    cuda_current_device()

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    Returns the index of a currently selected device.

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    cuda_device_count()

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    Returns the number of GPUs available.

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    cuda_is_available()

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    dataloader()

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    Data loader. Combines a dataset and a sampler, and provides -single- or multi-process iterators over the dataset.

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    dataloader_make_iter()

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    Creates an iterator from a DataLoader

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    dataloader_next()

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    Get the next element of a dataloader iterator

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    dataset()

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    An abstract class representing a Dataset.

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    torch_set_default_dtype() torch_get_default_dtype()

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    enumerate()

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    Enumerate an iterator

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    enumerate(<dataloader>)

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    Enumerate an iterator

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    install_torch()

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    is_dataloader()

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    is_torch_dtype()

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    Check if object is a torch data type

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    is_torch_layout()

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    Check if an object is a torch layout.

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    is_torch_memory_format()

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    Check if an object is a memory format

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    is_torch_qscheme()

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    kmnist_dataset()

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    Kuzushiji-MNIST

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    mnist_dataset()

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    MNIST dataset

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    nn_adaptive_log_softmax_with_loss()

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    nn_batch_norm1d()

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    BatchNorm1D module

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    nn_batch_norm2d()

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    BatchNorm2D

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    nn_bce_loss()

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    Binary cross entropy loss

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    nn_celu()

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    nn_conv1d()

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    Conv1D module

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    nn_conv2d()

    -

    Conv2D module

    -

    nn_conv3d()

    -

    Conv3D module

    -

    nn_conv_transpose1d()

    -

    ConvTranspose1D

    -

    nn_conv_transpose2d()

    -

    ConvTranpose2D module

    -

    nn_conv_transpose3d()

    -

    ConvTranpose3D module

    -

    nn_cross_entropy_loss()

    -

    CrossEntropyLoss module

    -

    nn_dropout()

    -

    Dropout module

    -

    nn_dropout2d()

    -

    Dropout2D module

    -

    nn_dropout3d()

    -

    Dropout3D module

    -

    nn_elu()

    -

    ELU module

    -

    nn_embedding()

    -

    Embedding module

    -

    nn_gelu()

    -

    GELU module

    -

    nn_glu()

    -

    GLU module

    -

    nn_hardshrink()

    -

    Hardshwink module

    -

    nn_hardsigmoid()

    -

    Hardsigmoid module

    -

    nn_hardswish()

    -

    Hardswish module

    -

    nn_hardtanh()

    -

    Hardtanh module

    -

    nn_identity()

    -

    Identity module

    -

    nn_init_calculate_gain()

    -

    Calculate gain

    -

    nn_init_constant_()

    -

    Constant initialization

    -

    nn_init_dirac_()

    -

    Dirac initialization

    -

    nn_init_eye_()

    -

    Eye initialization

    -

    nn_init_kaiming_normal_()

    -

    Kaiming normal initialization

    -

    nn_init_kaiming_uniform_()

    -

    Kaiming uniform initialization

    -

    nn_init_normal_()

    -

    Normal initialization

    -

    nn_init_ones_()

    -

    Ones initialization

    -

    nn_init_orthogonal_()

    -

    Orthogonal initialization

    -

    nn_init_sparse_()

    -

    Sparse initialization

    -

    nn_init_trunc_normal_()

    -

    Truncated normal initialization

    -

    nn_init_uniform_()

    -

    Uniform initialization

    -

    nn_init_xavier_normal_()

    -

    Xavier normal initialization

    -

    nn_init_xavier_uniform_()

    -

    Xavier uniform initialization

    -

    nn_init_zeros_()

    -

    Zeros initialization

    -

    nn_leaky_relu()

    -

    LeakyReLU module

    -

    nn_linear()

    -

    Linear module

    -

    nn_log_sigmoid()

    -

    LogSigmoid module

    -

    nn_log_softmax()

    -

    LogSoftmax module

    -

    nn_max_pool1d()

    -

    MaxPool1D module

    -

    nn_max_pool2d()

    -

    MaxPool2D module

    -

    nn_module()

    -

    Base class for all neural network modules.

    -

    nn_module_list()

    -

    Holds submodules in a list.

    -

    nn_multihead_attention()

    -

    MultiHead attention

    -

    nn_prelu()

    -

    PReLU module

    -

    nn_relu()

    -

    ReLU module

    -

    nn_relu6()

    -

    ReLu6 module

    -

    nn_rnn()

    -

    RNN module

    -

    nn_rrelu()

    -

    RReLU module

    -

    nn_selu()

    -

    SELU module

    -

    nn_sequential()

    -

    A sequential container

    -

    nn_sigmoid()

    -

    Sigmoid module

    -

    nn_softmax()

    -

    Softmax module

    -

    nn_softmax2d()

    -

    Softmax2d module

    -

    nn_softmin()

    -

    Softmin

    -

    nn_softplus()

    -

    Softplus module

    -

    nn_softshrink()

    -

    Softshrink module

    -

    nn_softsign()

    -

    Softsign module

    -

    nn_tanh()

    -

    Tanh module

    -

    nn_tanhshrink()

    -

    Tanhshrink module

    -

    nn_threshold()

    -

    Threshoold module

    -

    nn_utils_rnn_pack_padded_sequence()

    -

    Packs a Tensor containing padded sequences of variable length.

    -

    nn_utils_rnn_pack_sequence()

    -

    Packs a list of variable length Tensors

    -

    nn_utils_rnn_pad_packed_sequence()

    -

    Pads a packed batch of variable length sequences.

    -

    nn_utils_rnn_pad_sequence()

    -

    Pad a list of variable length Tensors with padding_value

    -

    nnf_adaptive_avg_pool1d()

    -

    Adaptive_avg_pool1d

    -

    nnf_adaptive_avg_pool2d()

    -

    Adaptive_avg_pool2d

    -

    nnf_adaptive_avg_pool3d()

    -

    Adaptive_avg_pool3d

    -

    nnf_adaptive_max_pool1d()

    -

    Adaptive_max_pool1d

    -

    nnf_adaptive_max_pool2d()

    -

    Adaptive_max_pool2d

    -

    nnf_adaptive_max_pool3d()

    -

    Adaptive_max_pool3d

    -

    nnf_affine_grid()

    -

    Affine_grid

    -

    nnf_alpha_dropout()

    -

    Alpha_dropout

    -

    nnf_avg_pool1d()

    -

    Avg_pool1d

    -

    nnf_avg_pool2d()

    -

    Avg_pool2d

    -

    nnf_avg_pool3d()

    -

    Avg_pool3d

    -

    nnf_batch_norm()

    -

    Batch_norm

    -

    nnf_bilinear()

    -

    Bilinear

    -

    nnf_binary_cross_entropy()

    -

    Binary_cross_entropy

    -

    nnf_binary_cross_entropy_with_logits()

    -

    Binary_cross_entropy_with_logits

    -

    nnf_celu() nnf_celu_()

    -

    Celu

    -

    nnf_conv1d()

    -

    Conv1d

    -

    nnf_conv2d()

    -

    Conv2d

    -

    nnf_conv3d()

    -

    Conv3d

    -

    nnf_conv_tbc()

    -

    Conv_tbc

    -

    nnf_conv_transpose1d()

    -

    Conv_transpose1d

    -

    nnf_conv_transpose2d()

    -

    Conv_transpose2d

    -

    nnf_conv_transpose3d()

    -

    Conv_transpose3d

    -

    nnf_cosine_embedding_loss()

    -

    Cosine_embedding_loss

    -

    nnf_cosine_similarity()

    -

    Cosine_similarity

    -

    nnf_cross_entropy()

    -

    Cross_entropy

    -

    nnf_ctc_loss()

    -

    Ctc_loss

    -

    nnf_dropout()

    -

    Dropout

    -

    nnf_dropout2d()

    -

    Dropout2d

    -

    nnf_dropout3d()

    -

    Dropout3d

    -

    nnf_elu() nnf_elu_()

    -

    Elu

    -

    nnf_embedding()

    -

    Embedding

    -

    nnf_embedding_bag()

    -

    Embedding_bag

    -

    nnf_fold()

    -

    Fold

    -

    nnf_fractional_max_pool2d()

    -

    Fractional_max_pool2d

    -

    nnf_fractional_max_pool3d()

    -

    Fractional_max_pool3d

    -

    nnf_gelu()

    -

    Gelu

    -

    nnf_glu()

    -

    Glu

    -

    nnf_grid_sample()

    -

    Grid_sample

    -

    nnf_group_norm()

    -

    Group_norm

    -

    nnf_gumbel_softmax()

    -

    Gumbel_softmax

    -

    nnf_hardshrink()

    -

    Hardshrink

    -

    nnf_hardsigmoid()

    -

    Hardsigmoid

    -

    nnf_hardswish()

    -

    Hardswish

    -

    nnf_hardtanh() nnf_hardtanh_()

    -

    Hardtanh

    -

    nnf_hinge_embedding_loss()

    -

    Hinge_embedding_loss

    -

    nnf_instance_norm()

    -

    Instance_norm

    -

    nnf_interpolate()

    -

    Interpolate

    -

    nnf_kl_div()

    -

    Kl_div

    -

    nnf_l1_loss()

    -

    L1_loss

    -

    nnf_layer_norm()

    -

    Layer_norm

    -

    nnf_leaky_relu()

    -

    Leaky_relu

    -

    nnf_linear()

    -

    Linear

    -

    nnf_local_response_norm()

    -

    Local_response_norm

    -

    nnf_log_softmax()

    -

    Log_softmax

    -

    nnf_logsigmoid()

    -

    Logsigmoid

    -

    nnf_lp_pool1d()

    -

    Lp_pool1d

    -

    nnf_lp_pool2d()

    -

    Lp_pool2d

    -

    nnf_margin_ranking_loss()

    -

    Margin_ranking_loss

    -

    nnf_max_pool1d()

    -

    Max_pool1d

    -

    nnf_max_pool2d()

    -

    Max_pool2d

    -

    nnf_max_pool3d()

    -

    Max_pool3d

    -

    nnf_max_unpool1d()

    -

    Max_unpool1d

    -

    nnf_max_unpool2d()

    -

    Max_unpool2d

    -

    nnf_max_unpool3d()

    -

    Max_unpool3d

    -

    nnf_mse_loss()

    -

    Mse_loss

    -

    nnf_multi_head_attention_forward()

    -

    Multi head attention forward

    -

    nnf_multi_margin_loss()

    -

    Multi_margin_loss

    -

    nnf_multilabel_margin_loss()

    -

    Multilabel_margin_loss

    -

    nnf_multilabel_soft_margin_loss()

    -

    Multilabel_soft_margin_loss

    -

    nnf_nll_loss()

    -

    Nll_loss

    -

    nnf_normalize()

    -

    Normalize

    -

    nnf_one_hot()

    -

    One_hot

    -

    nnf_pad()

    -

    Pad

    -

    nnf_pairwise_distance()

    -

    Pairwise_distance

    -

    nnf_pdist()

    -

    Pdist

    -

    nnf_pixel_shuffle()

    -

    Pixel_shuffle

    -

    nnf_poisson_nll_loss()

    -

    Poisson_nll_loss

    -

    nnf_prelu()

    -

    Prelu

    -

    nnf_relu() nnf_relu_()

    -

    Relu

    -

    nnf_relu6()

    -

    Relu6

    -

    nnf_rrelu() nnf_rrelu_()

    -

    Rrelu

    -

    nnf_selu() nnf_selu_()

    -

    Selu

    -

    nnf_smooth_l1_loss()

    -

    Smooth_l1_loss

    -

    nnf_soft_margin_loss()

    -

    Soft_margin_loss

    -

    nnf_softmax()

    -

    Softmax

    -

    nnf_softmin()

    -

    Softmin

    -

    nnf_softplus()

    -

    Softplus

    -

    nnf_softshrink()

    -

    Softshrink

    -

    nnf_softsign()

    -

    Softsign

    -

    nnf_tanhshrink()

    -

    Tanhshrink

    -

    nnf_threshold() nnf_threshold_()

    -

    Threshold

    -

    nnf_triplet_margin_loss()

    -

    Triplet_margin_loss

    -

    nnf_unfold()

    -

    Unfold

    -

    optim_adam()

    -

    Implements Adam algorithm.

    -

    optim_required()

    -

    Dummy value indicating a required value.

    -

    optim_sgd()

    -

    SGD optimizer

    -

    tensor_dataset()

    -

    Dataset wrapping tensors.

    -

    torch_abs

    -

    Abs

    -

    torch_acos

    -

    Acos

    -

    torch_adaptive_avg_pool1d

    -

    Adaptive_avg_pool1d

    -

    torch_add

    -

    Add

    -

    torch_addbmm

    -

    Addbmm

    -

    torch_addcdiv

    -

    Addcdiv

    -

    torch_addcmul

    -

    Addcmul

    -

    torch_addmm

    -

    Addmm

    -

    torch_addmv

    -

    Addmv

    -

    torch_addr

    -

    Addr

    -

    torch_allclose

    -

    Allclose

    -

    torch_angle

    -

    Angle

    -

    torch_arange

    -

    Arange

    -

    torch_argmax

    -

    Argmax

    -

    torch_argmin

    -

    Argmin

    -

    torch_argsort

    -

    Argsort

    -

    torch_as_strided

    -

    As_strided

    -

    torch_asin

    -

    Asin

    -

    torch_atan

    -

    Atan

    -

    torch_atan2

    -

    Atan2

    -

    torch_avg_pool1d

    -

    Avg_pool1d

    -

    torch_baddbmm

    -

    Baddbmm

    -

    torch_bartlett_window

    -

    Bartlett_window

    -

    torch_bernoulli

    -

    Bernoulli

    -

    torch_bincount

    -

    Bincount

    -

    torch_bitwise_and

    -

    Bitwise_and

    -

    torch_bitwise_not

    -

    Bitwise_not

    -

    torch_bitwise_or

    -

    Bitwise_or

    -

    torch_bitwise_xor

    -

    Bitwise_xor

    -

    torch_blackman_window

    -

    Blackman_window

    -

    torch_bmm

    -

    Bmm

    -

    torch_broadcast_tensors

    -

    Broadcast_tensors

    -

    torch_can_cast

    -

    Can_cast

    -

    torch_cartesian_prod

    -

    Cartesian_prod

    -

    torch_cat

    -

    Cat

    -

    torch_cdist

    -

    Cdist

    -

    torch_ceil

    -

    Ceil

    -

    torch_celu_

    -

    Celu_

    -

    torch_chain_matmul

    -

    Chain_matmul

    -

    torch_cholesky

    -

    Cholesky

    -

    torch_cholesky_inverse

    -

    Cholesky_inverse

    -

    torch_cholesky_solve

    -

    Cholesky_solve

    -

    torch_chunk

    -

    Chunk

    -

    torch_clamp

    -

    Clamp

    -

    torch_combinations

    -

    Combinations

    -

    torch_conj

    -

    Conj

    -

    torch_conv1d

    -

    Conv1d

    -

    torch_conv2d

    -

    Conv2d

    -

    torch_conv3d

    -

    Conv3d

    -

    torch_conv_tbc

    -

    Conv_tbc

    -

    torch_conv_transpose1d

    -

    Conv_transpose1d

    -

    torch_conv_transpose2d

    -

    Conv_transpose2d

    -

    torch_conv_transpose3d

    -

    Conv_transpose3d

    -

    torch_cos

    -

    Cos

    -

    torch_cosh

    -

    Cosh

    -

    torch_cosine_similarity

    -

    Cosine_similarity

    -

    torch_cross

    -

    Cross

    -

    torch_cummax

    -

    Cummax

    -

    torch_cummin

    -

    Cummin

    -

    torch_cumprod

    -

    Cumprod

    -

    torch_cumsum

    -

    Cumsum

    -

    torch_det

    -

    Det

    -

    torch_device()

    -

    Create a Device object

    -

    torch_diag

    -

    Diag

    -

    torch_diag_embed

    -

    Diag_embed

    -

    torch_diagflat

    -

    Diagflat

    -

    torch_diagonal

    -

    Diagonal

    -

    torch_digamma

    -

    Digamma

    -

    torch_dist

    -

    Dist

    -

    torch_div

    -

    Div

    -

    torch_dot

    -

    Dot

    -

    torch_float32() torch_float() torch_float64() torch_double() torch_float16() torch_half() torch_uint8() torch_int8() torch_int16() torch_short() torch_int32() torch_int() torch_int64() torch_long() torch_bool() torch_quint8() torch_qint8() torch_qint32()

    -

    Torch data types

    -

    torch_eig

    -

    Eig

    -

    torch_einsum

    -

    Einsum

    -

    torch_empty

    -

    Empty

    -

    torch_empty_like

    -

    Empty_like

    -

    torch_empty_strided

    -

    Empty_strided

    -

    torch_eq

    -

    Eq

    -

    torch_equal

    -

    Equal

    -

    torch_erf

    -

    Erf

    -

    torch_erfc

    -

    Erfc

    -

    torch_erfinv

    -

    Erfinv

    -

    torch_exp

    -

    Exp

    -

    torch_expm1

    -

    Expm1

    -

    torch_eye

    -

    Eye

    -

    torch_fft

    -

    Fft

    -

    torch_flatten

    -

    Flatten

    -

    torch_flip

    -

    Flip

    -

    torch_floor

    -

    Floor

    -

    torch_floor_divide

    -

    Floor_divide

    -

    torch_fmod

    -

    Fmod

    -

    torch_frac

    -

    Frac

    -

    torch_full

    -

    Full

    -

    torch_full_like

    -

    Full_like

    -

    torch_gather

    -

    Gather

    -

    torch_ge

    -

    Ge

    -

    torch_generator()

    -

    Create a Generator object

    -

    torch_geqrf

    -

    Geqrf

    -

    torch_ger

    -

    Ger

    -

    torch_gt

    -

    Gt

    -

    torch_hamming_window

    -

    Hamming_window

    -

    torch_hann_window

    -

    Hann_window

    -

    torch_histc

    -

    Histc

    -

    torch_ifft

    -

    Ifft

    -

    torch_imag

    -

    Imag

    -

    torch_index_select

    -

    Index_select

    -

    torch_inverse

    -

    Inverse

    -

    torch_irfft

    -

    Irfft

    -

    torch_is_complex

    -

    Is_complex

    -

    torch_is_floating_point

    -

    Is_floating_point

    -

    torch_isfinite

    -

    Isfinite

    -

    torch_isinf

    -

    Isinf

    -

    torch_isnan

    -

    Isnan

    -

    torch_kthvalue

    -

    Kthvalue

    -

    torch_strided() torch_sparse_coo()

    -

    Creates the corresponding layout

    -

    torch_le

    -

    Le

    -

    torch_lerp

    -

    Lerp

    -

    torch_lgamma

    -

    Lgamma

    -

    torch_linspace

    -

    Linspace

    -

    torch_load()

    -

    Loads a saved object

    -

    torch_log

    -

    Log

    -

    torch_log10

    -

    Log10

    -

    torch_log1p

    -

    Log1p

    -

    torch_log2

    -

    Log2

    -

    torch_logdet

    -

    Logdet

    -

    torch_logical_and

    -

    Logical_and

    -

    torch_logical_not

    -

    Logical_not

    -

    torch_logical_or

    -

    Logical_or

    -

    torch_logical_xor

    -

    Logical_xor

    -

    torch_logspace

    -

    Logspace

    -

    torch_logsumexp

    -

    Logsumexp

    -

    torch_lstsq

    -

    Lstsq

    -

    torch_lt

    -

    Lt

    -

    torch_lu()

    -

    LU

    -

    torch_lu_solve

    -

    Lu_solve

    -

    torch_masked_select

    -

    Masked_select

    -

    torch_matmul

    -

    Matmul

    -

    torch_matrix_power

    -

    Matrix_power

    -

    torch_matrix_rank

    -

    Matrix_rank

    -

    torch_max

    -

    Max

    -

    torch_mean

    -

    Mean

    -

    torch_median

    -

    Median

    -

    torch_contiguous_format() torch_preserve_format() torch_channels_last_format()

    -

    Memory format

    -

    torch_meshgrid

    -

    Meshgrid

    -

    torch_min

    -

    Min

    -

    torch_mm

    -

    Mm

    -

    torch_mode

    -

    Mode

    -

    torch_mul

    -

    Mul

    -

    torch_multinomial

    -

    Multinomial

    -

    torch_mv

    -

    Mv

    -

    torch_mvlgamma

    -

    Mvlgamma

    -

    torch_narrow

    -

    Narrow

    -

    torch_ne

    -

    Ne

    -

    torch_neg

    -

    Neg

    -

    torch_nonzero

    -

    Nonzero

    -

    torch_norm

    -

    Norm

    -

    torch_normal

    -

    Normal

    -

    torch_ones

    -

    Ones

    -

    torch_ones_like

    -

    Ones_like

    -

    torch_orgqr

    -

    Orgqr

    -

    torch_ormqr

    -

    Ormqr

    -

    torch_pdist

    -

    Pdist

    -

    torch_pinverse

    -

    Pinverse

    -

    torch_pixel_shuffle

    -

    Pixel_shuffle

    -

    torch_poisson

    -

    Poisson

    -

    torch_polygamma

    -

    Polygamma

    -

    torch_pow

    -

    Pow

    -

    torch_prod

    -

    Prod

    -

    torch_promote_types

    -

    Promote_types

    -

    torch_qr

    -

    Qr

    -

    torch_per_channel_affine() torch_per_tensor_affine() torch_per_channel_symmetric() torch_per_tensor_symmetric()

    -

    Creates the corresponding Scheme object

    -

    torch_quantize_per_channel

    -

    Quantize_per_channel

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    torch_quantize_per_tensor

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    Quantize_per_tensor

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    torch_rand

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    Rand

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    torch_rand_like

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    Rand_like

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    torch_randint

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    Randint

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    torch_randint_like

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    Randint_like

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    torch_randn

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    Randn

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    torch_randn_like

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    Randn_like

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    torch_randperm

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    Randperm

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    torch_range

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    Range

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    torch_real

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    Real

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    torch_reciprocal

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    Reciprocal

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    torch_reduction_sum() torch_reduction_mean() torch_reduction_none()

    -

    Creates the reduction objet

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    torch_relu_

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    Relu_

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    torch_remainder

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    Remainder

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    torch_renorm

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    Renorm

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    torch_repeat_interleave

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    Repeat_interleave

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    torch_reshape

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    Reshape

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    torch_result_type

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    Result_type

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    torch_rfft

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    Rfft

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    torch_roll

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    Roll

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    torch_rot90

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    Rot90

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    torch_round

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    Round

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    torch_rrelu_

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    Rrelu_

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    torch_rsqrt

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    Rsqrt

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    torch_save()

    -

    Saves an object to a disk file.

    -

    torch_selu_

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    Selu_

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    torch_sigmoid

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    Sigmoid

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    torch_sign

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    Sign

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    torch_sin

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    Sin

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    torch_sinh

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    Sinh

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    torch_slogdet

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    Slogdet

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    torch_solve

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    Solve

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    torch_sort

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    Sort

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    torch_sparse_coo_tensor

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    Sparse_coo_tensor

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    torch_split

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    Split

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    torch_sqrt

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    Sqrt

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    torch_square

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    Square

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    torch_squeeze

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    Squeeze

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    torch_stack

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    Stack

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    torch_std

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    Std

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    torch_std_mean

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    Std_mean

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    torch_stft

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    Stft

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    torch_sum

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    Sum

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    torch_svd

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    Svd

    -

    torch_symeig

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    Symeig

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    torch_t

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    T

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    torch_take

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    Take

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    torch_tan

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    Tan

    -

    torch_tanh

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    Tanh

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    torch_tensor()

    -

    Converts R objects to a torch tensor

    -

    torch_tensordot

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    Tensordot

    -

    torch_threshold_

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    Threshold_

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    torch_topk

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    Topk

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    torch_trace

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    Trace

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    torch_transpose

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    Transpose

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    torch_trapz

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    Trapz

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    torch_triangular_solve

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    Triangular_solve

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    torch_tril

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    Tril

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    torch_tril_indices

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    Tril_indices

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    torch_triu

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    Triu

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    torch_triu_indices

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    Triu_indices

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    torch_true_divide

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    True_divide

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    torch_trunc

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    Trunc

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    torch_unbind

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    Unbind

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    torch_unique_consecutive

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    Unique_consecutive

    -

    torch_unsqueeze

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    Unsqueeze

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    torch_var

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    Var

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    torch_var_mean

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    Var_mean

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    torch_where

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    Where

    -

    torch_zeros

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    Zeros

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    torch_zeros_like

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    Zeros_like

    -

    vision_make_grid()

    -

    A simplified version of torchvision.utils.make_grid.

    -

    with_enable_grad()

    -

    Enable grad

    -

    with_no_grad()

    -

    Temporarily modify gradient recording.

    -
    - - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
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    -
    - - - - - - - - diff --git a/docs/reference/install_torch.html b/docs/reference/install_torch.html deleted file mode 100644 index 22442f16f80f42832437bf21f26b53d88a3350ef..0000000000000000000000000000000000000000 --- a/docs/reference/install_torch.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Install Torch — install_torch • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Install Torch

    - Source: R/install.R - -
    - -
    -

    Installs Torch and its dependencies.

    -
    - - 
    install_torch(
-  version = "1.5.0",
-  type = install_type(version = version),
-  reinstall = FALSE,
-  path = install_path(),
-  ...
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    version

    The Torch version to install.

    type

    The installation type for Torch. Valid values are "cpu" or the 'CUDA' version.

    reinstall

    Re-install Torch even if its already installed?

    path

    Optional path to install or check for an already existing installation.

    ...

    other optional arguments (like load for manual installation.)

    - -

    Details

    - -

    When using path to install in a specific location, make sure the TORCH_HOME environment -variable is set to this same path to reuse this installation. The TORCH_INSTALL environment -variable can be set to 0 to prevent auto-installing torch and TORCH_LOAD set to 0 -to avoid loading dependencies automatically. These environment variables are meant for advanced use -cases and troubleshootinng only.

    - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/is_dataloader.html b/docs/reference/is_dataloader.html deleted file mode 100644 index 4963f41a6779ef28cd7ac246b2e5f90b571a97d2..0000000000000000000000000000000000000000 --- a/docs/reference/is_dataloader.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Checks if the object is a dataloader — is_dataloader • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Checks if the object is a dataloader

    - Source: R/utils-data-dataloader.R - -
    - -
    -

    Checks if the object is a dataloader

    -
    - - 
    is_dataloader(x)
    - -

    Arguments

    - - - - - - -
    x

    object to check

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/is_torch_dtype.html b/docs/reference/is_torch_dtype.html deleted file mode 100644 index a25b3fb7acd9979f4983c0518267ec43ee4cbaf6..0000000000000000000000000000000000000000 --- a/docs/reference/is_torch_dtype.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Check if object is a torch data type — is_torch_dtype • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Check if object is a torch data type

    - Source: R/dtype.R - -
    - -
    -

    Check if object is a torch data type

    -
    - - 
    is_torch_dtype(x)
    - -

    Arguments

    - - - - - - -
    x

    object to check.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/is_torch_layout.html b/docs/reference/is_torch_layout.html deleted file mode 100644 index f3d02486c008a2dd618d19feda484b5a061aa3de..0000000000000000000000000000000000000000 --- a/docs/reference/is_torch_layout.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Check if an object is a torch layout. — is_torch_layout • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Check if an object is a torch layout.

    - Source: R/layout.R - -
    - -
    -

    Check if an object is a torch layout.

    -
    - - 
    is_torch_layout(x)
    - -

    Arguments

    - - - - - - -
    x

    object to check

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/is_torch_memory_format.html b/docs/reference/is_torch_memory_format.html deleted file mode 100644 index 9352ef7d6a86d9f5b4719cd903cd0bee2e59777f..0000000000000000000000000000000000000000 --- a/docs/reference/is_torch_memory_format.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Check if an object is a memory format — is_torch_memory_format • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Check if an object is a memory format

    - Source: R/memory_format.R - -
    - -
    -

    Check if an object is a memory format

    -
    - - 
    is_torch_memory_format(x)
    - -

    Arguments

    - - - - - - -
    x

    object to check

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/is_torch_qscheme.html b/docs/reference/is_torch_qscheme.html deleted file mode 100644 index 4ab4b994369a3f4252c3cfb0637f9ddd862d88ab..0000000000000000000000000000000000000000 --- a/docs/reference/is_torch_qscheme.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Checks if an object is a QScheme — is_torch_qscheme • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Checks if an object is a QScheme

    - Source: R/qscheme.R - -
    - -
    -

    Checks if an object is a QScheme

    -
    - - 
    is_torch_qscheme(x)
    - -

    Arguments

    - - - - - - -
    x

    object to check

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kmnist_dataset.html b/docs/reference/kmnist_dataset.html deleted file mode 100644 index e6723725c65d26a8082f42f8d09355ace511ec7e..0000000000000000000000000000000000000000 --- a/docs/reference/kmnist_dataset.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - -Kuzushiji-MNIST — kmnist_dataset • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Kuzushiji-MNIST

    - Source: R/dataset-mnist.R - -
    - -
    -

    The Kuzushiji-MNIST dataset.

    -
    - - 
    kmnist_dataset(
-  root,
-  train = TRUE,
-  transform = NULL,
-  target_transform = NULL,
-  download = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    root

    (string): Root directory of dataset where KMNIST/processed/training.pt -and KMNIST/processed/test.pt exist.

    train

    (bool, optional): If TRUE, creates dataset from training.pt, -otherwise from test.pt.

    transform

    (callable, optional): A function/transform that takes in an PIL image -and returns a transformed version. E.g, transforms.RandomCrop

    target_transform

    (callable, optional): A function/transform that takes in the -target and transforms it.

    download

    (bool, optional): If true, downloads the dataset from the internet and -puts it in root directory. If dataset is already downloaded, it is not -downloaded again.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mnist_dataset.html b/docs/reference/mnist_dataset.html deleted file mode 100644 index d9eb3855ed9ba92b3ed1b0afac64f3a91a684fb0..0000000000000000000000000000000000000000 --- a/docs/reference/mnist_dataset.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - -MNIST dataset — mnist_dataset • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    MNIST dataset

    - Source: R/dataset-mnist.R - -
    - -
    -

    Prepares the MNIST dataset and optionally downloads it.

    -
    - - 
    mnist_dataset(
-  root,
-  train = TRUE,
-  transform = NULL,
-  target_transform = NULL,
-  download = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    root

    (string): Root directory of dataset where MNIST/processed/training.pt -and MNIST/processed/test.pt exist.

    train

    (bool, optional): If True, creates dataset from training.pt, -otherwise from test.pt.

    transform

    (callable, optional): A function/transform that takes in an PIL image -and returns a transformed version. E.g, transforms.RandomCrop

    target_transform

    (callable, optional): A function/transform that takes in the -target and transforms it.

    download

    (bool, optional): If true, downloads the dataset from the internet and -puts it in root directory. If dataset is already downloaded, it is not -downloaded again.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_adaptive_log_softmax_with_loss.html b/docs/reference/nn_adaptive_log_softmax_with_loss.html deleted file mode 100644 index 8bb8c2fa7623e2fa4a943a146eca8b6ed831db35..0000000000000000000000000000000000000000 --- a/docs/reference/nn_adaptive_log_softmax_with_loss.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - -AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    AdaptiveLogSoftmaxWithLoss module

    - Source: R/nn_adaptive.R - -
    - -
    -

    Efficient softmax approximation as described in -Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou

    -
    - - 
    nn_adaptive_log_softmax_with_loss(
-  in_features,
-  n_classes,
-  cutoffs,
-  div_value = 4,
-  head_bias = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    in_features

    (int): Number of features in the input tensor

    n_classes

    (int): Number of classes in the dataset

    cutoffs

    (Sequence): Cutoffs used to assign targets to their buckets

    div_value

    (float, optional): value used as an exponent to compute sizes -of the clusters. Default: 4.0

    head_bias

    (bool, optional): If True, adds a bias term to the 'head' of the -adaptive softmax. Default: False

    - -

    Value

    - -

    NamedTuple with output and loss fields:

      -

    • output is a Tensor of size N containing computed target -log probabilities for each example

      • -

    • loss is a Scalar representing the computed negative -log likelihood loss

      • -
    - -

    Details

    - -

    Adaptive softmax is an approximate strategy for training models with large -output spaces. It is most effective when the label distribution is highly -imbalanced, for example in natural language modelling, where the word -frequency distribution approximately follows the Zipf's law.

    -

    Adaptive softmax partitions the labels into several clusters, according to -their frequency. These clusters may contain different number of targets -each.

    -

    Additionally, clusters containing less frequent labels assign lower -dimensional embeddings to those labels, which speeds up the computation. -For each minibatch, only clusters for which at least one target is -present are evaluated.

    -

    The idea is that the clusters which are accessed frequently -(like the first one, containing most frequent labels), should also be cheap -to compute -- that is, contain a small number of assigned labels. -We highly recommend taking a look at the original paper for more details.

      -

    • cutoffs should be an ordered Sequence of integers sorted -in the increasing order. -It controls number of clusters and the partitioning of targets into -clusters. For example setting cutoffs = c(10, 100, 1000) -means that first 10 targets will be assigned -to the 'head' of the adaptive softmax, targets 11, 12, ..., 100 will be -assigned to the first cluster, and targets 101, 102, ..., 1000 will be -assigned to the second cluster, while targets -1001, 1002, ..., n_classes - 1 will be assigned -to the last, third cluster.

      • -

    • div_value is used to compute the size of each additional cluster, -which is given as -\(\left\lfloor\frac{\mbox{in\_features}}{\mbox{div\_value}^{idx}}\right\rfloor\), -where \(idx\) is the cluster index (with clusters -for less frequent words having larger indices, -and indices starting from \(1\)).

      • -

    • head_bias if set to True, adds a bias term to the 'head' of the -adaptive softmax. See paper for details. Set to False in the official -implementation.

      • -
    - -

    Note

    - -

    This module returns a NamedTuple with output -and loss fields. See further documentation for details.

    -

    To compute log-probabilities for all classes, the log_prob -method can be used.

    -

    Warning

    - - - -

    Labels passed as inputs to this module should be sorted according to -their frequency. This means that the most frequent label should be -represented by the index 0, and the least frequent -label should be represented by the index n_classes - 1.

    -

    Shape

    - - - -
      -

    • input: \((N, \mbox{in\_features})\)

      • -

    • target: \((N)\) where each value satisfies \(0 <= \mbox{target[i]} <= \mbox{n\_classes}\)

      • -

    • output1: \((N)\)

      • -

    • output2: Scalar

      • -
    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_batch_norm1d.html b/docs/reference/nn_batch_norm1d.html deleted file mode 100644 index ca25d62f31ce9a0a147cb41fab2e2a0a31cfd24f..0000000000000000000000000000000000000000 --- a/docs/reference/nn_batch_norm1d.html +++ /dev/null @@ -1,287 +0,0 @@ - - - - - - - - -BatchNorm1D module — nn_batch_norm1d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    BatchNorm1D module

    - Source: R/nn-batchnorm.R - -
    - -
    -

    Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D -inputs with optional additional channel dimension) as described in the paper -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

    -
    - - 
    nn_batch_norm1d(
-  num_features,
-  eps = 1e-05,
-  momentum = 0.1,
-  affine = TRUE,
-  track_running_stats = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    num_features

    \(C\) from an expected input of size -\((N, C, L)\) or \(L\) from input of size \((N, L)\)

    eps

    a value added to the denominator for numerical stability. -Default: 1e-5

    momentum

    the value used for the running_mean and running_var -computation. Can be set to NULL for cumulative moving average -(i.e. simple average). Default: 0.1

    affine

    a boolean value that when set to TRUE, this module has -learnable affine parameters. Default: TRUE

    track_running_stats

    a boolean value that when set to TRUE, this -module tracks the running mean and variance, and when set to FALSE, -this module does not track such statistics and always uses batch -statistics in both training and eval modes. Default: TRUE

    - -

    Details

    - -

    $$ -y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta -$$

    -

    The mean and standard-deviation are calculated per-dimension over -the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors -of size C (where C is the input size). By default, the elements of \(\gamma\) -are set to 1 and the elements of \(\beta\) are set to 0.

    -

    Also by default, during training this layer keeps running estimates of its -computed mean and variance, which are then used for normalization during -evaluation. The running estimates are kept with a default :attr:momentum -of 0.1. -If track_running_stats is set to FALSE, this layer then does not -keep running estimates, and batch statistics are instead used during -evaluation time as well.

    -

    Note

    - - - - -

    This momentum argument is different from one used in optimizer -classes and the conventional notion of momentum. Mathematically, the -update rule for running statistics here is -\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\), -where \(\hat{x}\) is the estimated statistic and \(x_t\) is the -new observed value.

    -

    Because the Batch Normalization is done over the C dimension, computing statistics -on (N, L) slices, it's common terminology to call this Temporal Batch Normalization.

    -

    Shape

    - - - -
      -

    • Input: \((N, C)\) or \((N, C, L)\)

      • -

    • Output: \((N, C)\) or \((N, C, L)\) (same shape as input)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With Learnable Parameters
-m <- nn_batch_norm1d(100)
-# Without Learnable Parameters
-m <- nn_batch_norm1d(100, affine = FALSE)
-input <- torch_randn(20, 100)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_batch_norm2d.html b/docs/reference/nn_batch_norm2d.html deleted file mode 100644 index 34a8884a54e0e772c09f2b2e085e50bdbd9c7ec5..0000000000000000000000000000000000000000 --- a/docs/reference/nn_batch_norm2d.html +++ /dev/null @@ -1,286 +0,0 @@ - - - - - - - - -BatchNorm2D — nn_batch_norm2d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    BatchNorm2D

    - Source: R/nn-batchnorm.R - -
    - -
    -

    Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs -additional channel dimension) as described in the paper -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

    -
    - - 
    nn_batch_norm2d(
-  num_features,
-  eps = 1e-05,
-  momentum = 0.1,
-  affine = TRUE,
-  track_running_stats = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    num_features

    \(C\) from an expected input of size -\((N, C, H, W)\)

    eps

    a value added to the denominator for numerical stability. -Default: 1e-5

    momentum

    the value used for the running_mean and running_var -computation. Can be set to None for cumulative moving average -(i.e. simple average). Default: 0.1

    affine

    a boolean value that when set to TRUE, this module has -learnable affine parameters. Default: TRUE

    track_running_stats

    a boolean value that when set to TRUE, this -module tracks the running mean and variance, and when set to FALSE, -this module does not track such statistics and uses batch statistics instead -in both training and eval modes if the running mean and variance are None. -Default: TRUE

    - -

    Details

    - -

    $$ - y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta -$$

    -

    The mean and standard-deviation are calculated per-dimension over -the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors -of size C (where C is the input size). By default, the elements of \(\gamma\) are set -to 1 and the elements of \(\beta\) are set to 0. The standard-deviation is calculated -via the biased estimator, equivalent to torch_var(input, unbiased=FALSE). -Also by default, during training this layer keeps running estimates of its -computed mean and variance, which are then used for normalization during -evaluation. The running estimates are kept with a default momentum -of 0.1.

    -

    If track_running_stats is set to FALSE, this layer then does not -keep running estimates, and batch statistics are instead used during -evaluation time as well.

    -

    Note

    - -

    This momentum argument is different from one used in optimizer -classes and the conventional notion of momentum. Mathematically, the -update rule for running statistics here is -\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\), -where \(\hat{x}\) is the estimated statistic and \(x_t\) is the -new observed value. -Because the Batch Normalization is done over the C dimension, computing statistics -on (N, H, W) slices, it's common terminology to call this Spatial Batch Normalization.

    -

    Shape

    - - - -
      -

    • Input: \((N, C, H, W)\)

      • -

    • Output: \((N, C, H, W)\) (same shape as input)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With Learnable Parameters
-m <- nn_batch_norm2d(100)
-# Without Learnable Parameters
-m <- nn_batch_norm2d(100, affine=FALSE)
-input <- torch_randn(20, 100, 35, 45)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_bce_loss.html b/docs/reference/nn_bce_loss.html deleted file mode 100644 index 9f6987f694ebba6780eaa85888154abfc1177502..0000000000000000000000000000000000000000 --- a/docs/reference/nn_bce_loss.html +++ /dev/null @@ -1,271 +0,0 @@ - - - - - - - - -Binary cross entropy loss — nn_bce_loss • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Binary cross entropy loss

    - Source: R/nn-loss.R - -
    - -
    -

    Creates a criterion that measures the Binary Cross Entropy -between the target and the output:

    -
    - - 
    nn_bce_loss(weight = NULL, reduction = "mean")
    - -

    Arguments

    - - - - - - - - - - -
    weight

    (Tensor, optional): a manual rescaling weight given to the loss -of each batch element. If given, has to be a Tensor of size nbatch.

    reduction

    (string, optional): Specifies the reduction to apply to the output: -'none' | 'mean' | 'sum'. 'none': no reduction will be applied, -'mean': the sum of the output will be divided by the number of -elements in the output, 'sum': the output will be summed. Note: size_average -and reduce are in the process of being deprecated, and in the meantime, -specifying either of those two args will override reduction. Default: 'mean'

    - -

    Details

    - -

    The unreduced (i.e. with reduction set to 'none') loss can be described as: -$$ - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad -l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right] -$$ -where \(N\) is the batch size. If reduction is not 'none' -(default 'mean'), then

    -

    $$ - \ell(x, y) = \left\{ \begin{array}{ll} -\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ -\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} -\end{array} -\right. -$$

    -

    This is used for measuring the error of a reconstruction in for example -an auto-encoder. Note that the targets \(y\) should be numbers -between 0 and 1.

    -

    Notice that if \(x_n\) is either 0 or 1, one of the log terms would be -mathematically undefined in the above loss equation. PyTorch chooses to set -\(\log (0) = -\infty\), since \(\lim_{x\to 0} \log (x) = -\infty\).

    -

    However, an infinite term in the loss equation is not desirable for several reasons. -For one, if either \(y_n = 0\) or \((1 - y_n) = 0\), then we would be -multiplying 0 with infinity. Secondly, if we have an infinite loss value, then -we would also have an infinite term in our gradient, since -\(\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty\).

    -

    This would make BCELoss's backward method nonlinear with respect to \(x_n\), -and using it for things like linear regression would not be straight-forward. -Our solution is that BCELoss clamps its log function outputs to be greater than -or equal to -100. This way, we can always have a finite loss value and a linear -backward method.

    -

    Shape

    - - - -
      -

    • Input: \((N, *)\) where \(*\) means, any number of additional -dimensions

      • -

    • Target: \((N, *)\), same shape as the input

      • -

    • Output: scalar. If reduction is 'none', then \((N, *)\), same -shape as input.

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_sigmoid()
-loss <- nn_bce_loss()
-input <- torch_randn(3, requires_grad=TRUE)
-target <- torch_rand(3)
-output <- loss(m(input), target)
-output$backward()
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_bilinear.html b/docs/reference/nn_bilinear.html deleted file mode 100644 index e2cd608a09c8c1bac9e7f2b12d619e2a86f59ed3..0000000000000000000000000000000000000000 --- a/docs/reference/nn_bilinear.html +++ /dev/null @@ -1,257 +0,0 @@ - - - - - - - - -Bilinear module — nn_bilinear • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Bilinear module

    - Source: R/nn-linear.R - -
    - -
    -

    Applies a bilinear transformation to the incoming data -\(y = x_1^T A x_2 + b\)

    -
    - - 
    nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    in1_features

    size of each first input sample

    in2_features

    size of each second input sample

    out_features

    size of each output sample

    bias

    If set to FALSE, the layer will not learn an additive bias. -Default: TRUE

    - -

    Shape

    - - - -
      -

    • Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and -\(*\) means any number of additional dimensions. All but the last -dimension of the inputs should be the same.

      • -

    • Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).

      • -

    • Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\) -and all but the last dimension are the same shape as the input.

      • -
    - -

    Attributes

    - - - -
      -

    • weight: the learnable weights of the module of shape -\((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\). -The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where -\(k = \frac{1}{\mbox{in1\_features}}\)

      • -

    • bias: the learnable bias of the module of shape \((\mbox{out\_features})\). -If bias is TRUE, the values are initialized from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where -\(k = \frac{1}{\mbox{in1\_features}}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_bilinear(20, 30, 50)
-input1 <- torch_randn(128, 20)
-input2 <- torch_randn(128, 30)
-output = m(input1, input2)
-print(output$size())
    #> [1] 128  50
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_celu.html b/docs/reference/nn_celu.html deleted file mode 100644 index 28f07fdebb647dd876e35fd4628f0d732aefd189..0000000000000000000000000000000000000000 --- a/docs/reference/nn_celu.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - -CELU module — nn_celu • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    CELU module

    - Source: R/nn-activation.R - -
    - -
    -

    Applies the element-wise function:

    -
    - - 
    nn_celu(alpha = 1, inplace = FALSE)
    - -

    Arguments

    - - - - - - - - - - -
    alpha

    the \(\alpha\) value for the CELU formulation. Default: 1.0

    inplace

    can optionally do the operation in-place. Default: FALSE

    - -

    Details

    - -

    $$ - \mbox{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1)) -$$

    -

    More details can be found in the paper -Continuously Differentiable Exponential Linear Units.

    -

    Shape

    - - - -
      -

    • Input: \((N, *)\) where * means, any number of additional -dimensions

      • -

    • Output: \((N, *)\), same shape as the input

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_celu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv1d.html b/docs/reference/nn_conv1d.html deleted file mode 100644 index 5c37fbd8a65c20c4c065ce2b194ae977e6f25d6f..0000000000000000000000000000000000000000 --- a/docs/reference/nn_conv1d.html +++ /dev/null @@ -1,344 +0,0 @@ - - - - - - - - -Conv1D module — nn_conv1d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Conv1D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 1D convolution over an input signal composed of several input -planes. -In the simplest case, the output value of the layer with input size -\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be -precisely described as:

    -
    - - 
    nn_conv1d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): Zero-padding added to both sides of -the input. Default: 0

    dilation

    (int or tuple, optional): Spacing between kernel -elements. Default: 1

    groups

    (int, optional): Number of blocked connections from input -channels to output channels. Default: 1

    bias

    (bool, optional): If TRUE, adds a learnable bias to the -output. Default: TRUE

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    $$ -\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) + - \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) -\star \mbox{input}(N_i, k) -$$

    -

    where \(\star\) is the valid -cross-correlation operator, -\(N\) is a batch size, \(C\) denotes a number of channels, -\(L\) is a length of signal sequence.

      -

    • stride controls the stride for the cross-correlation, a single -number or a one-element tuple.

      • -

    • padding controls the amount of implicit zero-paddings on both sides -for padding number of points.

      • -

    • dilation controls the spacing between the kernel points; also -known as the à trous algorithm. It is harder to describe, but this -link -has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters, -of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).

        • -
      • -
    - -

    Note

    - - - - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid -cross-correlation, and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    When groups == in_channels and out_channels == K * in_channels, -where K is a positive integer, this operation is also termed in -literature as depthwise convolution. -In other words, for an input of size \((N, C_{in}, L_{in})\), -a depthwise convolution with a depthwise multiplier K, can be constructed by arguments -\((C_{\mbox{in}}=C_{in}, C_{\mbox{out}}=C_{in} \times K, ..., \mbox{groups}=C_{in})\).

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, L_{in})\)

      • -

    • Output: \((N, C_{out}, L_{out})\) where

      • -
    - -

    $$ - L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation} - \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor -$$

    -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape -(out_channels). If bias is TRUE, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_conv1d(16, 33, 3, stride=2)
-input <- torch_randn(20, 16, 50)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv2d.html b/docs/reference/nn_conv2d.html deleted file mode 100644 index 3c2a0ce4b442735c89840f929248325546cb305f..0000000000000000000000000000000000000000 --- a/docs/reference/nn_conv2d.html +++ /dev/null @@ -1,361 +0,0 @@ - - - - - - - - -Conv2D module — nn_conv2d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Conv2D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 2D convolution over an input signal composed of several input -planes.

    -
    - - 
    nn_conv2d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): Zero-padding added to both sides of -the input. Default: 0

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    groups

    (int, optional): Number of blocked connections from input -channels to output channels. Default: 1

    bias

    (bool, optional): If TRUE, adds a learnable bias to the -output. Default: TRUE

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    In the simplest case, the output value of the layer with input size -\((N, C_{\mbox{in}}, H, W)\) and output \((N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})\) -can be precisely described as:

    -

    $$ -\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) + - \sum_{k = 0}^{C_{\mbox{in}} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k) -$$

    -

    where \(\star\) is the valid 2D cross-correlation operator, -\(N\) is a batch size, \(C\) denotes a number of channels, -\(H\) is a height of input planes in pixels, and \(W\) is -width in pixels.

      -

    • stride controls the stride for the cross-correlation, a single -number or a tuple.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for padding number of points for each dimension.

      • -

    • dilation controls the spacing between the kernel points; also -known as the à trous algorithm. It is harder to describe, but this link_ -has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters, of size: -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).

        • -
      • -
    - -

    The parameters kernel_size, stride, padding, dilation can either be:

      -

    • a single int -- in which case the same value is used for the height and -width dimension

      • -

    • a tuple of two ints -- in which case, the first int is used for the height dimension, -and the second int for the width dimension

      • -
    - -

    Note

    - - - - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    When groups == in_channels and out_channels == K * in_channels, -where K is a positive integer, this operation is also termed in -literature as depthwise convolution. -In other words, for an input of size :math:(N, C_{in}, H_{in}, W_{in}), -a depthwise convolution with a depthwise multiplier K, can be constructed by arguments -\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting backends_cudnn_deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, H_{out}, W_{out})\) where -$$ - H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] - \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor -$$ -$$ - W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] - \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\), -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape -(out_channels). If bias is TRUE, -then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-
-# With square kernels and equal stride
-m <- nn_conv2d(16, 33, 3, stride = 2)
-# non-square kernels and unequal stride and with padding
-m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
-# non-square kernels and unequal stride and with padding and dilation
-m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2), dilation=c(3, 1))
-input <- torch_randn(20, 16, 50, 100)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv3d.html b/docs/reference/nn_conv3d.html deleted file mode 100644 index b31fe1dc99d6c032222f0600390678dca3bf1387..0000000000000000000000000000000000000000 --- a/docs/reference/nn_conv3d.html +++ /dev/null @@ -1,349 +0,0 @@ - - - - - - - - -Conv3D module — nn_conv3d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Conv3D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 3D convolution over an input signal composed of several input -planes. -In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\) -and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:

    -
    - - 
    nn_conv3d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If TRUE, adds a learnable bias to the output. Default: TRUE

    padding_mode

    (string, optional): 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    $$ - out(N_i, C_{out_j}) = bias(C_{out_j}) + - \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k) -$$

    -

    where \(\star\) is the valid 3D cross-correlation operator

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for padding number of points for each dimension.

      • -

    • dilation controls the spacing between the kernel points; also known as the à trous algorithm. -It is harder to describe, but this link_ has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

      • -

    • At groups=1, all inputs are convolved to all outputs.

      • -

    • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

      • -

    • At groups= in_channels, each input channel is convolved with -its own set of filters, of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).

      • -
    - -

    The parameters kernel_size, stride, padding, dilation can either be:

      -

    • a single int -- in which case the same value is used for the depth, height and width dimension

      • -

    • a tuple of three ints -- in which case, the first int is used for the depth dimension, -the second int for the height dimension and the third int for the width dimension

      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    When groups == in_channels and out_channels == K * in_channels, -where K is a positive integer, this operation is also termed in -literature as depthwise convolution. -In other words, for an input of size \((N, C_{in}, D_{in}, H_{in}, W_{in})\), -a depthwise convolution with a depthwise multiplier K, can be constructed by arguments -\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE. -Please see the notes on :doc:/notes/randomness for background.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where -$$ - D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] - \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor - $$ -$$ - H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] - \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor - $$ -$$ - W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] - \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor - $$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels). If bias is True, -then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With square kernels and equal stride
-m <- nn_conv3d(16, 33, 3, stride=2)
-# non-square kernels and unequal stride and with padding
-m <- nn_conv3d(16, 33, c(3, 5, 2), stride=c(2, 1, 1), padding=c(4, 2, 0))
-input <- torch_randn(20, 16, 10, 50, 100)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv_transpose1d.html b/docs/reference/nn_conv_transpose1d.html deleted file mode 100644 index f499ebd0be95420d53b56f8afc5022658d6c81f4..0000000000000000000000000000000000000000 --- a/docs/reference/nn_conv_transpose1d.html +++ /dev/null @@ -1,342 +0,0 @@ - - - - - - - - -ConvTranspose1D — nn_conv_transpose1d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    ConvTranspose1D

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 1D transposed convolution operator over an input image -composed of several input planes.

    -
    - - 
    nn_conv_transpose1d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): dilation * (kernel_size - 1) - padding zero-padding -will be added to both sides of the input. Default: 0

    output_padding

    (int or tuple, optional): Additional size added to one side -of the output shape. Default: 0

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If True, adds a learnable bias to the output. Default: TRUE

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    This module can be seen as the gradient of Conv1d with respect to its input. -It is also known as a fractionally-strided convolution or -a deconvolution (although it is not an actual deconvolution operation).

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for dilation * (kernel_size - 1) - padding number of points. See note -below for details.

      • -

    • output_padding controls the additional size added to one side -of the output shape. See note below for details.

      • -

    • dilation controls the spacing between the kernel points; also known as the -à trous algorithm. It is harder to describe, but this link -has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters (of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

        • -
      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    The padding argument effectively adds dilation * (kernel_size - 1) - padding -amount of zero padding to both sizes of the input. This is set so that -when a ~torch.nn.Conv1d and a ~torch.nn.ConvTranspose1d -are initialized with same parameters, they are inverses of each other in -regard to the input and output shapes. However, when stride > 1, -~torch.nn.Conv1d maps multiple input shapes to the same output -shape. output_padding is provided to resolve this ambiguity by -effectively increasing the calculated output shape on one side. Note -that output_padding is only used to find output shape, but does -not actually add zero-padding to output.

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, L_{in})\)

      • -

    • Output: \((N, C_{out}, L_{out})\) where -$$ - L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation} -\times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1 -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels). -If bias is TRUE, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_conv_transpose1d(32, 16, 2)
-input <- torch_randn(10, 32, 2)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv_transpose2d.html b/docs/reference/nn_conv_transpose2d.html deleted file mode 100644 index fec88b2fb8a43558ebcfc0c05ec99e7b35cd04b1..0000000000000000000000000000000000000000 --- a/docs/reference/nn_conv_transpose2d.html +++ /dev/null @@ -1,361 +0,0 @@ - - - - - - - - -ConvTranpose2D module — nn_conv_transpose2d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    ConvTranpose2D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 2D transposed convolution operator over an input image -composed of several input planes.

    -
    - - 
    nn_conv_transpose2d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): dilation * (kernel_size - 1) - padding zero-padding -will be added to both sides of each dimension in the input. Default: 0

    output_padding

    (int or tuple, optional): Additional size added to one side -of each dimension in the output shape. Default: 0

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If True, adds a learnable bias to the output. Default: True

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    This module can be seen as the gradient of Conv2d with respect to its input. -It is also known as a fractionally-strided convolution or -a deconvolution (although it is not an actual deconvolution operation).

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for dilation * (kernel_size - 1) - padding number of points. See note -below for details.

      • -

    • output_padding controls the additional size added to one side -of the output shape. See note below for details.

      • -

    • dilation controls the spacing between the kernel points; also known as the à trous algorithm. -It is harder to describe, but this link_ has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters (of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

        • -
      • -
    - -

    The parameters kernel_size, stride, padding, output_padding -can either be:

      -

    • a single int -- in which case the same value is used for the height and width dimensions

      • -

    • a tuple of two ints -- in which case, the first int is used for the height dimension, -and the second int for the width dimension

      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation_, -and not a full cross-correlation. It is up to the user to add proper padding.

    -

    The padding argument effectively adds dilation * (kernel_size - 1) - padding -amount of zero padding to both sizes of the input. This is set so that -when a nn_conv2d and a nn_conv_transpose2d are initialized with same -parameters, they are inverses of each other in -regard to the input and output shapes. However, when stride > 1, -nn_conv2d maps multiple input shapes to the same output -shape. output_padding is provided to resolve this ambiguity by -effectively increasing the calculated output shape on one side. Note -that output_padding is only used to find output shape, but does -not actually add zero-padding to output.

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, H_{out}, W_{out})\) where -$$ - H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0] -\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1 -$$ -$$ - W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1] -\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1 -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels) -If bias is True, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With square kernels and equal stride
-m <- nn_conv_transpose2d(16, 33, 3, stride=2)
-# non-square kernels and unequal stride and with padding
-m <- nn_conv_transpose2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
-input <- torch_randn(20, 16, 50, 100)
-output <- m(input)
-# exact output size can be also specified as an argument
-input <- torch_randn(1, 16, 12, 12)
-downsample <- nn_conv2d(16, 16, 3, stride=2, padding=1)
-upsample <- nn_conv_transpose2d(16, 16, 3, stride=2, padding=1)
-h <- downsample(input)
-h$size()
    #> [1]  1 16  6  6
    output <- upsample(h, output_size=input$size())
-output$size()
    #> [1]  1 16 12 12
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv_transpose3d.html b/docs/reference/nn_conv_transpose3d.html deleted file mode 100644 index e929e4949f461b8a1cebd9b157b33c42b4eef7ed..0000000000000000000000000000000000000000 --- a/docs/reference/nn_conv_transpose3d.html +++ /dev/null @@ -1,354 +0,0 @@ - - - - - - - - -ConvTranpose3D module — nn_conv_transpose3d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    ConvTranpose3D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 3D transposed convolution operator over an input image composed of several input -planes.

    -
    - - 
    nn_conv_transpose3d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): dilation * (kernel_size - 1) - padding zero-padding -will be added to both sides of each dimension in the input. Default: 0 -output_padding (int or tuple, optional): Additional size added to one side -of each dimension in the output shape. Default: 0

    output_padding

    (int or tuple, optional): Additional size added to one side -of each dimension in the output shape. Default: 0

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If True, adds a learnable bias to the output. Default: True

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    padding_mode

    (string, optional): 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    The transposed convolution operator multiplies each input value element-wise by a learnable kernel, -and sums over the outputs from all input feature planes.

    -

    This module can be seen as the gradient of Conv3d with respect to its input. -It is also known as a fractionally-strided convolution or -a deconvolution (although it is not an actual deconvolution operation).

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for dilation * (kernel_size - 1) - padding number of points. See note -below for details.

      • -

    • output_padding controls the additional size added to one side -of the output shape. See note below for details.

      • -

    • dilation controls the spacing between the kernel points; also known as the à trous algorithm. -It is harder to describe, but this link_ has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters (of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

        • -
      • -
    - -

    The parameters kernel_size, stride, padding, output_padding -can either be:

      -

    • a single int -- in which case the same value is used for the depth, height and width dimensions

      • -

    • a tuple of three ints -- in which case, the first int is used for the depth dimension, -the second int for the height dimension and the third int for the width dimension

      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    The padding argument effectively adds dilation * (kernel_size - 1) - padding -amount of zero padding to both sizes of the input. This is set so that -when a ~torch.nn.Conv3d and a ~torch.nn.ConvTranspose3d -are initialized with same parameters, they are inverses of each other in -regard to the input and output shapes. However, when stride > 1, -~torch.nn.Conv3d maps multiple input shapes to the same output -shape. output_padding is provided to resolve this ambiguity by -effectively increasing the calculated output shape on one side. Note -that output_padding is only used to find output shape, but does -not actually add zero-padding to output.

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where -$$ - D_{out} = (D_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0] -\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1 -$$ -$$ - H_{out} = (H_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1] -\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1 -$$ -$$ - W_{out} = (W_{in} - 1) \times \mbox{stride}[2] - 2 \times \mbox{padding}[2] + \mbox{dilation}[2] -\times (\mbox{kernel\_size}[2] - 1) + \mbox{output\_padding}[2] + 1 -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels) -If bias is True, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    
-  
    
-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/nn_cross_entropy_loss.html b/docs/reference/nn_cross_entropy_loss.html
deleted file mode 100644
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-CrossEntropyLoss module — nn_cross_entropy_loss • torch
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-    
    CrossEntropyLoss module
    
-    Source: R/nn-loss.R
-    
-    
    
-
-    
    
-    
    This criterion combines nn_log_softmax() and nn_nll_loss() in one single class.
-It is useful when training a classification problem with C classes.
    
-    
    
-
-    
    nn_cross_entropy_loss(weight = NULL, ignore_index = -100, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
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-      
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-    
    weight
    (Tensor, optional): a manual rescaling weight given to each class.
-If given, has to be a Tensor of size C
    ignore_index
    (int, optional): Specifies a target value that is ignored
-and does not contribute to the input gradient. When size_average is
-TRUE, the loss is averaged over non-ignored targets.
    reduction
    (string, optional): Specifies the reduction to apply to the output:
-'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
-'mean': the sum of the output will be divided by the number of
-elements in the output, 'sum': the output will be summed. Note: size_average
-and reduce are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override reduction. Default: 'mean'
    
-
-    
    Details
    
-
-    
    If provided, the optional argument weight should be a 1D Tensor
-assigning weight to each of the classes.
    
-
    This is particularly useful when you have an unbalanced training set.
-The input is expected to contain raw, unnormalized scores for each class.
-input has to be a Tensor of size either \((minibatch, C)\) or
-\((minibatch, C, d_1, d_2, ..., d_K)\)
-with \(K \geq 1\) for the K-dimensional case (described later).
    
-
    This criterion expects a class index in the range \([0, C-1]\) as the
-target for each value of a 1D tensor of size minibatch; if ignore_index
-is specified, this criterion also accepts this class index (this index may not
-necessarily be in the class range).
    
-
    The loss can be described as:
-$$
-  \mbox{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
-= -x[class] + \log\left(\sum_j \exp(x[j])\right)
-$$
-or in the case of the weight argument being specified:
-$$
-  \mbox{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right)
-$$
    
-
    The losses are averaged across observations for each minibatch.
-Can also be used for higher dimension inputs, such as 2D images, by providing
-an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
-where \(K\) is the number of dimensions, and a target of appropriate shape
-(see below).
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C)\) where C = number of classes, or
-\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
-in the case of K-dimensional loss.
      • 
-
    • Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
-K-dimensional loss.
      • 
-
    • Output: scalar.
-If reduction is 'none', then the same size as the target:
-\((N)\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
-of K-dimensional loss.
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-loss <- nn_cross_entropy_loss()
-input <- torch_randn(3, 5, requires_grad=TRUE)
-target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long())
-output <- loss(input, target)
-output$backward()
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_dropout.html b/docs/reference/nn_dropout.html
deleted file mode 100644
index 95fa808ba10a0d9ae69d3bbb87883eb19566e0fa..0000000000000000000000000000000000000000
--- a/docs/reference/nn_dropout.html
+++ /dev/null
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-
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-  
-  
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-Dropout module — nn_dropout • torch
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-    
    Dropout module
    
-    Source: R/nn-dropout.R
-    
-    
    
-
-    
    
-    
    During training, randomly zeroes some of the elements of the input
-tensor with probability p using samples from a Bernoulli
-distribution. Each channel will be zeroed out independently on every forward
-call.
    
-    
    
-
-    
    nn_dropout(p = 0.5, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    p
    probability of an element to be zeroed. Default: 0.5
    inplace
    If set to TRUE, will do this operation in-place. Default: FALSE.
    
-
-    
    Details
    
-
-    
    This has proven to be an effective technique for regularization and
-preventing the co-adaptation of neurons as described in the paper
-Improving neural networks by preventing co-adaptation of feature detectors.
    
-
    Furthermore, the outputs are scaled by a factor of :math:\frac{1}{1-p} during
-training. This means that during evaluation the module simply computes an
-identity function.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((*)\). Input can be of any shape
      • 
-
    • Output: \((*)\). Output is of the same shape as input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_dropout(p = 0.2)
-input <- torch_randn(20, 16)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_dropout2d.html b/docs/reference/nn_dropout2d.html
deleted file mode 100644
index efcc07a6ba6dc0c4f6e37af555bfeaa190f8e20a..0000000000000000000000000000000000000000
--- a/docs/reference/nn_dropout2d.html
+++ /dev/null
@@ -1,243 +0,0 @@
-
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-Dropout2D module — nn_dropout2d • torch
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-    Source: R/nn-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(\mbox{input}[i, j]\)).
    
-    
    
-
-    
    nn_dropout2d(p = 0.5, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    p
    (float, optional): probability of an element to be zero-ed.
    inplace
    (bool, optional): If set to TRUE, will do this operation
-in-place
    
-
-    
    Details
    
-
-    
    Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
-Usually the input comes from nn_conv2d modules.
    
-
    As described in the paper
-Efficient Object Localization Using Convolutional Networks ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.
-In this case, nn_dropout2d will help promote independence between
-feature maps and should be used instead.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, H, W)\)
      • 
-
    • Output: \((N, C, H, W)\) (same shape as input)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_dropout2d(p = 0.2)
-input <- torch_randn(20, 16, 32, 32)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_dropout3d.html b/docs/reference/nn_dropout3d.html
deleted file mode 100644
index 32d4545dfbb9ab138232d56407cafe9cf86847ee..0000000000000000000000000000000000000000
--- a/docs/reference/nn_dropout3d.html
+++ /dev/null
@@ -1,243 +0,0 @@
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-Dropout3D module — nn_dropout3d • torch
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    Dropout3D module
    
-    Source: R/nn-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(\mbox{input}[i, j]\)).
    
-    
    
-
-    
    nn_dropout3d(p = 0.5, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    p
    (float, optional): probability of an element to be zeroed.
    inplace
    (bool, optional): If set to TRUE, will do this operation
-in-place
    
-
-    
    Details
    
-
-    
    Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
-Usually the input comes from nn_conv2d modules.
    
-
    As described in the paper
-Efficient Object Localization Using Convolutional Networks ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.
    
-
    In this case, nn_dropout3d will help promote independence between
-feature maps and should be used instead.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, D, H, W)\)
      • 
-
    • Output: \((N, C, D, H, W)\) (same shape as input)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_dropout3d(p = 0.2)
-input <- torch_randn(20, 16, 4, 32, 32)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_elu.html b/docs/reference/nn_elu.html
deleted file mode 100644
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-ELU module — nn_elu • torch
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    ELU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_elu(alpha = 1, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    alpha
    the \(\alpha\) value for the ELU formulation. Default: 1.0
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
-$$
    
-    
    Shape
    
-
-    
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-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
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      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_elu()
-input <- torch_randn(2)
-output <-  m(input)
-
-# }
    
-  
    
-  
-
    
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/nn_embedding.html b/docs/reference/nn_embedding.html
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-    Source: R/nn-sparse.R
-    
-    
    
-
-    
    
-    
    A simple lookup table that stores embeddings of a fixed dictionary and size.
-This module is often used to store word embeddings and retrieve them using indices.
-The input to the module is a list of indices, and the output is the corresponding
-word embeddings.
    
-    
    
-
-    
    nn_embedding(
-  num_embeddings,
-  embedding_dim,
-  padding_idx = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  sparse = FALSE,
-  .weight = NULL
-)
    
-
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    Arguments
    
-    
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-      
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    num_embeddings
    (int): size of the dictionary of embeddings
    embedding_dim
    (int): the size of each embedding vector
    padding_idx
    (int, optional): If given, pads the output with the embedding vector at padding_idx
-(initialized to zeros) whenever it encounters the index.
    max_norm
    (float, optional): If given, each embedding vector with norm larger than max_norm
-is renormalized to have norm max_norm.
    norm_type
    (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
    scale_grad_by_freq
    (boolean, optional): If given, this will scale gradients by the inverse of frequency of
-the words in the mini-batch. Default False.
    sparse
    (bool, optional): If True, gradient w.r.t. weight matrix will be a sparse tensor.
    .weight
    (Tensor) embeddings weights (in case you want to set it manually)
    
-
    See Notes for more details regarding sparse gradients.
    
-
-    
    Note
    
-
-    
    Keep in mind that only a limited number of optimizers support
-sparse gradients: currently it's optim.SGD (CUDA and CPU),
-optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)
    
-
    With padding_idx set, the embedding vector at
-padding_idx is initialized to all zeros. However, note that this
-vector can be modified afterwards, e.g., using a customized
-initialization method, and thus changing the vector used to pad the
-output. The gradient for this vector from nn_embedding
-is always zero.
    
-    
    Attributes
    
-
-    
-
-
      
-
    • weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
-initialized from \(\mathcal{N}(0, 1)\)
      • 
-
    
-
-    
    Shape
    
-
-    
-
-
      
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    • Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract
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      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-# an Embedding module containing 10 tensors of size 3
-embedding <- nn_embedding(10, 3)
-# a batch of 2 samples of 4 indices each
-input <- torch_tensor(rbind(c(1,2,4,5),c(4,3,2,9)), dtype = torch_long())
-embedding(input)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.5531  0.2969 -1.9168
-#>  -0.7095 -0.1328 -0.7352
-#>  -1.5311 -0.6539  0.7804
-#>   1.5343  0.1139  1.1985
-#> 
-#> (2,.,.) = 
-#>  -1.5311 -0.6539  0.7804
-#>  -0.1120  0.9578  0.1195
-#>  -0.7095 -0.1328 -0.7352
-#>  -0.4247  0.6266 -0.1286
-#> [ CPUFloatType{2,4,3} ]
    # example with padding_idx
-embedding <- nn_embedding(10, 3, padding_idx=1)
-input <- torch_tensor(matrix(c(1,3,1,6), nrow = 1), dtype = torch_long())
-embedding(input)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.0000  0.0000  0.0000
-#>  -1.2943 -1.0279  0.6483
-#>   0.0000  0.0000  0.0000
-#>   0.4053  0.7866 -0.3922
-#> [ CPUFloatType{1,4,3} ]
    
-# }
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diff --git a/docs/reference/nn_gelu.html b/docs/reference/nn_gelu.html
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-    Source: R/nn-activation.R
-    
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-    
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-$$\mbox{GELU}(x) = x * \Phi(x)$$
    
-    
    
-
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    nn_gelu()
    
-
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    Details
    
-
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    where \(\Phi(x)\) is the Cumulative Distribution Function for Gaussian Distribution.
    
-    
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      • 
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    Examples
    
-    
    # \dontrun{
-m = nn_gelu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
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diff --git a/docs/reference/nn_glu.html b/docs/reference/nn_glu.html
deleted file mode 100644
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-GLU module — nn_glu • torch
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    GLU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
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-    
    Applies the gated linear unit function
-\({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
-of the input matrices and \(b\) is the second half.
    
-    
    
-
-    
    nn_glu(dim = -1)
    
-
-    
    Arguments
    
-    
-    
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-      
-      
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    dim
    (int): the dimension on which to split the input. Default: -1
    
-
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      • 
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-
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    Examples
    
-    
    # \dontrun{
-m <- nn_glu()
-input <- torch_randn(4, 2)
-output <- m(input)
-
-# }
    
-  
    
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diff --git a/docs/reference/nn_hardshrink.html b/docs/reference/nn_hardshrink.html
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-Hardshwink module — nn_hardshrink • torch
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-    Source: R/nn-activation.R
-    
-    
    
-
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-    
    Applies the hard shrinkage function element-wise:
    
-    
    
-
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    nn_hardshrink(lambd = 0.5)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    lambd
    the \(\lambda\) value for the Hardshrink formulation. Default: 0.5
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{HardShrink}(x) =
-  \left\{ \begin{array}{ll}
-x, & \mbox{ if } x > \lambda \\
-x, & \mbox{ if } x < -\lambda \\
-0, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-    
    Shape
    
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-dimensions
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      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_hardshrink()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_hardsigmoid.html b/docs/reference/nn_hardsigmoid.html
deleted file mode 100644
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-Hardsigmoid module — nn_hardsigmoid • torch
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-    Source: R/nn-activation.R
-    
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    Applies the element-wise function:
    
-    
    
-
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    nn_hardsigmoid()
    
-
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    Details
    
-
-    
    $$
-\mbox{Hardsigmoid}(x) = \left\{ \begin{array}{ll}
-  0 & \mbox{if~} x \le -3, \\
-  1 & \mbox{if~} x \ge +3, \\
-  x / 6 + 1 / 2 & \mbox{otherwise}
-\end{array}
-\right.
-$$
    
-    
    Shape
    
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-dimensions
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      • 
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-
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-    
    Examples
    
-    
    # \dontrun{
-m <- nn_hardsigmoid()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
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diff --git a/docs/reference/nn_hardswish.html b/docs/reference/nn_hardswish.html
deleted file mode 100644
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-Hardswish module — nn_hardswish • torch
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-    Source: R/nn-activation.R
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-    
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-Searching for MobileNetV3
    
-    
    
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-
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    Details
    
-
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    $$ \mbox{Hardswish}(x) = \left\{
-  \begin{array}{ll}
-  0 & \mbox{if } x \le -3, \\
-  x & \mbox{if } x \ge +3, \\
-  x \cdot (x + 3)/6 & \mbox{otherwise}
-  \end{array}
-  \right. $$
    
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diff --git a/docs/reference/nn_hardtanh.html b/docs/reference/nn_hardtanh.html
deleted file mode 100644
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-    Source: R/nn-activation.R
-    
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-    
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-HardTanh is defined as:
    
-    
    
-
-    
    nn_hardtanh(min_val = -1, max_val = 1, inplace = FALSE)
    
-
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    Arguments
    
-    
-    
-    
-      
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    min_val
    minimum value of the linear region range. Default: -1
    max_val
    maximum value of the linear region range. Default: 1
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$
-\mbox{HardTanh}(x) = \left\{ \begin{array}{ll}
-  1 & \mbox{ if } x > 1 \\
-  -1 & \mbox{ if } x < -1 \\
-  x & \mbox{ otherwise } \\
-\end{array}
-\right.
-$$
    
-
    The range of the linear region :math:[-1, 1] can be adjusted using
-min_val and max_val.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_hardtanh(-2, 2)
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
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diff --git a/docs/reference/nn_identity.html b/docs/reference/nn_identity.html
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-    Source: R/nn-linear.R
-    
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-
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-    
    A placeholder identity operator that is argument-insensitive.
    
-    
    
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-
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    Arguments
    
-    
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-    
-      
-      
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-    
    ...
    any arguments (unused)
    
-
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-    
    Examples
    
-    
    # \dontrun{
-m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE)
-input <- torch_randn(128, 20)
-output <- m(input)
-print(output$size())
    #> [1] 128  20
    
-# }
-
    
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diff --git a/docs/reference/nn_init_calculate_gain.html b/docs/reference/nn_init_calculate_gain.html
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-    
    
-
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-
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    the non-linear function
    param
    optional parameter for the non-linear function
    
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    tensor
    an n-dimensional Tensor
    val
    the value to fill the tensor with
    
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-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_constant_(w, 0.3)
    #> torch_tensor 
-#>  0.3000  0.3000  0.3000  0.3000  0.3000
-#>  0.3000  0.3000  0.3000  0.3000  0.3000
-#>  0.3000  0.3000  0.3000  0.3000  0.3000
-#> [ CPUFloatType{3,5} ]
    
-# }
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diff --git a/docs/reference/nn_init_dirac_.html b/docs/reference/nn_init_dirac_.html
deleted file mode 100644
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+++ /dev/null
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-Dirac initialization — nn_init_dirac_ • torch
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-    
    
-    
    Dirac initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the 3, 4, 5-dimensional input Tensor with the Dirac
-delta function. Preserves the identity of the inputs in Convolutional
-layers, where as many input channels are preserved as possible. In case
-of groups>1, each group of channels preserves identity.
    
-    
    
-
-    
    nn_init_dirac_(tensor, groups = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    a 3, 4, 5-dimensional torch.Tensor
    groups
    (optional) number of groups in the conv layer (default: 1)
    
-
-
-    
    Examples
    
-    
    
-  
    
-  
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-  
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diff --git a/docs/reference/nn_init_eye_.html b/docs/reference/nn_init_eye_.html
deleted file mode 100644
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--- a/docs/reference/nn_init_eye_.html
+++ /dev/null
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-    
    Eye initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the 2-dimensional input Tensor with the identity matrix.
-Preserves the identity of the inputs in Linear layers, where as
-many inputs are preserved as possible.
    
-    
    
-
-    
    nn_init_eye_(tensor)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    a 2-dimensional torch tensor.
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_eye_(w)
    #> torch_tensor 
-#>  1  0  0  0  0
-#>  0  1  0  0  0
-#>  0  0  1  0  0
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_init_kaiming_normal_.html b/docs/reference/nn_init_kaiming_normal_.html
deleted file mode 100644
index d5d58686d6bf410b67a7dc5e6739bdaf3d4a1a5c..0000000000000000000000000000000000000000
--- a/docs/reference/nn_init_kaiming_normal_.html
+++ /dev/null
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-
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-Kaiming normal initialization — nn_init_kaiming_normal_ • torch
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-    
    Kaiming normal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values according to the method
-described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a
-normal distribution.
    
-    
    
-
-    
    nn_init_kaiming_normal_(
-  tensor,
-  a = 0,
-  mode = "fan_in",
-  nonlinearity = "leaky_relu"
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional torch.Tensor
    a
    the negative slope of the rectifier used after this layer (only used
-with 'leaky_relu')
    mode
    either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.
    nonlinearity
    the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_kaiming_normal_(w, mode = "fan_in", nonlinearity = "leaky_relu")
    #> torch_tensor 
-#> -0.5594  0.2408  0.3946  0.5860 -0.4834
-#> -0.0442  0.7170 -0.3028  0.4015 -0.8906
-#> -0.5157 -0.1763  0.9366  0.4640 -0.5356
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
-  
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diff --git a/docs/reference/nn_init_kaiming_uniform_.html b/docs/reference/nn_init_kaiming_uniform_.html
deleted file mode 100644
index 62e5e6ce159b33c2a84a8003a25100983196f0d7..0000000000000000000000000000000000000000
--- a/docs/reference/nn_init_kaiming_uniform_.html
+++ /dev/null
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-Kaiming uniform initialization — nn_init_kaiming_uniform_ • torch
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-  
    
-    
    
-    
    Kaiming uniform initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values according to the method
-described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a
-uniform distribution.
    
-    
    
-
-    
    nn_init_kaiming_uniform_(
-  tensor,
-  a = 0,
-  mode = "fan_in",
-  nonlinearity = "leaky_relu"
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional torch.Tensor
    a
    the negative slope of the rectifier used after this layer (only used
-with 'leaky_relu')
    mode
    either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.
    nonlinearity
    the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_kaiming_uniform_(w, mode = "fan_in", nonlinearity = "leaky_relu")
    #> torch_tensor 
-#> -0.7460  0.2070 -0.1066 -0.4344 -0.4666
-#> -0.5351 -0.4524  0.0950 -1.0077 -0.2169
-#> -0.9525  0.8753  0.0070 -0.4553 -0.3445
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_init_normal_.html b/docs/reference/nn_init_normal_.html
deleted file mode 100644
index a7962cbc0d84088cf15c30b8d15b5ce9a8c12dc9..0000000000000000000000000000000000000000
--- a/docs/reference/nn_init_normal_.html
+++ /dev/null
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-Normal initialization — nn_init_normal_ • torch
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-    
    
-    
    Normal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values drawn from the normal distribution
    
-    
    
-
-    
    nn_init_normal_(tensor, mean = 0, std = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    mean
    the mean of the normal distribution
    std
    the standard deviation of the normal distribution
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_normal_(w)
    #> torch_tensor 
-#> -1.0569 -1.0900  1.2740 -1.7728  0.0593
-#> -1.7131 -0.1353  0.8191  0.1481 -0.9940
-#> -0.7544 -1.0298  0.4237  1.4650  0.0575
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
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diff --git a/docs/reference/nn_init_ones_.html b/docs/reference/nn_init_ones_.html
deleted file mode 100644
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+++ /dev/null
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-Ones initialization — nn_init_ones_ • torch
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    Ones initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with the scalar value 1
    
-    
    
-
-    
    nn_init_ones_(tensor)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_ones_(w)
    #> torch_tensor 
-#>  1  1  1  1  1
-#>  1  1  1  1  1
-#>  1  1  1  1  1
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
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diff --git a/docs/reference/nn_init_orthogonal_.html b/docs/reference/nn_init_orthogonal_.html
deleted file mode 100644
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--- a/docs/reference/nn_init_orthogonal_.html
+++ /dev/null
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-Orthogonal initialization — nn_init_orthogonal_ • torch
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-    
    
-    
    Orthogonal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with a (semi) orthogonal matrix, as
-described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have
-at least 2 dimensions, and for tensors with more than 2 dimensions the
-trailing dimensions are flattened.
    
-    
    
-
-    
    nn_init_orthogonal_(tensor, gain = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    gain
    optional scaling factor
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3,5)
-nn_init_orthogonal_(w)
    #> torch_tensor 
-#> -0.2147  0.0073 -0.0312 -0.0439  0.9752
-#> -0.8268  0.5222  0.0419  0.0979 -0.1802
-#>  0.3963  0.5329 -0.0498  0.7371  0.1148
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_init_sparse_.html b/docs/reference/nn_init_sparse_.html
deleted file mode 100644
index c9b963d67878426e2aca519360eda1f7d86189c7..0000000000000000000000000000000000000000
--- a/docs/reference/nn_init_sparse_.html
+++ /dev/null
@@ -1,220 +0,0 @@
-
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-  
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-Sparse initialization — nn_init_sparse_ • torch
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-    
    Sparse initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the 2D input Tensor as a sparse matrix, where the
-non-zero elements will be drawn from the normal distribution
-as described in Deep learning via Hessian-free optimization - Martens, J. (2010).
    
-    
    
-
-    
    nn_init_sparse_(tensor, sparsity, std = 0.01)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    sparsity
    The fraction of elements in each column to be set to zero
    std
    the standard deviation of the normal distribution used to generate
-the non-zero values
    
-
-
-    
    Examples
    
-    
    
-  
    
-  
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-  
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diff --git a/docs/reference/nn_init_trunc_normal_.html b/docs/reference/nn_init_trunc_normal_.html
deleted file mode 100644
index 9ed52ecb0d7ffe5b6a7be855592507dcb1521046..0000000000000000000000000000000000000000
--- a/docs/reference/nn_init_trunc_normal_.html
+++ /dev/null
@@ -1,233 +0,0 @@
-
-
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-  
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-
-Truncated normal initialization — nn_init_trunc_normal_ • torch
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-    
    Truncated normal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values drawn from a truncated
-normal distribution.
    
-    
    
-
-    
    nn_init_trunc_normal_(tensor, mean = 0, std = 1, a = -2, b = -2)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
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-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    mean
    the mean of the normal distribution
    std
    the standard deviation of the normal distribution
    a
    the minimum cutoff value
    b
    the maximum cutoff value
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_trunc_normal_(w)
    #> torch_tensor 
-#> -2 -2 -2 -2 -2
-#> -2 -2 -2 -2 -2
-#> -2 -2 -2 -2 -2
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
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diff --git a/docs/reference/nn_init_uniform_.html b/docs/reference/nn_init_uniform_.html
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    Fills the input Tensor with values drawn from the uniform distribution
    
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    a
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    b
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    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_uniform_(w)
    #> torch_tensor 
-#>  0.8556  0.9331  0.3515  0.8071  0.4948
-#>  0.6075  0.9042  0.7181  0.7329  0.7563
-#>  0.2584  0.5293  0.9757  0.3030  0.3341
-#> [ CPUFloatType{3,5} ]
    
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-    Source: R/nn-init.R
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-    
    Fills the input Tensor with values according to the method
-described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using a normal
-distribution.
    
-    
    
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    tensor
    an n-dimensional Tensor
    gain
    an optional scaling factor
    
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    Examples
    
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    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_xavier_normal_(w)
    #> torch_tensor 
-#>  1.2535 -0.2197  0.5425 -3.0052 -4.2446
-#> -0.3570 -1.6970 -2.0154 -0.5348  2.7582
-#>  0.8714 -0.8924  0.7675  3.2553 -1.4333
-#> [ CPUFloatType{3,5} ]
    
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-described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using a uniform
-distribution.
    
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    tensor
    an n-dimensional Tensor
    gain
    an optional scaling factor
    
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    Examples
    
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    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_xavier_uniform_(w)
    #> torch_tensor 
-#>  1.3397  1.1040 -3.0453 -1.7935  0.9545
-#> -0.0194 -2.4483  2.9345  2.2750 -2.4048
-#> -0.4406 -2.2409  0.4155 -0.1573  1.9776
-#> [ CPUFloatType{3,5} ]
    
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diff --git a/docs/reference/nn_init_zeros_.html b/docs/reference/nn_init_zeros_.html
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    tensor
    an n-dimensional tensor
    
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    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_zeros_(w)
    #> torch_tensor 
-#>  0  0  0  0  0
-#>  0  0  0  0  0
-#>  0  0  0  0  0
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diff --git a/docs/reference/nn_leaky_relu.html b/docs/reference/nn_leaky_relu.html
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-LeakyReLU module — nn_leaky_relu • torch
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    LeakyReLU module
    
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    negative_slope
    Controls the angle of the negative slope. Default: 1e-2
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-  \mbox{LeakyReLU}(x) = \max(0, x) + \mbox{negative\_slope} * \min(0, x)
-$$
-or
    
-
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-  \mbox{LeakyRELU}(x) =
-  \left\{ \begin{array}{ll}
-x, & \mbox{ if } x \geq 0 \\
-\mbox{negative\_slope} \times x, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-    
    Shape
    
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      • 
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    Examples
    
-    
    # \dontrun{
-m <- nn_leaky_relu(0.1)
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
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    in_features
    size of each input sample
    out_features
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    bias
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-Default: TRUE
    
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    • weight: the learnable weights of the module of shape
-(out_features, in_features). The values are
-initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
-\(k = \frac{1}{\mbox{in\_features}}\)
      • 
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-If bias is TRUE, the values are initialized from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{1}{\mbox{in\_features}}\)
      • 
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    Examples
    
-    
    # \dontrun{
-m <- nn_linear(20, 30)
-input <- torch_randn(128, 20)
-output <- m(input)
-print(output$size())
    #> [1] 128  30
    
-# }
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diff --git a/docs/reference/nn_log_sigmoid.html b/docs/reference/nn_log_sigmoid.html
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-
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-    
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-$$
-  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
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-m <- nn_log_sigmoid()
-input <- torch_randn(2)
-output <- m(input)
-
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diff --git a/docs/reference/nn_log_softmax.html b/docs/reference/nn_log_softmax.html
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    Arguments
    
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    (int): A dimension along which LogSoftmax will be computed.
    
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    Value
    
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-    
    a Tensor of the same dimension and shape as the input with
-values in the range [-inf, 0)
    
-    
    Details
    
-
-    
    $$
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-$$
    
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-dimensions
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    Examples
    
-    
    # \dontrun{
-m <- nn_log_softmax(1)
-input <- torch_randn(2, 3)
-output <- m(input)
-
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-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  return_indices = FALSE,
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    kernel_size
    the size of the window to take a max over
    stride
    the stride of the window. Default value is kernel_size
    padding
    implicit zero padding to be added on both sides
    dilation
    a parameter that controls the stride of elements in the window
    return_indices
    if TRUE, will return the max indices along with the outputs.
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    ceil_mode
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    Details
    
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-and output \((N, C, L_{out})\) can be precisely described as:
    
-
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-  out(N_i, C_j, k) = \max_{m=0, \ldots, \mbox{kernel\_size} - 1}
-input(N_i, C_j, stride \times k + m)
-$$
    
-
    If padding is non-zero, then the input is implicitly zero-padded on both sides
-for padding number of points. dilation controls the spacing between the kernel points.
-It is harder to describe, but this link
-has a nice visualization of what dilation does.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, L_{in})\)
      • 
-
    • Output: \((N, C, L_{out})\), where
      • 
-
    
-
-
    $$
-  L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
-    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-# pool of size=3, stride=2
-m <- nn_max_pool1d(3, stride=2)
-input <- torch_randn(20, 16, 50)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nn_max_pool2d.html b/docs/reference/nn_max_pool2d.html
deleted file mode 100644
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--- a/docs/reference/nn_max_pool2d.html
+++ /dev/null
@@ -1,283 +0,0 @@
-
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-MaxPool2D module — nn_max_pool2d • torch
-
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-  
    
-    
    
-    
    MaxPool2D module
    
-    Source: R/nn-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 2D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nn_max_pool2d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  return_indices = FALSE,
-  ceil_mode = FALSE
-)
    
-
-    
    Arguments
    
-    
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    kernel_size
    the size of the window to take a max over
    stride
    the stride of the window. Default value is kernel_size
    padding
    implicit zero padding to be added on both sides
    dilation
    a parameter that controls the stride of elements in the window
    return_indices
    if TRUE, will return the max indices along with the outputs.
-Useful for nn_max_unpool2d() later.
    ceil_mode
    when TRUE, will use ceil instead of floor to compute the output shape
    
-
-    
    Details
    
-
-    
    In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
-output \((N, C, H_{out}, W_{out})\) and kernel_size \((kH, kW)\)
-can be precisely described as:
    
-
    $$
-  \begin{array}{ll}
-out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
-& \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
-               \mbox{stride[1]} \times w + n)
-\end{array}
-$$
    
-
    If padding is non-zero, then the input is implicitly zero-padded on both sides
-for padding number of points. dilation controls the spacing between the kernel points.
-It is harder to describe, but this link has a nice visualization of what dilation does.
    
-
    The parameters kernel_size, stride, padding, dilation can either be:
      
-
    • a single int -- in which case the same value is used for the height and width dimension
      • 
-
    • a tuple of two ints -- in which case, the first int is used for the height dimension,
-and the second int for the width dimension
      • 
-
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, H_{in}, W_{in})\)
      • 
-
    • Output: \((N, C, H_{out}, W_{out})\), where
      • 
-
    
-
-
    $$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 * \mbox{padding[0]} - \mbox{dilation[0]}
-    \times (\mbox{kernel\_size[0]} - 1) - 1}{\mbox{stride[0]}} + 1\right\rfloor
-$$
    
-
    $$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 * \mbox{padding[1]} - \mbox{dilation[1]}
-    \times (\mbox{kernel\_size[1]} - 1) - 1}{\mbox{stride[1]}} + 1\right\rfloor
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-# pool of square window of size=3, stride=2
-m <- nn_max_pool2d(3, stride=2)
-# pool of non-square window
-m <- nn_max_pool2d(c(3, 2), stride=c(2, 1))
-input <- torch_randn(20, 16, 50, 32)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
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diff --git a/docs/reference/nn_module.html b/docs/reference/nn_module.html
deleted file mode 100644
index 8848bc1feac4965996564770ef19085726cc17db..0000000000000000000000000000000000000000
--- a/docs/reference/nn_module.html
+++ /dev/null
@@ -1,234 +0,0 @@
-
-
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-  
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-Base class for all neural network modules. — nn_module • torch
-
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-    
    
-    
    Base class for all neural network modules.
    
-    Source: R/nn.R
-    
-    
    
-
-    
    
-    
    Your models should also subclass this class.
    
-    
    
-
-    
    nn_module(classname = NULL, inherit = nn_Module, ...)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-      
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-    
    classname
    an optional name for the module
    inherit
    an optional module to inherit from
    ...
    methods implementation
    
-
-    
    Details
    
-
-    
    Modules can also contain other Modules, allowing to nest them in a tree
-structure. You can assign the submodules as regular attributes.
    
-
-    
    Examples
    
-    
    # \dontrun{
-model <- nn_module(
- initialize = function() {
-   self$conv1 <- nn_conv2d(1, 20, 5)
-   self$conv2 <- nn_conv2d(20, 20, 5)
- },
- forward = function(input) {
-   input <- self$conv1(input)
-   input <- nnf_relu(input)
-   input <- self$conv2(input)
-   input <- nnf_relu(input)
-   input
- }
-)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nn_module_list.html b/docs/reference/nn_module_list.html
deleted file mode 100644
index b03a9e1bfd1b176b08eb83fde6b04c0aa3f9fa8b..0000000000000000000000000000000000000000
--- a/docs/reference/nn_module_list.html
+++ /dev/null
@@ -1,224 +0,0 @@
-
-
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-  
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-Holds submodules in a list. — nn_module_list • torch
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-    
    Holds submodules in a list.
    
-    Source: R/nn.R
-    
-    
    
-
-    
    
-    
    nn_module_list can be indexed like a regular R list, but
-modules it contains are properly registered, and will be visible by all
-nn_module methods.
    
-    
    
-
-    
    nn_module_list(modules = list())
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    modules
    a list of modules to add
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-my_module <- nn_module(
- initialize = function() {
-   self$linears <- nn_module_list(lapply(1:10, function(x) nn_linear(10, 10)))
- },
- forward = function(x) {
-  for (i in 1:length(self$linears))
-    x <- self$linears[[i]](x)
-  x
- }
-)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nn_multihead_attention.html b/docs/reference/nn_multihead_attention.html
deleted file mode 100644
index f7b9e3a961f95ffa22401e6963111f06de242906..0000000000000000000000000000000000000000
--- a/docs/reference/nn_multihead_attention.html
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@@ -1,289 +0,0 @@
-
-
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-MultiHead attention — nn_multihead_attention • torch
-
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-    
    MultiHead attention
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Allows the model to jointly attend to information
-from different representation subspaces.
-See reference: Attention Is All You Need
    
-    
    
-
-    
    nn_multihead_attention(
-  embed_dim,
-  num_heads,
-  dropout = 0,
-  bias = TRUE,
-  add_bias_kv = FALSE,
-  add_zero_attn = FALSE,
-  kdim = NULL,
-  vdim = NULL
-)
    
-
-    
    Arguments
    
-    
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-    
-    
    embed_dim
    total dimension of the model.
    num_heads
    parallel attention heads.
    dropout
    a Dropout layer on attn_output_weights. Default: 0.0.
    bias
    add bias as module parameter. Default: True.
    add_bias_kv
    add bias to the key and value sequences at dim=0.
    add_zero_attn
    add a new batch of zeros to the key and
-value sequences at dim=1.
    kdim
    total number of features in key. Default: NULL
    vdim
    total number of features in value. Default: NULL.
-Note: if kdim and vdim are NULL, they will be set to embed_dim such that
-query, key, and value have the same number of features.
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{MultiHead}(Q, K, V) = \mbox{Concat}(head_1,\dots,head_h)W^O
-\mbox{where} head_i = \mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V)
-$$
    
-    
    Shape
    
-
-    
-
-
-
    Inputs:
      
-
    • query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.
      • 
-
    • key: \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
      • 
-
    • value: \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
      • 
-
    • key_padding_mask: \((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of True will be ignored while the position with the value of False will be unchanged.
      • 
-
    • attn_mask: 2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with True
-is not allowed to attend while False values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.
      • 
-
    
-
-
    Outputs:
      
-
    • attn_output: \((L, N, E)\) where L is the target sequence length, N is the batch size,
-E is the embedding dimension.
      • 
-
    • attn_output_weights: \((N, L, S)\) where N is the batch size,
-L is the target sequence length, S is the source sequence length.
      • 
-
    
-
-
-    
    Examples
    
-    
    
-  
    
-  
-
    
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_prelu.html b/docs/reference/nn_prelu.html
deleted file mode 100644
index ec2da259c793fbb1d12b906fd9ba75685094c4dd..0000000000000000000000000000000000000000
--- a/docs/reference/nn_prelu.html
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@@ -1,270 +0,0 @@
-
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-PReLU module — nn_prelu • torch
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-    
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-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
-$$
-  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
-$$
-or
-$$
-  \mbox{PReLU}(x) =
-  \left\{ \begin{array}{ll}
-x, & \mbox{ if } x \geq 0 \\
-ax, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-    
    
-
-    
    nn_prelu(num_parameters = 1, init = 0.25)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    num_parameters
    (int): number of \(a\) to learn.
-Although it takes an int as input, there is only two values are legitimate:
-1, or the number of channels at input. Default: 1
    init
    (float): the initial value of \(a\). Default: 0.25
    
-
-    
    Details
    
-
-    
    Here \(a\) is a learnable parameter. When called without arguments, nn.prelu() uses a single
-parameter \(a\) across all input channels. If called with nn_prelu(nChannels),
-a separate \(a\) is used for each input channel.
    
-    
    Note
    
-
-    
    weight decay should not be used when learning \(a\) for good performance.
    
-
    Channel dim is the 2nd dim of input. When input has dims < 2, then there is
-no channel dim and the number of channels = 1.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-    
    Attributes
    
-
-    
-
-
      
-
    • weight (Tensor): the learnable weights of shape (num_parameters).
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_prelu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_relu.html b/docs/reference/nn_relu.html
deleted file mode 100644
index 21a181a9b61cb56a9d170f77e82ce857fe1e0d9c..0000000000000000000000000000000000000000
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-    
    ReLU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the rectified linear unit function element-wise
-$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$
    
-    
    
-
-    
    nn_relu(inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_relu()
-input <- torch_randn(2)
-m(input)
    #> torch_tensor 
-#>  0.2952
-#>  0.0000
-#> [ CPUFloatType{2} ]
    
-# }
-
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_relu6.html b/docs/reference/nn_relu6.html
deleted file mode 100644
index 3bc8d5d38286802c0d35938cf0b51f0c5b39c8b8..0000000000000000000000000000000000000000
--- a/docs/reference/nn_relu6.html
+++ /dev/null
@@ -1,227 +0,0 @@
-
-
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-ReLu6 module — nn_relu6 • torch
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-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_relu6(inplace = FALSE)
    
-
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    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{ReLU6}(x) = \min(\max(0,x), 6)
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_relu6()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nn_rnn.html b/docs/reference/nn_rnn.html
deleted file mode 100644
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--- a/docs/reference/nn_rnn.html
+++ /dev/null
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-
-
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-RNN module — nn_rnn • torch
-
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-    
    RNN module
    
-    Source: R/nn-rnn.R
-    
-    
    
-
-    
    
-    
    Applies a multi-layer Elman RNN with \(\tanh\) or \(\mbox{ReLU}\) non-linearity
-to an input sequence.
    
-    
    
-
-    
    nn_rnn(
-  input_size,
-  hidden_size,
-  num_layers = 1,
-  nonlinearity = NULL,
-  bias = TRUE,
-  batch_first = FALSE,
-  dropout = 0,
-  bidirectional = FALSE,
-  ...
-)
    
-
-    
    Arguments
    
-    
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-      
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    input_size
    The number of expected features in the input x
    hidden_size
    The number of features in the hidden state h
    num_layers
    Number of recurrent layers. E.g., setting num_layers=2
-would mean stacking two RNNs together to form a stacked RNN,
-with the second RNN taking in outputs of the first RNN and
-computing the final results. Default: 1
    nonlinearity
    The non-linearity to use. Can be either 'tanh' or
-'relu'. Default: 'tanh'
    bias
    If FALSE, then the layer does not use bias weights b_ih and
-b_hh. Default: TRUE
    batch_first
    If TRUE, then the input and output tensors are provided
-as (batch, seq, feature). Default: FALSE
    dropout
    If non-zero, introduces a Dropout layer on the outputs of each
-RNN layer except the last layer, with dropout probability equal to
-dropout. Default: 0
    bidirectional
    If TRUE, becomes a bidirectional RNN. Default: FALSE
    ...
    other arguments that can be passed to the super class.
    
-
-    
    Details
    
-
-    
    For each element in the input sequence, each layer computes the following
-function:
    
-
    $$
-h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})
-$$
    
-
    where \(h_t\) is the hidden state at time t, \(x_t\) is
-the input at time t, and \(h_{(t-1)}\) is the hidden state of the
-previous layer at time t-1 or the initial hidden state at time 0.
-If nonlinearity is 'relu', then \(\mbox{ReLU}\) is used instead of
-\(\tanh\).
    
-    
    Inputs
    
-
-    
-
-
      
-
    • input of shape (seq_len, batch, input_size): tensor containing the features
-of the input sequence. The input can also be a packed variable length
-sequence.
      • 
-
    • h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor
-containing the initial hidden state for each element in the batch.
-Defaults to zero if not provided. If the RNN is bidirectional,
-num_directions should be 2, else it should be 1.
      • 
-
    
-
-    
    Outputs
    
-
-    
-
-
      
-
    • output of shape (seq_len, batch, num_directions * hidden_size): tensor
-containing the output features (h_t) from the last layer of the RNN,
-for each t.  If a :class:nn_packed_sequence has
-been given as the input, the output will also be a packed sequence.
-For the unpacked case, the directions can be separated
-using output$view(seq_len, batch, num_directions, hidden_size),
-with forward and backward being direction 0 and 1 respectively.
-Similarly, the directions can be separated in the packed case.
      • 
-
    • h_n of shape (num_layers * num_directions, batch, hidden_size): tensor
-containing the hidden state for t = seq_len.
-Like output, the layers can be separated using
-h_n$view(num_layers, num_directions, batch, hidden_size).
      • 
-
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input1: \((L, N, H_{in})\) tensor containing input features where
-\(H_{in}=\mbox{input\_size}\) and L represents a sequence length.
      • 
-
    • Input2: \((S, N, H_{out})\) tensor
-containing the initial hidden state for each element in the batch.
-\(H_{out}=\mbox{hidden\_size}\)
-Defaults to zero if not provided. where \(S=\mbox{num\_layers} * \mbox{num\_directions}\)
-If the RNN is bidirectional, num_directions should be 2, else it should be 1.
      • 
-
    • Output1: \((L, N, H_{all})\) where \(H_{all}=\mbox{num\_directions} * \mbox{hidden\_size}\)
      • 
-
    • Output2: \((S, N, H_{out})\) tensor containing the next hidden state
-for each element in the batch
      • 
-
    
-
-    
    Attributes
    
-
-    
-
-
      
-
    • weight_ih_l[k]: the learnable input-hidden weights of the k-th layer,
-of shape (hidden_size, input_size) for k = 0. Otherwise, the shape is
-(hidden_size, num_directions * hidden_size)
      • 
-
    • weight_hh_l[k]: the learnable hidden-hidden weights of the k-th layer,
-of shape (hidden_size, hidden_size)
      • 
-
    • bias_ih_l[k]: the learnable input-hidden bias of the k-th layer,
-of shape (hidden_size)
      • 
-
    • bias_hh_l[k]: the learnable hidden-hidden bias of the k-th layer,
-of shape (hidden_size)
      • 
-
    
-
-    
    Note
    
-
-    
-
-
-
    All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
-where \(k = \frac{1}{\mbox{hidden\_size}}\)
    
-
-    
    Examples
    
-    
    # \dontrun{
-rnn <- nn_rnn(10, 20, 2)
-input <- torch_randn(5, 3, 10)
-h0 <- torch_randn(2, 3, 20)
-rnn(input, h0)
    #> [[1]]
-#> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 9  0.1563  0.2797  0.4653  0.8483 -0.5044  0.5032 -0.2032  0.8317 -0.0350
-#>   0.1632 -0.1904  0.5733  0.3198 -0.3562  0.3904 -0.6873  0.7901 -0.9472
-#>  -0.6539 -0.6025  0.4797 -0.8375  0.7255 -0.4198  0.5030 -0.1419  0.2092
-#> 
-#> Columns 10 to 18  0.9481 -0.2581 -0.6185 -0.7787 -0.2685 -0.7144 -0.3700  0.4854  0.0008
-#>   0.8805 -0.2755 -0.9071 -0.5580 -0.0061 -0.8662 -0.7541  0.7855 -0.6157
-#>   0.5480  0.5395 -0.1759  0.2965  0.7805 -0.2651 -0.8080  0.2643  0.7689
-#> 
-#> Columns 19 to 20  0.3487 -0.1631
-#>   0.2535  0.5113
-#>  -0.0188  0.3766
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 9  0.1416  0.4498  0.1932  0.1637 -0.5115 -0.7125 -0.4544 -0.1721 -0.0345
-#>  -0.7202  0.1668  0.1129  0.6700 -0.6155  0.2947 -0.4558  0.1086  0.1323
-#>   0.3006 -0.0124 -0.0014  0.5341 -0.0718  0.3542 -0.1502  0.4042 -0.5156
-#> 
-#> Columns 10 to 18  0.8057  0.2908 -0.1019 -0.2748  0.2799  0.6493 -0.7338  0.1910  0.1886
-#>  -0.1154  0.1593 -0.5129 -0.6648  0.1733 -0.2496 -0.0886  0.2975  0.5137
-#>   0.8810  0.4194 -0.0103 -0.8285 -0.0348 -0.0102 -0.6628  0.1669 -0.4613
-#> 
-#> Columns 19 to 20 -0.0403  0.0080
-#>  -0.0600 -0.4162
-#>   0.0908  0.6937
-#> 
-#> (3,.,.) = 
-#>  Columns 1 to 9 -0.1978  0.2242  0.0024  0.3932 -0.4801 -0.4895 -0.5400 -0.0527 -0.4520
-#>   0.4544  0.0302  0.4917  0.2736 -0.5769 -0.1859 -0.4959  0.0229 -0.2535
-#>  -0.5535  0.3675  0.5847  0.6636 -0.3288 -0.2481 -0.1065 -0.0289 -0.5147
-#> 
-#> Columns 10 to 18  0.8121  0.5476 -0.5889 -0.2491  0.5971  0.3482 -0.4202  0.5075  0.0695
-#>   0.8887  0.3603 -0.1642 -0.3072  0.2559 -0.0096 -0.6545  0.5044  0.5036
-#>   0.6373 -0.1207  0.0495 -0.3367  0.4293  0.4361 -0.3157  0.3224  0.6757
-#> 
-#> Columns 19 to 20 -0.0226 -0.0955
-#>   0.6364  0.2054
-#>   0.1772 -0.2871
-#> 
-#> (4,.,.) = 
-#>  Columns 1 to 9 -0.5002  0.2480 -0.0165  0.4973 -0.7685  0.0885 -0.3330 -0.2697 -0.1477
-#>  -0.5379  0.1719  0.2126  0.1891 -0.5105  0.2180 -0.5122 -0.1882 -0.4472
-#>   0.0806  0.0901  0.5329  0.3643 -0.6769  0.4601 -0.5399 -0.3066 -0.0994
-#> 
-#> Columns 10 to 18  0.4677 -0.0194 -0.3609 -0.2897  0.3666  0.0276  0.0770  0.5985  0.5201
-#>   0.6046 -0.2503 -0.4701 -0.1266  0.3423  0.1259 -0.2631  0.5912  0.1230
-#>   0.6537 -0.2490 -0.3203 -0.3803  0.0304 -0.0077  0.1981  0.6495 -0.0583
-#> 
-#> Columns 19 to 20 -0.0329 -0.2124
-#>   0.1306  0.0613
-#>   0.0430 -0.0534
-#> 
-#> (5,.,.) = 
-#>  Columns 1 to 9 -0.3560  0.0896  0.2468  0.0908 -0.3990 -0.1175 -0.3947 -0.0834  0.1421
-#>   0.1891 -0.0772  0.2671  0.0296 -0.1929 -0.2009 -0.5507 -0.2240  0.2121
-#>  -0.1833  0.2226 -0.0158  0.5592 -0.5925  0.0255 -0.6282 -0.1562  0.0561
-#> 
-#> Columns 10 to 18  0.5649 -0.0882 -0.4652 -0.2057  0.0088  0.0349 -0.0315  0.3252  0.6167
-#>   0.5626  0.3505 -0.2768 -0.4894 -0.0599  0.4348 -0.1352  0.2022  0.2273
-#>   0.2647  0.0037 -0.3756 -0.3976 -0.0172 -0.1532 -0.4150  0.3451  0.3110
-#> 
-#> Columns 19 to 20  0.2250  0.1283
-#>  -0.0717  0.2627
-#>   0.1909 -0.1445
-#> [ CPUFloatType{5,3,20} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 9 -0.2977 -0.3901 -0.3494 -0.6523  0.3627  0.1448 -0.3341  0.2196  0.1126
-#>   0.2888 -0.7529  0.1781 -0.0379 -0.2393  0.3807  0.1044 -0.0212 -0.5096
-#>  -0.1402 -0.3835 -0.2036  0.3084  0.1285 -0.3805  0.1103  0.0476  0.2100
-#> 
-#> Columns 10 to 18 -0.6325 -0.1108 -0.1481  0.0602  0.7081 -0.3749  0.6918 -0.4901 -0.2858
-#>   0.2888  0.4654  0.2154 -0.3173  0.4848  0.3496 -0.1522 -0.0645 -0.5102
-#>   0.2073  0.5197  0.0807  0.4554  0.0247 -0.2980 -0.3274 -0.1698 -0.0551
-#> 
-#> Columns 19 to 20  0.0842  0.0867
-#>   0.1297 -0.4188
-#>   0.6599 -0.5773
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 9 -0.3560  0.0896  0.2468  0.0908 -0.3990 -0.1175 -0.3947 -0.0834  0.1421
-#>   0.1891 -0.0772  0.2671  0.0296 -0.1929 -0.2009 -0.5507 -0.2240  0.2121
-#>  -0.1833  0.2226 -0.0158  0.5592 -0.5925  0.0255 -0.6282 -0.1562  0.0561
-#> 
-#> Columns 10 to 18  0.5649 -0.0882 -0.4652 -0.2057  0.0088  0.0349 -0.0315  0.3252  0.6167
-#>   0.5626  0.3505 -0.2768 -0.4894 -0.0599  0.4348 -0.1352  0.2022  0.2273
-#>   0.2647  0.0037 -0.3756 -0.3976 -0.0172 -0.1532 -0.4150  0.3451  0.3110
-#> 
-#> Columns 19 to 20  0.2250  0.1283
-#>  -0.0717  0.2627
-#>   0.1909 -0.1445
-#> [ CPUFloatType{2,3,20} ]
-#> 
    
-# }
-
    
-  
    
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-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_rrelu.html b/docs/reference/nn_rrelu.html
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-RReLU module — nn_rrelu • torch
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    RReLU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the randomized leaky rectified liner unit function, element-wise,
-as described in the paper:
    
-    
    
-
-    
    nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    lower
    lower bound of the uniform distribution. Default: \(\frac{1}{8}\)
    upper
    upper bound of the uniform distribution. Default: \(\frac{1}{3}\)
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    Empirical Evaluation of Rectified Activations in Convolutional Network.
    
-
    The function is defined as:
    
-
    $$
-\mbox{RReLU}(x) =
-\left\{ \begin{array}{ll}
-x & \mbox{if } x \geq 0 \\
-ax & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-
    where \(a\) is randomly sampled from uniform distribution
-\(\mathcal{U}(\mbox{lower}, \mbox{upper})\).
-See: https://arxiv.org/pdf/1505.00853.pdf
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
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    • Output: \((N, *)\), same shape as the input
      • 
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-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_rrelu(0.1, 0.3)
-input <- torch_randn(2)
-m(input)
    #> torch_tensor 
-#> -0.0421
-#>  1.4246
-#> [ CPUFloatType{2} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_selu.html b/docs/reference/nn_selu.html
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-    Source: R/nn-activation.R
-    
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-
-    
    
-    
    Applied element-wise, as:
    
-    
    
-
-    
    nn_selu(inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    inplace
    (bool, optional): can optionally do the operation in-place. Default: FALSE
    
-
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    Details
    
-
-    
    $$
-  \mbox{SELU}(x) = \mbox{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
-$$
    
-
    with \(\alpha = 1.6732632423543772848170429916717\) and
-\(\mbox{scale} = 1.0507009873554804934193349852946\).
    
-
    More details can be found in the paper
-Self-Normalizing Neural Networks.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_selu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/nn_sequential.html b/docs/reference/nn_sequential.html
deleted file mode 100644
index afd123a4acd5933edfef6323f7fb501068aaaeec..0000000000000000000000000000000000000000
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-
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-A sequential container — nn_sequential • torch
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    A sequential container
    
-    Source: R/nn.R
-    
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-
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-    
    A sequential container.
-Modules will be added to it in the order they are passed in the constructor.
-See examples.
    
-    
    
-
-    
    nn_sequential(..., name = NULL)
    
-
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    Arguments
    
-    
-    
-    
-      
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-      
-      
-    
-    
    ...
    sequence of modules to be added
    name
    optional name for the generated module.
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-model <- nn_sequential(
-  nn_conv2d(1, 20, 5),
-  nn_relu(),
-  nn_conv2d(20, 64, 5),
-  nn_relu()
-)
-input <- torch_randn(32, 1, 28, 28)
-output <- model(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
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-      
    
-   
    
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-  
-
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-
diff --git a/docs/reference/nn_sigmoid.html b/docs/reference/nn_sigmoid.html
deleted file mode 100644
index 912e815e93ab78dc5515fd7d442ee64740816272..0000000000000000000000000000000000000000
--- a/docs/reference/nn_sigmoid.html
+++ /dev/null
@@ -1,219 +0,0 @@
-
-
-
-  
-  
-
-
-
-Sigmoid module — nn_sigmoid • torch
-
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-    
    
-      
    
-      
-
-      
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-      
    
-
-
    
-  
    
-    
    
-    
    Sigmoid module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_sigmoid()
    
-
-
-    
    Details
    
-
-    
    $$
-  \mbox{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_sigmoid()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
-   
    
-
-  
-
-
-  
-
-
-
diff --git a/docs/reference/nn_softmax.html b/docs/reference/nn_softmax.html
deleted file mode 100644
index 9ffface0c9bc32f19266ccf9b99a0f895d7f5080..0000000000000000000000000000000000000000
--- a/docs/reference/nn_softmax.html
+++ /dev/null
@@ -1,246 +0,0 @@
-
-
-
-  
-  
-
-
-
-Softmax module — nn_softmax • torch
-
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-    
    
-      
    
-      
-
-      
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-      
    
-
-
    
-  
    
-    
    
-    
    Softmax module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the Softmax function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range [0,1] and sum to 1.
-Softmax is defined as:
    
-    
    
-
-    
    nn_softmax(dim)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    dim
    (int): A dimension along which Softmax will be computed (so every slice
-along dim will sum to 1).
    
-
-    
    Value
    
-
-    
    :
-a Tensor of the same dimension and shape as the input with
-values in the range [0, 1]
    
-    
    Details
    
-
-    
    $$
-  \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
-$$
    
-
    When the input Tensor is a sparse tensor then the unspecifed
-values are treated as -Inf.
    
-    
    Note
    
-
-    
    This module doesn't work directly with NLLLoss,
-which expects the Log to be computed between the Softmax and itself.
-Use LogSoftmax instead (it's faster and has better numerical properties).
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((*)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((*)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softmax(1)
-input <- torch_randn(2, 3)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
-   
    
-
-  
-
-
-  
-
-
-
diff --git a/docs/reference/nn_softmax2d.html b/docs/reference/nn_softmax2d.html
deleted file mode 100644
index 7288011ff4910bebde7ebf27e108b4c71ed8686e..0000000000000000000000000000000000000000
--- a/docs/reference/nn_softmax2d.html
+++ /dev/null
@@ -1,221 +0,0 @@
-
-
-
-  
-  
-
-
-
-Softmax2d module — nn_softmax2d • torch
-
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-      
    
-      
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-      
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-      
    
-
-
    
-  
    
-    
    
-    
    Softmax2d module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies SoftMax over features to each spatial location.
-When given an image of Channels x Height x Width, it will
-apply Softmax to each location \((Channels, h_i, w_j)\)
    
-    
    
-
-    
    nn_softmax2d()
    
-
-
-    
    Value
    
-
-    
    a Tensor of the same dimension and shape as the input with
-values in the range [0, 1]
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, H, W)\)
      • 
-
    • Output: \((N, C, H, W)\) (same shape as input)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softmax2d()
-input <- torch_randn(2, 3, 12, 13)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
-   
    
-
-  
-
-
-  
-
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-
diff --git a/docs/reference/nn_softmin.html b/docs/reference/nn_softmin.html
deleted file mode 100644
index 59aa790e75defb8b2eec24afc6d033cd3dbc3fb2..0000000000000000000000000000000000000000
--- a/docs/reference/nn_softmin.html
+++ /dev/null
@@ -1,238 +0,0 @@
-
-
-
-  
-  
-
-
-
-Softmin — nn_softmin • torch
-
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-
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-  
    
-    
    
-    
    Softmin
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the Softmin function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range [0, 1] and sum to 1.
-Softmin is defined as:
    
-    
    
-
-    
    nn_softmin(dim)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    dim
    (int): A dimension along which Softmin will be computed (so every slice
-along dim will sum to 1).
    
-
-    
    Value
    
-
-    
    a Tensor of the same dimension and shape as the input, with
-values in the range [0, 1].
    
-    
    Details
    
-
-    
    $$
-  \mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((*)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((*)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softmin(dim = 1)
-input <- torch_randn(2, 2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
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diff --git a/docs/reference/nn_softplus.html b/docs/reference/nn_softplus.html
deleted file mode 100644
index 9ed55c7ea9a7324cdb33f50c78898f5362da1c36..0000000000000000000000000000000000000000
--- a/docs/reference/nn_softplus.html
+++ /dev/null
@@ -1,238 +0,0 @@
-
-
-
-  
-  
-
-
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-Softplus module — nn_softplus • torch
-
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-      
    
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-  
    
-    
    
-    
    Softplus module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
-$$
-  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
-$$
    
-    
    
-
-    
    nn_softplus(beta = 1, threshold = 20)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    beta
    the \(\beta\) value for the Softplus formulation. Default: 1
    threshold
    values above this revert to a linear function. Default: 20
    
-
-    
    Details
    
-
-    
    SoftPlus is a smooth approximation to the ReLU function and can be used
-to constrain the output of a machine to always be positive.
-For numerical stability the implementation reverts to the linear function
-when \(input \times \beta > threshold\).
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softplus()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
-   
    
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-  
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diff --git a/docs/reference/nn_softshrink.html b/docs/reference/nn_softshrink.html
deleted file mode 100644
index db79678e3a1781e1b3318413632b984b7bb69967..0000000000000000000000000000000000000000
--- a/docs/reference/nn_softshrink.html
+++ /dev/null
@@ -1,233 +0,0 @@
-
-
-
-  
-  
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-Softshrink module — nn_softshrink • torch
-
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-  
    
-    
    
-    
    Softshrink module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the soft shrinkage function elementwise:
    
-    
    
-
-    
    nn_softshrink(lambd = 0.5)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    lambd
    the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{SoftShrinkage}(x) =
-  \left\{ \begin{array}{ll}
-x - \lambda, & \mbox{ if } x > \lambda \\
-x + \lambda, & \mbox{ if } x < -\lambda \\
-0, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softshrink()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
-   
    
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diff --git a/docs/reference/nn_softsign.html b/docs/reference/nn_softsign.html
deleted file mode 100644
index 30815b625c0fe05b288b8456a5abf57a68f91e0b..0000000000000000000000000000000000000000
--- a/docs/reference/nn_softsign.html
+++ /dev/null
@@ -1,220 +0,0 @@
-
-
-
-  
-  
-
-
-
-Softsign module — nn_softsign • torch
-
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-      
    
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-
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-  
    
-    
    
-    
    Softsign module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
-$$
-  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
-$$
    
-    
    
-
-    
    nn_softsign()
    
-
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softsign()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nn_tanh.html b/docs/reference/nn_tanh.html
deleted file mode 100644
index 7a1a1f3106323faff4d2448b23bc4c4f6a6343f1..0000000000000000000000000000000000000000
--- a/docs/reference/nn_tanh.html
+++ /dev/null
@@ -1,219 +0,0 @@
-
-
-
-  
-  
-
-
-
-Tanh module — nn_tanh • torch
-
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-  
    
-    
    
-    
    Tanh module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_tanh()
    
-
-
-    
    Details
    
-
-    
    $$
-  \mbox{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_tanh()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_tanhshrink.html b/docs/reference/nn_tanhshrink.html
deleted file mode 100644
index 0bd1f23cfb945141ee20c509390c6ca52919560a..0000000000000000000000000000000000000000
--- a/docs/reference/nn_tanhshrink.html
+++ /dev/null
@@ -1,219 +0,0 @@
-
-
-
-  
-  
-
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-
-Tanhshrink module — nn_tanhshrink • torch
-
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-    
    
-    
    Tanhshrink module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_tanhshrink()
    
-
-
-    
    Details
    
-
-    
    $$
-  \mbox{Tanhshrink}(x) = x - \tanh(x)
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_tanhshrink()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
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-   
    
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deleted file mode 100644
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-    Source: R/nn-activation.R
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-    
    Thresholds each element of the input Tensor.
    
-    
    
-
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    nn_threshold(threshold, value, inplace = FALSE)
    
-
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    Arguments
    
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    threshold
    The value to threshold at
    value
    The value to replace with
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    Threshold is defined as:
-$$
-  y = 
-  \left\{ \begin{array}{ll}
-  x, &\mbox{ if } x > \mbox{threshold} \\
-  \mbox{value}, &\mbox{ otherwise }
-  \end{array}
-  \right.
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_threshold(0.1, 20)
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_utils_rnn_pack_padded_sequence.html b/docs/reference/nn_utils_rnn_pack_padded_sequence.html
deleted file mode 100644
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--- a/docs/reference/nn_utils_rnn_pack_padded_sequence.html
+++ /dev/null
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-Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence • torch
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    Packs a Tensor containing padded sequences of variable length.
    
-    Source: R/nn-utils-rnn.R
-    
-    
    
-
-    
    
-    
    input can be of size T x B x * where T is the length of the
-longest sequence (equal to lengths[1]), B is the batch size, and
-* is any number of dimensions (including 0). If batch_first is
-TRUE, B x T x * input is expected.
    
-    
    
-
-    
    nn_utils_rnn_pack_padded_sequence(
-  input,
-  lengths,
-  batch_first = FALSE,
-  enforce_sorted = TRUE
-)
    
-
-    
    Arguments
    
-    
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    input
    (Tensor): padded batch of variable length sequences.
    lengths
    (Tensor): list of sequences lengths of each batch element.
    batch_first
    (bool, optional): if TRUE, the input is expected in B x T x *
-format.
    enforce_sorted
    (bool, optional): if TRUE, the input is expected to
-contain sequences sorted by length in a decreasing order. If
-FALSE, the input will get sorted unconditionally. Default: TRUE.
    
-
-    
    Value
    
-
-    
    a PackedSequence object
    
-    
    Details
    
-
-    
    For unsorted sequences, use enforce_sorted = FALSE. If enforce_sorted is
-TRUE, the sequences should be sorted by length in a decreasing order, i.e.
-input[,1] should be the longest sequence, and input[,B] the shortest
-one. enforce_sorted = TRUE is only necessary for ONNX export.
    
-    
    Note
    
-
-    
    This function accepts any input that has at least two dimensions. You
-can apply it to pack the labels, and use the output of the RNN with
-them to compute the loss directly. A Tensor can be retrieved from
-a PackedSequence object by accessing its .data attribute.
    
-
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diff --git a/docs/reference/nn_utils_rnn_pack_sequence.html b/docs/reference/nn_utils_rnn_pack_sequence.html
deleted file mode 100644
index afddfe3283138ddaa819dd44e6a6409ca0f8d3ea..0000000000000000000000000000000000000000
--- a/docs/reference/nn_utils_rnn_pack_sequence.html
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-Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence • torch
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    Packs a list of variable length Tensors
    
-    Source: R/nn-utils-rnn.R
-    
-    
    
-
-    
    
-    
    sequences should be a list of Tensors of size L x *, where L is
-the length of a sequence and * is any number of trailing dimensions,
-including zero.
    
-    
    
-
-    
    nn_utils_rnn_pack_sequence(sequences, enforce_sorted = TRUE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-    
-      
-      
-    
-    
    sequences
    (list[Tensor]): A list of sequences of decreasing length.
    enforce_sorted
    (bool, optional): if TRUE, checks that the input
-contains sequences sorted by length in a decreasing order. If
-FALSE, this condition is not checked. Default: TRUE.
    
-
-    
    Value
    
-
-    
    a PackedSequence object
    
-    
    Details
    
-
-    
    For unsorted sequences, use enforce_sorted = FALSE. If enforce_sorted
-is TRUE, the sequences should be sorted in the order of decreasing length.
-enforce_sorted = TRUE is only necessary for ONNX export.
    
-
-    
    Examples
    
-    
    # \dontrun{
-x <- torch_tensor(c(1,2,3), dtype = torch_long())
-y <- torch_tensor(c(4, 5), dtype = torch_long())
-z <- torch_tensor(c(6), dtype = torch_long())
-
-p <- nn_utils_rnn_pack_sequence(list(x, y, z))
-
-# }
    
-  
    
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diff --git a/docs/reference/nn_utils_rnn_pad_packed_sequence.html b/docs/reference/nn_utils_rnn_pad_packed_sequence.html
deleted file mode 100644
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-Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence • torch
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    Pads a packed batch of variable length sequences.
    
-    Source: R/nn-utils-rnn.R
-    
-    
    
-
-    
    
-    
    It is an inverse operation to nn_utils_rnn_pack_padded_sequence().
    
-    
    
-
-    
    nn_utils_rnn_pad_packed_sequence(
-  sequence,
-  batch_first = FALSE,
-  padding_value = 0,
-  total_length = NULL
-)
    
-
-    
    Arguments
    
-    
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-      
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    sequence
    (PackedSequence): batch to pad
    batch_first
    (bool, optional): if True, the output will be in ``B x T x *`
-format.
    padding_value
    (float, optional): values for padded elements.
    total_length
    (int, optional): if not NULL, the output will be padded to
-have length total_length. This method will throw ValueError
-if total_length is less than the max sequence length in
-sequence.
    
-
-    
    Value
    
-
-    
    Tuple of Tensor containing the padded sequence, and a Tensor
-containing the list of lengths of each sequence in the batch.
-Batch elements will be re-ordered as they were ordered originally when
-the batch was passed to nn_utils_rnn_pack_padded_sequence() or
-nn_utils_rnn_pack_sequence().
    
-    
    Details
    
-
-    
    The returned Tensor's data will be of size T x B x *, where T is the length
-of the longest sequence and B is the batch size. If batch_first is TRUE,
-the data will be transposed into B x T x * format.
    
-    
    Note
    
-
-    
    total_length is useful to implement the
-pack sequence -> recurrent network -> unpack sequence pattern in a
-nn_module wrapped in ~torch.nn.DataParallel.
    
-
-    
    Examples
    
-    
    # \dontrun{
-seq <- torch_tensor(rbind(c(1,2,0), c(3,0,0), c(4,5,6)))
-lens <- c(2,1,3)
-packed <- nn_utils_rnn_pack_padded_sequence(seq, lens, batch_first = TRUE,
-                                            enforce_sorted = FALSE)
-packed
    #> <PackedSequence>
-#>   Public:
-#>     batch_sizes: active binding
-#>     clone: function (deep = FALSE) 
-#>     data: active binding
-#>     initialize: function (ptr = NULL) 
-#>     ptr: externalptr
-#>     sorted_indices: active binding
-#>     unsorted_indices: active binding
    nn_utils_rnn_pad_packed_sequence(packed, batch_first=TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  1  2  0
-#>  3  0  0
-#>  4  5  6
-#> [ CPUFloatType{3,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  2
-#>  1
-#>  3
-#> [ CPULongType{3} ]
-#> 
    
-# }
-
    
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diff --git a/docs/reference/nn_utils_rnn_pad_sequence.html b/docs/reference/nn_utils_rnn_pad_sequence.html
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-Pad a list of variable length Tensors with <code>padding_value</code> — nn_utils_rnn_pad_sequence • torch
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-    Source: R/nn-utils-rnn.R
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-    
    
-
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-    
    pad_sequence stacks a list of Tensors along a new dimension,
-and pads them to equal length. For example, if the input is list of
-sequences with size L x * and if batch_first is False, and T x B x *
-otherwise.
    
-    
    
-
-    
    nn_utils_rnn_pad_sequence(sequences, batch_first = FALSE, padding_value = 0)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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    sequences
    (list[Tensor]): list of variable length sequences.
    batch_first
    (bool, optional): output will be in B x T x * if TRUE,
-or in T x B x * otherwise
    padding_value
    (float, optional): value for padded elements. Default: 0.
    
-
-    
    Value
    
-
-    
    Tensor of size T x B x * if batch_first is FALSE.
-Tensor of size B x T x * otherwise
    
-    
    Details
    
-
-    
    B is batch size. It is equal to the number of elements in sequences.
-T is length of the longest sequence.
-L is length of the sequence.
-* is any number of trailing dimensions, including none.
    
-    
    Note
    
-
-    
    This function returns a Tensor of size T x B x * or B x T x *
-where T is the length of the longest sequence. This function assumes
-trailing dimensions and type of all the Tensors in sequences are same.
    
-
-    
    Examples
    
-    
    # \dontrun{
-a <- torch_ones(25, 300)
-b <- torch_ones(22, 300)
-c <- torch_ones(15, 300)
-nn_utils_rnn_pad_sequence(list(a, b, c))$size()
    #> [1]  25   3 300
    
-# }
-
    
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diff --git a/docs/reference/nnf_adaptive_avg_pool1d.html b/docs/reference/nnf_adaptive_avg_pool1d.html
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-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
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-several input planes.
    
-    
    
-
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-
-    
    Arguments
    
-    
-    
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-      
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-      
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    input
    input tensor of shape (minibatch , in_channels , iW)
    output_size
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diff --git a/docs/reference/nnf_adaptive_avg_pool2d.html b/docs/reference/nnf_adaptive_avg_pool2d.html
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-    
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-
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-    
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-several input planes.
    
-    
    
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-
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-    
-    
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-      
-      
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-      
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    input
    input tensor (minibatch, in_channels , iH , iW)
    output_size
    the target output size (single integer or double-integer tuple)
    
-
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diff --git a/docs/reference/nnf_adaptive_avg_pool3d.html b/docs/reference/nnf_adaptive_avg_pool3d.html
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-    
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-several input planes.
    
-    
    
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-
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-    
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    input
    input tensor (minibatch, in_channels , iT * iH , iW)
    output_size
    the target output size (single integer or triple-integer tuple)
    
-
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diff --git a/docs/reference/nnf_adaptive_max_pool1d.html b/docs/reference/nnf_adaptive_max_pool1d.html
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-Adaptive_max_pool1d — nnf_adaptive_max_pool1d • torch
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-    Source: R/nnf-pooling.R
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    Applies a 1D adaptive max pooling over an input signal composed of
-several input planes.
    
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    input
    input tensor of shape (minibatch , in_channels , iW)
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    whether to return pooling indices. Default: FALSE
    
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diff --git a/docs/reference/nnf_adaptive_max_pool2d.html b/docs/reference/nnf_adaptive_max_pool2d.html
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-    Source: R/nnf-pooling.R
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-    
    Applies a 2D adaptive max pooling over an input signal composed of
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    input
    input tensor (minibatch, in_channels , iH , iW)
    output_size
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diff --git a/docs/reference/nnf_adaptive_max_pool3d.html b/docs/reference/nnf_adaptive_max_pool3d.html
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-Adaptive_max_pool3d — nnf_adaptive_max_pool3d • torch
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    Adaptive_max_pool3d
    
-    Source: R/nnf-pooling.R
-    
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-    
    Applies a 3D adaptive max pooling over an input signal composed of
-several input planes.
    
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    nnf_adaptive_max_pool3d(input, output_size, return_indices = FALSE)
    
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    input
    input tensor (minibatch, in_channels , iT * iH , iW)
    output_size
    the target output size (single integer or triple-integer tuple)
    return_indices
    whether to return pooling indices. Default:FALSE
    
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diff --git a/docs/reference/nnf_affine_grid.html b/docs/reference/nnf_affine_grid.html
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-Affine_grid — nnf_affine_grid • torch
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    Affine_grid
    
-    Source: R/nnf-vision.R
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-    
    Generates a 2D or 3D flow field (sampling grid), given a batch of
-affine matrices theta.
    
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    nnf_affine_grid(theta, size, align_corners = FALSE)
    
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    theta
    (Tensor) input batch of affine matrices with shape
-(\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D
    size
    (torch.Size) the target output image size. (\(N \times C \times H \times W\)
-for 2D or \(N \times C \times D \times H \times W\) for 3D)
-Example: torch.Size((32, 3, 24, 24))
    align_corners
    (bool, optional) if True, consider -1 and 1
-to refer to the centers of the corner pixels rather than the image corners.
-Refer to nnf_grid_sample() for a more complete description. A grid generated by
-nnf_affine_grid() should be passed to nnf_grid_sample()  with the same setting for
-this option. Default: False
    
-
-    
    Note
    
-
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    This function is often used in conjunction with nnf_grid_sample()
-to build Spatial Transformer Networks_ .
    
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diff --git a/docs/reference/nnf_alpha_dropout.html b/docs/reference/nnf_alpha_dropout.html
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-Alpha_dropout — nnf_alpha_dropout • torch
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    Alpha_dropout
    
-    Source: R/nnf-dropout.R
-    
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-
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-    
    Applies alpha dropout to the input.
    
-    
    
-
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    nnf_alpha_dropout(input, p = 0.5, training = FALSE, inplace = FALSE)
    
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    input
    the input tensor
    p
    probability of an element to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE
    inplace
    If set to TRUE, will do this operation in-place.
-Default: FALSE
    
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diff --git a/docs/reference/nnf_avg_pool1d.html b/docs/reference/nnf_avg_pool1d.html
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-Avg_pool1d — nnf_avg_pool1d • torch
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    Avg_pool1d
    
-    Source: R/nnf-pooling.R
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-
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-    
    Applies a 1D average pooling over an input signal composed of several
-input planes.
    
-    
    
-
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    nnf_avg_pool1d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE
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    input
    input tensor of shape (minibatch , in_channels , iW)
    kernel_size
    the size of the window. Can be a single number or a
-tuple (kW,).
    stride
    the stride of the window. Can be a single number or a tuple
-(sW,). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padW,). Default: 0
    ceil_mode
    when True, will use ceil instead of floor to compute the
-output shape. Default: FALSE
    count_include_pad
    when True, will include the zero-padding in the
-averaging calculation. Default: TRUE
    
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diff --git a/docs/reference/nnf_avg_pool2d.html b/docs/reference/nnf_avg_pool2d.html
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-Avg_pool2d — nnf_avg_pool2d • torch
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    Avg_pool2d
    
-    Source: R/nnf-pooling.R
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-    
    Applies 2D average-pooling operation in \(kH * kW\) regions by step size
-\(sH * sW\) steps. The number of output features is equal to the number of
-input planes.
    
-    
    
-
-    
    nnf_avg_pool2d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
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    Arguments
    
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    input
    input tensor (minibatch, in_channels , iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape. Default: FALSE
    count_include_pad
    when True, will include the zero-padding in the
-averaging calculation. Default: TRUE
    divisor_override
    if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: NULL
    
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diff --git a/docs/reference/nnf_avg_pool3d.html b/docs/reference/nnf_avg_pool3d.html
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-Avg_pool3d — nnf_avg_pool3d • torch
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    Avg_pool3d
    
-    Source: R/nnf-pooling.R
-    
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-
-    
    
-    
    Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
-size \(sT * sH * sW\) steps. The number of output features is equal to
-\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).
    
-    
    
-
-    
    nnf_avg_pool3d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
-
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    Arguments
    
-    
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    input
    input tensor (minibatch, in_channels , iT * iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kT, kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sT, sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW), Default: 0
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape
    count_include_pad
    when True, will include the zero-padding in the
-averaging calculation
    divisor_override
    NA if specified, it will be used as divisor, otherwise
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diff --git a/docs/reference/nnf_batch_norm.html b/docs/reference/nnf_batch_norm.html
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    Batch_norm
    
-    Source: R/nnf-batchnorm.R
-    
-    
    
-
-    
    
-    
    Applies Batch Normalization for each channel across a batch of data.
    
-    
    
-
-    
    nnf_batch_norm(
-  input,
-  running_mean,
-  running_var,
-  weight = NULL,
-  bias = NULL,
-  training = FALSE,
-  momentum = 0.1,
-  eps = 1e-05
-)
    
-
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    Arguments
    
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    input
    input tensor
    running_mean
    the running_mean tensor
    running_var
    the running_var tensor
    weight
    the weight tensor
    bias
    the bias tensor
    training
    bool wether it's training. Default: FALSE
    momentum
    the value used for the running_mean and running_var computation.
-Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1
    eps
    a value added to the denominator for numerical stability. Default: 1e-5
    
-
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diff --git a/docs/reference/nnf_bilinear.html b/docs/reference/nnf_bilinear.html
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-Bilinear — nnf_bilinear • torch
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-    
    Bilinear
    
-    Source: R/nnf-linear.R
-    
-    
    
-
-    
    
-    
    Applies a bilinear transformation to the incoming data:
-\(y = x_1 A x_2 + b\)
    
-    
    
-
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    nnf_bilinear(input1, input2, weight, bias = NULL)
    
-
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    input1
    \((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
-and \(*\) means any number of additional dimensions.
-All but the last dimension of the inputs should be the same.
    input2
    \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)
    weight
    \((\mbox{out\_features}, \mbox{in1\_features},
-\mbox{in2\_features})\)
    bias
    \((\mbox{out\_features})\)
    
-
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    Value
    
-
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    output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
-and all but the last dimension are the same shape as the input.
    
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diff --git a/docs/reference/nnf_binary_cross_entropy.html b/docs/reference/nnf_binary_cross_entropy.html
deleted file mode 100644
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--- a/docs/reference/nnf_binary_cross_entropy.html
+++ /dev/null
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-Binary_cross_entropy — nnf_binary_cross_entropy • torch
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    Binary_cross_entropy
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Function that measures the Binary Cross Entropy
-between the target and the output.
    
-    
    
-
-    
    nnf_binary_cross_entropy(
-  input,
-  target,
-  weight = NULL,
-  reduction = c("mean", "sum", "none")
-)
    
-
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    Arguments
    
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    weight
    (tensor) weight for each value.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_binary_cross_entropy_with_logits.html b/docs/reference/nnf_binary_cross_entropy_with_logits.html
deleted file mode 100644
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+++ /dev/null
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-Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits • torch
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-    
    Binary_cross_entropy_with_logits
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Function that measures Binary Cross Entropy between target and output
-logits.
    
-    
    
-
-    
    nnf_binary_cross_entropy_with_logits(
-  input,
-  target,
-  weight = NULL,
-  reduction = c("mean", "sum", "none"),
-  pos_weight = NULL
-)
    
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    Arguments
    
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    input
    Tensor of arbitrary shape
    target
    Tensor of the same shape as input
    weight
    (Tensor, optional) a manual rescaling weight if provided it's
-repeated to match input tensor shape.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    pos_weight
    (Tensor, optional) a weight of positive examples.
-Must be a vector with length equal to the number of classes.
    
-
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diff --git a/docs/reference/nnf_celu.html b/docs/reference/nnf_celu.html
deleted file mode 100644
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+++ /dev/null
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-Celu — nnf_celu • torch
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    Celu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).
    
-    
    
-
-    
    nnf_celu(input, alpha = 1, inplace = FALSE)
-
-nnf_celu_(input, alpha = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-      
-      
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-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    alpha
    the alpha value for the CELU formulation. Default: 1.0
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
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-  
    
-  
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    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_conv1d.html b/docs/reference/nnf_conv1d.html
deleted file mode 100644
index 3f8a34f33fa02fbdee6f7e03bd0fbe542ff39d81..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_conv1d.html
+++ /dev/null
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-Conv1d — nnf_conv1d • torch
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-    
    Conv1d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 1D convolution over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_conv1d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
-
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    Arguments
    
-    
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    input
    input tensor of shape (minibatch, in_channels , iW)
    weight
    filters of shape (out_channels, in_channels/groups , kW)
    bias
    optional bias of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or
-a one-element tuple (sW,). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple (padW,). Default: 0
    dilation
    the spacing between kernel elements. Can be a single number or
-a one-element tuple (dW,). Default: 1
    groups
    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    
-
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diff --git a/docs/reference/nnf_conv2d.html b/docs/reference/nnf_conv2d.html
deleted file mode 100644
index 076a7144069a2115993230d98aae88cd429a4137..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_conv2d.html
+++ /dev/null
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-Conv2d — nnf_conv2d • torch
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-    
    Conv2d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 2D convolution over an input image composed of several input
-planes.
    
-    
    
-
-    
    nnf_conv2d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
-
-    
    Arguments
    
-    
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    input
    input tensor of shape (minibatch, in_channels, iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dH, dW). Default: 1
    groups
    split input into groups, in_channels should be divisible by the
-number of groups. Default: 1
    
-
-
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-  
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diff --git a/docs/reference/nnf_conv3d.html b/docs/reference/nnf_conv3d.html
deleted file mode 100644
index 2095988606d8737376c326f2413ebebfc0453efa..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_conv3d.html
+++ /dev/null
@@ -1,243 +0,0 @@
-
-
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-Conv3d — nnf_conv3d • torch
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-    
    Conv3d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 3D convolution over an input image composed of several input
-planes.
    
-    
    
-
-    
    nnf_conv3d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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    input
    input tensor of shape (minibatch, in_channels , iT , iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kT , kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sT, sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW). Default: 0
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dT, dH, dW). Default: 1
    groups
    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    
-
-
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-  
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diff --git a/docs/reference/nnf_conv_tbc.html b/docs/reference/nnf_conv_tbc.html
deleted file mode 100644
index b9f90e3b76e38db954e70d27a50aae81657d2714..0000000000000000000000000000000000000000
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-Conv_tbc — nnf_conv_tbc • torch
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    Conv_tbc
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 1-dimensional sequence convolution over an input sequence.
-Input and output dimensions are (Time, Batch, Channels) - hence TBC.
    
-    
    
-
-    
    nnf_conv_tbc(input, weight, bias, pad = 0)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-      
-      
-    
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-      
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-      
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-    
    input
    input tensor of shape \((\mbox{sequence length} \times 
-batch \times \mbox{in\_channels})\)
    weight
    filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} 
-\times \mbox{out\_channels}\))
    bias
    bias of shape (\(\mbox{out\_channels}\))
    pad
    number of timesteps to pad. Default: 0
    
-
-
-  
    
-  
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diff --git a/docs/reference/nnf_conv_transpose1d.html b/docs/reference/nnf_conv_transpose1d.html
deleted file mode 100644
index 9cd618c6b6d0ef0297ab6af2d89a00b1980f0507..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_conv_transpose1d.html
+++ /dev/null
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-Conv_transpose1d — nnf_conv_transpose1d • torch
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    Conv_transpose1d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 1D transposed convolution operator over an input signal
-composed of several input planes, sometimes also called "deconvolution".
    
-    
    
-
-    
    nnf_conv_transpose1d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
-
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    Arguments
    
-    
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    input
    input tensor of shape (minibatch, in_channels , iW)
    weight
    filters of shape (out_channels, in_channels/groups , kW)
    bias
    optional bias of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or
-a one-element tuple (sW,). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple (padW,). Default: 0
    output_padding
    padding applied to the output
    groups
    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    dilation
    the spacing between kernel elements. Can be a single number or
-a one-element tuple (dW,). Default: 1
    
-
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diff --git a/docs/reference/nnf_conv_transpose2d.html b/docs/reference/nnf_conv_transpose2d.html
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-
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-Conv_transpose2d — nnf_conv_transpose2d • torch
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-    
    Conv_transpose2d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 2D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution".
    
-    
    
-
-    
    nnf_conv_transpose2d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
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    input
    input tensor of shape (minibatch, in_channels, iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    output_padding
    padding applied to the output
    groups
    split input into groups, in_channels should be divisible by the
-number of groups. Default: 1
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dH, dW). Default: 1
    
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diff --git a/docs/reference/nnf_conv_transpose3d.html b/docs/reference/nnf_conv_transpose3d.html
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-Conv_transpose3d — nnf_conv_transpose3d • torch
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-    
    
-    
    Conv_transpose3d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 3D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution"
    
-    
    
-
-    
    nnf_conv_transpose3d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
-
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    Arguments
    
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    input
    input tensor of shape (minibatch, in_channels , iT , iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kT , kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sT, sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW). Default: 0
    output_padding
    padding applied to the output
    groups
    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dT, dH, dW). Default: 1
    
-
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diff --git a/docs/reference/nnf_cosine_embedding_loss.html b/docs/reference/nnf_cosine_embedding_loss.html
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-Cosine_embedding_loss — nnf_cosine_embedding_loss • torch
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-    
    
-    
    Cosine_embedding_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that measures the loss given input tensors x_1, x_2 and a
-Tensor label y with values 1 or -1. This is used for measuring whether two inputs
-are similar or dissimilar, using the cosine distance, and is typically used
-for learning nonlinear embeddings or semi-supervised learning.
    
-    
    
-
-    
    nnf_cosine_embedding_loss(
-  input1,
-  input2,
-  target,
-  margin = 0,
-  reduction = c("mean", "sum", "none")
-)
    
-
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    Arguments
    
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    input1
    the input x_1 tensor
    input2
    the input x_2 tensor
    target
    the target tensor
    margin
    Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
-is missing, the default value is 0.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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diff --git a/docs/reference/nnf_cosine_similarity.html b/docs/reference/nnf_cosine_similarity.html
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-Cosine_similarity — nnf_cosine_similarity • torch
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-    
    
-    
    Cosine_similarity
    
-    Source: R/nnf-distance.R
-    
-    
    
-
-    
    
-    
    Returns cosine similarity between x1 and x2, computed along dim.
    
-    
    
-
-    
    nnf_cosine_similarity(x1, x2, dim = 1, eps = 1e-08)
    
-
-    
    Arguments
    
-    
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    x1
    (Tensor) First input.
    x2
    (Tensor) Second input (of size matching x1).
    dim
    (int, optional) Dimension of vectors. Default: 1
    eps
    (float, optional) Small value to avoid division by zero.
-Default: 1e-8
    
-
-    
    Details
    
-
-    
    $$
-    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-$$
    
-
-  
    
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diff --git a/docs/reference/nnf_cross_entropy.html b/docs/reference/nnf_cross_entropy.html
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-Cross_entropy — nnf_cross_entropy • torch
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-    
    Cross_entropy
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    This criterion combines log_softmax and nll_loss in a single
-function.
    
-    
    
-
-    
    nnf_cross_entropy(
-  input,
-  target,
-  weight = NULL,
-  ignore_index = -100,
-  reduction = c("mean", "sum", "none")
-)
    
-
-    
    Arguments
    
-    
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    input
    (Tensor) \((N, C)\) where C = number of classes or \((N, C, H, W)\)
-in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
-in the case of K-dimensional loss.
    target
    (Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.
    weight
    (Tensor, optional) a manual rescaling weight given to each class. If
-given, has to be a Tensor of size C
    ignore_index
    (int, optional) Specifies a target value that is ignored
-and does not contribute to the input gradient.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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diff --git a/docs/reference/nnf_ctc_loss.html b/docs/reference/nnf_ctc_loss.html
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-Ctc_loss — nnf_ctc_loss • torch
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    Ctc_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    The Connectionist Temporal Classification loss.
    
-    
    
-
-    
    nnf_ctc_loss(
-  log_probs,
-  targets,
-  input_lengths,
-  target_lengths,
-  blank = 0,
-  reduction = c("mean", "sum", "none"),
-  zero_infinity = FALSE
-)
    
-
-    
    Arguments
    
-    
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    log_probs
    \((T, N, C)\) where C = number of characters in alphabet including blank,
-T = input length, and N = batch size. The logarithmized probabilities of
-the outputs (e.g. obtained with nnf_log_softmax).
    targets
    \((N, S)\) or (sum(target_lengths)). Targets cannot be blank.
-In the second form, the targets are assumed to be concatenated.
    input_lengths
    \((N)\). Lengths of the inputs (must each be \(\leq T\))
    target_lengths
    \((N)\). Lengths of the targets
    blank
    (int, optional) Blank label. Default \(0\).
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    zero_infinity
    (bool, optional) Whether to zero infinite losses and the
-associated gradients. Default: FALSE Infinite losses mainly occur when the
-inputs are too short to be aligned to the targets.
    
-
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diff --git a/docs/reference/nnf_dropout.html b/docs/reference/nnf_dropout.html
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-    
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-    Source: R/nnf-dropout.R
-    
-    
    
-
-    
    
-    
    During training, randomly zeroes some of the elements of the input
-tensor with probability p using samples from a Bernoulli
-distribution.
    
-    
    
-
-    
    nnf_dropout(input, p = 0.5, training = TRUE, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
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    input
    the input tensor
    p
    probability of an element to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE
    inplace
    If set to TRUE, will do this operation in-place.
-Default: FALSE
    
-
-
-  
    
-  
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diff --git a/docs/reference/nnf_dropout2d.html b/docs/reference/nnf_dropout2d.html
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-Dropout2d — nnf_dropout2d • torch
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-    
    
-    
    Dropout2d
    
-    Source: R/nnf-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
    
-    
    
-
-    
    nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
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-      
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-      
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-    
    input
    the input tensor
    p
    probability of a channel to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE.
    inplace
    If set to TRUE, will do this operation in-place.
-Default: FALSE
    
-
-
-  
    
-  
-
    
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diff --git a/docs/reference/nnf_dropout3d.html b/docs/reference/nnf_dropout3d.html
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-
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-Dropout3d — nnf_dropout3d • torch
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-    
    
-    
    Dropout3d
    
-    Source: R/nnf-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
    
-    
    
-
-    
    nnf_dropout3d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
-
-    
    Arguments
    
-    
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    input
    the input tensor
    p
    probability of a channel to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE.
    inplace
    If set to TRUE, will do this operation in-place.
-Default: FALSE
    
-
-
-  
    
-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_elu.html b/docs/reference/nnf_elu.html
deleted file mode 100644
index 0fda511504fb75425743a996b2f76e507dae952d..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_elu.html
+++ /dev/null
@@ -1,228 +0,0 @@
-
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-  
-  
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-Elu — nnf_elu • torch
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-    
    
-    
    Elu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise,
-$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.
    
-    
    
-
-    
    nnf_elu(input, alpha = 1, inplace = FALSE)
-
-nnf_elu_(input, alpha = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-      
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    alpha
    the alpha value for the ELU formulation. Default: 1.0
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-x <- torch_randn(2, 2)
-y <- nnf_elu(x, alpha = 1)
-nnf_elu_(x, alpha = 1)
    #> torch_tensor 
-#> -0.7520  0.2844
-#>  1.3381  0.9215
-#> [ CPUFloatType{2,2} ]
    torch_equal(x, y)
    #> [1] TRUE
    
-# }
-
    
-  
    
-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_embedding.html b/docs/reference/nnf_embedding.html
deleted file mode 100644
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--- a/docs/reference/nnf_embedding.html
+++ /dev/null
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-
-
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-  
-  
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-Embedding — nnf_embedding • torch
-
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-    
    
-    
    Embedding
    
-    Source: R/nnf-embedding.R
-    
-    
    
-
-    
    
-    
    A simple lookup table that looks up embeddings in a fixed dictionary and size.
    
-    
    
-
-    
    nnf_embedding(
-  input,
-  weight,
-  padding_idx = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  sparse = FALSE
-)
    
-
-    
    Arguments
    
-    
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-      
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-    
    input
    (LongTensor) Tensor containing indices into the embedding matrix
    weight
    (Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size
    padding_idx
    (int, optional) If given, pads the output with the embedding
-vector at padding_idx (initialized to zeros) whenever it encounters the index.
    max_norm
    (float, optional) If given, each embedding vector with norm larger
-than max_norm is renormalized to have norm max_norm. Note: this will modify
-weight in-place.
    norm_type
    (float, optional) The p of the p-norm to compute for the max_norm
-option. Default 2.
    scale_grad_by_freq
    (boolean, optional) If given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default FALSE.
    sparse
    (bool, optional) If TRUE, gradient w.r.t. weight will be a
-sparse tensor. See Notes under nn_embedding for more details regarding
-sparse gradients.
    
-
-    
    Details
    
-
-    
    This module is often used to retrieve word embeddings using indices.
-The input to the module is a list of indices, and the embedding matrix,
-and the output is the corresponding word embeddings.
    
-
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nnf_embedding_bag.html b/docs/reference/nnf_embedding_bag.html
deleted file mode 100644
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--- a/docs/reference/nnf_embedding_bag.html
+++ /dev/null
@@ -1,267 +0,0 @@
-
-
-
-  
-  
-
-
-
-Embedding_bag — nnf_embedding_bag • torch
-
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-    
    
-    
    Embedding_bag
    
-    Source: R/nnf-embedding.R
-    
-    
    
-
-    
    
-    
    Computes sums, means or maxes of bags of embeddings, without instantiating the
-intermediate embeddings.
    
-    
    
-
-    
    nnf_embedding_bag(
-  input,
-  weight,
-  offsets = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  mode = "mean",
-  sparse = FALSE,
-  per_sample_weights = NULL,
-  include_last_offset = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-      
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-    
-    
-      
-      
-    
-    
    input
    (LongTensor) Tensor containing bags of indices into the embedding matrix
    weight
    (Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size
    offsets
    (LongTensor, optional) Only used when input is 1D. offsets
-determines the starting index position of each bag (sequence) in input.
    max_norm
    (float, optional) If given, each embedding vector with norm
-larger than max_norm is renormalized to have norm max_norm.
-Note: this will modify weight in-place.
    norm_type
    (float, optional) The p in the p-norm to compute for the
-max_norm option. Default 2.
    scale_grad_by_freq
    (boolean, optional) if given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default FALSE.                                            Note: this option is not supported when mode="max".
    mode
    (string, optional) "sum", "mean" or "max". Specifies
-the way to reduce the bag. Default: 'mean'
    sparse
    (bool, optional) if TRUE, gradient w.r.t. weight will be a
-sparse tensor. See Notes under nn_embedding for more details regarding
-sparse gradients. Note: this option is not supported when mode="max".
    per_sample_weights
    (Tensor, optional) a tensor of float / double weights,
-or NULL to indicate all weights should be taken to be 1. If specified,
-per_sample_weights must have exactly the same shape as input and is treated
-as having the same offsets, if those are not NULL.
    include_last_offset
    (bool, optional) if TRUE, the size of offsets is
-equal to the number of bags + 1.
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nnf_fold.html b/docs/reference/nnf_fold.html
deleted file mode 100644
index b934f6ec5539447058bc8bb9ae310f2511f4cb6e..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_fold.html
+++ /dev/null
@@ -1,245 +0,0 @@
-
-
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-  
-  
-
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-Fold — nnf_fold • torch
-
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-  
    
-    
    
-    
    Fold
    
-    Source: R/nnf-fold.R
-    
-    
    
-
-    
    
-    
    Combines an array of sliding local blocks into a large containing
-tensor.
    
-    
    
-
-    
    nnf_fold(
-  input,
-  output_size,
-  kernel_size,
-  dilation = 1,
-  padding = 0,
-  stride = 1
-)
    
-
-    
    Arguments
    
-    
-    
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-      
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-    
    input
    the input tensor
    output_size
    the shape of the spatial dimensions of the output (i.e.,
-output$sizes()[-c(1,2)])
    kernel_size
    the size of the sliding blocks
    dilation
    a parameter that controls the stride of elements within the
-neighborhood. Default: 1
    padding
    implicit zero padding to be added on both sides of input.
-Default: 0
    stride
    the stride of the sliding blocks in the input spatial dimensions.
-Default: 1
    
-
-    
    Warning
    
-
-    
-
-
-
    Currently, only 4-D output tensors (batched image-like tensors) are
-supported.
    
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nnf_fractional_max_pool2d.html b/docs/reference/nnf_fractional_max_pool2d.html
deleted file mode 100644
index 5f41f39d9bd4c93bebdee1b09a16b087138b3dde..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_fractional_max_pool2d.html
+++ /dev/null
@@ -1,242 +0,0 @@
-
-
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-  
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-Fractional_max_pool2d — nnf_fractional_max_pool2d • torch
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-    
    Fractional_max_pool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies 2D fractional max pooling over an input signal composed of several input planes.
    
-    
    
-
-    
    nnf_fractional_max_pool2d(
-  input,
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE,
-  random_samples = NULL
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
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-      
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    input
    the input tensor
    kernel_size
    the size of the window to take a max over. Can be a
-single number \(k\) (for a square kernel of \(k * k\)) or
-a tuple (kH, kW)
    output_size
    the target output size of the image of the form \(oH * oW\).
-Can be a tuple (oH, oW) or a single number \(oH\) for a square image \(oH * oH\)
    output_ratio
    If one wants to have an output size as a ratio of the input size,
-this option can be given. This has to be a number or tuple in the range (0, 1)
    return_indices
    if True, will return the indices along with the outputs.
    random_samples
    optional random samples.
    
-
-    
    Details
    
-
-    
    Fractional MaxPooling is described in detail in the paper Fractional MaxPooling_ by Ben Graham
    
-
    The max-pooling operation is applied in \(kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.
    
-
-  
    
-  
-
    
-
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_fractional_max_pool3d.html b/docs/reference/nnf_fractional_max_pool3d.html
deleted file mode 100644
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+++ /dev/null
@@ -1,243 +0,0 @@
-
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-Fractional_max_pool3d — nnf_fractional_max_pool3d • torch
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-    
    Fractional_max_pool3d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies 3D fractional max pooling over an input signal composed of several input planes.
    
-    
    
-
-    
    nnf_fractional_max_pool3d(
-  input,
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE,
-  random_samples = NULL
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
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-      
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    input
    the input tensor
    kernel_size
    the size of the window to take a max over. Can be a single number \(k\)
-(for a square kernel of \(k * k * k\)) or a tuple (kT, kH, kW)
    output_size
    the target output size of the form \(oT * oH * oW\).
-Can be a tuple (oT, oH, oW) or a single number \(oH\) for a cubic output
-\(oH * oH * oH\)
    output_ratio
    If one wants to have an output size as a ratio of the
-input size, this option can be given. This has to be a number or tuple in the
-range (0, 1)
    return_indices
    if True, will return the indices along with the outputs.
    random_samples
    undocumented argument.
    
-
-    
    Details
    
-
-    
    Fractional MaxPooling is described in detail in the paper Fractional MaxPooling_ by Ben Graham
    
-
    The max-pooling operation is applied in \(kT * kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.
    
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nnf_gelu.html b/docs/reference/nnf_gelu.html
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-
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-Gelu — nnf_gelu • torch
-
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-    
    
-    
    Gelu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Gelu
    
-    
    
-
-    
    nnf_gelu(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
-
-    
    gelu(input) -> Tensor 
    
-
-    
-
-
-
    Applies element-wise the function
-\(GELU(x) = x * \Phi(x)\)
    
-
    where \(\Phi(x)\) is the Cumulative Distribution Function for
-Gaussian Distribution.
    
-
    See Gaussian Error Linear Units (GELUs).
    
-
-  
    
-  
-
    
-
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_glu.html b/docs/reference/nnf_glu.html
deleted file mode 100644
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-
-
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-Glu — nnf_glu • torch
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-    
    
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    input
    (Tensor) input tensor
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    (int) dimension on which to split the input. Default: -1
    
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-
    where input is split in half along dim to form a and b, \(\sigma\)
-is the sigmoid function and \(\otimes\) is the element-wise product
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diff --git a/docs/reference/nnf_grid_sample.html b/docs/reference/nnf_grid_sample.html
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-    Source: R/nnf-vision.R
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-
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-    
    Given an input and a flow-field grid, computes the
-output using input values and pixel locations from grid.
    
-    
    
-
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    nnf_grid_sample(
-  input,
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    input
    (Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)
    grid
    (Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)
    mode
    (str) interpolation mode to calculate output values 'bilinear' | 'nearest'.
-Default: 'bilinear'
    padding_mode
    (str) padding mode for outside grid values 'zeros' | 'border'
-| 'reflection'. Default: 'zeros'
    align_corners
    (bool, optional) Geometrically, we consider the pixels of the
-input  as squares rather than points. If set to True, the extrema (-1 and
-1) are considered as referring to the center points of the input's corner pixels.
-If set to False, they are instead considered as referring to the corner
-points of the input's corner pixels, making the sampling more resolution
-agnostic. This option parallels the align_corners option in  nnf_interpolate(), and
-so whichever option is used here should also be used there to resize the input
-image before grid sampling. Default: False
    
-
-    
    Details
    
-
-    
    Currently, only spatial (4-D) and volumetric (5-D) input are
-supported.
    
-
    In the spatial (4-D) case, for input with shape
-\((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) and grid with shape
-\((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\), the output will have shape
-\((N, C, H_{\mbox{out}}, W_{\mbox{out}})\).
    
-
    For each output location output[n, :, h, w], the size-2 vector
-grid[n, h, w] specifies input pixel locations x and y,
-which are used to interpolate the output value output[n, :, h, w].
-In the case of 5D inputs, grid[n, d, h, w] specifies the
-x, y, z pixel locations for interpolating
-output[n, :, d, h, w]. mode argument specifies nearest or
-bilinear interpolation method to sample the input pixels.
    
-
    grid specifies the sampling pixel locations normalized by the
-input spatial dimensions. Therefore, it should have most values in
-the range of [-1, 1]. For example, values x = -1, y = -1 is the
-left-top pixel of input, and values  x = 1, y = 1 is the
-right-bottom pixel of input.
    
-
    If grid has values outside the range of [-1, 1], the corresponding
-outputs are handled as defined by padding_mode. Options are
      
-
    • padding_mode="zeros": use 0 for out-of-bound grid locations,
      • 
-
    • padding_mode="border": use border values for out-of-bound grid locations,
      • 
-
    • padding_mode="reflection": use values at locations reflected by
-the border for out-of-bound grid locations. For location far away
-from the border, it will keep being reflected until becoming in bound,
-e.g., (normalized) pixel location x = -3.5 reflects by border -1
-and becomes x' = 1.5, then reflects by border 1 and becomes
-x'' = -0.5.
      • 
-
    
-
-    
    Note
    
-
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-
-
-
    This function is often used in conjunction with nnf_affine_grid()
-to build Spatial Transformer Networks_ .
    
-
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diff --git a/docs/reference/nnf_group_norm.html b/docs/reference/nnf_group_norm.html
deleted file mode 100644
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-    Source: R/nnf-normalization.R
-    
-    
    
-
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-    
    Applies Group Normalization for last certain number of dimensions.
    
-    
    
-
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    input
    the input tensor
    num_groups
    number of groups to separate the channels into
    weight
    the weight tensor
    bias
    the bias tensor
    eps
    a value added to the denominator for numerical stability. Default: 1e-5
    
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diff --git a/docs/reference/nnf_gumbel_softmax.html b/docs/reference/nnf_gumbel_softmax.html
deleted file mode 100644
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-Gumbel_softmax — nnf_gumbel_softmax • torch
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    Gumbel_softmax
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Samples from the Gumbel-Softmax distribution and
-optionally discretizes.
    
-    
    
-
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    nnf_gumbel_softmax(logits, tau = 1, hard = FALSE, dim = -1)
    
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    Arguments
    
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    logits
    [..., num_features] unnormalized log probabilities
    tau
    non-negative scalar temperature
    hard
    if True, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd
    dim
    (int) A dimension along which softmax will be computed. Default: -1.
    
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diff --git a/docs/reference/nnf_hardshrink.html b/docs/reference/nnf_hardshrink.html
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-    Source: R/nnf-activation.R
-    
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-
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-    
    Applies the hard shrinkage function element-wise
    
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    Arguments
    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    lambd
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diff --git a/docs/reference/nnf_hardsigmoid.html b/docs/reference/nnf_hardsigmoid.html
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-    Source: R/nnf-activation.R
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-
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-    
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-    
    
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-
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    Arguments
    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    inplace
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diff --git a/docs/reference/nnf_hardswish.html b/docs/reference/nnf_hardswish.html
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-    Source: R/nnf-activation.R
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-    
    Applies the hardswish function, element-wise, as described in the paper:
-Searching for MobileNetV3.
    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    inplace
    can optionally do the operation in-place. Default: FALSE
    
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    Details
    
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    $$ \mbox{Hardswish}(x) = \left\{
-  \begin{array}{ll}
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-  \right. $$
    
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diff --git a/docs/reference/nnf_hardtanh.html b/docs/reference/nnf_hardtanh.html
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-    Source: R/nnf-activation.R
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    Applies the HardTanh function element-wise.
    
-    
    
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-
-nnf_hardtanh_(input, min_val = -1, max_val = 1)
    
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    input
    (N,*) tensor, where * means, any number of additional
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    min_val
    minimum value of the linear region range. Default: -1
    max_val
    maximum value of the linear region range. Default: 1
    inplace
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diff --git a/docs/reference/nnf_hinge_embedding_loss.html b/docs/reference/nnf_hinge_embedding_loss.html
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-    
    Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
-This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
-using the L1 pairwise distance as xx , and is typically used for learning nonlinear
-embeddings or semi-supervised learning.
    
-    
    
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    margin
    Has a default value of 1.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
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diff --git a/docs/reference/nnf_instance_norm.html b/docs/reference/nnf_instance_norm.html
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-
    
-  
    
-    
    
-    
    Instance_norm
    
-    Source: R/nnf-instancenorm.R
-    
-    
    
-
-    
    
-    
    Applies Instance Normalization for each channel in each data sample in a
-batch.
    
-    
    
-
-    
    nnf_instance_norm(
-  input,
-  running_mean = NULL,
-  running_var = NULL,
-  weight = NULL,
-  bias = NULL,
-  use_input_stats = TRUE,
-  momentum = 0.1,
-  eps = 1e-05
-)
    
-
-    
    Arguments
    
-    
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-    
    input
    the input tensor
    running_mean
    the running_mean tensor
    running_var
    the running var tensor
    weight
    the weight tensor
    bias
    the bias tensor
    use_input_stats
    whether to use input stats
    momentum
    a double for the momentum
    eps
    an eps double for numerical stability
    
-
-
-  
    
-  
-
    
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-  
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diff --git a/docs/reference/nnf_interpolate.html b/docs/reference/nnf_interpolate.html
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+++ /dev/null
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-
-
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-  
-  
-
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-Interpolate — nnf_interpolate • torch
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-    
    
-    
    Interpolate
    
-    Source: R/nnf-upsampling.R
-    
-    
    
-
-    
    
-    
    Down/up samples the input to either the given size or the given
-scale_factor
    
-    
    
-
-    
    nnf_interpolate(
-  input,
-  size = NULL,
-  scale_factor = NULL,
-  mode = "nearest",
-  align_corners = FALSE,
-  recompute_scale_factor = NULL
-)
    
-
-    
    Arguments
    
-    
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-    
    input
    (Tensor) the input tensor
    size
    (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int])
-output spatial size.
    scale_factor
    (float or Tuple[float]) multiplier for spatial size.
-Has to match input size if it is a tuple.
    mode
    (str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
-| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'
    align_corners
    (bool, optional) Geometrically, we consider the pixels
-of the input and output as squares rather than points. If set to TRUE,
-the input and output tensors are aligned by the center points of their corner
-pixels, preserving the values at the corner pixels. If set to False, the
-input and output tensors are aligned by the corner points of their corner pixels,
-and the interpolation uses edge value padding for out-of-boundary values,
-making this operation independent of input size when scale_factor is kept
-the same. This only has an effect when mode  is 'linear', 'bilinear',
-'bicubic' or 'trilinear'.  Default: False
    recompute_scale_factor
    (bool, optional) recompute the scale_factor
-for use in the interpolation calculation.  When scale_factor is passed
-as a parameter, it is used to compute the output_size.  If recompute_scale_factor
-is ```True`` or not specified, a new scale_factor will be computed based on
-the output and input sizes for use in the interpolation computation (i.e. the
-computation will be identical to if the computed `output_size` were passed-in
-explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
-interpolation computation.  Note that when `scale_factor` is floating-point,
-the recomputed scale_factor may differ from the one passed in due to rounding
-and precision issues.
    
-
-    
    Details
    
-
-    
    The algorithm used for interpolation is determined by mode.
    
-
    Currently temporal, spatial and volumetric sampling are supported, i.e.
-expected inputs are 3-D, 4-D or 5-D in shape.
    
-
    The input dimensions are interpreted in the form:
-mini-batch x channels x [optional depth] x [optional height] x width.
    
-
    The modes available for resizing are: nearest, linear (3D-only),
-bilinear, bicubic (4D-only), trilinear (5D-only), area
    
-
-  
    
-  
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-
    
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diff --git a/docs/reference/nnf_kl_div.html b/docs/reference/nnf_kl_div.html
deleted file mode 100644
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--- a/docs/reference/nnf_kl_div.html
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-
-
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-Kl_div — nnf_kl_div • torch
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-  
    
-    
    
-    
    Kl_div
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    The Kullback-Leibler divergence Loss.
    
-    
    
-
-    
    nnf_kl_div(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nnf_l1_loss.html b/docs/reference/nnf_l1_loss.html
deleted file mode 100644
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--- a/docs/reference/nnf_l1_loss.html
+++ /dev/null
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-
-
-
-  
-  
-
-
-
-L1_loss — nnf_l1_loss • torch
-
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-  
    
-    
    
-    
    L1_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Function that takes the mean element-wise absolute value difference.
    
-    
    
-
-    
    nnf_l1_loss(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-      
-      
-    
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-      
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-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_layer_norm.html b/docs/reference/nnf_layer_norm.html
deleted file mode 100644
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-
-
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-  
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-Layer_norm — nnf_layer_norm • torch
-
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-  
    
-    
    
-    
    Layer_norm
    
-    Source: R/nnf-normalization.R
-    
-    
    
-
-    
    
-    
    Applies Layer Normalization for last certain number of dimensions.
    
-    
    
-
-    
    nnf_layer_norm(
-  input,
-  normalized_shape,
-  weight = NULL,
-  bias = NULL,
-  eps = 1e-05
-)
    
-
-    
    Arguments
    
-    
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-      
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-    
    input
    the input tensor
    normalized_shape
    input shape from an expected input of size. If a single
-integer is used, it is treated as a singleton list, and this module will normalize
-over the last dimension which is expected to be of that specific size.
    weight
    the weight tensor
    bias
    the bias tensor
    eps
    a value added to the denominator for numerical stability. Default: 1e-5
    
-
-
-  
    
-  
-
    
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-      
    
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_leaky_relu.html b/docs/reference/nnf_leaky_relu.html
deleted file mode 100644
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-
-
-
-  
-  
-
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-Leaky_relu — nnf_leaky_relu • torch
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    Leaky_relu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise,
-\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)
    
-    
    
-
-    
    nnf_leaky_relu(input, negative_slope = 0.01, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    negative_slope
    Controls the angle of the negative slope. Default: 1e-2
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-
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-  
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diff --git a/docs/reference/nnf_linear.html b/docs/reference/nnf_linear.html
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-
-
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-Linear — nnf_linear • torch
-
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-    
    Linear
    
-    Source: R/nnf-linear.R
-    
-    
    
-
-    
    
-    
    Applies a linear transformation to the incoming data: \(y = xA^T + b\).
    
-    
    
-
-    
    nnf_linear(input, weight, bias = NULL)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    \((N, *, in\_features)\) where * means any number of
-additional dimensions
    weight
    \((out\_features, in\_features)\) the weights tensor.
    bias
    optional tensor \((out\_features)\)
    
-
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-  
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diff --git a/docs/reference/nnf_local_response_norm.html b/docs/reference/nnf_local_response_norm.html
deleted file mode 100644
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-
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-
-Local_response_norm — nnf_local_response_norm • torch
-
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-    
    
-    
    Local_response_norm
    
-    Source: R/nnf-normalization.R
-    
-    
    
-
-    
    
-    
    Applies local response normalization over an input signal composed of
-several input planes, where channels occupy the second dimension.
-Applies normalization across channels.
    
-    
    
-
-    
    nnf_local_response_norm(input, size, alpha = 1e-04, beta = 0.75, k = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
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-      
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-    
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-    
    input
    the input tensor
    size
    amount of neighbouring channels used for normalization
    alpha
    multiplicative factor. Default: 0.0001
    beta
    exponent. Default: 0.75
    k
    additive factor. Default: 1
    
-
-
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-  
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diff --git a/docs/reference/nnf_log_softmax.html b/docs/reference/nnf_log_softmax.html
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-
-
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-
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-
-Log_softmax — nnf_log_softmax • torch
-
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-    
    
-    
    Log_softmax
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies a softmax followed by a logarithm.
    
-    
    
-
-    
    nnf_log_softmax(input, dim = NULL, dtype = NULL)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    (Tensor) input
    dim
    (int) A dimension along which log_softmax will be computed.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.
-If specified, the input tensor is casted to dtype before the operation
-is performed. This is useful for preventing data type overflows.
-Default: NULL.
    
-
-    
    Details
    
-
-    
    While mathematically equivalent to log(softmax(x)), doing these two
-operations separately is slower, and numerically unstable. This function
-uses an alternative formulation to compute the output and gradient correctly.
    
-
-  
    
-  
-
    
-
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-
    
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diff --git a/docs/reference/nnf_logsigmoid.html b/docs/reference/nnf_logsigmoid.html
deleted file mode 100644
index 159a3a9b583b58d1c874cda2107b2de1ce73cbb8..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,206 +0,0 @@
-
-
-
-  
-  
-
-
-
-Logsigmoid — nnf_logsigmoid • torch
-
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    Logsigmoid
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise \(LogSigmoid(x_i) = log(\frac{1}{1 + exp(-x_i)})\)
    
-    
    
-
-    
    nnf_logsigmoid(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
-
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diff --git a/docs/reference/nnf_lp_pool1d.html b/docs/reference/nnf_lp_pool1d.html
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+++ /dev/null
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-Lp_pool1d — nnf_lp_pool1d • torch
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-    
    Lp_pool1d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 1D power-average pooling over an input signal composed of
-several input planes. If the sum of all inputs to the power of p is
-zero, the gradient is set to zero as well.
    
-    
    
-
-    
    nnf_lp_pool1d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
-
-    
    Arguments
    
-    
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    input
    the input tensor
    norm_type
    if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)
    kernel_size
    a single int, the size of the window
    stride
    a single int, the stride of the window. Default value is kernel_size
    ceil_mode
    when True, will use ceil instead of floor to compute the output shape
    
-
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diff --git a/docs/reference/nnf_lp_pool2d.html b/docs/reference/nnf_lp_pool2d.html
deleted file mode 100644
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--- a/docs/reference/nnf_lp_pool2d.html
+++ /dev/null
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-
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-Lp_pool2d — nnf_lp_pool2d • torch
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-    
    
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    Lp_pool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 2D power-average pooling over an input signal composed of
-several input planes. If the sum of all inputs to the power of p is
-zero, the gradient is set to zero as well.
    
-    
    
-
-    
    nnf_lp_pool2d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
-
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    Arguments
    
-    
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    input
    the input tensor
    norm_type
    if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)
    kernel_size
    a single int, the size of the window
    stride
    a single int, the stride of the window. Default value is kernel_size
    ceil_mode
    when True, will use ceil instead of floor to compute the output shape
    
-
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diff --git a/docs/reference/nnf_margin_ranking_loss.html b/docs/reference/nnf_margin_ranking_loss.html
deleted file mode 100644
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--- a/docs/reference/nnf_margin_ranking_loss.html
+++ /dev/null
@@ -1,226 +0,0 @@
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    Margin_ranking_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
-mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).
    
-    
    
-
-    
    nnf_margin_ranking_loss(input1, input2, target, margin = 0, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
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    input1
    the first tensor
    input2
    the second input tensor
    target
    the target tensor
    margin
    Has a default value of 00 .
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
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-  
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    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_max_pool1d.html b/docs/reference/nnf_max_pool1d.html
deleted file mode 100644
index 3bb6df07c7eb8b24ea98c682b7ece215a3f57b46..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_max_pool1d.html
+++ /dev/null
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-
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-Max_pool1d — nnf_max_pool1d • torch
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-    
    
-    
    Max_pool1d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 1D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_max_pool1d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
-
-    
    Arguments
    
-    
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-    
-    
    input
    input tensor of shape (minibatch , in_channels , iW)
    kernel_size
    the size of the window. Can be a single number or a
-tuple (kW,).
    stride
    the stride of the window. Can be a single number or a tuple
-(sW,). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padW,). Default: 0
    dilation
    controls the spacing between the kernel points; also known as
-the à trous algorithm.
    ceil_mode
    when True, will use ceil instead of floor to compute the
-output shape. Default: FALSE
    return_indices
    whether to return the indices where the max occurs.
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_max_pool2d.html b/docs/reference/nnf_max_pool2d.html
deleted file mode 100644
index 745db5f012170a9e8577baa7e869f53f710d7290..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_max_pool2d.html
+++ /dev/null
@@ -1,244 +0,0 @@
-
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-Max_pool2d — nnf_max_pool2d • torch
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-  
    
-    
    
-    
    Max_pool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 2D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_max_pool2d(
-  input,
-  kernel_size,
-  stride = kernel_size,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
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-      
-    
-    
-      
-      
-    
-    
    input
    input tensor (minibatch, in_channels , iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    dilation
    controls the spacing between the kernel points; also known as
-the à trous algorithm.
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape. Default: FALSE
    return_indices
    whether to return the indices where the max occurs.
    
-
-
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
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diff --git a/docs/reference/nnf_max_pool3d.html b/docs/reference/nnf_max_pool3d.html
deleted file mode 100644
index 900fc670b4861ea0704f9511e5da183443a3976f..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_max_pool3d.html
+++ /dev/null
@@ -1,244 +0,0 @@
-
-
-
-  
-  
-
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-
-Max_pool3d — nnf_max_pool3d • torch
-
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-    
    
-      
    
-      
-
-      
-
-      
    
-
-
    
-  
    
-    
    
-    
    Max_pool3d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 3D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_max_pool3d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
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-      
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-    
-      
-      
-    
-    
    input
    input tensor (minibatch, in_channels , iT * iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kT, kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sT, sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW), Default: 0
    dilation
    controls the spacing between the kernel points; also known as
-the à trous algorithm.
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape
    return_indices
    whether to return the indices where the max occurs.
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nnf_max_unpool1d.html b/docs/reference/nnf_max_unpool1d.html
deleted file mode 100644
index 9baabdb74e76cd48bdef4a9f2f67b7d1576c8aac..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_max_unpool1d.html
+++ /dev/null
@@ -1,232 +0,0 @@
-
-
-
-  
-  
-
-
-
-Max_unpool1d — nnf_max_unpool1d • torch
-
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-      
    
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-
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-  
    
-    
    
-    
    Max_unpool1d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Computes a partial inverse of MaxPool1d.
    
-    
    
-
-    
    nnf_max_unpool1d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
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-      
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-    
-    
-      
-      
-    
-    
    input
    the input Tensor to invert
    indices
    the indices given out by max pool
    kernel_size
    Size of the max pooling window.
    stride
    Stride of the max pooling window. It is set to kernel_size by default.
    padding
    Padding that was added to the input
    output_size
    the targeted output size
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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-
    
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diff --git a/docs/reference/nnf_max_unpool2d.html b/docs/reference/nnf_max_unpool2d.html
deleted file mode 100644
index cd9c095d9098c9f48ee0a3551cf869363545d790..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_max_unpool2d.html
+++ /dev/null
@@ -1,232 +0,0 @@
-
-
-
-  
-  
-
-
-
-Max_unpool2d — nnf_max_unpool2d • torch
-
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-  
    
-    
    
-    
    Max_unpool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Computes a partial inverse of MaxPool2d.
    
-    
    
-
-    
    nnf_max_unpool2d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
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-    
-    
-      
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-    
-    
-      
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-    
-    
-      
-      
-    
-    
    input
    the input Tensor to invert
    indices
    the indices given out by max pool
    kernel_size
    Size of the max pooling window.
    stride
    Stride of the max pooling window. It is set to kernel_size by default.
    padding
    Padding that was added to the input
    output_size
    the targeted output size
    
-
-
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_max_unpool3d.html b/docs/reference/nnf_max_unpool3d.html
deleted file mode 100644
index de981fa25b9011e312e3a26c1d422c541d3ed33b..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_max_unpool3d.html
+++ /dev/null
@@ -1,232 +0,0 @@
-
-
-
-  
-  
-
-
-
-Max_unpool3d — nnf_max_unpool3d • torch
-
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-    
    
-    
    Max_unpool3d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Computes a partial inverse of MaxPool3d.
    
-    
    
-
-    
    nnf_max_unpool3d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-    
-      
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-    
-    
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-    
-    
-      
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-    
    input
    the input Tensor to invert
    indices
    the indices given out by max pool
    kernel_size
    Size of the max pooling window.
    stride
    Stride of the max pooling window. It is set to kernel_size by default.
    padding
    Padding that was added to the input
    output_size
    the targeted output size
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nnf_mse_loss.html b/docs/reference/nnf_mse_loss.html
deleted file mode 100644
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+++ /dev/null
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-
-
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-  
-  
-
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-Mse_loss — nnf_mse_loss • torch
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-
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-  
    
-    
    
-    
    Mse_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Measures the element-wise mean squared error.
    
-    
    
-
-    
    nnf_mse_loss(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-
    
-  
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diff --git a/docs/reference/nnf_multi_head_attention_forward.html b/docs/reference/nnf_multi_head_attention_forward.html
deleted file mode 100644
index 65ae1ae73ea87912886034c0e558cf6cce00c1f1..0000000000000000000000000000000000000000
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+++ /dev/null
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-
-
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-  
-  
-
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-
-Multi head attention forward — nnf_multi_head_attention_forward • torch
-
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-    
    
-    
    Multi head attention forward
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Allows the model to jointly attend to information from different representation
-subspaces. See reference: Attention Is All You Need
    
-    
    
-
-    
    nnf_multi_head_attention_forward(
-  query,
-  key,
-  value,
-  embed_dim_to_check,
-  num_heads,
-  in_proj_weight,
-  in_proj_bias,
-  bias_k,
-  bias_v,
-  add_zero_attn,
-  dropout_p,
-  out_proj_weight,
-  out_proj_bias,
-  training = TRUE,
-  key_padding_mask = NULL,
-  need_weights = TRUE,
-  attn_mask = NULL,
-  use_separate_proj_weight = FALSE,
-  q_proj_weight = NULL,
-  k_proj_weight = NULL,
-  v_proj_weight = NULL,
-  static_k = NULL,
-  static_v = NULL
-)
    
-
-    
    Arguments
    
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-    
-      
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-    
-    
    query
    \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.
    key
    \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
    value
    \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
    embed_dim_to_check
    total dimension of the model.
    num_heads
    parallel attention heads.
    in_proj_weight
    input projection weight and bias.
    in_proj_bias
    currently undocumented.
    bias_k
    bias of the key and value sequences to be added at dim=0.
    bias_v
    currently undocumented.
    add_zero_attn
    add a new batch of zeros to the key and
-value sequences at dim=1.
    dropout_p
    probability of an element to be zeroed.
    out_proj_weight
    the output projection weight and bias.
    out_proj_bias
    currently undocumented.
    training
    apply dropout if is TRUE.
    key_padding_mask
    \((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of True will be ignored while the position with the value of False will be unchanged.
    need_weights
    output attn_output_weights.
    attn_mask
    2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with True
-is not allowed to attend while False values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.
    use_separate_proj_weight
    the function accept the proj. weights for
-query, key, and value in different forms. If false, in_proj_weight will be used,
-which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.
    q_proj_weight
    input projection weight and bias.
    k_proj_weight
    currently undocumented.
    v_proj_weight
    currently undocumented.
    static_k
    static key and value used for attention operators.
    static_v
    currently undocumented.
    
-
-
-  
    
-  
-
    
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_multi_margin_loss.html b/docs/reference/nnf_multi_margin_loss.html
deleted file mode 100644
index 05a13bbcb8ed891511a377b18ec79acd4048e878..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_multi_margin_loss.html
+++ /dev/null
@@ -1,240 +0,0 @@
-
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-Multi_margin_loss — nnf_multi_margin_loss • torch
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-    
    
-    
    Multi_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that optimizes a multi-class classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
-(which is a 1D tensor of target class indices, 0 <= y <= x$size(2) - 1 ).
    
-    
    
-
-    
    nnf_multi_margin_loss(
-  input,
-  target,
-  p = 1,
-  margin = 1,
-  weight = NULL,
-  reduction = "mean"
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
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-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    p
    Has a default value of 1. 1 and 2 are the only supported values.
    margin
    Has a default value of 1.
    weight
    a manual rescaling weight given to each class. If given, it has to
-be a Tensor of size C. Otherwise, it is treated as if having all ones.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
-  
    
-  
-
    
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nnf_multilabel_margin_loss.html b/docs/reference/nnf_multilabel_margin_loss.html
deleted file mode 100644
index 096c4f8931a984e416bd0d73033688bd1c4019f2..0000000000000000000000000000000000000000
--- a/docs/reference/nnf_multilabel_margin_loss.html
+++ /dev/null
@@ -1,220 +0,0 @@
-
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-  
-  
-
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-
-Multilabel_margin_loss — nnf_multilabel_margin_loss • torch
-
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-  
    
-    
    
-    
    Multilabel_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that optimizes a multi-class multi-classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
-is a 2D Tensor of target class indices).
    
-    
    
-
-    
    nnf_multilabel_margin_loss(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
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-
    
-
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-  
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diff --git a/docs/reference/nnf_multilabel_soft_margin_loss.html b/docs/reference/nnf_multilabel_soft_margin_loss.html
deleted file mode 100644
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-
-
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-  
-  
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-Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss • torch
-
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-  
    
-    
    
-    
    Multilabel_soft_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that optimizes a multi-label one-versus-all loss based on
-max-entropy, between input x and target y of size (N, C).
    
-    
    
-
-    
    nnf_multilabel_soft_margin_loss(input, target, weight, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    weight
    weight tensor to apply on the loss.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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diff --git a/docs/reference/nnf_nll_loss.html b/docs/reference/nnf_nll_loss.html
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-Nll_loss — nnf_nll_loss • torch
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-    
    
-    
    Nll_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    The negative log likelihood loss.
    
-    
    
-
-    
    nnf_nll_loss(
-  input,
-  target,
-  weight = NULL,
-  ignore_index = -100,
-  reduction = "mean"
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
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-      
-    
-    
-      
-      
-    
-    
    input
    \((N, C)\) where C = number of classes or \((N, C, H, W)\) in
-case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
-the case of K-dimensional loss.
    target
    \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.
    weight
    (Tensor, optional) a manual rescaling weight given to each class.
-If given, has to be a Tensor of size C
    ignore_index
    (int, optional) Specifies a target value that is ignored and
-does not contribute to the input gradient.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_normalize.html b/docs/reference/nnf_normalize.html
deleted file mode 100644
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-
-
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-Normalize — nnf_normalize • torch
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-    
    
-    
    Normalize
    
-    Source: R/nnf-normalization.R
-    
-    
    
-
-    
    
-    
    Performs \(L_p\) normalization of inputs over specified dimension.
    
-    
    
-
-    
    nnf_normalize(input, p = 2, dim = 1, eps = 1e-12, out = NULL)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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    input
    input tensor of any shape
    p
    (float) the exponent value in the norm formulation. Default: 2
    dim
    (int) the dimension to reduce. Default: 1
    eps
    (float) small value to avoid division by zero. Default: 1e-12
    out
    (Tensor, optional) the output tensor. If out is used, this                            operation won't be differentiable.
    
-
-    
    Details
    
-
-    
    For a tensor input of sizes \((n_0, ..., n_{dim}, ..., n_k)\), each
-\(n_{dim}\) -element vector \(v\) along dimension dim is transformed as
    
-
    $$
-        v = \frac{v}{\max(\Vert v \Vert_p, \epsilon)}.
-$$
    
-
    With the default arguments it uses the Euclidean norm over vectors along
-dimension \(1\) for normalization.
    
-
-  
    
-  
-
    
-
-
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-  
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diff --git a/docs/reference/nnf_one_hot.html b/docs/reference/nnf_one_hot.html
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-One_hot — nnf_one_hot • torch
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-    
    One_hot
    
-    Source: R/nnf-embedding.R
-    
-    
    
-
-    
    
-    
    Takes LongTensor with index values of shape (*) and returns a tensor
-of shape (*, num_classes) that have zeros everywhere except where the
-index of last dimension matches the corresponding value of the input tensor,
-in which case it will be 1.
    
-    
    
-
-    
    nnf_one_hot(tensor, num_classes = -1)
    
-
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    Arguments
    
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-    
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    tensor
    (LongTensor) class values of any shape.
    num_classes
    (int) Total number of classes. If set to -1, the number
-of classes will be inferred as one greater than the largest class value in
-the input tensor.
    
-
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    Details
    
-
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    One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot
    
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diff --git a/docs/reference/nnf_pad.html b/docs/reference/nnf_pad.html
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-    Source: R/nnf-padding.R
-    
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-    
    Pads tensor.
    
-    
    
-
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    nnf_pad(input, pad, mode = "constant", value = 0)
    
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    Arguments
    
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    input
    (Tensor) N-dimensional tensor
    pad
    (tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
-and \(m\) is even.
    mode
    'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'
    value
    fill value for 'constant' padding. Default: 0.
    
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    Padding size
    
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-
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-
    The padding size by which to pad some dimensions of input
-are described starting from the last dimension and moving forward.
-\(\left\lfloor\frac{\mbox{len(pad)}}{2}\right\rfloor\) dimensions
-of input will be padded.
-For example, to pad only the last dimension of the input tensor, then
-pad has the form
-\((\mbox{padding\_left}, \mbox{padding\_right})\);
-to pad the last 2 dimensions of the input tensor, then use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom})\);
-to pad the last 3 dimensions, use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom}\)
-\(\mbox{padding\_front}, \mbox{padding\_back})\).
    
-    
    Padding mode
    
-
-    
-
-
-
    See nn_constant_pad_2d, nn_reflection_pad_2d, and
-nn_replication_pad_2d for concrete examples on how each of the
-padding modes works. Constant padding is implemented for arbitrary dimensions.
-tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of
-3D input tensor. Reflect padding is only implemented for padding the last 2
-dimensions of 4D input tensor, or the last dimension of 3D input tensor.
    
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-    
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    Computes the batchwise pairwise distance between vectors using the p-norm.
    
-    
    
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    x1
    (Tensor) First input.
    x2
    (Tensor) Second input (of size matching x1).
    p
    the norm degree. Default: 2
    eps
    (float, optional) Small value to avoid division by zero.
-Default: 1e-8
    keepdim
    Determines whether or not to keep the vector dimension. Default: False
    
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diff --git a/docs/reference/nnf_pdist.html b/docs/reference/nnf_pdist.html
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-    Source: R/nnf-distance.R
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-    
    Computes the p-norm distance between every pair of row vectors in the input.
-This is identical to the upper triangular portion, excluding the diagonal, of
-torch_norm(input[:, None] - input, dim=2, p=p). This function will be faster
-if the rows are contiguous.
    
-    
    
-
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    nnf_pdist(input, p = 2)
    
-
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    Arguments
    
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    input
    input tensor of shape \(N \times M\).
    p
    p value for the p-norm distance to calculate between each vector pair
-\(\in [0, \infty]\).
    
-
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    Details
    
-
-    
    If input has shape \(N \times M\) then the output will have shape
-\(\frac{1}{2} N (N - 1)\).
    
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diff --git a/docs/reference/nnf_pixel_shuffle.html b/docs/reference/nnf_pixel_shuffle.html
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-    Source: R/nnf-pixelshuffle.R
-    
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-    
    Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
-tensor of shape \((*, C, H \times r, W \times r)\).
    
-    
    
-
-    
    nnf_pixel_shuffle(input, upscale_factor)
    
-
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    Arguments
    
-    
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    input
    (Tensor) the input tensor
    upscale_factor
    (int) factor to increase spatial resolution by
    
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diff --git a/docs/reference/nnf_poisson_nll_loss.html b/docs/reference/nnf_poisson_nll_loss.html
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    Poisson_nll_loss
    
-    Source: R/nnf-loss.R
-    
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-
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-    
    Poisson negative log likelihood loss.
    
-    
    
-
-    
    nnf_poisson_nll_loss(
-  input,
-  target,
-  log_input = TRUE,
-  full = FALSE,
-  eps = 1e-08,
-  reduction = "mean"
-)
    
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    log_input
    if TRUE the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
-if FALSE then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
-Default: TRUE.
    full
    whether to compute full loss, i. e. to add the Stirling approximation
-term. Default: FALSE.
    eps
    (float, optional) Small value to avoid evaluation of \(\log(0)\) when
-log_input=FALSE. Default: 1e-8
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_prelu.html b/docs/reference/nnf_prelu.html
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-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise the function
-\(PReLU(x) = max(0,x) + weight * min(0,x)\)
-where weight is a learnable parameter.
    
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-
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    (N,*) tensor, where * means, any number of additional
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    weight
    (Tensor) the learnable weights
    
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diff --git a/docs/reference/nnf_relu.html b/docs/reference/nnf_relu.html
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-    
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-
-    
    
-    
    Applies the rectified linear unit function element-wise.
    
-    
    
-
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-
-nnf_relu_(input)
    
-
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    Arguments
    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    inplace
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diff --git a/docs/reference/nnf_relu6.html b/docs/reference/nnf_relu6.html
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-
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-    
    Applies the element-wise function \(ReLU6(x) = min(max(0,x), 6)\).
    
-    
    
-
-    
    nnf_relu6(input, inplace = FALSE)
    
-
-    
    Arguments
    
-    
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-      
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    input
    (N,*) tensor, where * means, any number of additional
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    inplace
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diff --git a/docs/reference/nnf_rrelu.html b/docs/reference/nnf_rrelu.html
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    Rrelu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Randomized leaky ReLU.
    
-    
    
-
-    
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-
-nnf_rrelu_(input, lower = 1/8, upper = 1/3, training = FALSE)
    
-
-    
    Arguments
    
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-      
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    lower
    lower bound of the uniform distribution. Default: 1/8
    upper
    upper bound of the uniform distribution. Default: 1/3
    training
    bool wether it's a training pass. DEfault: FALSE
    inplace
    can optionally do the operation in-place. Default: FALSE
    
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diff --git a/docs/reference/nnf_selu.html b/docs/reference/nnf_selu.html
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-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise,
-$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
-with \(\alpha=1.6732632423543772848170429916717\) and
-\(scale=1.0507009873554804934193349852946\).
    
-    
    
-
-    
    nnf_selu(input, inplace = FALSE)
-
-nnf_selu_(input)
    
-
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    input
    (N,*) tensor, where * means, any number of additional
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    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-x <- torch_randn(2, 2)
-y <- nnf_selu(x)
-nnf_selu_(x)
    #> torch_tensor 
-#>  0.3549  0.1844
-#> -1.4839 -0.8035
-#> [ CPUFloatType{2,2} ]
    torch_equal(x, y)
    #> [1] TRUE
    
-# }
-
    
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diff --git a/docs/reference/nnf_smooth_l1_loss.html b/docs/reference/nnf_smooth_l1_loss.html
deleted file mode 100644
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-Smooth_l1_loss — nnf_smooth_l1_loss • torch
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    Smooth_l1_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
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-    
    Function that uses a squared term if the absolute
-element-wise error falls below 1 and an L1 term otherwise.
    
-    
    
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    target
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    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_soft_margin_loss.html b/docs/reference/nnf_soft_margin_loss.html
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-Soft_margin_loss — nnf_soft_margin_loss • torch
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    Soft_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that optimizes a two-class classification logistic loss
-between input tensor x and target tensor y (containing 1 or -1).
    
-    
    
-
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    nnf_soft_margin_loss(input, target, reduction = "mean")
    
-
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    Arguments
    
-    
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_softmax.html b/docs/reference/nnf_softmax.html
deleted file mode 100644
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    Softmax
    
-    Source: R/nnf-activation.R
-    
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-    
    Applies a softmax function.
    
-    
    
-
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    nnf_softmax(input, dim, dtype = NULL)
    
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    input
    (Tensor) input
    dim
    (int) A dimension along which softmax will be computed.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to dtype before the operation      is performed. This is useful for preventing data type overflows.
-Default: NULL.
    
-
-    
    Details
    
-
-    
    Softmax is defined as:
    
-
    $$Softmax(x_{i}) = exp(x_i)/\sum_j exp(x_j)$$
    
-
    It is applied to all slices along dim, and will re-scale them so that the elements
-lie in the range [0, 1] and sum to 1.
    
-
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diff --git a/docs/reference/nnf_softmin.html b/docs/reference/nnf_softmin.html
deleted file mode 100644
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-Softmin — nnf_softmin • torch
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    Softmin
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies a softmin function.
    
-    
    
-
-    
    nnf_softmin(input, dim, dtype = NULL)
    
-
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    Arguments
    
-    
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-      
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-    
    input
    (Tensor) input
    dim
    (int) A dimension along which softmin will be computed
-(so every slice        along dim will sum to 1).
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to dtype before the operation      is performed.
-This is useful for preventing data type overflows. Default: NULL.
    
-
-    
    Details
    
-
-    
    Note that
    
-
    $$Softmin(x) = Softmax(-x)$$.
    
-
    See nnf_softmax definition for mathematical formula.
    
-
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diff --git a/docs/reference/nnf_softplus.html b/docs/reference/nnf_softplus.html
deleted file mode 100644
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-Softplus — nnf_softplus • torch
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    Softplus
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).
    
-    
    
-
-    
    nnf_softplus(input, beta = 1, threshold = 20)
    
-
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    Arguments
    
-    
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-      
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    beta
    the beta value for the Softplus formulation. Default: 1
    threshold
    values above this revert to a linear function. Default: 20
    
-
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    Details
    
-
-    
    For numerical stability the implementation reverts to the linear function
-when \(input * \beta > threshold\).
    
-
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diff --git a/docs/reference/nnf_softshrink.html b/docs/reference/nnf_softshrink.html
deleted file mode 100644
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-Softshrink — nnf_softshrink • torch
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    Softshrink
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies the soft shrinkage function elementwise
    
-    
    
-
-    
    nnf_softshrink(input, lambd = 0.5)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    lambd
    the lambda (must be no less than zero) value for the Softshrink
-formulation. Default: 0.5
    
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diff --git a/docs/reference/nnf_softsign.html b/docs/reference/nnf_softsign.html
deleted file mode 100644
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-    
    Softsign
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise, the function \(SoftSign(x) = x/(1 + |x|\)
    
-    
    
-
-    
    nnf_softsign(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
-
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diff --git a/docs/reference/nnf_tanhshrink.html b/docs/reference/nnf_tanhshrink.html
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    Tanhshrink
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise, \(Tanhshrink(x) = x - Tanh(x)\)
    
-    
    
-
-    
    nnf_tanhshrink(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
-
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diff --git a/docs/reference/nnf_threshold.html b/docs/reference/nnf_threshold.html
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    Threshold
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Thresholds each element of the input Tensor.
    
-    
    
-
-    
    nnf_threshold(input, threshold, value, inplace = FALSE)
-
-nnf_threshold_(input, threshold, value)
    
-
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    Arguments
    
-    
-    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    threshold
    The value to threshold at
    value
    The value to replace with
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
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diff --git a/docs/reference/nnf_triplet_margin_loss.html b/docs/reference/nnf_triplet_margin_loss.html
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-Triplet_margin_loss — nnf_triplet_margin_loss • torch
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    Triplet_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that measures the triplet loss given an input tensors x1 ,
-x2 , x3 and a margin with a value greater than 0 . This is used for measuring
-a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
-anchor, positive examples and negative examples respectively). The shapes of all
-input tensors should be (N, D).
    
-    
    
-
-    
    nnf_triplet_margin_loss(
-  anchor,
-  positive,
-  negative,
-  margin = 1,
-  p = 2,
-  eps = 1e-06,
-  swap = FALSE,
-  reduction = "mean"
-)
    
-
-    
    Arguments
    
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    anchor
    the anchor input tensor
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    the positive input tensor
    negative
    the negative input tensor
    margin
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    p
    The norm degree for pairwise distance. Default: 2.
    eps
    (float, optional) Small value to avoid division by zero.
    swap
    The distance swap is described in detail in the paper Learning shallow
-convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
-Default: FALSE.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_unfold.html b/docs/reference/nnf_unfold.html
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-    
    Unfold
    
-    Source: R/nnf-fold.R
-    
-    
    
-
-    
    
-    
    Extracts sliding local blocks from an batched input tensor.
    
-    
    
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    nnf_unfold(input, kernel_size, dilation = 1, padding = 0, stride = 1)
    
-
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    input
    the input tensor
    kernel_size
    the size of the sliding blocks
    dilation
    a parameter that controls the stride of elements within the
-neighborhood. Default: 1
    padding
    implicit zero padding to be added on both sides of input.
-Default: 0
    stride
    the stride of the sliding blocks in the input spatial dimensions.
-Default: 1
    
-
-    
    Warning
    
-
-    
-
-
-
    Currently, only 4-D input tensors (batched image-like tensors) are
-supported.
    
-
-
-
    More than one element of the unfolded tensor may refer to a single
-memory location. As a result, in-place operations (especially ones that
-are vectorized) may result in incorrect behavior. If you need to write
-to the tensor, please clone it first.
    
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diff --git a/docs/reference/optim_adam.html b/docs/reference/optim_adam.html
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-
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-Implements Adam algorithm. — optim_adam • torch
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-    
    Implements Adam algorithm.
    
-    Source: R/optim-adam.R
-    
-    
    
-
-    
    
-    
    It has been proposed in Adam: A Method for Stochastic Optimization.
    
-    
    
-
-    
    optim_adam(
-  params,
-  lr = 0.001,
-  betas = c(0.9, 0.999),
-  eps = 1e-08,
-  weight_decay = 0,
-  amsgrad = FALSE
-)
    
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    Arguments
    
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    params
    (iterable): iterable of parameters to optimize or dicts defining
-parameter groups
    lr
    (float, optional): learning rate (default: 1e-3)
    betas
    (Tuple[float, float], optional): coefficients used for computing
-running averages of gradient and its square (default: (0.9, 0.999))
    eps
    (float, optional): term added to the denominator to improve
-numerical stability (default: 1e-8)
    weight_decay
    (float, optional): weight decay (L2 penalty) (default: 0)
    amsgrad
    (boolean, optional): whether to use the AMSGrad variant of this
-algorithm from the paper On the Convergence of Adam and Beyond
-(default: FALSE)
    
-
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    Examples
    
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diff --git a/docs/reference/optim_required.html b/docs/reference/optim_required.html
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-Dummy value indicating a required value. — optim_required • torch
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    Dummy value indicating a required value.
    
-    Source: R/optim.R
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    export
    
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diff --git a/docs/reference/optim_sgd.html b/docs/reference/optim_sgd.html
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-    
    SGD optimizer
    
-    Source: R/optim-sgd.R
-    
-    
    
-
-    
    
-    
    Implements stochastic gradient descent (optionally with momentum).
-Nesterov momentum is based on the formula from
-On the importance of initialization and momentum in deep learning.
    
-    
    
-
-    
    optim_sgd(
-  params,
-  lr = optim_required(),
-  momentum = 0,
-  dampening = 0,
-  weight_decay = 0,
-  nesterov = FALSE
-)
    
-
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    Arguments
    
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    params
    (iterable): iterable of parameters to optimize or dicts defining
-parameter groups
    lr
    (float): learning rate
    momentum
    (float, optional): momentum factor (default: 0)
    dampening
    (float, optional): dampening for momentum (default: 0)
    weight_decay
    (float, optional): weight decay (L2 penalty) (default: 0)
    nesterov
    (bool, optional): enables Nesterov momentum (default: FALSE)
    
-
-    
    Note
    
-
-    
-
-
-
    The implementation of SGD with Momentum-Nesterov subtly differs from
-Sutskever et. al. and implementations in some other frameworks.
    
-
    Considering the specific case of Momentum, the update can be written as
-$$
-  \begin{array}{ll}
-v_{t+1} & = \mu * v_{t} + g_{t+1}, \\
-p_{t+1} & = p_{t} - \mbox{lr} * v_{t+1},
-\end{array}
-$$
    
-
    where \(p\), \(g\), \(v\) and \(\mu\) denote the
-parameters, gradient, velocity, and momentum respectively.
    
-
    This is in contrast to Sutskever et. al. and
-other frameworks which employ an update of the form
    
-
    $$
-  \begin{array}{ll}
-v_{t+1} & = \mu * v_{t} + \mbox{lr} * g_{t+1}, \\
-p_{t+1} & = p_{t} - v_{t+1}.
-\end{array}
-$$
-The Nesterov version is analogously modified.
    
-
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    Examples
    
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diff --git a/docs/reference/tensor_dataset.html b/docs/reference/tensor_dataset.html
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-
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-    Source: R/utils-data.R
-    
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-    
    Each sample will be retrieved by indexing tensors along the first dimension.
    
-    
    
-
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    tensor_dataset(...)
    
-
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    ...
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diff --git a/docs/reference/torch_abs.html b/docs/reference/torch_abs.html
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-Abs — torch_abs • torch
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    Abs
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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-    
    Abs
    
-    
    
-
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    Arguments
    
-    
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-    
-    
    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
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    abs(input, out=None) -> Tensor 
    
-
-    
-
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-
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-
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-    \mbox{out}_{i} = |\mbox{input}_{i}|
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_abs(torch_tensor(c(-1, -2, 3)))
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{3} ]
    # }
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diff --git a/docs/reference/torch_acos.html b/docs/reference/torch_acos.html
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-Acos — torch_acos • torch
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    Acos
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Acos
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
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-
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-
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-
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-    \mbox{out}_{i} = \cos^{-1}(\mbox{input}_{i})
-$$
    
-
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    Examples
    
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    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.6137
-#>  1.1598
-#>  0.0958
-#> -0.2733
-#> [ CPUFloatType{4} ]
    torch_acos(a)
    #> torch_tensor 
-#>  2.2315
-#>     nan
-#>  1.4748
-#>  1.8476
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_adaptive_avg_pool1d.html b/docs/reference/torch_adaptive_avg_pool1d.html
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-    
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-
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-    
    Adaptive_avg_pool1d
    
-    
    
-
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    Arguments
    
-    
-    
-    
-      
-      
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-    
    output_size
    NA the target output size (single integer)
    
-
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    adaptive_avg_pool1d(input, output_size) -> Tensor 
    
-
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-
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    value
    (Number) the number to be added to each element of input
    other
    (Tensor) the second input tensor
    alpha
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-
-    
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-
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-
-
-
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-and returns a new resulting tensor.
    
-
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-    \mbox{out} = \mbox{input} + \mbox{other}
-$$
-If input is of type FloatTensor or DoubleTensor, other must be
-a real number, otherwise it should be an integer.
    
-    
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-
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-alpha and added to each element of the tensor input.
-The resulting tensor is returned.
    
-
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-broadcastable .
    
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-$$
-If other is of type FloatTensor or DoubleTensor, alpha must be
-a real number, otherwise it should be an integer.
    
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    Examples
    
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    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.2160
-#>  0.1973
-#> -0.1795
-#> -0.9024
-#> [ CPUFloatType{4} ]
    torch_add(a, 20)
    #> torch_tensor 
-#>  20.2160
-#>  20.1973
-#>  19.8204
-#>  19.0976
-#> [ CPUFloatType{4} ]
    
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -1.6998
-#> -0.1848
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-#> [ CPUFloatType{4} ]
    b = torch_randn(c(4, 1))
-b
    #> torch_tensor 
-#>  0.9213
-#>  0.5193
-#>  0.3855
-#> -1.5317
-#> [ CPUFloatType{4,1} ]
    torch_add(a, b)
    #> torch_tensor 
-#> -0.7785  0.7364  0.4865  0.1738
-#> -1.1805  0.3345  0.0845 -0.2282
-#> -1.3142  0.2007 -0.0492 -0.3619
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-#> [ CPUFloatType{4,4} ]
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diff --git a/docs/reference/torch_addbmm.html b/docs/reference/torch_addbmm.html
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    batch1
    (Tensor) the first batch of matrices to be multiplied
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    input
    (Tensor) matrix to be added
    alpha
    (Number, optional) multiplier for batch1 @ batch2 (\(\alpha\))
    out
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-in batch1 and batch2,
-with a reduced add step (all matrix multiplications get accumulated
-along the first dimension).
-input is added to the final result.
    
-
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-same number of matrices.
    
-
    If batch1 is a \((b \times n \times m)\) tensor, batch2 is a
-\((b \times m \times p)\) tensor, input must be
-broadcastable  with a \((n \times p)\) tensor
-and out will be a \((n \times p)\) tensor.
    
-
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-    out = \beta\ \mbox{input} + \alpha\ (\sum_{i=0}^{b-1} \mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha
-must be real numbers, otherwise they should be integers.
    
-
-    
    Examples
    
-    
    # \dontrun{
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-M = torch_randn(c(3, 5))
-batch1 = torch_randn(c(10, 3, 4))
-batch2 = torch_randn(c(10, 4, 5))
-torch_addbmm(M, batch1, batch2)
    #> torch_tensor 
-#>   5.7025   7.7808   5.3946   0.1290   2.4487
-#>  -1.9730  -3.0379  -5.4090   0.6009  -3.1469
-#>   4.6785   6.4997 -11.4732   6.3957  10.2272
-#> [ CPUFloatType{3,5} ]
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diff --git a/docs/reference/torch_addcdiv.html b/docs/reference/torch_addcdiv.html
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    (Tensor) the tensor to be added
    tensor1
    (Tensor) the numerator tensor
    tensor2
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    value
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    out
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-multiply the result by the scalar value and add it to input.
    
-    
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-addcdiv will perform a true division of tensor1 and tensor2.
-The current addcdiv behavior can be replicated using torch_floor_divide()
-for integral inputs
-(input + value * tensor1 // tensor2)
-and torch_div() for float inputs
-(input + value * tensor1 / tensor2).
-The new addcdiv behavior can be implemented with torch_true_divide()
-(input + value * torch.true_divide(tensor1,
-tensor2).
    
-
    $$
-    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \frac{\mbox{tensor1}_i}{\mbox{tensor2}_i}
-$$
    
-
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-broadcastable .
    
-
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-a real number, otherwise an integer.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-t = torch_randn(c(1, 3))
-t1 = torch_randn(c(3, 1))
-t2 = torch_randn(c(1, 3))
-torch_addcdiv(t, t1, t2, 0.1)
    #> torch_tensor 
-#> -0.3050 -0.1424  0.5617
-#> -0.3424  0.0045  1.0519
-#> -0.2932 -0.1885  0.4079
-#> [ CPUFloatType{3,3} ]
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-and add it to input.
    
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-The shapes of tensor, tensor1, and tensor2 must be
-broadcastable .
    
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-a real number, otherwise an integer.
    
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    Examples
    
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    # \dontrun{
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-t = torch_randn(c(1, 3))
-t1 = torch_randn(c(3, 1))
-t2 = torch_randn(c(1, 3))
-torch_addcmul(t, t1, t2, 0.1)
    #> torch_tensor 
-#> -0.2008 -0.8560  0.9351
-#> -0.1664 -0.8433  0.9042
-#> -0.5670 -0.9908  1.2630
-#> [ CPUFloatType{3,3} ]
    # }
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diff --git a/docs/reference/torch_addmm.html b/docs/reference/torch_addmm.html
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-mat1 and mat2 and the added matrix input respectively.
    
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-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat1}_i \mathbin{@} \mbox{mat2}_i)
-$$
-For inputs of type FloatTensor or DoubleTensor, arguments beta and
-alpha must be real numbers, otherwise they should be integers.
    
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-    
    # \dontrun{
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-M = torch_randn(c(2, 3))
-mat1 = torch_randn(c(2, 3))
-mat2 = torch_randn(c(3, 3))
-torch_addmm(M, mat1, mat2)
    #> torch_tensor 
-#> -1.4411  0.9520  5.5685
-#>  2.0314  0.6255  2.2542
-#> [ CPUFloatType{2,3} ]
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diff --git a/docs/reference/torch_addmv.html b/docs/reference/torch_addmv.html
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-$$
-For inputs of type FloatTensor or DoubleTensor, arguments beta and
-alpha must be real numbers, otherwise they should be integers
    
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    # \dontrun{
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-mat = torch_randn(c(2, 3))
-vec = torch_randn(c(3))
-torch_addmv(M, mat, vec)
    #> torch_tensor 
-#>  1.9265
-#>  1.5524
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    # }
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-M = torch_zeros(c(3, 2))
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    rtol
    (float, optional) relative tolerance. Default: 1e-05
    equal_nan
    (bool, optional) if True, then two NaN s will be compared as equal. Default: False
    
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-    \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
-$$
-elementwise, for all elements of input and other. The behaviour of this function is analogous to
-numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>_
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08)))
    #> [1] FALSE
    torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09)))
    #> [1] FALSE
    torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)))
    #> [1] FALSE
    torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)), equal_nan=TRUE)
    #> [1] TRUE
    # }
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diff --git a/docs/reference/torch_angle.html b/docs/reference/torch_angle.html
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    out
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    angle(input, out=None) -> Tensor 
    
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-
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-    \mbox{out}_{i} = angle(\mbox{input}_{i})
-$$
    
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    Examples
    
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diff --git a/docs/reference/torch_arange.html b/docs/reference/torch_arange.html
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    (Number) the starting value for the set of points. Default: 0.
    end
    (Number) the ending value for the set of points
    step
    (Number) the gap between each pair of adjacent points. Default: 1.
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). If dtype is not given, infer the data type from the other input        arguments. If any of start, end, or stop are floating-point, the        dtype is inferred to be the default dtype, see        ~torch.get_default_dtype. Otherwise, the dtype is inferred to        be torch.int64.
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-with values from the interval [start, end) taken with common difference
-step beginning from start.
    
-
    Note that non-integer step is subject to floating point rounding errors when
-comparing against end; to avoid inconsistency, we advise adding a small epsilon to end
-in such cases.
    
-
    $$
-    \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_arange(start = 0, end = 5)
    #> torch_tensor 
-#>  0
-#>  1
-#>  2
-#>  3
-#>  4
-#> [ CPUFloatType{5} ]
    torch_arange(1, 4)
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{3} ]
    torch_arange(1, 2.5, 0.5)
    #> torch_tensor 
-#>  1.0000
-#>  1.5000
-#>  2.0000
-#> [ CPUFloatType{3} ]
    # }
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diff --git a/docs/reference/torch_argmax.html b/docs/reference/torch_argmax.html
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-documentation for the exact semantics of this method.
    
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diff --git a/docs/reference/torch_argmin.html b/docs/reference/torch_argmin.html
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    (Tensor) the input tensor.
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    Returns the indices of the minimum values of a tensor across a dimension.
    
-
    This is the second value returned by torch_min. See its
-documentation for the exact semantics of this method.
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  1.6530 -1.9398 -0.7858 -0.6979
-#>  1.3467  2.4378  2.4695 -0.0903
-#>  0.5428 -0.8464 -0.8918 -0.2703
-#>  1.0460 -0.3144  0.2131 -0.1355
-#> [ CPUFloatType{4,4} ]
    torch_argmin(a)
    #> torch_tensor 
-#> 1
-#> [ CPULongType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.3917 -1.7360  2.1245 -0.4908
-#> -1.2249 -0.1974  0.3145 -1.2540
-#>  2.5169  0.8670  1.2077  0.5393
-#>  0.2843 -0.6558 -0.7945  1.3721
-#> [ CPUFloatType{4,4} ]
    torch_argmin(a, dim=1)
    #> torch_tensor 
-#>  1
-#>  0
-#>  3
-#>  1
-#> [ CPULongType{4} ]
    # }
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diff --git a/docs/reference/torch_argsort.html b/docs/reference/torch_argsort.html
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    input
    (Tensor) the input tensor.
    dim
    (int, optional) the dimension to sort along
    descending
    (bool, optional) controls the sorting order (ascending or descending)
    
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-order by value.
    
-
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-for the exact semantics of this method.
    
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#> -0.8250 -0.5984 -1.2454  0.4598
-#> -0.9256  0.0695 -1.6829  1.5544
-#>  2.1622  0.7200  0.7667 -0.4872
-#>  1.1699  0.8607  2.5965  0.0434
-#> [ CPUFloatType{4,4} ]
    torch_argsort(a, dim=1)
    #> torch_tensor 
-#>  1  0  1  2
-#>  0  1  0  3
-#>  3  2  2  0
-#>  2  3  3  1
-#> [ CPULongType{4,4} ]
    # }
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diff --git a/docs/reference/torch_as_strided.html b/docs/reference/torch_as_strided.html
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    input
    (Tensor) the input tensor.
    size
    (tuple or ints) the shape of the output tensor
    stride
    (tuple or ints) the stride of the output tensor
    storage_offset
    (int, optional) the offset in the underlying storage of the output tensor
    
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-size, stride and storage_offset.
    
-    
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-location. As a result, in-place operations (especially ones that are
-vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.
    Many PyTorch functions, which return a view of a tensor, are internally
-implemented with this function. Those functions, like
-`torch_Tensor.expand`, are easier to read and are therefore more
-advisable to use.
-
    
-
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    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 3))
-x
    #> torch_tensor 
-#> -1.5576  0.5216 -0.6254
-#>  0.5108 -0.2964  0.5801
-#> -0.7827  0.2806  0.0976
-#> [ CPUFloatType{3,3} ]
    t = torch_as_strided(x, list(2, 2), list(1, 2))
-t
    #> torch_tensor 
-#> -1.5576 -0.6254
-#>  0.5216  0.5108
-#> [ CPUFloatType{2,2} ]
    t = torch_as_strided(x, list(2, 2), list(1, 2), 1)
-t
    #> torch_tensor 
-#>  0.5216  0.5108
-#> -0.6254 -0.2964
-#> [ CPUFloatType{2,2} ]
    # }
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diff --git a/docs/reference/torch_asin.html b/docs/reference/torch_asin.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-    
    Asin
    
-    
    
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    out
    (Tensor, optional) the output tensor.
    
-
-    
    asin(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a new tensor with the arcsine  of the elements of input.
    
-
    $$
-    \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.3857
-#>  1.8551
-#>  0.4113
-#>  0.7013
-#> [ CPUFloatType{4} ]
    torch_asin(a)
    #> torch_tensor 
-#> -0.3959
-#>     nan
-#>  0.4239
-#>  0.7773
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_atan.html b/docs/reference/torch_atan.html
deleted file mode 100644
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--- a/docs/reference/torch_atan.html
+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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-    
    Atan
    
-    
    
-
-
-    
    Arguments
    
-    
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-      
-      
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
-    
    atan(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a new tensor with the arctangent  of the elements of input.
    
-
    $$
-    \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.7742
-#>  0.3914
-#> -0.0984
-#>  0.7190
-#> [ CPUFloatType{4} ]
    torch_atan(a)
    #> torch_tensor 
-#> -0.6588
-#>  0.3730
-#> -0.0980
-#>  0.6234
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_atan2.html b/docs/reference/torch_atan2.html
deleted file mode 100644
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--- a/docs/reference/torch_atan2.html
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-    
    Atan2
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Atan2
    
-    
    
-
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-    
    Arguments
    
-    
-    
-    
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    input
    (Tensor) the first input tensor
    other
    (Tensor) the second input tensor
    out
    (Tensor, optional) the output tensor.
    
-
-    
    atan2(input, other, out=None) -> Tensor 
    
-
-    
-
-
-
    Element-wise arctangent of \(\mbox{input}_{i} / \mbox{other}_{i}\)
-with consideration of the quadrant. Returns a new tensor with the signed angles
-in radians between vector \((\mbox{other}_{i}, \mbox{input}_{i})\)
-and vector \((1, 0)\). (Note that \(\mbox{other}_{i}\), the second
-parameter, is the x-coordinate, while \(\mbox{input}_{i}\), the first
-parameter, is the y-coordinate.)
    
-
    The shapes of input and other must be
-broadcastable .
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.7384
-#>  1.5533
-#>  0.0480
-#>  0.5090
-#> [ CPUFloatType{4} ]
    torch_atan2(a, torch_randn(c(4)))
    #> torch_tensor 
-#>  0.3252
-#>  1.4989
-#>  3.0686
-#>  1.3146
-#> [ CPUFloatType{4} ]
    # }
-
    
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diff --git a/docs/reference/torch_avg_pool1d.html b/docs/reference/torch_avg_pool1d.html
deleted file mode 100644
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+++ /dev/null
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-Avg_pool1d — torch_avg_pool1d • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-    
    Avg_pool1d
    
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    input
    NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)
    kernel_size
    NA the size of the window. Can be a single number or a      tuple (kW,)
    stride
    NA the stride of the window. Can be a single number or a tuple      (sW,). Default: kernel_size
    padding
    NA implicit zero paddings on both sides of the input. Can be a      single number or a tuple (padW,). Default: 0
    ceil_mode
    NA when True, will use ceil instead of floor to compute the        output shape. Default: False
    count_include_pad
    NA when True, will include the zero-padding in the        averaging calculation. Default: True
    
-
-    
    avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor 
    
-
-    
-
-
-
    Applies a 1D average pooling over an input signal composed of several
-input planes.
    
-
    See ~torch.nn.AvgPool1d for details and output shape.
    
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diff --git a/docs/reference/torch_baddbmm.html b/docs/reference/torch_baddbmm.html
deleted file mode 100644
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    Baddbmm
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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-    
    Baddbmm
    
-    
    
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    Arguments
    
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    input
    (Tensor) the tensor to be added
    batch1
    (Tensor) the first batch of matrices to be multiplied
    batch2
    (Tensor) the second batch of matrices to be multiplied
    beta
    (Number, optional) multiplier for input (\(\beta\))
    alpha
    (Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))
    out
    (Tensor, optional) the output tensor.
    
-
-    
    baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor 
    
-
-    
-
-
-
    Performs a batch matrix-matrix product of matrices in batch1
-and batch2.
-input is added to the final result.
    
-
    batch1 and batch2 must be 3-D tensors each containing the same
-number of matrices.
    
-
    If batch1 is a \((b \times n \times m)\) tensor, batch2 is a
-\((b \times m \times p)\) tensor, then input must be
-broadcastable  with a
-\((b \times n \times p)\) tensor and out will be a
-\((b \times n \times p)\) tensor. Both alpha and beta mean the
-same as the scaling factors used in torch_addbmm.
    
-
    $$
-    \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type FloatTensor or DoubleTensor, arguments beta and
-alpha must be real numbers, otherwise they should be integers.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-M = torch_randn(c(10, 3, 5))
-batch1 = torch_randn(c(10, 3, 4))
-batch2 = torch_randn(c(10, 4, 5))
-torch_baddbmm(M, batch1, batch2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   3.2697 -5.0643  0.0743 -0.2398 -2.5402
-#>  -0.3596 -0.1524 -2.2537 -0.6132  0.4815
-#>   1.1825 -2.2500  0.8243  1.5010 -2.4894
-#> 
-#> (2,.,.) = 
-#>   0.4770 -0.8900  3.0012  2.0244  2.9934
-#>  -2.0624 -0.7371 -0.6249 -1.4119 -1.0305
-#>   0.4525  1.1938  1.2075  2.4423  0.5840
-#> 
-#> (3,.,.) = 
-#>  -1.5483  0.0002 -0.7736 -0.1712  2.3502
-#>   1.3820  1.9069 -1.1504  2.8244 -0.5037
-#>  -0.7816  0.0485  3.1307 -0.7125  2.3957
-#> 
-#> (4,.,.) = 
-#>   3.2263  1.9973 -2.7929 -0.6880 -1.8358
-#>   3.9498  0.1835 -3.6300 -0.7907 -2.9265
-#>   1.5720 -1.5571 -0.5235  0.2169 -0.7204
-#> 
-#> (5,.,.) = 
-#>  -1.5198 -1.4044  0.6454  1.6571  1.6412
-#>   0.6481 -0.1620  0.7348 -2.5747 -1.5232
-#>  -3.9663  0.6486 -0.1782 -0.2130 -0.2005
-#> 
-#> (6,.,.) = 
-#>  -0.7923 -0.1696 -0.0210 -1.4651  0.1979
-#>  -0.2874  2.4903 -2.5324  0.1213  4.3363
-#>   0.8367  0.5843  2.6930 -0.5081 -0.7514
-#> 
-#> (7,.,.) = 
-#>   0.9376 -5.8062 -2.4161 -2.2368  1.7258
-#>  -0.5255  2.0584  1.1016 -2.1323 -1.1418
-#>  -1.8125  0.8110 -0.2142  1.9131  2.4363
-#> 
-#> (8,.,.) = 
-#>  -1.3094  0.0064 -0.5161  4.1986 -0.5380
-#>  -4.8329 -0.6216  0.9426 -3.9339 -1.2310
-#>   7.8403  0.3146 -1.0314  3.4608  2.3111
-#> 
-#> (9,.,.) = 
-#>  -0.9910 -4.0243  4.6838 -4.8655  0.7247
-#>   1.0314  0.6343  0.5493 -0.0418 -0.5915
-#>   0.1801  0.7773 -1.0913  0.2247 -2.3853
-#> 
-#> (10,.,.) = 
-#>  -1.1792  0.4361 -0.6693 -0.4414  0.9327
-#>  -4.9029 -6.8475 -4.1729 -2.2513  0.6501
-#>   1.3470  1.4167 -0.9282  0.5063 -2.4436
-#> [ CPUFloatType{10,3,5} ]
    # }
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diff --git a/docs/reference/torch_bartlett_window.html b/docs/reference/torch_bartlett_window.html
deleted file mode 100644
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Bartlett_window
    
-    
    
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    window_length
    (int) the size of returned window
    periodic
    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    Note
    
-
-    
-
    If `window_length` \eqn{=1}, the returned window contains a single value 1.
-
    
-
-    
    bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Bartlett window function.
    
-
    $$
-    w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \left\{ \begin{array}{ll}
-        \frac{2n}{N - 1} & \mbox{if } 0 \leq n \leq \frac{N - 1}{2} \\
-        2 - \frac{2n}{N - 1} & \mbox{if } \frac{N - 1}{2} < n < N \\
-    \end{array}
-    \right. ,
-$$
-where \(N\) is the full window size.
    
-
    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_bartlett_window(L, periodic=True) equal to
-torch_bartlett_window(L + 1, periodic=False)[:-1]).
    
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diff --git a/docs/reference/torch_bernoulli.html b/docs/reference/torch_bernoulli.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Bernoulli
    
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    input
    (Tensor) the input tensor of probability values for the Bernoulli distribution
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    out
    (Tensor, optional) the output tensor.
    
-
-    
    bernoulli(input, *, generator=None, out=None) -> Tensor 
    
-
-    
-
-
-
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-
    The input tensor should be a tensor containing probabilities
-to be used for drawing the binary random number.
-Hence, all values in input have to be in the range:
-\(0 \leq \mbox{input}_i \leq 1\).
    
-
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-value \(1\) according to the \(\mbox{i}^{th}\) probability value given
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-
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-$$
-The returned out tensor only has values 0 or 1 and is of the same
-shape as input.
    
-
    out can have integral dtype, but input must have floating
-point dtype.
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1)
-a
    #> torch_tensor 
-#>  0.8765  0.8092  0.3962
-#>  0.4623  0.3192  0.0298
-#>  0.7755  0.1732  0.0310
-#> [ CPUFloatType{3,3} ]
    torch_bernoulli(a)
    #> torch_tensor 
-#>  0  1  1
-#>  0  1  0
-#>  1  0  0
-#> [ CPUFloatType{3,3} ]
    a = torch_ones(c(3, 3)) # probability of drawing "1" is 1
-torch_bernoulli(a)
    #> torch_tensor 
-#>  1  1  1
-#>  1  1  1
-#>  1  1  1
-#> [ CPUFloatType{3,3} ]
    a = torch_zeros(c(3, 3)) # probability of drawing "1" is 0
-torch_bernoulli(a)
    #> torch_tensor 
-#>  0  0  0
-#>  0  0  0
-#>  0  0  0
-#> [ CPUFloatType{3,3} ]
    # }
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-input unless input is empty, in which case the result is a
-tensor of size 0. If minlength is specified, the number of bins is at least
-minlength and if input is empty, then the result is tensor of size
-minlength filled with zeros. If n is the value at position i,
-out[n] += weights[i] if weights is specified else
-out[n] += 1.
    
-
    .. include:: cuda_deterministic.rst
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-input = torch_randint(0, 8, list(5), dtype=torch_int64())
-weights = torch_linspace(0, 1, steps=5)
-input
    #> torch_tensor 
-#>  2
-#>  7
-#>  5
-#>  3
-#>  6
-#> [ CPULongType{5} ]
    weights
    #> torch_tensor 
-#>  0.0000
-#>  0.2500
-#>  0.5000
-#>  0.7500
-#>  1.0000
-#> [ CPUFloatType{5} ]
    torch_bincount(input, weights)
    #> torch_tensor 
-#>  0.0000
-#>  0.0000
-#>  0.0000
-#>  0.7500
-#>  0.0000
-#>  0.5000
-#>  1.0000
-#>  0.2500
-#> [ CPUFloatType{8} ]
    input$bincount(weights)
    #> torch_tensor 
-#>  0.0000
-#>  0.0000
-#>  0.0000
-#>  0.7500
-#>  0.0000
-#>  0.5000
-#>  1.0000
-#>  0.2500
-#> [ CPUFloatType{8} ]
    # }
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diff --git a/docs/reference/torch_bitwise_and.html b/docs/reference/torch_bitwise_and.html
deleted file mode 100644
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    NA the first input tensor
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    NA the second input tensor
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    (Tensor, optional) the output tensor.
    
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    bitwise_and(input, other, out=None) -> Tensor 
    
-
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-integral or Boolean types. For bool tensors, it computes the logical AND.
    
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diff --git a/docs/reference/torch_bitwise_not.html b/docs/reference/torch_bitwise_not.html
deleted file mode 100644
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-    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
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    bitwise_not(input, out=None) -> Tensor 
    
-
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-integral or Boolean types. For bool tensors, it computes the logical NOT.
    
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diff --git a/docs/reference/torch_bitwise_or.html b/docs/reference/torch_bitwise_or.html
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    input
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-integral or Boolean types. For bool tensors, it computes the logical OR.
    
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diff --git a/docs/reference/torch_bitwise_xor.html b/docs/reference/torch_bitwise_xor.html
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    input
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    other
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-integral or Boolean types. For bool tensors, it computes the logical XOR.
    
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diff --git a/docs/reference/torch_blackman_window.html b/docs/reference/torch_blackman_window.html
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-    
    
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    window_length
    (int) the size of returned window
    periodic
    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-
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-
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-
    
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    blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
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-
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    Blackman window function.
    
-
    $$
-    w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
-$$
-where \(N\) is the full window size.
    
-
    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_blackman_window(L, periodic=True) equal to
-torch_blackman_window(L + 1, periodic=False)[:-1]).
    
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diff --git a/docs/reference/torch_bmm.html b/docs/reference/torch_bmm.html
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    mat2
    (Tensor) the second batch of matrices to be multiplied
    out
    (Tensor, optional) the output tensor.
    
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    Note
    
-
-    
    This function does not broadcast .
-For broadcasting matrix products, see torch_matmul.
    
-    
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-\((b \times m \times p)\) tensor, out will be a
-\((b \times n \times p)\) tensor.
    
-
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-    \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
-$$
    
-
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    Examples
    
-    
    # \dontrun{
-
-input = torch_randn(c(10, 3, 4))
-mat2 = torch_randn(c(10, 4, 5))
-res = torch_bmm(input, mat2)
-res
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -1.1937  1.0490 -1.3460 -1.3636  0.2908
-#>  -0.7399 -0.3916 -0.0894  1.5547 -0.5792
-#>   1.6370 -1.8825  0.6914  0.4735  0.2958
-#> 
-#> (2,.,.) = 
-#>   3.0209 -2.4298  0.3410  0.0615 -2.5501
-#>  -3.8228  0.7082  0.9869  0.1536  1.1400
-#>   2.5718 -0.3476  1.3377  0.6290 -0.2315
-#> 
-#> (3,.,.) = 
-#>  -0.2813 -0.3510 -0.6811 -0.8482  1.3861
-#>   3.3843 -1.2077 -1.9622 -1.1351 -1.8477
-#>   2.6732 -2.4184  0.7855  2.8759 -1.4808
-#> 
-#> (4,.,.) = 
-#>  -6.8546  1.0791  2.1027 -2.8185  0.7520
-#>  -0.9041  0.8896  2.4743  0.6284  0.2519
-#>   2.6052 -1.4564 -1.6375  1.3288  0.3487
-#> 
-#> (5,.,.) = 
-#>   2.5222 -1.6164 -2.2116 -1.0754  0.7719
-#>  -3.6324  2.5302  0.9988 -2.1378  0.6788
-#>   5.6221  0.7932  2.1447  4.9035 -5.1887
-#> 
-#> (6,.,.) = 
-#>   0.2683 -1.0509  2.6643 -0.2398  0.4529
-#>  -2.3240 -3.0188  2.6981  1.3544  0.8555
-#>  -0.4469  0.3477  1.0020  4.7555  1.9801
-#> 
-#> (7,.,.) = 
-#>  -1.5234  0.5375  0.0234  2.3384 -3.3980
-#>   1.3228  3.1686  1.4053 -2.2938  7.3319
-#>   1.9968 -5.2192 -0.6723 -1.0900 -3.2833
-#> 
-#> (8,.,.) = 
-#>  -2.3741  2.0837 -0.4425 -1.5224  2.2040
-#>  -0.7937  1.1621  6.6647  0.5726  1.9161
-#>  -1.2275 -0.9221 -1.9841 -0.4629  0.8082
-#> 
-#> (9,.,.) = 
-#>  -1.4034 -0.0159 -0.7663  0.1020  0.4187
-#>  -1.1376  2.4816  1.0544  1.9942 -1.1878
-#>  -0.8727  0.0230 -0.5804  0.0939  0.1387
-#> 
-#> (10,.,.) = 
-#>  -0.4053 -0.3109 -2.1835 -0.1594  1.8523
-#>  -1.8857  1.2782  3.0087 -2.7136 -0.9552
-#>  -1.9781  1.0313  2.1664 -2.4844 -0.7711
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-y = torch_arange(0, 2)$view(c(2, 1))
-out = torch_broadcast_tensors(list(x, y))
-out[[1]]
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-#>  0  1  2
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-#> [ CPUFloatType{2,3} ]
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-a = c(1, 2, 3)
-b = c(4, 5)
-tensor_a = torch_tensor(a)
-tensor_b = torch_tensor(b)
-torch_cartesian_prod(list(tensor_a, tensor_b))
    #> torch_tensor 
-#>  1  4
-#>  1  5
-#>  2  4
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-#> [ CPUFloatType{6,2} ]
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    tensors
    (sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.
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-All tensors must either have the same shape (except in the concatenating
-dimension) or be empty.
    
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-and torch_chunk.
    
-
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-
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    Examples
    
-    
    # \dontrun{
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-x = torch_randn(c(2, 3))
-x
    #> torch_tensor 
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#> [ CPUFloatType{2,3} ]
    torch_cat(list(x, x, x), 1)
    #> torch_tensor 
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#> [ CPUFloatType{6,3} ]
    torch_cat(list(x, x, x), 2)
    #> torch_tensor 
-#>  1.5078  0.8533  1.7774  1.5078  0.8533  1.7774  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703 -0.7864  1.4110  0.6703 -0.7864  1.4110  0.6703
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-#>  0.9465
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    torch_ceil(a)
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diff --git a/docs/reference/torch_celu_.html b/docs/reference/torch_celu_.html
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-using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
-of arithmetic operations ([CLRS]_). Note that since this is a function to compute the product, \(N\)
-needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
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-c = torch_randn(c(5, 6))
-d = torch_randn(c(6, 7))
-torch_chain_matmul(list(a, b, c, d))
    #> torch_tensor 
-#>   2.2025   6.9263 -12.0433  -1.8318   6.1157  -1.9091  -2.5474
-#>  -9.2675   3.6580 -10.9555   3.7499  -0.9984  -2.1468  18.3629
-#>  -4.3318 -10.0159  20.3315   2.5116  -9.5372   3.4920   7.3516
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    input
    (Tensor) the input tensor \(A\) of size \((*, n, n)\) where * is zero or more                batch dimensions consisting of symmetric positive-definite matrices.
    upper
    (bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: False
    out
    (Tensor, optional) the output matrix
    
-
-    
    cholesky(input, upper=False, out=None) -> Tensor 
    
-
-    
-
-
-
    Computes the Cholesky decomposition of a symmetric positive-definite
-matrix \(A\) or for batches of symmetric positive-definite matrices.
    
-
    If upper is True, the returned matrix U is upper-triangular, and
-the decomposition has the form:
    
-
    $$
-  A = U^TU
-$$
-If upper is False, the returned matrix L is lower-triangular, and
-the decomposition has the form:
    
-
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-    A = LL^T
-$$
-If upper is True, and \(A\) is a batch of symmetric positive-definite
-matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
-of each of the individual matrices. Similarly, when upper is False, the returned
-tensor will be composed of lower-triangular Cholesky factors of each of the individual
-matrices.
    
-
-    
    Examples
    
-    
    
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diff --git a/docs/reference/torch_cholesky_inverse.html b/docs/reference/torch_cholesky_inverse.html
deleted file mode 100644
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+++ /dev/null
@@ -1,232 +0,0 @@
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor
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    (Tensor, optional) the output tensor for inv
    
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-
-    
-
-
-
    Computes the inverse of a symmetric positive-definite matrix \(A\) using its
-Cholesky factor \(u\): returns matrix inv. The inverse is computed using
-LAPACK routines dpotri and spotri (and the corresponding MAGMA routines).
    
-
    If upper is False, \(u\) is lower triangular
-such that the returned tensor is
    
-
    $$
-    inv = (uu^{{T}})^{{-1}}
-$$
-If upper is True or not provided, \(u\) is upper
-triangular such that the returned tensor is
    
-
    $$
-    inv = (u^T u)^{{-1}}
-$$
    
-
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    Examples
    
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    (Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions
    input2
    (Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor
    upper
    (bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: False.
    out
    (Tensor, optional) the output tensor for c
    
-
-    
    cholesky_solve(input, input2, upper=False, out=None) -> Tensor 
    
-
-    
-
-
-
    Solves a linear system of equations with a positive semidefinite
-matrix to be inverted given its Cholesky factor matrix \(u\).
    
-
    If upper is False, \(u\) is and lower triangular and c is
-returned such that:
    
-
    $$
-    c = (u u^T)^{{-1}} b
-$$
-If upper is True or not provided, \(u\) is upper triangular
-and c is returned such that:
    
-
    $$
-    c = (u^T u)^{{-1}} b
-$$
-torch_cholesky_solve(b, u) can take in 2D inputs b, u or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs c
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-a = torch_mm(a, a$t()) # make symmetric positive definite
-u = torch_cholesky(a)
-a
    #> torch_tensor 
-#>  4.8833 -0.7896 -0.4785
-#> -0.7896  1.0348 -0.2048
-#> -0.4785 -0.2048  0.8552
-#> [ CPUFloatType{3,3} ]
    b = torch_randn(c(3, 2))
-b
    #> torch_tensor 
-#>  0.5712 -0.1153
-#> -1.2014  0.0291
-#>  1.1547  0.9237
-#> [ CPUFloatType{3,2} ]
    torch_cholesky_solve(b, u)
    #> torch_tensor 
-#>  0.0975  0.1667
-#> -0.8489  0.4068
-#>  1.2015  1.2708
-#> [ CPUFloatType{3,2} ]
    torch_mm(a$inverse(), b)
    #> torch_tensor 
-#>  0.0975  0.1667
-#> -0.8489  0.4068
-#>  1.2015  1.2708
-#> [ CPUFloatType{3,2} ]
    # }
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    input
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-
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Clamp
    
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    (Tensor) the input tensor.
    min
    (Number) lower-bound of the range to be clamped to
    max
    (Number) upper-bound of the range to be clamped to
    out
    (Tensor, optional) the output tensor.
    value
    (Number) minimal value of each element in the output
    
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    clamp(input, min, max, out=None) -> Tensor 
    
-
-    
-
-
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-a resulting tensor:
    
-
    $$
-    y_i = \left\{ \begin{array}{ll}
-        \mbox{min} & \mbox{if } x_i < \mbox{min} \\
-        x_i & \mbox{if } \mbox{min} \leq x_i \leq \mbox{max} \\
-        \mbox{max} & \mbox{if } x_i > \mbox{max}
-    \end{array}
-    \right.
-$$
-If input is of type FloatTensor or DoubleTensor, args min
-and max must be real numbers, otherwise they should be integers.
    
-    
    clamp(input, *, min, out=None) -> Tensor 
    
-
-    
-
-
-
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-
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-
-    
-
-
-
    Clamps all elements in input to be smaller or equal max.
    
-
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-should be a real number, otherwise it should be an integer.
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.9506
-#>  2.2284
-#> -0.7040
-#> -0.4355
-#> [ CPUFloatType{4} ]
    torch_clamp(a, min=-0.5, max=0.5)
    #> torch_tensor 
-#> -0.5000
-#>  0.5000
-#> -0.5000
-#> -0.4355
-#> [ CPUFloatType{4} ]
    
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.9982
-#> -1.4524
-#> -1.4201
-#>  0.5077
-#> [ CPUFloatType{4} ]
    torch_clamp(a, min=0.5)
    #> torch_tensor 
-#>  0.9982
-#>  0.5000
-#>  0.5000
-#>  0.5077
-#> [ CPUFloatType{4} ]
    
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  1.9805
-#> -1.3783
-#>  0.7469
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-#> [ CPUFloatType{4} ]
    torch_clamp(a, max=0.5)
    #> torch_tensor 
-#>  0.5000
-#> -1.3783
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    # }
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    input
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    r
    (int, optional) number of elements to combine
    with_replacement
    (boolean, optional) whether to allow duplication in combination
    
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-python's itertools.combinations when with_replacement is set to False, and
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-
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    Examples
    
-    
    # \dontrun{
-
-a = c(1, 2, 3)
-tensor_a = torch_tensor(a)
-torch_combinations(tensor_a)
    #> torch_tensor 
-#>  1  2
-#>  1  3
-#>  2  3
-#> [ CPUFloatType{3,2} ]
    torch_combinations(tensor_a, r=3)
    #> torch_tensor 
-#>  1  2  3
-#> [ CPUFloatType{1,3} ]
    torch_combinations(tensor_a, with_replacement=TRUE)
    #> torch_tensor 
-#>  1  1
-#>  1  2
-#>  1  3
-#>  2  2
-#>  2  3
-#>  3  3
-#> [ CPUFloatType{6,2} ]
    # }
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diff --git a/docs/reference/torch_conj.html b/docs/reference/torch_conj.html
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    out
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    NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)
    weight
    NA filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)
    bias
    NA optional bias of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or      a one-element tuple (sW,). Default: 1
    padding
    NA implicit paddings on both sides of the input. Can be a      single number or a one-element tuple (padW,). Default: 0
    dilation
    NA the spacing between kernel elements. Can be a single number or      a one-element tuple (dW,). Default: 1
    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1
    
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-planes.
    
-
    See ~torch.nn.Conv1d for details and output shape.
    
-
    .. include:: cudnn_deterministic.rst
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-filters = torch_randn(c(33, 16, 3))
-inputs = torch_randn(c(20, 16, 50))
-nnf_conv1d(inputs, filters)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 8   8.7199   1.0094 -13.1435   0.0848  10.3420   0.6087   4.9930  -1.3194
-#>    4.9928  -0.9228  -0.9986   6.1375  -2.7563   6.2688  -4.3247   1.3281
-#>   -9.2853   2.2857   5.2282  -3.3548  -1.1207   0.3963   0.6191   9.8601
-#>   -2.9242  10.2787   0.3779   3.2870  -0.1333   5.6178   6.8783   0.1472
-#>   -3.6903  -1.2163  11.9645  -7.1134  -7.8627   8.8960  -5.3701   6.3564
-#>    6.0586 -13.3361   8.7976   5.4943  -0.7770  16.2518  -1.2318  -3.5980
-#>   -2.6689   7.9637  -3.4276  -8.6586 -11.7605   7.9358   3.0729  -3.9882
-#>    4.5930  -2.0741  -3.8087   2.1398   0.5135  -4.2471  -6.8810  -0.7988
-#>    8.2812   1.7671   0.9025  -1.8145  -4.3674  -2.3088   0.6176   3.8401
-#>    9.8749   2.9536   2.1752   0.1801  -4.7035  -4.8083  -2.0966   7.7104
-#>   -4.8894  -5.0632  10.0122 -17.8657  14.6597  -3.3046 -14.7129   8.5806
-#>   -8.5251 -10.4482  13.8896   8.8005   1.1426  10.7473  -8.9987  -1.4510
-#>   -0.4190   6.8991  -5.3740   4.5935 -17.9217  -4.3224  -7.6038   5.2284
-#>    4.8968   7.2210  -3.0931   1.1218  -3.9569  -6.7213   7.2451   0.0891
-#>    2.0933   2.8404  -0.0990   2.7687  -4.0503  12.9643   9.7891   6.1938
-#>   -0.1327   4.4571  -4.0763   0.9068  -4.0663 -10.0672  -0.0225   1.3389
-#>   -9.7440  14.3030  -2.2208   2.3737   4.2655  -4.8818  -6.3157 -14.4964
-#>   -3.8432 -18.8804   5.3663   1.4313  -1.3751  14.9081  -9.7717  -4.8676
-#>   -3.7739   0.8794   1.7302 -10.1387   2.1366 -12.9728  -9.7771  22.9757
-#>    2.7529   9.6604  -0.1306  -3.2509  -3.3979  -0.0270   9.5813   1.2052
-#>   -1.1247  -1.7801   1.5065  -2.9020   6.6147   0.8074  -6.8228  -8.4092
-#>   10.0025   6.9134  -9.1085   8.0784 -11.5030  -9.0789  10.6144  -9.2909
-#>   -7.9290   6.5677   6.5561  -1.8320  11.5627  -5.7025   6.3629   4.9404
-#>  -11.5437   8.3430 -13.9013  -4.2953   6.8770 -10.3218   6.7874   2.5812
-#>   -1.8688  -0.4796  -2.9052   1.1717  10.4609   3.9477   2.4715   1.7198
-#>   -2.8262   7.9209  -3.2288  -6.6791  -5.2385  -4.3199   2.6352   2.9522
-#>    6.2078  -0.4560   6.5245  -3.6146  -1.6950  14.1103   5.0830  -5.4658
-#>  -11.5664   6.8556   2.3652   4.2702   1.0913  -5.0231   1.3486 -16.3077
-#>    8.8823  -0.4775  -1.9547  10.6024   1.7667  -2.6328  12.5331   9.6971
-#>  -11.2919  12.3110   6.9462  -8.8800  -1.5003 -14.4211  -7.1624   7.2424
-#>    2.9990  -6.5829  10.0200   5.3001   2.1273  -3.6609 -12.9631   7.9098
-#>   -3.6909  11.1726   7.8032   2.4063   7.1658   4.1017   4.1518   0.2259
-#>    1.0179   6.3694  -1.5287   5.6457  -8.5666  -2.8587   5.0281   7.5907
-#> 
-#> Columns 9 to 16   2.7100  12.4777  -0.7324  -7.0967  -8.4348   0.8085   6.8330   0.4832
-#>   -8.0836  -5.8170   3.4022  -9.3509  10.5116  -4.9803   6.4073   0.3220
-#>    0.1177  -4.8427  -9.6902  -4.1170   4.2751   4.1760   7.5186  -2.3325
-#>   -0.2762 -10.6201  -1.7163   0.3247  21.2705  -3.6339   2.3409  -1.9524
-#>   -5.4666   1.8799   7.4607   6.1014  -5.0309  -0.7846  -7.0460  -4.5347
-#>  -14.8343   1.0224   2.7581   1.0455  -8.7523   6.5568   2.4569   0.8886
-#>    1.1306   8.4311   6.7053   7.2054   4.8025   0.7849  12.7978  10.1318
-#>    1.7782   0.2690  -2.7999   2.6726  -4.2082  -5.3613  13.9660  -4.6798
-#>    2.3140  -8.0598  12.3022   4.7510   0.3521   0.5352  -5.9258   3.5649
-#>   -3.9661  -1.8614   1.3968  -5.5604   3.0089  -9.1651  -4.2411   1.4253
-#>   -6.1841  17.0519   3.7948   8.4733 -14.7091   8.7843  -6.6218   4.4110
-#>    1.9766   7.1204 -14.9206 -10.7534  -8.4386  -3.9708   4.7609 -10.6990
-#>   15.3699  -7.0543   5.9875   2.9363   9.7296  -1.8657   3.1239  -3.5793
-#>   -3.2610   3.2806   7.3320   9.7861  -1.4700   4.6751  -1.0152   5.4633
-#>    4.6915  -7.1968   1.9738   9.5179  -6.9344   6.6227  -1.7615  -4.0994
-#>   12.9562  -5.4254  -4.4734  -3.8013   1.8657   8.0770 -13.3441   3.3371
-#>   -8.7960   1.4661   7.6683 -12.3310  11.6751 -13.0036  -1.9325  -2.9310
-#>   -8.7788  10.2001   7.3673  -9.2273  -6.8593  -1.2400   0.5811  -6.0189
-#>    2.1427   6.4209   0.6752   9.6093   6.0686   2.9096   9.4132   6.9639
-#>   -6.5691   5.4340  -2.0061 -11.7639  -5.7487 -12.4004  12.9011   3.9542
-#>   -7.6302   3.3180  -3.8970 -15.9881  -3.6270  -1.2098   1.5831  -4.1935
-#>   11.1702  -0.4212   1.5514   3.8853   4.7498  13.8274  -3.8186   8.3544
-#>   -9.0928   2.6163  -5.1443   9.0578   6.0795  -3.2900   0.8558   5.4144
-#>   -3.7635  14.0854  -0.3645  -2.8304  11.9087   6.8293   0.3143   4.1031
-#>   -3.6399   3.6001   0.3297 -10.6195 -22.8159  -4.6401  -6.4364  -2.2262
-#>    4.5382   0.7714  -5.7943  15.3865  -6.2597   2.9687   7.7204  -7.6626
-#>   -1.1954   1.3419   3.5047   6.7636   0.5723  -3.9505   2.3417   0.7854
-#>   -3.7300   4.1058  -8.3983   2.9162 -12.2176  10.6979 -12.7928  -4.0314
-#>   -6.2145  -8.7189 -11.9263  -8.0741 -15.0889 -15.3207   3.2689  -9.3045
-#>   -3.6201  -1.4019  -4.1480   8.8221   3.6578  -1.4179 -12.9980  -2.9236
-#>    3.1550  -3.8547  -9.1263   5.0425   6.9819  -7.1466  -8.7011 -15.4836
-#>    3.3937 -11.3399  -8.4114  -9.2620   2.7354 -16.1142  -0.4008  -9.2902
-#>   14.1278 -11.2060   0.8862  -2.8045  -7.4519   9.9396  -3.1323   0.3984
-#> 
-#> Columns 17 to 24   3.1652   2.7114   3.4294   3.0415  -4.4903   7.3292  -7.9222   5.6389
-#>    1.4022  -2.9618   4.4276   5.1812  -3.8627  -7.9557   1.3882  -5.7022
-#>   -0.7707   0.4761   1.1342 -13.3373   0.1454  -9.6590 -10.9706  -8.7114
-#>    2.3547   3.2183  11.7203  -4.3086 -10.5229   7.3772  -9.7069 -10.1956
-#>    6.7410 -11.6240  -0.2382   4.6611   7.6330  -1.6570   3.7988  14.0638
-#>    6.2723   5.4443   5.0403 -11.4443   9.5750   9.6919  -5.5292   4.9020
-#>    3.5478   9.5459  -7.2635   0.9142   7.1907   0.9225  -0.5947   9.1271
-#>   -7.7508  13.0199   0.7921  -3.7426  -1.0259  -3.3604  -5.9647   5.9305
-#>    9.3377   0.7645  -0.9862  -2.8337  -1.9219  -2.1858  -2.1245  -5.5627
-#>   -0.3122  -6.7192   1.9777   6.8633  12.8850  -9.8542   2.4686  -7.5124
-#>   11.0900 -14.9795   3.5174  -3.2619  16.1453  -6.4522  12.5797  12.6404
-#>   -5.9324  -2.8032   5.1964  -1.0824  -1.9832  11.7757  -1.7279 -12.9944
-#>   -0.9892   1.0205   2.8183  -7.3203   2.3917  -2.5111   1.4563  -3.8504
-#>    7.3162   1.6155   5.1525 -12.5349  11.1861  -7.4035  -3.2327  -2.9030
-#>    7.1859   5.1134  -0.2741  -4.0530  -1.2997  -3.2875  -5.5183  15.7646
-#>   -8.1788   2.5854   5.3686  -2.9653   8.9160  -4.7501  -5.7845  -0.0098
-#>  -12.0708  -1.6919  -3.5657   2.9483  10.7586   0.2813   2.4652  -9.4908
-#>    0.5983  -5.5621  -1.6855  12.0777   7.8527   6.7515  -3.6835  -2.2488
-#>    4.0004  13.8937  17.0219 -16.7366   1.2763  -9.2189  12.1063   6.0094
-#>   -3.6126  -3.3000  -1.6039  10.1845  -2.7015 -11.2980   8.8211  -1.4717
-#>   -4.4065  -2.8283   5.3857   7.3212 -15.1150  -6.8182  -3.2339 -11.4930
-#>    5.9961  14.4878  -7.1496   3.3353  -8.7106   1.7533  -4.1133  -3.2559
-#>    7.3600  19.3142   8.8778  -5.9481   8.3435   0.9410  11.6001   3.9670
-#>   10.8095  -7.0179  13.0225  -2.6706   6.3683  -3.4559  24.2514 -12.8641
-#>    3.2058  -7.5246   7.2564   2.0544  -1.8178  -7.5897   7.9397  -2.8945
-#>   -5.4792  13.4542   5.1535  -3.1458   4.5727   7.0280  -3.7451  19.4841
-#>    4.7460  -2.8855 -13.2574  -4.7093  -8.3299  -5.1462  -5.0900   8.6444
-#>  -10.8531  -6.6294  -9.2172  -6.9031  14.4248  -4.8154  -2.9415   1.8398
-#>   -6.2820  -4.4764  -2.3139   4.8700   4.6623 -12.3261  -3.9658  -0.6185
-#>    1.0061  -5.7484   3.3581   0.1569  19.8596   7.6068  -0.7496   2.7470
-#>   -2.2016   6.4260  -2.1709 -11.6076  -5.6707   0.9438  -6.6296   9.2304
-#>  -13.8891  -0.0952   2.0144  -1.3961  -1.1307   0.9833  -5.6071  -2.3964
-#>    7.1253   3.4768  -1.7254   3.0641   2.3469   4.7614   0.0285   5.2199
-#> 
-#> Columns 25 to 32  -5.7640   1.9080   1.2179  -7.9288  12.6428  -4.0817  -2.0562  11.2799
-#>   -2.2999   3.3650  -3.3909   4.6736   2.4997  -0.5393  -8.2946  11.0434
-#>    0.4672   3.7375  -6.1090   5.6665   0.1840  -1.2466   3.4214   2.1810
-#>   16.8931   1.4244  -2.1984  -6.3534   9.9076   5.2015  -8.9448   2.1251
-#>    3.4824   6.7484  -2.2112   3.6076   0.1031   6.6900   7.2862  -4.4783
-#>    1.2703  -7.0325 -10.5537   2.3520 -14.0131  -8.6101   2.5236   4.7246
-#>    6.5031   2.1143  -7.2054  -3.5723   1.5327  -2.3223  13.5636   1.2073
-#>    2.6270 -10.5701   0.3349   7.4437   1.2394 -11.9173   2.2026   1.5314
-#>    3.6801 -10.6600   7.3837  -3.9585   1.3472   3.7613 -11.5850  -5.6722
-#>   -0.3205   0.1430  -8.6536   5.9411   0.8691  -1.4013   0.8575   9.8608
-#>    6.6989   4.2341   4.1919   9.8073 -13.1589  -0.6517  10.5697  -4.4361
-#>   10.4385   1.2819 -12.3164   7.7010  -3.0824  -7.2327   3.1755   0.4660
-#>   -0.6344  11.4283   1.1485   3.0558   0.5925  -0.7444  -0.7708   4.2235
-#>   13.7591  -4.8287   2.2579  -7.3116  -9.8958  10.0413  -1.5291  -8.6872
-#>    2.3379  15.0105   6.5214   8.6823  -7.9255  -2.5331   6.9940  -9.1324
-#>   17.5126  -5.5142   8.5011  -5.0201   5.6609   9.2456  -6.1677   3.4066
-#>    2.7979  -5.5795  -8.3789  -8.7183   2.8860   1.6986   1.9887   8.5494
-#>    1.4318  -1.6808  -7.4402  -0.1204   7.6516   5.0772  13.7477   3.2697
-#>   12.2700  15.0287  -0.2295  15.0833   0.3382 -10.1724  -0.2717 -12.9545
-#>   -2.9782  -0.0518  -3.3041   0.6671  -6.4112  -0.1517  10.1593  -1.5037
-#>   -8.0853 -10.9545   3.6043  -9.2561   1.8679   5.7242 -10.7073  -2.8222
-#>    9.8356  -9.7044  16.5548  -3.7865  -4.3693   8.5636  -6.1174  -5.1056
-#>    7.9162   3.0483  -3.3383   4.8383  -7.7680  -6.6377   3.4518  -2.8278
-#>   -7.1266   7.2581  -1.7810  -8.9348   4.6869  -5.0878  -7.0578   6.4613
-#>  -14.1705  -1.9353  -5.3650   2.5194   0.2712   1.1505  -7.4139 -11.8516
-#>    6.0882   2.5719  -1.0495  12.0958   1.7734  -4.2679  16.0315  -4.3643
-#>    5.2256  -1.9650   0.6906  -2.4795  -8.6869  -5.6020 -11.8749  -6.4854
-#>    0.8150  -3.5122  -8.1720  -7.3898 -15.8716  -2.2908  14.4807  -9.1374
-#>   -5.1087  -3.3034   0.2836   4.4421  -9.0222   7.1890  -0.6107 -11.7751
-#>   -0.4007   4.7543  -6.2101  -4.1952  -6.2441  10.7278  12.6808   6.7488
-#>   12.5466   1.6176  -6.0263   5.8591  -2.0679 -16.3913  -3.3983  -2.6660
-#>   -5.7903   3.3609  -9.2619   3.9730  -6.3071  -0.9969  -5.8838  -4.9410
-#>    1.3117   1.3398  10.9463  -3.0687  -8.4949   7.5141  -6.1267   0.4574
-#> 
-#> Columns 33 to 40   5.2150  -0.8799   8.5244   8.7060   7.5206   5.6204   3.4728  -2.8515
-#>   -7.1945  -2.2905  -5.6205  -9.4417   2.0720  -7.6105   6.4689  -3.4050
-#>   -3.1793   3.3743   1.3336 -13.1295  -2.0912  -1.7854   4.5045  -2.4045
-#>  -12.8614   5.7727  -6.9273  -9.1353   4.9521  -4.8614   3.4216   3.9188
-#>   -3.9518  -1.9401   0.1467   6.3261   1.4010   8.1285  11.6837  -2.6874
-#>    3.4330  -5.8609  -5.7545   0.2943   2.2022  -3.8233   4.7208  -8.1877
-#>   -4.1200  -3.3579  -1.6158  -2.2032  -4.3891   2.5318  11.8702  -1.2812
-#>   -4.3724  -2.4815  -7.6249  -0.3565  -6.4236  -6.1582   3.4271  -2.6058
-#>  -10.7067  -2.7378  -0.1568   0.4353  -2.4969  13.3673  -4.5670   1.2707
-#>  -15.9498   4.6342  -3.5398  -2.3704   2.9344  -3.9178   7.6102   3.9654
-#>    5.2910  -6.9972  -1.8721   4.3733   1.1638   4.6027   5.1656   7.6044
-#>   -6.5072   0.3133  -9.6089  -2.3442  -2.4470  -1.7199   5.8528  -6.2555
-#>   -4.4756   2.8104   8.7608  -6.9807  -1.2307   2.2073  -9.4628  14.5121
-#>   -0.8752   0.9772  -0.6482  -1.2494   4.9640   4.4118   1.1982  -2.9184
-#>   -1.4969  -2.8102   8.4710  -4.1947  13.4710  15.7840   8.0034   6.4894
-#>   -0.2018  -2.0787   3.6000  -5.0466  -6.3977  -0.3397  -2.1683   5.1826
-#>  -10.4754  -0.3989  -2.1770   7.0205   3.4139  -9.8878  -0.6335   6.0262
-#>   -2.2778   7.0196  -4.0415  -0.7292  -0.8091  -2.1652   5.3104 -10.2415
-#>   -4.7832   6.1902  -6.4281 -16.2450  -0.6198  -1.2905   7.3274  13.9358
-#>    0.0250   0.0059   2.8493   7.5505   7.3472   3.1271   3.9622  14.2142
-#>   -7.1615  -4.8397  -3.9963  -2.8790 -11.1888  -3.5380  -8.0007 -17.4729
-#>    6.1410  -7.7925   3.5944  11.6301   6.0241   5.4524  -3.4412   1.0908
-#>    9.0182  -4.6797  -7.1141   7.6652   1.9939  -8.9450   4.6569   2.0778
-#>    8.6951  -0.7674   8.8390  -2.0409  -1.4114  -5.7077 -10.2438  -6.6126
-#>   -0.7592  -7.9487   6.5387 -19.3390   0.3024  -2.0058   3.5009  -6.7498
-#>    6.0412  -5.7854   6.8616   3.3522   2.0884   2.2317  12.6853   5.7927
-#>   -1.7431 -10.2967  -2.6837  13.5075   1.5764  -0.3518   1.4739   1.8059
-#>   11.0760 -16.0045   0.1230  -9.1960   4.6352  -1.8363   3.4795  -4.1980
-#>   -6.6729   3.4266  -1.6780 -10.0093   6.0663   0.8971  13.7264  -5.1570
-#>    2.7899  -3.6841   7.6861  -0.9843  -1.9141  -4.7011  -3.1319   7.7179
-#>   -9.1538  -4.4516  -8.9869  -2.2559   1.1936   0.6045   0.1807  -3.2672
-#>   -8.1173   5.0338  -8.0610   1.1754  -1.3898   1.5216  -1.7338   3.0696
-#>    4.5485  -4.0602  14.4818   1.3694  -0.8208   3.5496   6.0590  -6.4393
-#> 
-#> Columns 41 to 48   9.4318   8.2171   4.8322   5.6457  -2.4645  -3.5706  -0.8075   4.8571
-#>   -3.2900   0.4726  -2.3633   1.8494  -2.6713  -3.3732  -5.7716  -4.3053
-#>   -0.2045 -11.8373 -13.0920  -5.4249   2.9510  -3.4678 -15.6115  -8.6613
-#>   -1.0374  -2.1204  -9.4928  -0.0420   5.7950   3.1081  -1.5467   1.4160
-#>    2.2251  -6.1046  -3.2651   2.6999   2.5576  10.5800   0.0160  -3.9492
-#>    9.1404   4.3808   6.6542  -0.1789   0.4081   8.8088   0.6046  -0.0137
-#>    1.1672  -9.2990   3.7059   5.6774   2.8614  -2.1775  -7.2604   0.4836
-#>   -2.4184  -0.9147  -0.6182   1.0150   3.7945  -2.1332   1.4857  -7.8693
-#>   -6.0545  -8.6096   1.3435   0.2288   4.7465  -0.0030  -7.0416  -1.5885
-#>  -11.9655   0.3257  -4.6491   2.0680  -3.0774  -0.2861   8.1254  -4.1858
-#>    0.5986  12.9359  -1.3932   3.2026   4.7596   4.9578   0.6192  -0.7744
-#>    0.2748   4.1774  -1.7001  -4.7775   0.3853   0.3061  -3.2432   8.5125
-#>  -11.3921 -10.8563  -7.5818  -1.9139  -3.8404   3.6539   5.9986  -0.6856
-#>    7.9886  -7.6804  -0.7950   2.8417   4.4662   9.4306   0.1284  11.4431
-#>   -1.4610  -1.3248   0.5573  -4.3987  -2.9653  -3.4342  -0.6057  -0.4561
-#>   -1.3849  -9.5639  -2.3433   1.1506   7.7221   1.7906   3.4410   1.6867
-#>   -1.6394   5.4429  -8.0806  -0.0369   0.6217  -3.6089   7.8226   6.9275
-#>   13.5281  -1.9440   4.2859  -6.6300  -2.5808  10.4666   6.6420   5.9351
-#>   -7.1382   0.0638  -2.3715   7.4408   9.4569   0.4644  -4.9758  -6.1014
-#>   -3.6463  10.5673   2.6788   2.2272   7.1652  -0.5320 -10.1751 -13.5356
-#>    6.0480  -6.9958 -12.2500  -4.8509   2.3519  -1.9778 -14.9620   3.8193
-#>    4.9883  -8.4463  10.3489   3.0552   0.4646  -7.8505   1.3567   7.2660
-#>   13.4826  10.6962   4.5866   4.7238   8.7162   5.6177   5.2107   6.1307
-#>   -3.1070   0.8094  -0.3173   4.1823  -7.6181  -4.1219   2.5528  20.6973
-#>    4.1262   0.0812  10.5087   6.9784   5.2514   3.9877 -13.4908  -0.9355
-#>   -6.2435  -0.0535  -5.1731  -1.5473   4.3737  -7.7088  -2.1305  -1.1972
-#>   -4.0223   3.8423  -0.1538   2.1298   3.1662   4.6899  -4.0121  -8.6478
-#>    1.9742   0.8946  -9.8064  -1.7442  -1.2579  -0.8046  14.5273   9.2468
-#>   -5.3971   1.5371  -2.6447   2.3005  -2.7475  -1.9236 -11.5593  -9.2965
-#>    5.1362   3.7165  -5.0594   1.5171   6.5340   4.1253  13.0958   8.7482
-#>   -2.3735  -2.9054   4.5685   1.3250   8.4590  16.7741   3.8116   1.4827
-#>  -24.0291   1.0120  -9.4348  -3.3122   4.4437  -0.8187   1.9867  -5.7997
-#>   11.1663  -8.7557   4.2210   2.3879  -1.6097   0.6809  -6.8775  -2.3145
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 6 -7.1509e-01 -4.9349e-01  2.9598e+00  7.5520e+00  4.0061e+00 -1.3150e+00
-#>   6.1158e-01 -6.8831e+00  4.3357e+00  2.3711e+00  5.0670e+00  4.3449e+00
-#>  -5.1513e+00 -1.9952e+00  3.0471e-01 -1.4798e+00 -2.9636e+00  1.1523e+00
-#>   7.4474e-02  1.0948e+00 -6.2086e+00 -5.7473e+00  3.9698e+00 -3.6172e+00
-#>  -1.0056e+01  1.9371e+00 -1.2662e+00 -1.7345e+00 -1.1842e+01  3.6392e+00
-#>   3.7177e+00 -8.3396e-01 -6.0881e+00  4.9506e+00 -5.1576e+00 -4.8864e+00
-#>   2.7897e+00  3.9768e+00 -1.1350e+01 -5.2308e+00 -2.3766e+00  1.1203e+01
-#>   5.9575e+00  3.1428e+00 -5.9637e+00  5.7530e+00  7.2610e-01  5.6093e+00
-#>   1.1167e+00  4.4847e+00  7.6154e+00 -1.3624e+00  8.2594e+00 -9.0724e+00
-#>  -4.8284e+00 -1.0704e+01  2.0032e+00  2.9894e+00 -6.4011e-01  8.4435e+00
-#>   5.1141e+00 -1.6564e+00  4.4674e+00  6.0807e+00 -6.4299e+00  5.6468e-01
-#>   2.3772e-01  6.5366e+00  8.5935e+00  3.8245e+00 -1.4917e+01 -9.2021e-01
-#>  -5.6431e+00 -1.2715e+00 -2.1304e+00  6.5598e+00 -5.9600e+00 -3.6707e+00
-#>  -4.7215e+00 -1.9434e-01  2.5668e+00  1.0392e+01 -3.2271e+00 -6.6734e+00
-#>  -3.0612e+00 -2.3705e+00 -7.3181e-01  9.4870e+00 -7.3197e-01 -2.5297e+00
-#>  -5.7609e+00 -8.1802e+00 -7.4856e+00 -1.3887e+01  2.7613e+00 -5.9915e+00
-#>   7.5561e+00  6.5578e+00  5.6733e+00  9.0824e+00  1.1058e+01  1.6211e+01
-#>  -2.8835e+00 -4.0863e+00  2.6817e+00  6.0484e+00 -3.5239e+00  3.4253e+00
-#>   8.4178e+00 -1.9318e+00 -8.1999e+00  5.6324e+00 -4.5409e+00  6.2325e+00
-#>  -4.9633e+00  1.1019e+01  3.7780e+00  1.7794e+00 -4.6708e+00 -5.6795e-01
-#>   5.0768e+00  5.8880e+00 -2.3387e+00 -2.4749e+00  4.1631e+00  2.0668e-01
-#>   3.3728e+00  1.9753e+00  1.2435e+01 -9.1491e+00  6.8930e+00 -1.4042e+01
-#>  -2.3147e+00  1.1081e+00 -4.7888e+00  3.3629e+00 -5.7519e+00  7.9487e+00
-#>  -4.1190e+00 -6.3947e-01 -1.4461e+01 -6.3951e+00 -6.1554e+00  3.6205e+00
-#>   1.3226e+00  5.5740e+00 -4.9470e+00 -3.4072e+00  5.9575e-01 -7.4203e+00
-#>  -2.0237e+00  6.2413e+00 -1.3620e+01 -4.2507e+00  7.2215e+00  6.4567e+00
-#>   4.3053e+00  7.2772e+00  8.3178e+00  3.6260e+00 -4.1942e+00 -1.9330e+00
-#>  -1.4517e+00  7.2922e-01  4.5117e+00 -2.4268e+00  7.4411e+00  6.0721e+00
-#>  -1.0672e+01 -5.0188e+00  1.7881e-01  6.3910e+00 -4.9103e+00 -1.8536e+00
-#>  -6.9459e+00 -5.0136e+00 -2.8328e+00  4.1931e+00 -1.0879e+00  8.8608e+00
-#>  -1.3535e+00 -6.3363e-01  6.6245e+00  8.1844e+00 -4.6843e+00 -3.0662e+00
-#>  -1.7060e+00 -5.8109e+00 -2.4222e+00 -1.0309e+01 -8.6567e+00  3.7424e-01
-#>  -9.0191e+00 -5.1409e+00 -2.7915e+00 -1.0410e+01 -5.4462e+00 -1.1519e+01
-#> 
-#> Columns 7 to 12 -5.9516e+00  7.0156e+00  5.0552e+00  8.3654e-01  4.9012e+00  3.2226e+00
-#>  -2.9401e-03 -8.3668e+00 -3.8962e+00  1.0038e+01 -3.0033e+00 -8.2599e-01
-#>   3.8983e+00 -8.2420e+00 -2.4075e+00  1.6270e+00 -1.6823e+01  1.3287e+00
-#>  -1.0277e+00  1.8767e-01 -1.2692e+01  1.2294e+00 -1.2903e+01  1.2650e+01
-#>  -4.3204e+00 -8.9256e+00 -5.2164e-01 -1.7049e+00 -2.2844e+00 -8.9570e+00
-#>   5.9251e+00  2.8970e+00 -2.0009e-01 -3.1560e+00  5.2960e+00 -5.3078e+00
-#>   1.7958e+00  4.7085e-02 -7.6300e+00  8.0213e+00 -1.4238e+01 -3.1199e+00
-#>   4.0685e-01  8.8669e-01 -1.9305e+00  9.5970e+00  4.0827e+00 -5.9016e+00
-#>   3.5766e+00  3.4390e+00 -5.6902e+00 -1.2657e+01 -5.3466e+00 -3.8703e+00
-#>   1.9449e+00 -9.8307e+00  3.2450e+00  2.4425e+00 -3.7293e+00  8.7565e-01
-#>  -1.5291e+00 -8.5767e+00  6.2224e+00  4.7074e+00 -1.7078e+00  3.0609e+00
-#>  -3.9321e+00  6.1785e+00  5.0283e+00  4.1094e+00 -2.6448e+00  5.3437e-01
-#>   1.5058e+00 -1.2001e+00 -9.9301e+00  7.9571e+00 -4.7609e+00 -1.0210e+01
-#>   4.2221e+00 -3.1459e+00 -2.5754e+00  3.1745e+00 -1.0673e+01 -4.2940e+00
-#>  -1.5951e+00 -2.1660e+01 -6.0590e-01  1.2761e+00 -5.5640e+00 -4.4963e+00
-#>   3.5914e+00 -7.2424e+00 -4.2301e-01 -2.1719e+00 -5.9560e+00 -6.6360e+00
-#>   6.0457e+00  9.1813e-01  9.8854e+00  1.5404e+01  8.7826e+00 -1.0195e+01
-#>   7.1532e+00  1.1020e+01 -7.9245e-01  1.8356e-01 -1.0282e+01 -2.5896e-01
-#>   4.4396e+00 -6.3894e+00 -3.1537e+00 -1.0250e+01 -9.5840e+00 -3.5273e+00
-#>  -1.0148e+01 -6.4415e+00  7.3811e+00  1.6038e+01 -1.2954e+00 -7.3381e-01
-#>  -1.1356e+01  1.0164e+01  3.7982e+00  1.1379e+01 -1.3215e+01 -4.7850e+00
-#>  -7.0512e+00  3.6091e+00 -1.1422e+00 -7.8434e+00  8.2024e-01  3.2626e+00
-#>  -5.2845e+00 -3.2287e+00  1.0512e+01  3.9971e+00 -5.6834e+00 -9.3933e+00
-#>  -1.4016e+00  9.6409e-01  8.2726e+00  1.9828e+00  4.4276e+00  3.6313e+00
-#>   1.9920e+00 -9.7004e-01  5.1981e+00 -9.5342e+00 -5.0760e+00  3.3793e+00
-#>   2.1394e+00 -1.4262e+01  3.0598e+00  7.2985e+00  1.9970e+00 -9.2220e+00
-#>  -1.0893e+01 -2.5566e+00 -6.1387e+00  1.0846e+01  6.5455e+00  1.6814e+00
-#>   3.2075e+00 -1.5686e+01  1.4660e+01  6.6920e+00  2.2215e+00 -1.4393e+01
-#>   3.2561e+00 -3.0157e+00 -2.8714e+00 -8.9107e-02 -8.5308e-01  9.6009e-01
-#>   6.7504e+00  9.9947e+00  1.3110e+01 -1.0746e+01  8.8372e+00 -1.4285e+01
-#>   9.1524e-01  2.6967e+00  1.3832e+00  7.8529e+00  4.4973e+00  4.9536e+00
-#>  -2.6921e+00 -1.4808e+01 -2.9850e+00  1.8306e+00  2.6230e-01  4.2523e-01
-#>  -1.2366e+01 -7.5979e-01 -2.1096e+00 -6.5637e-02  2.7424e+00 -8.5872e+00
-#> 
-#> Columns 13 to 18  2.7000e+00  8.5381e+00 -8.1091e+00 -3.7162e+00  2.0213e+00 -6.0458e+00
-#>  -4.4232e+00 -1.0786e+01  1.4304e+01  7.0479e-01 -3.7465e+00 -1.9488e+00
-#>   2.8797e+00  4.6008e-01  7.6718e+00 -2.5439e+00  5.3121e+00  1.3722e+00
-#>   1.9172e+00  3.2784e+00  7.7748e+00 -1.2554e+01  6.0663e+00  3.9239e+00
-#>   1.9770e+00  1.4078e+01 -6.5417e-01  9.7094e+00 -2.4134e+00  5.5477e+00
-#>  -3.2059e+00  6.4861e-02  4.7822e+00 -6.6546e+00 -4.4499e+00 -1.5853e+00
-#>  -2.4581e+00 -1.5303e+00  4.5577e+00 -3.0146e+00  1.0402e+01  9.5618e+00
-#>  -6.2991e+00 -5.5900e+00 -5.3919e+00 -1.0916e+00  9.6959e+00 -5.6915e+00
-#>   1.0740e+01  9.5805e-01 -4.2546e+00  8.1128e+00 -7.7091e+00 -2.4287e+00
-#>  -1.1507e+00  5.1993e+00  8.7242e+00  1.1963e+01 -6.3006e-01 -3.7769e-01
-#>  -6.9138e+00 -1.0091e+01 -1.2608e+01  8.1199e+00  1.6238e+00  3.1721e+00
-#>  -1.6155e+01  7.7175e-01  1.0307e+01 -2.4856e+00  1.0481e+01 -6.2984e+00
-#>   3.8670e+00 -2.1379e+00 -1.2605e+00  2.7508e+00  7.6017e-01 -8.3812e+00
-#>   5.5454e+00  6.7137e+00 -1.7055e+00 -7.7823e+00 -3.4515e+00  4.7732e+00
-#>   7.6535e+00  9.3814e+00 -8.8797e+00 -1.1818e+01 -8.5267e+00 -1.4578e+00
-#>   2.7738e+00  1.0396e+01  3.6270e+00  3.0116e+00  2.7857e-01  1.8120e+01
-#>   3.4237e+00 -1.0749e+01  5.3512e+00  2.4723e-01 -7.9703e+00  1.9826e+01
-#>   6.5276e+00  9.1472e+00  2.5897e+00  5.6845e-01  2.4023e+00  9.8481e-01
-#>  -8.0553e+00 -1.1198e+01 -1.0056e+01  1.4838e+00  6.0982e+00  1.8073e+00
-#>   7.9459e+00 -8.7685e+00  2.4101e+00  4.0183e+00 -2.7991e-01  8.6546e+00
-#>  -1.1821e+01  4.5998e+00  6.9386e-01 -4.8410e+00  3.9323e+00  3.4028e-02
-#>   6.9009e+00  6.5700e+00 -2.3530e+00 -2.3630e+00 -3.5682e-01 -3.1213e+00
-#>  -6.3269e+00  3.8343e+00 -7.5480e+00 -1.1199e+01 -8.8526e+00  1.1089e+01
-#>  -7.9600e+00 -2.6078e-01  7.3612e+00  3.3860e+00 -5.8622e+00 -5.7458e+00
-#>   1.6788e+00 -4.5105e+00  4.8601e+00  2.6536e+00  6.3036e-01  1.6862e+00
-#>  -8.7453e+00  1.9969e+00 -2.7020e+00  1.2625e+00  8.6836e+00  4.4179e+00
-#>  -3.8124e+00 -5.3169e+00 -1.0316e+01 -6.2008e+00 -1.0265e+01 -4.3392e+00
-#>  -1.1196e+01  1.0953e+00  1.2303e+01 -3.2832e+00 -2.6740e+00 -8.8153e-02
-#>  -4.3849e+00  9.3394e-02  1.0517e+01  6.6309e+00 -1.0079e-01 -2.0375e+00
-#>   2.8490e+00  1.0117e+01 -5.2329e+00  9.0272e-01  4.3376e+00  7.2262e+00
-#>   1.2064e+00  2.9931e+00 -1.0497e+01 -1.0496e+01  5.1515e+00 -1.6065e+00
-#>   1.3753e+01 -3.8291e+00  1.2658e+01 -2.3645e+00 -1.1169e+01  4.3685e+00
-#>  -2.9462e+00  3.2668e+00  4.3312e+00  4.1991e+00  6.5311e+00 -3.7727e+00
-#> 
-#> Columns 19 to 24 -2.6109e-01  4.8078e+00 -1.0124e+01  7.8367e+00 -4.4960e+00 -9.2124e+00
-#>   3.4842e+00 -8.3740e-01  1.0619e+01  2.2888e-01 -9.4949e+00  4.2166e-01
-#>   7.1282e-02  5.1503e+00  8.8166e+00 -3.2567e+00 -3.4595e+00  2.0536e+00
-#>   7.7086e+00 -1.0302e+01  2.3211e+00 -1.3219e+00 -6.5876e-01 -9.8129e+00
-#>  -3.6886e+00  1.1903e+01 -2.9881e+00 -7.3948e+00 -1.5627e+00  8.1673e+00
-#>   1.1306e+01 -6.6918e+00 -3.6204e+00 -4.8952e+00 -9.2597e+00 -6.2369e+00
-#>  -6.9676e+00 -1.6389e+00 -2.3276e+00  3.5074e+00 -2.0643e+00 -2.0283e-02
-#>  -4.5878e+00 -2.5588e+00  8.6070e+00  3.8829e+00 -3.9444e+00 -3.7486e+00
-#>  -5.5344e+00  8.6413e+00  8.5727e-02  9.4498e-01  8.3532e+00 -1.1366e+01
-#>  -5.5513e+00  2.5488e+00  7.4590e+00 -3.5227e+00 -5.4778e+00  7.7005e+00
-#>   6.5902e+00 -6.7006e+00 -1.9534e+00 -4.8660e+00 -7.5983e+00  1.9447e+01
-#>  -1.2766e+01  6.2877e+00  7.3611e+00 -7.5964e+00  9.8168e+00  3.7307e+00
-#>  -1.1571e+00  3.2945e+00  3.3334e+00 -6.9614e-01  1.3975e+00  8.6417e+00
-#>  -2.3076e+00 -3.3192e-01  3.1283e+00 -1.2837e+01  5.1154e+00 -5.8279e+00
-#>   5.5291e+00  7.0720e+00 -6.0436e-02 -5.0244e+00 -1.6342e+01  5.6834e+00
-#>   4.0753e+00 -8.6386e-01 -1.2163e+00 -3.3384e-01  8.5434e+00 -6.3283e+00
-#>  -8.1850e+00  7.5152e-02 -5.8536e-01  1.3266e+00 -7.6313e+00  1.1692e+01
-#>   1.2035e+01  2.2418e+00  1.5214e+00  2.7325e+00 -4.7900e+00 -8.3553e+00
-#>   1.3710e+00 -5.7864e+00  3.9161e+00  4.7640e+00  9.8295e-01 -8.4390e-01
-#>  -5.9023e+00 -7.4171e+00 -5.0537e+00 -4.7018e+00 -8.9029e-01  9.8228e+00
-#>  -9.1306e+00 -1.9524e+00  8.9289e+00  3.8039e+00  1.5887e+01 -5.0845e+00
-#>   7.2528e+00 -3.6954e+00 -3.4254e+00 -4.3860e-02  5.0786e+00 -8.6250e+00
-#>  -1.7334e+00  3.8103e+00 -2.5993e-01 -1.1297e+01 -1.1125e+01  6.7070e-01
-#>  -1.3782e+01  5.9581e+00 -3.7445e+00  7.2762e+00  8.2591e+00  1.3241e+00
-#>   7.8407e+00 -3.1490e+00 -2.0267e+00  2.5580e+00  1.0221e+01 -6.0490e+00
-#>  -4.8752e-01 -9.7989e+00  3.6149e+00  6.3880e-01 -6.0927e+00  6.9155e+00
-#>  -5.2781e+00  2.3281e+00 -2.9401e+00 -5.2770e+00 -4.5600e+00  4.0959e-02
-#>  -1.6510e+00 -4.4628e+00 -4.4317e-01 -3.7385e+00 -7.0257e-01  1.2179e+01
-#>  -1.4263e+00  2.4627e-02  1.5154e+01 -8.5295e+00  1.1547e+01 -1.7261e+00
-#>   3.3542e-01  3.9312e+00 -1.0944e+01  2.7019e+00  6.4122e+00  2.6792e+00
-#>   4.4669e-01  1.9376e+00  7.9560e+00 -3.4105e+00 -3.0819e+00  2.3268e+00
-#>  -9.2093e+00 -2.2787e+00  6.0683e-01 -5.0750e+00 -4.6972e+00  2.2780e+01
-#>   3.7401e+00  2.3183e-01 -5.3432e+00 -7.5792e+00  1.1843e+01  2.6668e+00
-#> 
-#> Columns 25 to 30  1.3633e+01  1.1635e+01 -1.4768e+00 -1.0947e+00 -1.4935e+00  7.3367e+00
-#>   1.8347e+00 -5.9848e+00 -1.5196e+01  8.0386e+00 -8.6412e-01  5.7371e+00
-#>   3.3975e-01 -8.7520e-01 -2.1590e+00  6.9778e+00  2.7767e+00  4.6087e+00
-#>   1.4119e+01 -3.1869e+00 -7.2740e+00  9.6347e+00  8.3509e+00 -9.9526e+00
-#>   1.7852e+00 -6.6034e+00  1.3139e+00  5.3587e+00  6.7300e+00  1.1495e+01
-#>   1.0884e+01 -3.6163e+00 -6.1070e-01  9.3564e+00  1.5856e+00 -1.4446e+01
-#>   4.5013e+00 -3.0506e+00 -1.3709e+00 -6.1256e+00 -1.4894e+00  6.7897e+00
-#>   7.1776e+00 -3.4181e+00 -5.2673e+00 -8.4402e+00 -1.3334e+01  9.0230e+00
-#>  -4.5190e+00 -8.1797e+00  1.2852e+00 -1.2342e+00  1.4442e+01  3.5929e+00
-#>  -4.9083e+00 -8.2237e+00 -7.7480e+00  1.4833e+00 -3.7700e+00  8.9328e+00
-#>   1.3677e+01 -1.1703e+01 -3.4264e+00  8.4007e+00  3.0622e+00 -3.9277e+00
-#>  -1.2444e+00 -7.7115e+00 -1.1126e+00 -4.7788e+00 -9.1684e+00  1.7096e+00
-#>  -7.9805e+00  1.9402e+00  5.8282e+00 -1.0984e+01  5.7442e-01  4.8096e+00
-#>   3.5863e+00 -6.9901e+00  2.0761e+01 -6.0529e+00  1.0542e+01 -5.5653e+00
-#>   4.7324e-01  1.0031e+01  1.2682e+01  5.8382e+00  2.4513e+01  1.7300e+01
-#>   4.3019e+00 -3.9498e+00  4.4878e+00 -2.3613e+00  8.3003e+00 -1.0475e+01
-#>   6.1743e+00 -6.1079e+00 -1.0839e+01 -4.8769e+00 -6.2840e+00  7.5247e+00
-#>   1.1283e+01 -1.8325e+00 -7.0253e+00  5.0291e+00 -2.7680e+00 -7.1598e+00
-#>   2.6188e-01 -6.6821e+00 -1.2407e+00  7.5910e+00 -1.4712e+01  4.1615e+00
-#>   1.2098e+00  7.7760e+00  8.4132e+00 -1.8960e+00  9.5166e-02  1.1113e+01
-#>  -8.5597e-01 -1.1365e+01 -6.1514e-01 -7.5014e+00 -4.4777e+00  7.6538e-01
-#>  -5.3641e+00  8.4976e+00  7.7201e+00 -9.8481e+00  1.3061e+01  2.3271e+00
-#>  -5.7804e-01 -2.1340e+00  6.5594e+00  1.0442e+01  2.3031e+00  5.4915e+00
-#>  -2.4073e+00 -7.2948e+00 -7.1055e+00 -1.4424e+01 -1.8628e+01  6.5668e+00
-#>  -1.7887e+00 -1.0561e+01 -3.0666e+00  1.3548e+01  1.8072e+00 -8.3541e+00
-#>   1.4815e+01  2.1536e+00 -2.1050e+00 -2.4299e+00 -2.1874e+00  7.0530e+00
-#>   1.2392e+01  1.9511e+00 -5.1508e+00  4.0476e-01  1.3191e+01  1.0705e+01
-#>  -3.2279e+00  1.7266e+00 -4.3829e+00 -7.9983e+00  2.0262e+00  6.8517e+00
-#>  -8.2418e+00 -8.7575e+00  1.0860e+01  1.2190e+01 -1.1073e+00  7.0043e+00
-#>  -4.0054e+00  7.2396e-01 -1.3193e+00  4.3663e+00 -1.4141e+01  6.7344e+00
-#>   8.4796e+00 -7.0270e+00 -5.3384e+00 -9.5140e+00 -5.6786e-01  3.8349e+00
-#>  -1.6548e+01  7.2795e-01  7.1365e+00  8.1874e+00  2.1921e+00 -9.6058e+00
-#>  -3.1823e+00 -8.5109e-01  4.5027e+00  4.7013e+00  8.7304e+00 -3.1466e+00
-#> 
-#> Columns 31 to 36 -3.0502e+00 -5.4226e+00 -1.1866e+00 -7.7360e-04  1.0046e+01 -1.4339e+01
-#>   5.1785e+00  8.5924e+00  7.3401e+00 -9.1732e+00  6.5585e+00  4.2616e+00
-#>   4.7791e+00  1.9069e+00  1.2301e+00 -3.1632e+00  1.0141e+01 -7.9537e+00
-#>  -3.2250e+00  1.1265e+01  8.3408e+00 -1.5957e+01  1.1140e+01  3.0385e+00
-#>  -5.7449e+00  3.2804e+00 -3.5045e+00  3.9281e+00 -4.7560e+00  5.1381e+00
-#>  -3.3620e+00  3.1483e+00  6.3762e+00 -3.0654e+00 -5.8521e+00 -2.6027e+00
-#>  -6.8715e-01 -1.3580e+01  2.9583e+00 -2.8945e+00 -2.2106e+00 -1.0597e+00
-#>   2.3841e+00 -2.2713e+00  5.1123e-01  1.3546e+00  2.8196e+00 -4.8460e+00
-#>   4.1300e+00 -8.9717e-01 -2.0092e+00  8.0245e+00  8.9510e+00 -5.7317e+00
-#>   4.9242e+00  5.6605e+00  4.8302e-01 -1.6416e+00 -5.6424e+00 -3.1269e+00
-#>  -1.7472e+01  9.0286e+00 -1.1657e+00  1.5655e+01 -1.8243e+01  1.5363e+01
-#>  -1.8474e+00  4.0897e+00  7.5385e+00  6.4631e+00  1.1732e+01 -1.5875e+01
-#>  -2.9430e+00  1.0304e-02 -6.3027e+00  4.3613e+00  7.5438e+00 -7.5815e+00
-#>  -6.3004e+00 -2.0406e+00 -2.1198e+00 -1.7317e+00  5.3290e+00  1.1506e+00
-#>   4.4363e+00 -7.1078e+00 -2.2689e+00  9.1593e+00 -1.0209e+00  1.4142e+01
-#>  -1.0158e+00  3.4533e-01  1.0040e+01 -5.2964e+00 -3.8510e+00 -9.8925e+00
-#>   7.0940e+00 -1.9105e+00 -2.6435e+00 -6.7222e+00  4.9688e+00 -9.4728e+00
-#>  -1.4549e+01  5.3767e-01  2.3201e+00 -1.9899e+00 -5.1085e+00 -2.1957e+00
-#>   9.1778e+00  1.1308e+01 -4.9178e+00 -2.6880e+00  3.7381e+00 -4.8390e-02
-#>  -1.4211e+00 -4.6613e+00 -5.0277e+00  7.5841e+00 -5.6116e+00  1.0896e+01
-#>  -1.3916e+00  1.0658e+00  6.3661e+00 -3.2695e+00  8.0748e+00  1.1913e+01
-#>  -5.4840e-01 -7.0615e+00 -2.1461e-01  5.8277e+00  4.4998e+00  6.1279e-01
-#>  -7.2273e+00 -3.3353e+00 -2.2451e-01 -4.2120e+00  1.4607e+00  2.1736e+00
-#>   2.3888e+00  7.9467e+00 -1.4923e+01 -2.8997e+00 -1.4515e+00 -1.0238e+01
-#>   1.9038e+00 -2.1767e+00  1.7820e+00  5.0317e+00  9.6143e+00  3.5390e+00
-#>  -2.2836e+00 -6.7059e-01 -3.3277e+00  2.3968e+00 -6.2656e+00  7.1541e-02
-#>   1.8024e+00 -4.6275e+00  3.8931e+00  2.2845e+00  9.2879e+00  2.6737e+00
-#>  -2.7062e+00 -6.9413e+00 -2.2579e+00  9.6534e+00 -2.0443e+01  1.0024e+01
-#>   7.2623e+00  3.7980e+00  6.2083e+00 -5.2031e-01  6.6343e+00  2.8769e+00
-#>   4.6087e-01 -1.1342e+00 -1.0071e+01 -1.0014e+01 -1.0012e+00 -1.6799e+01
-#>   1.3046e+01  1.2031e+01 -5.9934e+00  2.7704e+00  5.1693e+00 -9.1025e+00
-#>   2.3357e+00  3.6832e+00  1.0729e+01  2.8181e+00  4.0101e+00  2.8323e+00
-#>  -3.7210e+00  9.5575e-01  9.1658e+00 -3.9453e+00  6.6812e+00 -9.8705e+00
-#> 
-#> Columns 37 to 42  8.0274e+00  1.3473e+00 -7.9683e+00 -2.5021e+00 -1.4562e+00  6.9043e+00
-#>   6.4800e+00 -6.4930e+00  6.5987e+00  1.6992e+00  3.3423e+00  2.9007e+00
-#>   4.1236e+00  2.8806e+00  3.4910e+00  9.0047e+00  1.3609e+00 -4.1131e+00
-#>   3.9045e+00 -2.7661e+00  8.0995e+00 -3.2099e+00 -6.3028e+00  3.6100e+00
-#>   2.8595e+00  8.5890e+00  6.9759e+00 -3.6375e+00  1.2716e+01 -5.1055e+00
-#>  -3.9744e+00 -3.7970e+00  4.5694e+00 -4.4010e+00  4.6595e+00  1.0435e+01
-#>   8.7296e+00  2.9286e+00 -1.9703e+00  2.7617e+00  1.0797e+00  2.7736e+00
-#>   4.3076e+00 -1.6706e+00 -7.8943e+00  7.5354e+00 -6.1941e+00  3.9286e+00
-#>   1.2360e+01 -5.4219e+00  1.6949e+01 -6.1366e+00  1.3807e+01 -6.2802e+00
-#>   1.7763e+00  6.6658e+00 -1.9258e+00  7.6167e+00  4.0521e+00 -4.5945e+00
-#>  -8.7879e+00  2.0683e+00 -1.3782e+00 -1.0502e+00  4.1240e+00 -9.3366e+00
-#>  -2.4919e+00  4.6335e+00  5.6993e-01  5.8056e+00  1.3667e+00 -3.8362e+00
-#>  -7.9760e+00 -9.7185e+00  6.3919e+00  5.8327e+00 -5.5591e+00 -1.0145e+01
-#>  -2.4361e+00 -1.3595e+00  5.3784e+00 -1.0564e+01  4.4731e+00 -7.7008e+00
-#>  -1.2498e+00  1.1055e+01  1.4361e+01  4.4318e+00 -9.1977e-01 -1.0886e+01
-#>   7.8499e+00 -4.4737e-01  4.0138e+00 -1.6863e+01  3.4583e+00 -8.8853e+00
-#>   5.6519e+00  7.7085e+00  1.0316e+00 -5.9979e+00  1.2462e+00 -5.8032e-02
-#>  -6.5523e+00 -4.4294e+00  1.6930e+00 -8.1491e+00 -1.3399e+00 -7.3746e-01
-#>  -2.9941e+00 -7.9544e+00 -9.6285e+00  2.6299e+00  3.5760e+00 -3.2629e+00
-#>   5.6632e+00  1.0023e+00 -3.3555e+00  8.9834e+00 -2.5112e-01 -3.7210e+00
-#>  -1.3401e+00  1.2614e+00 -1.3780e+00 -1.5681e+00  3.4846e+00 -2.9290e+00
-#>   7.9777e+00 -9.5111e+00  9.9007e+00  7.6525e-01 -7.4262e+00 -2.2465e+00
-#>  -1.0764e+01  1.0823e+01 -1.8126e+01 -8.9734e+00 -3.3309e+00  7.3897e+00
-#>  -2.4367e+00 -9.3883e+00  5.1017e+00  1.9732e+00  1.5514e+01 -9.8860e+00
-#>   1.1193e+01 -1.3586e+01  1.2199e+01 -6.2468e+00  4.8108e+00 -4.9684e+00
-#>   5.5543e+00 -7.2836e+00 -1.2312e+01  1.6544e+00 -7.8411e+00 -8.5071e-01
-#>   7.2632e+00  7.6805e+00  1.0372e+01  4.6467e+00  3.2371e+00  1.8547e+01
-#>   1.0000e+01  4.6672e+00  8.9041e+00  6.0691e+00  2.6341e+00 -1.5674e+00
-#>   1.2061e+01  3.0764e+00  5.6641e+00  4.6102e+00 -1.0707e+01 -2.9058e-01
-#>   9.3883e+00  4.1780e+00 -1.0755e+01 -3.1317e+00  1.2084e+00 -6.2495e+00
-#>   1.5567e+00  1.9683e+01  1.0854e+01  2.5242e+00  3.8949e+00  4.5147e+00
-#>  -8.4709e+00  2.0079e+01 -4.0331e+00  3.4815e+00 -1.0787e+01 -2.4349e+00
-#>   8.2260e+00 -1.2772e+01  5.0200e+00 -5.5445e+00 -2.8086e+00 -3.2149e+00
-#> 
-#> Columns 43 to 48 -3.8896e+00  6.5787e-01 -1.2978e+00 -6.1221e+00  1.3124e+00 -6.3121e-01
-#>   3.2791e+00 -2.4867e+00  1.8503e+00 -2.1419e+00 -2.8203e+00  6.7540e+00
-#>  -3.9395e+00  8.9447e-01  4.1483e+00  2.9906e+00 -1.8324e+00 -1.3498e+01
-#>   9.0459e+00  2.9160e+00  1.3991e+01 -3.4683e+00 -7.5850e+00  9.4991e-01
-#>  -4.8166e+00  4.3716e+00 -4.0342e+00  1.5501e+01  6.5063e+00  1.4848e+01
-#>   4.3978e+00 -1.7978e+00 -1.5569e+01  9.5838e-01 -4.2077e+00  1.1983e+01
-#>  -2.2132e+00  5.1870e+00 -2.2836e+00  7.6397e+00 -3.4778e-01  6.4687e+00
-#>  -3.0285e+00 -6.3110e+00 -7.4775e-01 -4.4857e+00 -6.5898e+00  1.2399e+00
-#>   8.5546e+00  9.6174e+00  5.5361e+00  1.3995e+00  7.3978e+00  6.0620e+00
-#>   9.5426e-01 -4.6008e-01  2.7788e+00 -1.6533e+00  7.6717e+00 -6.0897e+00
-#>  -2.3392e+00 -1.4206e+00  9.6478e-01  1.2578e+00  4.4325e+00  3.5684e+00
-#>  -1.2031e+01  3.2264e-01 -1.2314e+00  5.9877e+00 -1.8689e-01 -6.1992e+00
-#>   3.6786e+00  4.4235e+00  7.3856e+00 -3.5121e+00  1.8254e+00 -2.7950e+00
-#>   1.2859e+01  3.8752e+00  3.0138e+00  2.1199e+00 -2.3312e+00  8.4692e+00
-#>   3.6537e+00 -3.2655e+00  6.4011e+00  9.5566e+00  1.0377e+00  1.9657e+00
-#>   9.0634e+00 -6.8571e+00  4.1418e+00 -2.2360e+00 -4.1616e+00 -2.5369e+00
-#>  -1.2561e+01 -7.0413e+00  9.6420e-01 -1.5377e+00  7.4733e+00 -6.1888e+00
-#>  -1.2041e+01 -4.9432e+00 -6.8733e+00  7.2480e+00  4.7230e+00  1.5316e+00
-#>   4.2213e-01  2.5705e+00 -4.9289e+00  8.1845e+00 -1.2568e+01 -6.1808e+00
-#>   3.1592e+00 -2.7227e+00 -1.4107e+00  6.9731e+00  2.3468e+00 -2.5774e+00
-#>   4.4478e+00 -1.9829e+00  2.7573e+00 -2.3223e+00 -1.0544e+01  2.5697e-01
-#>   1.6394e+01  4.0235e+00  1.8288e+01 -1.6814e+00  2.0892e+00  1.2139e+01
-#>   3.9782e+00 -2.6290e+00 -4.9761e+00  1.6872e-01 -6.4128e+00  4.5709e+00
-#>   6.5372e+00  7.4668e+00 -6.9902e+00  1.1342e+01 -9.0094e+00  3.0885e+00
-#>  -2.4901e+00  1.1389e+01 -6.9001e+00  8.2024e+00 -1.1017e+01 -4.0060e+00
-#>  -4.9509e+00 -5.8122e+00 -1.0876e+00  7.2729e+00 -7.2931e+00  6.0226e+00
-#>  -9.5747e-01  1.3897e+00 -9.5474e-01 -5.0532e+00  3.3397e+00  1.9763e+00
-#>   2.7213e+00 -1.0482e+01  1.0440e-01  5.5811e+00 -1.7081e-01  3.2723e+00
-#>  -4.6305e+00  2.3510e+00 -2.4568e+00 -6.0458e+00 -6.0839e+00 -7.8381e+00
-#>  -1.0843e+01  6.2862e-01 -1.2367e+00  5.5577e+00 -3.5569e+00 -8.7925e-02
-#>  -2.7422e+00  1.8663e+00 -5.8871e-01 -4.5779e+00 -2.7187e+00 -1.7621e+00
-#>   3.8242e+00  4.7493e+00  8.6695e+00 -5.1289e+00 -1.1561e+00 -1.6367e+01
-#>  -2.5585e+00  4.8995e+00  5.4630e+00 -1.4587e+00 -6.8408e+00  1.0571e+01
-#> 
-#> (3,.,.) = 
-#>  Columns 1 to 6  1.4080e+00 -1.1899e+01 -4.1353e+00 -1.4412e+00  1.1742e+01  6.9489e+00
-#>  -6.5218e+00  1.3255e+01  9.3847e+00 -7.3147e+00  1.4185e+00  2.9938e+00
-#>  -9.4088e+00  9.8435e+00  7.5546e+00 -1.8330e+00  5.6999e-01  2.4329e+00
-#>  -2.1506e+00 -1.1168e+00  1.6671e-02  7.6816e+00  7.6185e+00 -1.3157e+01
-#>  -1.4543e+00 -7.0075e+00  3.7290e+00 -6.4805e+00 -4.2380e+00 -1.1300e+01
-#>  -6.9435e+00 -3.1541e+00  1.6184e+01  2.3992e+00  2.1464e+00 -2.0194e+01
-#>  -2.1261e+00 -6.1874e+00 -3.1801e+00 -9.9955e+00  4.5779e+00 -3.3643e+00
-#>  -1.3131e+00  6.6850e+00 -6.6825e-01 -3.8057e+00 -2.1247e-01 -1.0433e+01
-#>  -5.0201e+00 -5.4811e+00  1.9074e-02 -9.6455e-01 -6.4131e+00  6.0250e+00
-#>  -5.4076e+00  1.1422e+01  5.0324e+00 -2.3835e+00 -5.3782e+00  3.2404e+00
-#>  -8.0323e+00  2.0626e+00 -1.0257e+01  2.4690e+00 -4.1333e+00 -1.6341e+01
-#>   8.1692e-01 -4.8066e+00  6.5308e+00 -2.0152e+00  1.1058e+01 -1.1816e+01
-#>  -1.1623e+00  4.4053e+00  4.9868e+00 -1.4910e+00 -8.6254e+00  8.2777e+00
-#>  -8.3532e+00  2.3937e+00 -1.1702e+01  5.5676e+00 -4.8145e+00 -6.8998e+00
-#>  -8.3821e+00 -2.0785e+00 -8.5879e+00 -1.1150e+01  4.8695e+00 -3.8867e+00
-#>  -1.2349e+00  7.4407e+00  3.5352e+00 -2.9630e+00 -4.7452e+00  4.3327e+00
-#>  -7.6731e+00  9.8579e+00 -5.3611e+00 -2.5492e-01 -8.7314e+00  4.6041e+00
-#>  -5.3861e+00 -9.1160e-01  7.6677e+00  1.7004e+00  7.4107e+00 -6.2495e+00
-#>  -2.6204e+00  3.1218e+00 -5.8771e+00 -9.5579e+00 -9.5801e+00 -6.1502e+00
-#>   2.1210e-01  4.4249e+00 -1.3583e+01 -5.0736e+00 -1.4737e+00  9.9051e+00
-#>   1.1629e+01  5.7636e+00  4.6826e+00 -2.0306e+00  3.1641e+00 -3.3694e+00
-#>   4.8854e+00 -7.1501e+00 -1.1292e+01 -2.5348e+00  5.3458e+00  1.3115e+01
-#>   8.2982e-01 -1.4568e+00 -8.7658e+00  6.9908e+00  1.3195e+00 -7.1090e+00
-#>   1.1800e+00  1.6513e+00  6.2211e+00  2.4865e+00 -1.1569e+01  1.5910e+01
-#>  -1.0067e+01 -2.4248e+00  3.5204e+00 -7.3729e+00 -2.9552e+00  9.0173e+00
-#>  -3.5460e-01 -1.3474e+00  3.3893e+00  1.2785e+00  4.2084e+00 -1.0641e+01
-#>  -1.9705e+00 -1.6608e+01 -3.1503e+00 -4.9205e+00 -5.1441e+00 -7.2497e+00
-#>  -4.0333e+00  1.4794e+01 -1.0558e+01 -5.8430e+00 -1.0755e+01 -1.7470e-01
-#>  -1.1416e+01  1.0601e+01 -3.5518e+00 -1.1902e+00 -4.6416e+00  5.6309e+00
-#>  -6.7081e+00  3.0187e-01 -8.6661e-01 -5.8872e+00  2.0574e+00 -3.1048e+00
-#>  -7.0169e+00 -5.9670e+00 -8.7838e+00 -1.1772e+01 -4.3665e+00 -1.3870e+01
-#>   6.1087e-02  9.9251e+00  2.8213e+00  1.0690e+01 -1.8414e+00  2.7020e+00
-#>   7.8980e-01 -3.7714e+00  1.1692e+01 -1.0431e+00  3.6993e-01 -3.0131e+00
-#> 
-#> Columns 7 to 12  4.9066e+00 -4.1308e+00  6.7264e+00 -2.3971e+00  2.5494e+00  3.4488e+00
-#>   6.5412e-01  6.1340e-01  3.3838e+00 -8.8767e-01 -5.3286e-01  1.1592e+01
-#>   8.5030e+00  4.7017e+00  2.5309e+00  1.8400e+00 -2.3989e-01  5.0525e+00
-#>  -2.8190e+00  1.1768e+00  2.9686e+00  9.4875e-01 -2.0928e+00  2.8620e+00
-#>   8.0581e+00  2.0097e+00  7.7453e-01 -2.4825e+00  5.6598e+00 -5.6388e+00
-#>  -2.9977e+00  5.2568e+00 -8.4047e+00  4.8200e+00  9.3105e+00 -1.2028e+00
-#>  -1.1490e+01  4.0066e+00  9.3038e+00 -1.0326e+00  8.8266e-01  8.2433e+00
-#>  -1.9693e+00  9.3338e-02  6.7674e+00 -7.5838e+00 -1.2921e+00  4.9577e+00
-#>   1.2455e+01  1.2788e+01 -1.2791e+00  6.4981e-01 -3.5957e+00 -1.3183e+00
-#>   9.4231e+00 -2.2993e+00  4.4472e+00 -7.1652e+00 -2.1302e-02  3.9951e+00
-#>  -2.4195e+00 -2.5335e+00 -4.7614e+00 -2.5586e+00  2.0540e+00  2.6202e+00
-#>  -1.3300e+01  8.5292e+00  4.0447e+00 -6.2759e+00  7.4482e+00  7.2431e-01
-#>   4.9680e+00 -3.3235e+00 -4.6155e-01 -1.5015e+00 -1.1129e+01  7.0367e+00
-#>   1.1640e+01 -2.5281e+00 -6.0034e+00  1.1838e+01  8.8062e-01 -1.9703e+00
-#>   5.5088e+00  3.4637e+00 -8.0601e+00  3.6713e+00  9.8656e+00 -4.1082e+00
-#>   9.0013e-01 -7.9194e+00 -1.2030e+01  6.5047e+00 -4.0442e+00  3.4924e+00
-#>   1.9017e+00  2.0474e+00  7.3447e-01  4.4541e+00 -8.6128e+00 -1.1041e+01
-#>   3.9773e+00  5.6824e+00 -3.7343e+00 -6.4544e+00  5.0678e+00 -1.2625e+01
-#>  -1.3919e+01 -7.6696e+00 -8.0680e+00 -7.8409e-01  1.7832e-01  1.4216e+01
-#>  -8.4426e+00 -6.3279e-01  9.9402e+00  7.9904e+00  2.1366e+00  9.8434e+00
-#>   1.0340e+01  2.9289e+00  7.5236e+00 -3.0736e+00  5.6299e+00  2.1383e+00
-#>   4.8554e+00  2.0273e+00 -3.4944e+00  9.8330e+00 -1.5486e+00 -7.0196e+00
-#>  -1.3181e+01 -6.1692e+00 -4.3927e+00 -6.9112e-01  7.3283e+00 -4.3668e+00
-#>  -1.0650e+01 -1.0113e+01 -1.0493e+00 -4.0480e+00 -5.0376e+00  1.5352e+01
-#>  -1.2744e+00  1.3469e+01 -8.7806e+00  4.8216e+00  4.3525e+00  7.1390e+00
-#>  -7.6099e+00  8.2266e+00 -1.6163e+00 -5.2979e+00 -9.2686e-01  4.1946e+00
-#>  -6.8688e+00  6.8039e+00  4.5720e+00  2.1791e+00  3.2972e+00  2.7397e+00
-#>  -1.3702e+01  5.0406e+00 -2.5053e+00  1.6890e+01  2.2438e+00  7.1325e+00
-#>   1.4510e+01  3.9684e+00 -2.3852e-01  4.7563e+00  3.9079e+00  1.0018e+01
-#>   3.8940e+00 -7.0659e+00 -5.8890e+00  1.1028e+01 -8.5083e+00 -7.1714e+00
-#>  -7.7616e+00 -1.2060e+00  1.5313e+00 -1.0961e+00  4.9758e+00 -2.1184e+00
-#>   6.0980e+00 -1.2272e+00  5.8412e+00  1.2258e+00 -2.6513e+00  5.4603e+00
-#>   6.5693e+00 -2.6360e+00 -1.1088e+01  3.7412e+00  4.6298e+00  2.0834e+00
-#> 
-#> Columns 13 to 18  4.6010e+00  4.5067e+00 -2.9038e+00  3.2502e+00 -2.4335e+00 -6.4512e+00
-#>  -2.3157e+00 -6.3810e+00  1.3949e+01  9.6802e-01 -3.6449e-01  2.2528e+00
-#>  -3.6608e+00 -8.8017e+00  5.9599e+00 -3.7491e+00 -7.6035e+00  6.1058e+00
-#>  -4.8018e+00  2.0252e+00  4.9238e-01 -9.8625e+00  4.6778e+00 -2.1808e-01
-#>   3.1025e+00  8.5312e+00 -3.7468e+00  6.2848e+00 -5.9987e-01  4.7689e+00
-#>   3.6285e+00  1.5422e+01 -3.4136e+00  1.2394e+00 -6.1809e+00 -3.1180e+00
-#>   2.5461e+00 -5.0396e-01  3.0065e+00 -7.7716e+00 -1.0346e+01  4.0124e+00
-#>  -7.0728e-02  3.7410e+00  1.0579e+01 -4.4932e+00  1.1285e+00 -6.4617e+00
-#>  -7.7204e-01 -9.0712e+00 -1.7770e+00 -9.2219e+00 -8.7108e+00 -4.0555e-01
-#>   9.1434e+00  5.0025e+00  8.2765e+00  2.4627e-01 -1.8783e+00 -4.6973e+00
-#>  -3.2545e+00  5.0161e+00 -3.9063e+00 -1.1353e+01  5.4595e+00 -6.0954e+00
-#>  -4.9873e+00  1.4954e+01  5.8090e+00 -6.2336e-01 -4.7916e+00  3.4780e-01
-#>  -4.5335e+00 -3.1057e+00  1.1861e+00  2.7490e+00  2.5365e+00  1.1029e+01
-#>  -1.5909e+00  1.3848e+00 -5.3683e+00 -5.9884e+00 -3.4089e+00  6.0434e+00
-#>  -1.3089e+01  6.8337e+00 -8.6937e+00 -5.4593e+00 -2.6766e+00  1.0728e+01
-#>   2.8165e+00 -4.1133e+00  5.6637e+00 -4.4917e+00 -1.4141e-01 -1.4887e+00
-#>   9.4678e+00  4.1728e+00  1.1035e+01  9.4205e+00 -2.1007e+00 -7.6966e-01
-#>  -1.0080e+00  2.3142e+00  3.3514e+00  1.3970e+00 -8.1402e-01  8.2785e+00
-#>  -3.4290e+00 -3.6558e+00 -1.9663e+00 -1.4807e+01 -5.8848e+00 -3.5223e+00
-#>   9.5822e+00  5.5018e+00  1.1599e+01 -1.9601e+00 -2.5891e+00  5.5822e-01
-#>   5.1442e-01  4.4396e+00 -6.9159e+00 -6.6224e+00 -7.4509e+00 -5.4584e+00
-#>   3.9008e+00 -7.7933e+00 -3.8777e+00 -9.7138e+00  8.1011e+00 -1.8866e+00
-#>  -6.4980e-01  1.0553e+01  1.3711e+00  3.4205e+00  6.2344e+00 -6.0786e+00
-#>   1.8012e+00 -2.5126e+00 -6.1118e+00 -4.8493e+00 -2.3870e+00 -8.4272e-01
-#>  -8.4451e-01  3.0856e+00 -4.3986e+00 -5.1790e+00 -1.4728e+01 -3.6166e+00
-#>  -2.4438e+00  1.3056e+01  1.2298e+01 -5.6175e+00  8.8998e+00 -9.7530e-01
-#>   1.0519e+00  8.9182e-01 -3.7485e+00  1.4638e+00 -2.2603e+00 -1.1745e+01
-#>  -6.2835e-01 -3.9113e+00 -5.2103e-01 -1.3101e+01 -5.4891e+00 -3.1151e+00
-#>   3.8050e-01 -3.9765e+00  3.3705e+00 -5.4369e+00 -7.1983e+00 -4.7236e+00
-#>   5.7476e+00  4.3498e+00  1.7391e+00  9.4122e+00 -1.8259e+00  4.8131e+00
-#>  -5.2306e+00  1.1402e+01  2.8967e+00  5.5618e+00 -7.5690e-01 -2.9042e+00
-#>   2.3695e+00 -8.2757e+00 -4.8955e+00 -7.9524e-01 -2.1519e+00  2.2029e+00
-#>   5.9470e+00  5.0308e+00 -6.3217e+00  4.2271e+00 -5.8797e+00 -1.2538e+00
-#> 
-#> Columns 19 to 24  4.0035e+00 -5.2533e+00 -6.4376e+00 -6.5796e+00  1.4593e+01  3.5199e+00
-#>   5.3346e+00 -1.0014e+00  8.6347e-01  2.0348e+00 -5.6212e+00 -4.0300e+00
-#>  -2.2729e+00 -1.5539e+00  2.4310e+00  1.7948e+00  1.5205e+00  4.4759e+00
-#>  -1.0538e+01 -2.0725e+00  4.6372e+00 -3.4391e+00 -2.4950e-01 -2.3709e-01
-#>   4.2838e-01  2.8630e+00  3.5305e+00  1.2936e+00 -2.5255e+00 -4.0722e+00
-#>  -2.2387e+00  3.0365e+00  2.7049e+00 -1.3206e+01 -4.8693e+00  1.7665e+01
-#>  -1.0460e+00  3.7885e+00 -2.7064e+00  7.5554e+00 -7.9784e+00 -1.5450e+00
-#>   3.3267e+00 -2.1692e+00  5.9835e+00 -6.4481e+00 -7.5086e+00  3.9535e+00
-#>  -2.4311e+00 -3.8278e+00 -5.4211e-01 -8.2869e-02  3.1314e+00 -1.1352e+01
-#>   4.5032e+00 -1.3754e+00 -1.0011e+00  3.0971e+00 -6.2162e+00 -2.0818e+00
-#>   1.2915e+00 -6.8684e+00  6.6977e+00 -2.4609e+00  1.3056e+00  6.7367e+00
-#>  -8.9935e+00 -9.3973e+00  9.1078e+00  1.0718e+00 -1.8343e+01  2.3391e+00
-#>   6.7598e-01  1.4605e+01 -1.1669e+01  1.4837e+01 -4.9263e+00 -8.4995e+00
-#>  -8.4471e+00  3.9360e+00 -5.0993e+00  1.2821e+00 -5.0476e-01 -5.0610e-01
-#>  -3.1034e+00 -1.9458e+00 -1.6512e+00  4.0102e+00 -1.5717e+00  1.5329e+01
-#>  -9.0065e-01 -3.1436e-01 -1.4189e+00 -2.0881e+00  2.7689e+00 -9.9803e+00
-#>  -1.0392e+01  6.7957e+00 -8.4666e+00  1.5654e+01  8.5171e+00 -2.7840e-02
-#>   5.7474e+00 -7.4794e+00  2.5581e+00 -8.2204e+00 -2.3948e-02  4.2277e+00
-#>  -8.4238e+00  6.8594e+00 -7.1296e+00  1.5019e+00 -7.6827e+00  8.1506e+00
-#>  -7.1825e+00 -2.6377e+00 -4.2360e+00  1.5290e+01 -8.4564e+00 -1.9189e+00
-#>  -1.6717e+00 -6.7846e+00  1.0160e+01 -1.1922e+01  4.1626e-01 -7.3792e+00
-#>   5.8180e+00 -7.7029e+00  3.5438e+00 -5.4828e+00  1.0892e+01 -1.7366e+01
-#>  -4.1492e+00  5.8430e+00 -8.5405e-01  9.1512e-01 -9.1028e+00  8.8594e+00
-#>  -2.9390e+00  1.2791e+01 -1.4800e+01  8.0902e+00  9.1215e-02 -7.9433e+00
-#>  -1.1290e+01 -4.3278e+00 -6.7345e+00 -7.3926e+00 -7.8704e+00 -7.1902e+00
-#>   8.8550e-04  6.6792e+00  6.1034e+00  9.4450e-01 -8.4841e+00  9.1573e+00
-#>  -5.3408e+00 -4.3279e+00 -2.6200e+00  7.4204e+00  2.9669e+00  4.2542e+00
-#>  -4.2130e+00  3.1756e+00 -9.8007e-01 -3.4946e+00  3.6135e+00  4.5236e+00
-#>  -3.6319e+00 -1.5918e+00 -4.6717e+00  1.3942e+00 -4.0734e+00  1.0169e-02
-#>  -5.5155e-01  8.8678e+00 -2.8303e+00  4.7980e+00  1.5487e+01 -2.1578e+00
-#>  -1.0643e+01 -9.3310e+00 -5.1527e+00  5.4748e+00 -2.3128e+00  1.1993e+01
-#>  -4.4540e+00  8.2229e+00 -7.8324e+00  5.7167e+00 -2.1282e+01  5.6597e+00
-#>   1.6077e+00  5.2613e-01  6.0516e+00 -8.0079e+00  1.0206e+01 -9.7924e+00
-#> 
-#> Columns 25 to 30  1.1147e+01  3.6391e+00  1.1187e+01  6.0805e+00 -8.1869e+00 -2.5905e+00
-#>   5.2926e+00 -1.6961e+01 -5.4181e+00 -3.6479e+00 -4.9755e+00 -2.2046e+00
-#>   4.7799e+00 -2.3298e+00 -3.9841e+00  3.9436e+00 -2.6750e+00 -2.1315e-01
-#>   6.8501e+00 -8.3940e+00  8.5949e+00 -7.8190e+00 -1.7399e+00 -5.6473e+00
-#>  -1.1091e+01  1.1763e+00 -3.5075e+00 -3.0827e+00  4.8371e+00  1.9554e+00
-#>  -1.1648e+01 -7.4380e+00  2.7608e+00 -8.0752e-01  5.0588e+00  2.9817e-01
-#>  -7.2856e+00  9.5683e+00 -1.2037e+01  4.4913e+00  2.2812e+00 -5.4674e+00
-#>   3.0411e+00 -2.4650e+00 -6.0758e+00  6.5565e+00  3.3859e+00 -1.2842e+01
-#>   1.2164e+01 -1.1720e+01  4.6296e+00 -4.5861e+00 -1.8319e+00 -3.1555e+00
-#>   1.0095e+00  2.8889e+00 -3.4291e+00  1.0095e+00 -4.2277e+00  7.2100e+00
-#>  -8.5960e+00 -2.2444e+00 -4.6157e+00  2.9012e+00  3.8361e-01 -6.3091e+00
-#>  -5.5840e+00 -1.8335e+00  6.8453e-01  7.2422e+00  5.8593e+00 -6.2295e+00
-#>   2.0426e+00 -3.1267e+00 -3.0833e+00  1.8640e+00 -3.1724e+00  2.6324e+00
-#>   1.8683e+00 -4.9115e+00  5.0103e+00  2.4655e+00 -5.9479e+00  5.0536e+00
-#>   7.5598e-02 -7.1535e+00 -4.4489e-01  4.7277e+00 -5.1581e+00  3.8179e+00
-#>   1.1880e+01 -2.5464e+00  1.2672e+01 -5.9125e-02 -4.3747e+00  1.1664e+01
-#>  -1.0828e+01  3.0879e+00 -1.4164e+00  2.4949e+00 -8.1771e+00 -1.1576e+00
-#>  -6.1593e+00  3.6951e+00  1.3597e+01  6.8700e+00  3.7079e+00 -5.8486e+00
-#>   2.5296e+00  1.7619e+00 -4.1247e+00 -7.1495e+00 -9.2967e+00 -2.0392e+00
-#>   1.4032e+00  2.5314e+00 -1.4011e+01  4.0010e+00  1.7084e+01  4.9672e+00
-#>  -6.2591e-01 -4.5542e+00  5.4845e+00  8.8774e+00  6.3592e+00 -5.5222e+00
-#>   6.6866e+00 -6.4466e+00  3.6383e+00 -5.8592e+00 -2.6308e+00  2.5872e+00
-#>  -8.4094e+00  3.9811e+00 -9.8913e+00 -2.0240e+00 -8.8618e-01  5.7595e+00
-#>   8.8510e+00  4.8119e-01  5.9794e+00 -2.5156e+00 -7.9376e+00  6.2095e-03
-#>   5.2374e+00 -3.6271e+00  1.6991e+01  1.2048e+00  4.1467e+00  4.2666e+00
-#>  -9.1448e-01 -5.3934e+00 -2.2018e+00  4.8980e+00  3.2072e+00 -1.5705e+00
-#>  -1.4598e-01 -3.9422e+00 -1.3923e+01 -2.8437e+00  4.3032e+00 -6.9459e+00
-#>  -9.6621e+00 -4.9686e+00  6.9225e+00 -2.1751e+00 -1.7244e-01  1.2816e+01
-#>   9.4945e+00 -1.7000e+00  5.5441e+00  9.0715e+00  2.7920e+00  1.0386e+01
-#>  -7.8475e+00  5.6721e+00  2.7749e+00 -1.0268e+00  7.0137e-01  7.0984e+00
-#>   3.0558e+00 -1.8220e+00  5.1956e+00 -7.3876e+00 -9.1895e+00 -3.0264e+00
-#>  -3.3453e+00  9.7614e+00 -7.0201e+00  9.7704e+00  3.7403e+00  7.6579e+00
-#>   1.2433e+00 -8.8167e+00  7.2628e+00 -3.4548e+00  3.0066e+00  4.6408e+00
-#> 
-#> Columns 31 to 36  1.6280e+00  3.6802e+00 -1.1593e+01 -8.4596e-01 -1.7400e+00  6.1596e+00
-#>  -4.5575e+00  4.7161e+00  1.3676e+01  1.6125e+00  1.5714e+01  1.5754e+01
-#>  -1.8076e+00  2.8713e+00  3.8487e+00 -1.0413e+00  4.4413e+00  6.8242e+00
-#>   3.1493e+00  4.0503e+00 -1.4582e+01 -1.5412e+01  1.5087e+01 -2.7381e+00
-#>   3.5763e+00 -6.4330e+00 -2.3951e+00  1.6168e+01 -1.4622e+01  6.6627e+00
-#>  -2.4222e+00 -8.8587e+00 -3.3878e+00  1.8356e+00  2.1590e+00  6.4357e+00
-#>  -5.4561e+00 -4.7786e+00 -5.5594e+00  8.7320e+00 -5.4292e+00  6.9659e+00
-#>  -2.4925e+00  5.7615e+00  3.0381e+00  5.1190e+00  6.9788e+00 -3.4237e+00
-#>   4.2632e+00 -1.4803e+01  4.0556e+00  6.8307e+00 -3.7379e-01  2.1806e+00
-#>  -2.8327e+00 -3.2625e+00  9.2088e+00 -5.7277e+00  6.3689e+00  9.3465e+00
-#>   1.1018e+01  8.0445e+00  9.3478e+00 -5.5861e+00  1.5302e-01  3.1772e+00
-#>   8.2271e+00  1.5902e+01  1.8643e+01 -1.6779e+00  1.8905e+00  1.0224e+01
-#>   6.3268e+00 -6.0640e+00  1.0101e+01 -5.8077e+00 -4.8274e+00  1.0012e+01
-#>   5.0458e+00 -7.7735e+00 -9.2099e+00 -5.3822e+00 -2.3859e+00 -3.3018e+00
-#>  -4.3210e+00  1.1542e+01 -1.9341e-01  1.0544e+00 -5.4314e+00  7.5066e+00
-#>   5.5280e+00 -1.2632e+00 -4.0221e+00 -6.5578e+00 -6.1401e+00 -4.2676e+00
-#>   6.8111e+00 -6.7595e-01  1.1877e+01 -4.4416e+00  7.3254e+00  9.2634e+00
-#>  -9.3897e-01 -7.3323e+00 -5.6501e+00 -4.2338e+00 -1.6136e+01 -7.9817e+00
-#>   5.1431e+00  7.9962e+00  1.9244e+00 -1.3078e+01  9.7229e+00  1.4527e+01
-#>  -6.6730e+00  9.3903e-01  1.7861e+01  4.9748e+00  6.6223e+00  4.5511e+00
-#>   5.7490e+00  4.0505e+00  1.2850e+00  1.7379e+01  6.4363e+00 -3.4994e+00
-#>  -4.3550e+00 -1.3790e+00 -4.9837e-01  1.6309e+00  7.2332e+00 -9.8394e+00
-#>   5.2478e-01  6.5799e-01 -9.2388e+00 -3.2802e-01 -1.0465e+01 -7.3347e-01
-#>   1.2429e+00 -7.9775e+00  6.9857e+00 -4.2260e+00 -4.6722e+00  1.1488e+01
-#>   1.9202e+00 -3.3812e+00 -3.2940e-01 -1.7986e-01 -4.8731e+00  3.0378e+00
-#>  -1.2325e+00  1.7000e+01 -1.5116e+01  2.6105e+00  6.9141e+00  3.5265e+00
-#>  -4.5038e-02 -1.0016e+00  1.0130e+01  1.7839e+01 -2.6167e+00  7.1525e+00
-#>   3.8105e+00  1.5240e+01 -6.2467e+00  6.8072e+00  1.5233e+01 -1.2775e+01
-#>   4.2361e+00  1.1523e+01 -4.7938e+00  2.2597e+00  9.2862e+00 -5.7732e+00
-#>   8.5660e+00  1.7525e+00 -1.1605e+01 -1.7779e+01 -1.0388e+01  5.5783e+00
-#>   1.7085e+00  9.5260e+00 -3.2841e-01 -8.4488e+00  9.9827e+00  2.2759e+00
-#>  -8.8871e-01  1.1511e+01  7.0996e-01 -1.5810e+01  3.7052e+00  1.9111e+00
-#>   1.7095e+01  6.1128e+00 -3.5600e+00  7.4983e+00 -1.8792e+00  3.0595e+00
-#> 
-#> Columns 37 to 42  4.0209e+00  2.8236e+00  3.2228e+00  4.9068e+00 -1.0765e+01 -1.8393e+00
-#>   1.7493e+00 -1.6852e-01 -8.9894e+00 -7.7516e+00 -5.7265e+00  1.5782e+01
-#>  -7.5828e-01  5.2742e+00 -6.7648e+00 -7.0245e-01 -2.9208e+00  8.0485e+00
-#>   2.4751e+00  2.0166e+00 -2.7255e+00 -1.6698e+01  2.6687e+00  2.0290e+00
-#>   5.5240e-01 -2.9833e+00 -1.0485e+01 -4.2613e+00 -3.7978e+00 -4.5257e+00
-#>  -5.4438e+00 -4.8938e+00 -1.2284e+01  2.8564e+00  8.7059e+00 -2.1239e+00
-#>   3.2328e+00 -4.8299e+00 -8.4738e+00  5.2165e+00 -6.1057e+00 -2.3035e+00
-#>   9.0472e+00 -1.7072e+00  1.4483e+00  1.0006e+01  1.4309e+00  3.3362e+00
-#>  -3.5516e+00 -2.4131e+00 -7.4484e+00 -9.3446e+00 -2.2438e+00 -5.8856e+00
-#>   4.3916e+00  1.3265e+00 -9.2051e+00 -6.4683e+00 -1.0181e+01  1.0631e+01
-#>   2.4456e+00 -6.5465e+00  6.4855e+00 -2.8299e+00 -1.2262e+00  1.0795e+01
-#>  -4.5987e+00 -5.7546e+00 -3.0917e+00  1.2375e+01  8.1699e+00  4.7183e+00
-#>  -9.2398e+00  1.1088e+01  1.1573e+01 -1.9905e+00 -1.2787e+01  6.5136e+00
-#>  -7.4235e+00 -2.3060e-01  9.7640e+00 -1.0802e+01 -2.6236e+00  1.8933e-01
-#>  -1.0108e+01 -2.2754e+00  3.0799e+00 -8.2495e+00 -6.9606e+00  9.3663e+00
-#>  -7.5995e+00  7.7299e+00  3.7903e+00 -1.0401e+01  5.5817e+00  4.9853e+00
-#>  -5.1197e-01  1.0524e+01 -7.7887e+00  7.9674e+00 -1.1456e+01 -1.8496e+00
-#>   3.4752e+00 -7.9746e+00  1.3573e+00 -3.7112e+00 -7.2776e+00 -3.8574e+00
-#>  -1.8658e+00  3.9683e+00 -1.6261e+01  3.5811e+00  3.0788e+00  1.8561e+01
-#>   1.0342e+01 -2.8909e+00 -2.8083e+00  7.6817e+00 -4.0105e+00  4.0795e+00
-#>   3.1097e+00 -1.7027e+01  5.6430e+00  8.0713e+00  3.7843e+00 -2.7920e+00
-#>  -5.6453e+00 -8.9694e+00  1.8651e+01 -1.3044e+01 -4.0992e+00 -5.1164e+00
-#>  -4.7247e+00  2.1068e+00 -6.8930e+00 -3.1100e+00 -4.0142e+00  2.4032e+00
-#>  -8.0732e+00 -1.9362e+00 -1.1289e+00  1.5146e+01 -1.2932e+01  3.7773e-02
-#>  -3.3798e+00 -1.0183e+01 -2.1320e+01  4.0629e+00  7.0527e+00  5.3845e+00
-#>   1.3099e+01  2.9590e+00 -5.3575e+00 -1.3653e+00  1.5970e+00 -1.6306e+00
-#>   6.9089e-01 -4.2401e-02  6.2614e-01  8.6173e-02  5.2735e+00  5.8539e+00
-#>  -6.8782e+00 -8.2778e+00  8.0748e+00  7.1342e+00  5.1705e+00  2.6406e+00
-#>   6.2406e+00 -6.6000e+00 -6.3177e+00  1.2859e+00  9.0834e+00  1.1542e+01
-#>  -1.0031e+01  1.7066e+01 -4.2424e+00  1.1230e+01 -1.5471e+01 -1.2751e+01
-#>  -3.2882e+00  9.8471e+00  6.1404e+00 -4.0524e+00  8.4977e+00  5.9648e+00
-#>  -1.6528e+01  8.3521e+00 -9.0226e+00 -1.2466e+00  8.3971e+00  8.7195e+00
-#>  -3.5397e+00  5.0278e+00  3.5069e+00  3.1022e-01  1.0142e+01 -2.0094e+00
-#> 
-#> Columns 43 to 48  1.9703e+00 -1.1875e+01 -8.7695e+00  1.5051e+00 -5.7029e+00 -9.7498e+00
-#>  -1.3301e+00  3.2313e+00  8.9848e+00 -1.0432e+01 -4.4717e+00 -7.2384e+00
-#>   7.5183e+00  1.2554e+01  7.1470e+00 -3.7589e-01 -2.4162e+00  2.9486e+00
-#>   3.3490e-01  4.8699e+00  3.7211e+00 -1.0333e+01  9.6389e-01 -1.0573e+01
-#>   9.7084e+00 -4.1592e+00  5.4356e+00 -6.3258e+00  3.1986e-01  1.1790e+01
-#>  -9.9423e+00 -1.5987e+01 -6.0567e+00  8.4694e+00 -1.5238e+00 -1.0579e+00
-#>   8.9344e+00  5.8672e+00 -7.3183e+00  4.8573e+00 -1.2020e+01  2.9273e+00
-#>   1.1320e+01 -7.6255e+00 -3.5554e-01  1.0035e+00 -8.6486e+00 -6.9284e+00
-#>   1.2469e+01  3.7959e+00 -6.8073e+00  1.0302e+00  1.1334e+01  3.4938e+00
-#>   4.6032e+00 -2.1861e+00 -2.2557e+00 -8.5205e+00 -9.0304e+00  5.3963e+00
-#>   1.2137e+00 -7.5389e+00  1.7085e+01 -6.2938e+00  2.4187e+00  8.8205e-01
-#>   8.5736e+00  8.6609e+00 -7.3895e+00 -5.0710e-01 -1.4946e-01 -2.8082e+00
-#>   5.7984e+00  1.0131e+01  5.3564e+00 -7.1073e+00  2.3574e+00  8.9853e-01
-#>   1.5896e+00  1.8233e+00 -2.4928e+00 -2.7644e+00 -2.4445e+00  3.6724e+00
-#>   3.1770e+00 -1.2907e+01 -3.4434e+00 -7.8347e+00 -9.8390e+00  7.6037e+00
-#>   7.2079e+00  8.7224e+00 -6.1744e+00  6.8033e+00 -1.0701e+00 -3.7301e+00
-#>  -6.1776e+00  8.1282e+00 -1.6816e+01  4.8213e-01 -1.7013e+01 -6.6154e+00
-#>  -1.8096e+00 -8.0818e+00  7.6369e+00  4.6508e-01  1.3007e+00 -2.2696e+00
-#>   5.6270e+00  5.2678e+00 -1.9481e+00 -5.9225e+00 -1.8582e+01  1.7727e+00
-#>   7.5104e+00 -3.5144e+00  1.7004e+00 -6.3550e+00 -5.8357e+00  3.4854e-01
-#>   1.6668e+01 -1.5817e+00  1.3947e+01 -2.3133e+00  8.9867e+00 -5.4141e+00
-#>   5.5480e+00  4.2205e+00  2.1355e+00 -5.3126e+00  1.0447e+01 -3.6338e+00
-#>  -9.6799e+00  3.6726e-01 -1.1408e+01 -5.9977e-01 -1.0750e+01  1.0392e+00
-#>  -4.2397e-01 -3.0862e-01  5.3942e-01 -1.0992e+00  7.5872e+00  7.4528e+00
-#>   6.6743e-01 -3.1385e+00 -1.0539e+01  1.2112e+00 -1.9948e+00  2.6654e-01
-#>   1.2855e+01 -4.1853e+00 -4.4663e+00 -3.0913e+00 -2.1435e+01 -1.0684e+00
-#>  -4.6279e-03  1.4366e+00 -1.3314e+01 -4.3680e+00  2.9159e+00  5.4549e-01
-#>  -1.4838e+01  1.6832e+00  1.4140e+01  1.0610e+01  7.4335e+00  1.5544e+01
-#>  -5.9024e+00 -5.5648e+00 -8.7734e+00 -7.4661e+00 -1.0869e+01  1.4613e+00
-#>  -3.0699e+00  4.7420e+00 -4.3134e+00  5.4277e+00 -6.0626e+00  2.3346e+00
-#>   3.9549e+00 -4.3669e+00 -1.1736e+01 -6.6775e+00 -7.9589e+00 -2.7425e-01
-#>  -1.5342e+01  5.3356e+00 -1.0145e+01  3.7000e-01 -4.6578e+00  5.8591e+00
-#>   3.7243e+00  2.5135e+00  4.8050e-01 -6.2709e+00  5.8081e+00 -4.3069e+00
-#> 
-#> (4,.,.) = 
-#>  Columns 1 to 6 -2.4260e+00 -1.6090e+01 -7.1397e+00  1.0959e+01  1.3156e+01 -3.1173e+00
-#>  -8.2181e+00 -1.3502e+01  3.9274e+00 -2.2057e+00 -1.2749e+01  4.7140e+00
-#>  -1.4480e+01 -2.6388e+00  1.6621e+01  2.2499e+00 -1.5909e+01  9.4288e-02
-#>  -4.2047e+00  4.9874e+00  1.2744e+01 -2.2830e+00 -6.1745e+00 -3.4464e+00
-#>  -1.0277e+01 -1.6134e+00 -8.3036e-02 -1.7949e+00 -1.4716e+00 -1.4627e+00
-#>  -2.1850e+00 -6.9099e+00  3.0435e+00  8.3562e+00  2.9605e+00 -5.9025e+00
-#>  -1.0164e+00 -4.8070e-01  2.9863e+00 -1.1825e+00 -1.4028e+01  4.0764e+00
-#>  -4.2190e-02 -5.4516e+00  6.1648e+00 -5.6370e+00  4.4230e-01  5.5009e+00
-#>   3.7369e+00  6.2467e+00  6.4830e-01  3.9464e+00  4.0394e+00 -1.3070e+01
-#>  -3.4354e+00 -1.3006e+01  1.2709e+00  2.2926e+00 -7.9340e+00  7.8264e-01
-#>  -2.8094e+00 -6.3679e-01  3.3087e+00 -4.7293e+00  1.1885e+00  8.2875e+00
-#>  -9.3574e+00 -1.2533e+01  5.0777e+00  6.9207e+00  9.7479e+00  1.9588e+01
-#>  -4.3638e-01  1.3491e+01 -6.4486e+00 -7.1892e+00  7.7435e-01 -9.9856e+00
-#>  -1.7874e+00  9.6274e+00  1.0126e+01 -1.0308e+01  8.8940e+00 -8.6108e+00
-#>  -1.1105e+01  8.8923e+00  6.5329e+00 -8.6803e+00 -7.5524e+00 -8.0187e+00
-#>   4.4636e-01  4.6858e+00  1.1691e+01  1.0431e+00 -7.0344e-01 -8.0854e+00
-#>  -5.0888e+00 -1.2481e+01 -4.9545e+00  5.0573e+00 -1.8414e-01  3.1990e+00
-#>  -8.5629e-01 -9.0537e+00  4.4783e+00  1.1009e+01  4.8588e+00 -4.0643e+00
-#>  -3.3022e+00  2.5722e+00  1.6664e+01 -2.9806e+00 -1.4120e+01 -4.9962e+00
-#>  -5.7592e+00 -1.0260e+01 -8.1434e-01  4.3060e-01 -4.0868e+00  9.3831e+00
-#>  -1.3060e+01 -3.0198e+00  1.7335e+01  5.5972e+00  7.4274e+00  5.9574e+00
-#>   3.5454e-01  1.7126e+01 -2.0991e+00 -9.9543e+00  3.3188e-01 -6.5695e+00
-#>  -3.2862e+00  3.9254e+00  1.3965e+01 -1.2239e+00 -1.8622e-01  3.0077e+00
-#>   8.3303e+00 -9.2190e+00  3.1816e+00  1.9540e+00  8.2113e-01 -6.3173e+00
-#>   6.4037e+00 -6.7632e+00  7.7021e+00  1.1969e+01  5.9819e+00  4.0527e+00
-#>  -1.1441e+00 -7.0357e+00  5.4759e+00 -4.3076e+00 -8.1542e+00  2.4943e+00
-#>  -3.9106e+00 -7.4675e-01 -6.9691e+00 -2.8430e+00 -6.4367e-01  9.7454e+00
-#>  -9.9073e+00 -4.1061e+00  1.1572e+01 -1.0203e+00  2.5985e+00  1.9412e+01
-#>   3.7332e+00 -7.6847e+00  9.5770e+00 -3.4412e+00  5.8689e+00  1.1729e+01
-#>  -6.3888e+00  3.3612e+00  5.4892e+00  2.0501e+00  8.9452e+00  1.8355e+00
-#>  -1.4714e+01 -4.1082e+00 -1.1987e+00  4.5001e-01  3.4602e+00  2.8930e+00
-#>   8.4743e+00  1.4654e+01 -2.0721e+00  6.1471e+00 -3.8771e+00  3.2316e+00
-#>  -2.5601e+00  1.2247e+01 -3.2863e+00 -6.9482e+00  7.3815e+00  5.2209e-01
-#> 
-#> Columns 7 to 12  2.7039e+00  1.7928e+01  4.7722e+00 -9.0033e+00 -9.2337e+00  4.3650e+00
-#>   5.6106e-01 -3.7155e-01 -9.4022e-01  6.6389e-01 -1.0687e+00 -3.6676e+00
-#>  -1.5103e+00 -4.8982e+00  2.8042e+00  3.2289e+00  1.7656e+00  1.3508e+00
-#>  -1.7560e+00 -1.2062e+00 -4.4098e+00 -8.6041e+00 -9.1194e-01 -2.4839e+00
-#>  -7.7607e-02 -1.4647e+00 -1.4750e+00  2.2109e-01 -9.2688e+00 -1.6978e+00
-#>  -6.4080e+00 -1.4036e+01 -3.3036e+00  7.0697e+00 -2.8365e+00 -1.5730e+00
-#>  -8.7269e-02  2.7889e+00  6.2123e+00  3.2134e+00 -4.1613e+00 -4.4587e+00
-#>  -2.1674e+00 -1.1504e+00  5.9939e+00  1.1103e+01  1.1826e+01  5.6860e-01
-#>  -6.9300e+00 -6.7035e+00 -7.3591e+00 -7.8506e+00 -2.8402e+00 -7.3483e+00
-#>  -1.1257e+00  2.5082e+00  2.6712e+00 -3.2684e+00 -4.4637e+00  8.8603e-01
-#>  -7.6516e-02 -1.4251e-01 -2.3874e+00  1.1411e+00 -2.4513e+00 -1.6663e+01
-#>   4.6774e+00 -1.4641e+00  8.1328e-01 -1.1810e+01 -1.5955e+01  1.2982e+01
-#>  -8.5431e+00  3.9702e+00  9.8489e+00  4.6478e+00  1.2068e+01  4.8349e+00
-#>  -5.8179e+00 -8.1493e+00  1.6114e+00 -4.0638e+00  6.5257e+00 -5.3299e+00
-#>  -7.2035e-01 -4.5514e+00 -1.9175e+00  1.3576e+00  9.6702e+00 -6.4111e+00
-#>   3.2116e+00 -2.5445e+00 -4.9105e+00 -8.8980e-01  1.1377e+01 -3.6236e+00
-#>   1.0368e+01  2.7730e+00  8.0303e+00 -6.5360e+00 -1.2322e+01  2.1166e+01
-#>  -4.8024e+00  4.8266e+00  8.0571e-01  8.9774e-01 -8.2069e+00 -9.2629e+00
-#>   2.2174e-01 -6.4808e+00 -5.2895e+00  3.6308e-01 -1.2931e+00  2.7188e+00
-#>   9.5745e+00  1.2801e+00  7.4423e+00  1.8955e+00 -4.1224e+00 -4.7624e+00
-#>  -5.2236e+00 -1.0175e+00 -5.3479e+00  1.6259e-02  1.3361e+00  7.1376e+00
-#>  -7.6194e+00  6.1334e+00  1.1134e+00 -9.8276e+00  7.6407e+00 -1.5565e+01
-#>  -1.8772e+00 -1.9989e+01 -1.6736e+01  2.7651e+00 -3.2357e+00  6.1813e+00
-#>   8.6555e+00  1.2206e+01  7.3953e+00 -1.3803e+01 -9.0671e+00  1.8454e+01
-#>   6.6007e+00 -5.4982e+00 -7.9111e+00 -1.0212e+01 -1.3995e+01 -2.5820e+00
-#>   3.3626e+00 -2.6789e+00  7.5463e+00  8.0132e+00  7.1050e+00 -4.7084e+00
-#>   3.1330e+00  1.9449e+00 -7.4689e+00  1.5632e+00 -5.9301e+00  6.4092e+00
-#>   1.0197e+01 -7.0770e+00 -4.1250e+00 -8.2758e+00  1.3048e+01 -7.7559e+00
-#>  -2.1499e+00 -6.4168e+00 -2.3384e+00 -1.8222e+00  1.1717e-01  3.8442e+00
-#>  -6.0073e+00 -8.4099e+00  3.9294e+00 -6.3322e+00 -6.4771e+00  2.1426e+01
-#>   1.1076e+01  5.8835e+00 -4.9446e+00 -4.7285e+00  2.9562e+00  1.0060e+01
-#>   3.8793e+00 -1.3364e+01 -5.9460e+00 -5.2039e-01  4.9388e+00  9.8313e+00
-#>  -7.5788e+00 -4.2444e+00  2.7747e+00  2.6616e+00  4.5422e+00  3.9646e-01
-#> 
-#> Columns 13 to 18 -6.5245e+00  2.3812e+00 -2.7277e+00 -2.9581e+00 -1.6089e+01 -1.4964e+01
-#>  -5.1098e-01 -1.6415e+00 -1.0149e+01  5.5287e+00  4.1055e+00  1.2482e+00
-#>  -6.7670e-01 -1.7101e+00 -4.9976e+00  1.0970e+01 -3.4336e+00 -5.6223e+00
-#>  -4.6039e+00 -1.9907e+00 -2.7878e+00 -1.1976e-01  5.3809e-01  2.7278e-01
-#>  -4.1515e+00  6.1336e+00 -2.5220e+00  6.6164e+00  9.2006e+00  3.1525e+00
-#>  -3.0194e+00 -2.8319e+00 -2.1512e+01  2.1852e+00  9.3475e+00 -8.1556e-02
-#>  -2.9080e+01  2.7905e+00  7.8276e-01  2.3943e+00  1.5815e+00 -3.1924e+00
-#>  -4.5630e+00  4.8802e+00 -6.8771e+00 -8.7013e-01 -1.4001e+00  1.2690e-01
-#>   5.6586e+00  8.6122e+00  1.9926e+00 -2.5550e-01  1.9946e+00  3.0214e+00
-#>   5.9062e+00 -3.4057e+00  3.9138e+00 -3.8029e-01 -7.9910e+00  3.1446e+00
-#>   1.0425e+01 -7.9493e+00  3.6418e-01  1.0828e+01 -8.4890e+00  2.3251e+00
-#>   2.3559e+00  3.4553e+00  3.1563e-01  1.0489e+01 -6.5857e+00 -1.4040e+01
-#>  -7.9710e-01  4.0536e+00 -3.0672e+00  1.1442e+00  3.5079e+00  1.6066e+00
-#>  -7.5029e+00  1.2243e+00 -3.7898e-01 -1.5373e+00 -1.1446e+00  3.8205e-01
-#>   4.1850e+00  2.2051e+00 -2.0903e-01  2.1436e+00  1.8623e+00 -3.8784e+00
-#>  -4.1479e+00 -7.1423e+00 -3.1687e+00 -3.1377e+00  5.9941e+00  5.1574e+00
-#>   8.0884e+00  5.1589e-01  4.0641e+00 -1.9410e+00 -1.3332e+01 -7.8220e+00
-#>   8.4895e-01  8.6939e+00 -8.5540e+00  7.4821e+00  4.9653e+00 -1.1706e+01
-#>  -1.3710e+01 -9.1676e+00 -2.5768e+00  9.1837e+00 -1.1243e+01  2.3341e-01
-#>  -4.1771e+00 -2.4854e+00  2.1148e+00 -4.6356e+00 -2.6337e+00  9.0549e-01
-#>   5.2531e+00 -3.7544e+00 -5.2746e+00  4.3894e-01  6.3111e+00 -1.2990e+00
-#>  -3.5439e+00 -6.1807e-01  9.6415e+00 -8.0851e+00  4.2410e+00  7.7130e+00
-#>  -6.7433e+00 -1.0340e+01 -8.5594e+00  2.3174e+00 -2.7162e+00 -8.6086e-01
-#>  -2.7265e+00 -7.5501e+00 -3.1762e+00  1.0278e+01 -8.0296e+00  3.2575e-01
-#>  -4.1769e-01 -2.5689e+00 -9.3748e+00  2.6912e-01 -4.4217e+00  2.3118e-01
-#>  -4.7241e+00  3.1261e+00 -3.9767e+00 -3.7091e+00 -2.0489e+00  2.0303e+00
-#>   5.1013e+00  1.4020e+01 -4.9716e+00 -9.3442e+00  1.9353e+00 -4.5956e+00
-#>  -4.8233e+00 -8.7770e+00  1.9722e+01  9.5561e-01  3.9089e+00  1.3546e+01
-#>   9.2608e-01  3.2052e+00 -1.4554e-01 -1.4802e+01 -5.1632e+00  6.9924e+00
-#>  -5.7905e+00 -5.4141e+00  4.2075e+00  6.7678e+00 -1.7884e+01 -3.1346e+00
-#>   1.0392e+01  2.7993e+00 -2.3305e+00  7.8083e+00 -1.1962e+01 -1.3522e+01
-#>   5.6182e+00 -2.6997e+00  3.7665e+00  1.6071e+00 -3.7697e-01  1.3203e+00
-#>  -4.3505e+00 -1.3015e+00 -1.3011e+01 -7.3237e+00  4.3552e+00  6.8349e+00
-#> 
-#> Columns 19 to 24 -4.3832e+00  1.1854e+00  3.7849e+00 -8.7515e-01 -7.2142e+00  2.8712e+00
-#>   4.3580e+00  1.0377e+01  7.7775e+00  5.6452e+00 -5.5160e+00 -4.8094e+00
-#>   1.2200e+00  2.5590e+00  7.4564e-01  8.7451e+00  8.5624e+00  4.9214e-01
-#>  -4.0737e+00  1.1564e+01  3.7737e+00  5.8098e+00 -9.9780e+00  6.1329e+00
-#>   8.3322e+00 -8.3092e+00 -6.6719e+00 -1.1653e+00  9.0612e+00 -6.9816e-01
-#>   4.1872e+00  5.6975e+00  3.3598e+00  1.7068e+00 -1.0912e+01  1.0734e-01
-#>   6.7030e+00 -6.7618e+00  1.2650e+00 -1.8857e+00  1.7936e+00  1.0692e+01
-#>   1.0883e+00  4.4177e+00 -2.1538e+00  1.1302e+00 -3.1243e+00  1.4657e+00
-#>   2.5484e+00  2.1354e+00 -5.3882e+00  2.5681e+00  9.9810e+00 -5.4780e+00
-#>   7.7740e+00  1.0476e+00 -5.1584e+00  8.7806e-01  9.8774e-01 -1.1787e+00
-#>   2.5723e+00 -1.0043e+01 -2.5171e+00  1.2171e+00  6.1552e+00  1.3662e+00
-#>   4.5510e+00 -1.4972e+00  1.1797e+00  9.6993e+00  1.1976e+00 -4.3668e+00
-#>   2.2118e+00 -2.7551e+00 -8.4045e-01  3.4012e-01  9.2944e+00  3.2099e+00
-#>  -9.9009e+00  1.7620e+00 -3.0801e+00  7.2096e+00  8.1377e+00  8.4507e+00
-#>   1.2373e+00 -2.8196e+00 -2.6450e+00 -7.0677e+00  9.0234e+00  6.5156e+00
-#>  -4.0219e+00  8.0798e-01 -3.0108e+00 -1.4071e+00  6.3210e+00  6.3155e+00
-#>   4.1826e+00  1.0785e+01 -7.9641e-02 -8.0781e+00 -2.8322e+00  5.8314e+00
-#>  -1.6334e+00 -8.2829e-01 -5.2327e-01  9.1560e-01  2.7875e-01  1.0949e+00
-#>   1.1560e+01  7.1775e+00  2.4676e+00  4.3689e+00  1.2348e+00  1.5355e+00
-#>  -7.2999e+00 -8.7090e+00 -1.0109e+00  8.0552e+00  5.2843e+00 -5.5434e+00
-#>  -5.6621e+00  2.0950e+00  5.6926e+00  8.5302e+00 -1.8420e+00  4.9828e+00
-#>  -6.2905e+00 -3.2019e+00  3.9112e+00 -2.8016e+00 -2.4425e+00 -3.9356e+00
-#>  -1.2319e+00  9.3284e+00  7.1342e+00 -2.9960e-01  3.5464e+00  9.6238e+00
-#>  -2.5003e+00  3.7505e-01 -1.8017e+00 -2.4822e+00 -1.3950e+01  4.2405e+00
-#>   6.2448e-01  5.0793e+00 -2.1631e+00  4.3120e+00 -6.7088e+00 -6.2485e+00
-#>   1.0608e+00  8.4343e-01  1.4154e+00 -8.8833e-01 -4.9350e-02  5.2178e+00
-#>   5.0803e-01 -2.5242e-02  5.2353e+00 -4.7372e+00  1.6922e+00  2.6130e+00
-#>  -8.1313e-01 -1.4678e+01 -8.0687e+00 -7.8823e+00  5.9716e+00  9.7268e+00
-#>  -4.1983e+00  1.7083e+00 -4.8165e+00  1.0524e+01  2.4571e+00 -5.8272e-01
-#>   9.0055e+00  1.6781e+00 -3.9620e+00 -1.9208e-01  7.7751e+00  4.8966e+00
-#>   5.6446e+00  1.0289e+01 -2.6237e+00 -9.7269e+00 -1.8522e-01 -1.3218e+00
-#>  -3.3620e+00 -1.8233e+00 -3.1495e-01  1.1295e+00  5.1915e+00 -6.8499e+00
-#>  -1.2493e+00 -3.7749e+00 -4.4679e+00  4.6988e+00  6.2839e+00  1.0712e+00
-#> 
-#> Columns 25 to 30 -3.7678e+00 -5.7242e+00 -6.5666e+00 -6.8208e+00 -5.8472e+00 -4.0306e-01
-#>  -3.7068e+00 -1.8190e+00 -3.1648e+00  6.9638e+00  5.9712e+00  4.9328e+00
-#>  -1.0388e+01 -5.5448e+00 -1.4448e+00  2.6815e+00  3.7214e+00 -3.2213e-01
-#>  -3.6283e+00 -2.1454e+00  3.2638e-01  6.5090e+00  3.4894e+00  5.7031e+00
-#>  -5.3661e-02  4.1527e+00  2.4702e+00 -1.5813e-01 -1.6505e+00  1.4394e+00
-#>   1.6428e+01 -9.1191e+00  1.1358e+00 -3.5391e+00  8.9095e+00 -2.4697e+00
-#>  -4.4852e+00  1.1873e-01 -3.0818e+00  2.8583e+00  2.7108e+00  5.9310e-01
-#>   3.1718e-01  5.4803e+00 -7.4583e+00 -3.3241e+00  1.9538e+00 -9.9190e+00
-#>  -5.7366e+00  9.0325e+00 -3.6218e-01  1.2094e+01 -5.1567e+00  2.4442e+00
-#>  -1.0432e+01  4.0087e+00 -5.2226e+00 -3.0613e+00 -2.6841e+00  4.3142e+00
-#>   1.5683e+01 -4.8294e+00 -3.7603e+00 -3.9085e+00  6.4568e+00 -9.4047e-01
-#>  -8.2675e+00 -1.2175e+01 -2.7009e-01  3.1945e+00  7.9773e+00 -9.3286e+00
-#>  -1.1884e+00  6.6128e-01  5.1019e+00  5.5271e+00 -1.3284e+01 -6.6113e+00
-#>  -1.3139e+01  4.7815e+00  1.3458e+00  7.1244e+00 -8.1554e+00  5.0782e-01
-#>   4.6021e+00  5.5465e+00  1.0623e+01 -6.7340e+00 -1.0824e+01  9.9315e+00
-#>  -1.2730e+01  1.4776e+01 -2.8510e+00  1.9954e+00 -6.1477e+00  6.8560e+00
-#>  -8.4356e+00  3.8520e-01 -9.1840e+00  1.9059e+00 -8.1112e+00 -5.6377e+00
-#>  -7.3018e+00 -2.0424e+00  2.1029e+00 -1.0376e+00  2.2140e+00 -6.5560e+00
-#>   5.9514e+00  2.8834e+00  8.2532e+00 -1.2182e-01  1.4292e+01  6.1602e+00
-#>   2.3185e-01 -5.6710e+00 -1.6720e+01 -6.4373e+00 -8.8326e-01  1.2590e+00
-#>  -8.3497e+00 -1.0511e+01 -9.8981e+00  1.9591e+01  7.4912e+00  4.6404e+00
-#>  -6.4806e+00  9.1930e+00  3.6603e+00  6.1261e+00 -4.8635e+00 -1.2633e+01
-#>   1.6575e+00  1.2219e+01  1.8400e+00 -5.0897e-01  4.8157e-01 -1.5647e+00
-#>   1.0697e+01 -1.0563e+01 -1.9335e+00  5.2464e+00 -4.9309e+00  5.6560e+00
-#>   2.7579e+00 -1.0974e+01  3.7751e+00  2.4941e+00  1.2820e+01  1.2087e+01
-#>   1.0323e+01  9.6038e-01 -8.0909e+00 -1.2246e+01  5.8800e+00 -8.1793e+00
-#>  -3.2346e-01  5.5698e+00 -4.4115e+00  9.1758e-02 -5.0301e+00  3.8046e-02
-#>   5.3821e+00 -8.0975e+00  3.9839e+00 -1.6710e+00  1.3352e+01 -6.0166e+00
-#>  -1.8801e+01 -7.5684e-01 -6.5811e+00  2.7727e+00  8.5961e+00  8.7461e+00
-#>   5.9716e+00 -2.9117e-01  6.6215e+00 -6.3897e+00  3.0783e+00 -7.0034e+00
-#>   3.3100e+00  5.9482e+00  7.1441e+00 -7.2146e+00 -1.1453e+01 -2.1975e-01
-#>  -2.5174e+00  6.1783e-04  3.0374e+00  9.1811e-01  1.3462e+00  1.2635e+01
-#>  -1.1931e+00 -5.5093e+00 -6.1567e+00 -2.6138e+00  2.6214e+00  1.4476e+00
-#> 
-#> Columns 31 to 36  7.4223e-01  7.0131e+00  1.1629e+01 -5.2268e+00 -1.1719e+01  3.1106e+00
-#>   2.6765e+00  7.4811e-01  2.4381e+00 -6.1843e+00  8.3653e+00  1.0713e+01
-#>   5.0892e+00  1.7012e+01  5.5125e+00 -1.1288e+01  4.5462e+00  5.6505e+00
-#>   6.4937e+00  5.3456e+00  7.8426e+00 -1.6293e+01 -2.3455e+00 -5.8268e+00
-#>  -1.0484e+00  3.4260e+00 -8.2399e+00  4.6738e+00 -5.2327e+00  7.4706e+00
-#>   5.3485e+00 -1.8475e+00 -3.6222e+00 -1.6178e+01 -3.4572e+00  3.2129e+00
-#>   6.6275e+00 -2.6183e+00  1.6539e+00  4.8539e+00 -5.4083e+00  5.7612e+00
-#>  -2.7518e+00  5.2740e+00  1.5759e+01 -9.9001e+00  2.3535e+00  7.7896e+00
-#>   3.7825e+00 -3.4907e-01 -3.4481e+00 -2.5707e+00 -9.7787e+00  1.5958e+01
-#>  -3.8520e-01  1.4803e+01  3.4473e+00 -6.6064e+00  4.9638e+00  7.6164e+00
-#>  -2.5500e+00  4.3077e+00 -1.0892e+01 -4.6185e+00  3.9399e+00  9.3895e+00
-#>  -8.5954e+00  5.5902e+00  9.9969e+00  9.0837e+00 -7.0463e+00  1.6710e+00
-#>   2.7788e+00  7.4384e+00  8.5017e+00 -2.4923e+00  1.0762e+01  5.9373e+00
-#>   4.4875e+00  1.5368e+00 -1.6538e+00 -1.0821e+01  4.6451e+00 -1.2634e+00
-#>   1.2521e+00  1.4079e+01  2.2609e+00 -1.0919e+01  8.3839e+00 -3.6383e+00
-#>   1.1817e+01  2.9383e+00 -4.2285e+00  2.2827e+00 -1.1505e+01  3.6831e+00
-#>   5.6361e-01  1.7811e+00 -2.0413e-01  8.8456e-01 -2.2689e+00 -3.1501e+00
-#>  -9.8936e+00  3.8421e-02  1.3489e+00 -7.7164e+00  1.4918e+00  1.5608e+00
-#>   3.9265e+00  1.5115e+01 -4.5694e+00 -7.6374e+00  3.5958e+00  2.6907e+00
-#>  -2.7697e+00 -1.5410e-01  5.8248e+00  3.0651e+00  9.2409e+00 -4.3196e-01
-#>  -3.9996e+00  3.8280e+00  7.0900e+00 -9.7899e+00 -1.5215e+00  1.3456e+01
-#>   1.0321e+01 -9.7283e+00 -4.8336e+00  1.3471e+01  4.9134e+00  3.0929e+00
-#>   8.3774e-01  4.2876e+00 -1.7407e+01  2.7746e+00 -6.0241e+00 -9.6043e+00
-#>   3.1691e+00  8.8499e+00 -9.8104e-01  5.2230e+00 -8.6498e-01  1.2157e+01
-#>   9.0028e-01 -7.4834e+00 -3.0958e+00 -4.7875e+00 -1.0619e+01  1.0254e+00
-#>   5.0236e+00  1.2426e+01  1.5218e+01 -4.1620e+00 -5.4289e+00  7.4496e+00
-#>   1.5874e+01 -1.8470e+00 -5.5536e+00  1.0463e+01 -1.4672e+01 -4.7601e+00
-#>  -9.4839e-01  1.9456e-02 -2.4531e+01 -1.4408e+00  4.2728e+00 -7.0230e+00
-#>   9.5121e+00  1.6671e+00  8.0547e-01 -7.9168e+00  4.5085e+00 -9.3673e+00
-#>  -1.4331e+00 -3.4387e+00 -8.9806e+00  3.4347e-01 -1.6345e+01 -8.6711e-01
-#>   3.4529e+00  7.9604e+00  7.3007e+00 -1.6455e+01 -9.1844e+00 -7.5894e+00
-#>   4.7911e+00  6.6002e+00 -3.2026e+00 -9.1167e-01  3.9497e+00 -1.9500e+01
-#>   1.6120e+01 -1.0085e+00  2.9139e+00  9.0391e+00 -4.1158e+00  1.2126e+01
-#> 
-#> Columns 37 to 42 -4.2708e+00 -6.2193e+00  4.0405e+00 -2.4626e+00  2.6114e+00 -6.6725e+00
-#>  -2.6733e+00  5.4875e+00 -9.1574e-01  1.0587e+01  1.4299e+00  7.1512e+00
-#>  -1.1510e+01 -1.1695e+01 -6.2071e+00 -8.2175e+00  1.9661e+00  1.0048e+01
-#>  -2.6016e+00 -7.4609e+00 -5.6747e+00  9.3854e+00  9.3149e+00  5.0030e+00
-#>  -1.1570e+00  8.6847e-01  4.5684e+00  1.1916e+01  4.5815e+00  3.9475e+00
-#>   4.1254e+00  5.6743e+00 -2.8369e+00  8.3233e+00 -6.2624e+00 -6.4872e+00
-#>   6.2880e+00 -1.0629e+01  4.8851e+00  4.4807e+00  7.8539e+00  2.9114e+00
-#>   5.6523e+00 -9.5010e+00  1.1585e+01 -7.8069e+00 -1.9712e+00 -1.4402e+00
-#>  -9.8762e+00  1.1291e+01  3.9450e+00  7.5963e+00  1.1955e+00  1.0262e+01
-#>   6.1205e-01 -3.9418e-01  6.0771e-01  1.7916e+00  4.0193e+00  1.2516e+01
-#>  -5.3330e+00  5.4140e+00  1.0709e+01  5.9039e+00 -2.1101e+00  5.9015e+00
-#>   4.4010e+00 -5.8462e+00  2.4776e+00 -2.7191e-01  2.4018e+00 -5.4125e+00
-#>  -1.6926e+00  3.5603e+00  6.8201e-01 -1.0361e+01 -1.1898e+00  1.4208e+01
-#>  -8.8385e+00  9.4390e-01 -1.0417e-01  7.5702e-01  5.1242e+00  6.3443e+00
-#>  -1.0421e+01  3.4307e+00  1.0905e+00 -3.4570e-01 -4.2475e+00  3.7947e+00
-#>  -8.2029e+00 -7.5056e-02 -1.0317e+01  9.6688e-01  5.8398e+00  6.9069e+00
-#>   9.2056e+00  2.6418e+00 -6.2371e+00  3.8774e-01  1.1345e+00 -3.7623e+00
-#>  -4.0129e+00 -4.6145e+00 -4.4591e+00 -1.5093e-01  3.8242e+00 -2.6517e+00
-#>  -7.3270e-01  3.5610e+00  9.5007e+00  5.1863e+00  5.7790e+00 -9.9534e+00
-#>  -1.9771e+00 -4.4519e+00  2.3197e+00 -5.2061e+00 -4.5916e-01 -1.5046e-01
-#>  -6.4935e+00 -5.2702e+00  4.9656e+00 -7.4239e+00  9.6984e-01  5.0983e+00
-#>  -3.6458e+00  1.0499e+01 -3.4077e-02  2.9104e+00 -4.5600e+00  9.1534e+00
-#>   9.5156e+00 -1.5982e+00  4.5251e+00  3.1438e+00  4.4952e+00 -1.8068e+01
-#>  -2.3347e+00  8.9090e+00 -3.1034e+00 -5.2686e+00  1.9010e+00  3.1109e+00
-#>  -1.6672e+01  7.5201e+00 -1.2067e+00  3.2212e+00 -9.8372e-02 -1.1015e+01
-#>   1.0232e+01 -6.7055e+00  1.3532e+01 -2.2495e+00 -3.1926e+00 -3.1087e+00
-#>  -5.6343e-01 -2.0494e+00  3.1293e+00  8.3727e+00  6.9515e+00 -7.7970e+00
-#>  -3.8374e-01  4.6507e+00 -1.0472e+01 -1.6164e+00  9.6006e+00  6.4874e+00
-#>  -6.7306e+00 -5.4101e+00  5.1182e+00 -7.9834e+00  9.7989e+00 -5.6655e+00
-#>   3.6640e+00 -1.7696e+00 -4.1505e+00  1.7499e+00  8.3987e-01  4.7474e-01
-#>  -4.6932e+00 -3.6107e+00  6.9508e+00  8.5656e+00  6.0091e+00 -3.3539e+00
-#>   8.8946e+00 -6.8784e+00 -5.4160e-01  8.3567e+00 -8.4307e+00  1.9961e+00
-#>  -3.1567e-01  6.1672e+00 -1.2873e+00 -3.1027e+00 -7.7037e+00  4.4967e+00
-#> 
-#> Columns 43 to 48  7.6586e+00 -1.1518e-01 -3.6124e+00 -6.9019e-01  5.0078e+00 -7.1079e+00
-#>  -1.1350e+01  5.9555e+00  6.9061e+00  7.6141e+00  4.6341e-03 -9.8206e+00
-#>  -8.3167e+00 -4.8917e+00  6.2786e-01  3.4504e+00  3.6465e+00 -8.0655e+00
-#>  -9.8720e+00 -1.3697e+00  1.8162e+00 -1.2960e+00  5.4253e+00 -8.4163e+00
-#>  -4.6093e+00  3.1744e-01  1.3845e+01  5.9917e+00 -6.8247e+00  1.5222e+00
-#>   2.9278e+00  7.6591e+00  8.8834e+00 -7.3206e+00  3.5923e+00  1.1554e+00
-#>   1.7499e+01 -7.5501e+00  1.0134e+01 -2.2161e+00  2.7105e-01 -1.2830e+01
-#>   4.5848e+00 -6.8604e+00 -7.4688e-01  2.7601e+00  1.7959e+00 -4.4080e+00
-#>  -6.2159e+00  1.0061e+01  8.0681e+00  4.7329e+00 -1.4557e+01 -5.9783e+00
-#>  -1.4388e+01  6.6257e+00  5.5512e+00  1.4502e+01  2.5892e-01  1.5548e+00
-#>  -5.8538e+00  1.7142e+00  3.0457e-01  5.8071e+00 -1.3670e+01  1.0826e+01
-#>  -2.2469e+00 -8.5539e+00  2.7219e+00 -3.7882e+00  6.9489e-01  4.0771e-01
-#>  -1.2400e+00 -6.6279e-01 -5.0088e+00  1.0212e+00  4.5081e+00  2.4033e+00
-#>   6.0568e-01  1.0213e+00  4.9064e+00 -1.2058e+01 -5.7127e+00  1.8151e+00
-#>   7.8649e-01  3.0735e+00  9.0810e+00 -1.4522e+00 -2.7879e+00 -1.1300e+01
-#>  -6.7457e+00 -3.5366e+00 -8.0993e+00  2.4395e+00  3.8280e+00  2.8154e-01
-#>   3.8816e-01  4.0043e+00 -1.2529e-02  2.9227e+00  1.1307e+00 -2.1515e+00
-#>  -1.3813e+00 -7.7966e+00  6.1247e-01 -6.6544e+00  6.6106e-01  6.3762e+00
-#>  -2.4359e+00 -4.7479e+00 -1.0501e+01  3.6424e+00 -3.3815e+00 -7.2181e+00
-#>   3.5248e+00  4.5957e+00  1.1000e+01  2.9740e-01 -5.6136e+00  8.5737e+00
-#>   3.5406e+00  6.4048e+00  1.1231e+01 -8.7433e+00 -7.5803e+00  1.0159e+00
-#>   8.7339e+00  1.2499e+01  3.4785e-01 -1.0078e+01 -6.6816e+00 -5.8729e+00
-#>   6.4248e+00 -5.9144e-01  5.8935e+00 -6.0223e+00 -8.3323e+00  5.0163e-02
-#>   9.3207e+00  1.0922e+01 -2.6929e+00 -1.4812e-01  2.8792e+00  1.0079e+01
-#>  -1.1273e-01 -3.4145e+00  7.7179e+00 -3.9226e+00 -3.9568e+00 -3.7776e+00
-#>   6.0739e+00 -1.0924e+01  6.6230e+00  8.0777e+00  4.8874e-01 -8.7335e+00
-#>   6.0373e+00 -2.6559e+00  6.3964e+00 -3.3931e+00 -4.7059e+00 -4.6125e+00
-#>   1.0763e+01  3.2660e+00  1.2023e+00  1.4326e+01  2.6127e+00  5.9575e+00
-#>  -5.4855e+00 -7.0947e+00  9.2428e+00  6.1904e+00 -4.3986e+00 -5.3961e+00
-#>   6.7367e+00  2.6316e+00 -3.6548e+00  9.4662e+00  7.7264e+00  5.8823e+00
-#>  -6.9419e-01 -7.8668e+00  8.8550e-01  1.0576e+01  6.2078e+00 -4.0643e+00
-#>  -8.6446e+00  4.2446e+00 -2.4906e+00  5.2612e+00  9.2999e+00  7.0488e-01
-#>  -5.8305e-01 -7.3184e+00  2.9134e+00 -4.2438e+00  1.0961e+00 -5.6040e+00
-#> 
-#> (5,.,.) = 
-#>  Columns 1 to 6 -2.3830e+00  1.5045e+01  1.0755e+01  1.9093e+00  3.4693e+00  6.0668e+00
-#>  -9.1152e-01 -3.0114e+00 -2.5333e+00  5.3111e+00  7.9649e-01 -1.6620e-01
-#>   3.2936e+00 -8.9781e+00 -3.2925e+00  4.2987e+00  8.5330e+00 -2.5871e+00
-#>  -6.7483e+00 -1.5892e+00 -8.2601e-01  8.3264e+00  2.0445e+00  8.9829e-02
-#>   7.6095e-01 -3.3332e+00  2.8401e+00 -6.2223e+00  3.0014e+00  5.3401e+00
-#>   4.4991e+00  6.8539e+00 -7.9476e-01 -1.1602e+01 -1.2376e+01  5.7659e+00
-#>  -3.9050e-01 -6.8403e+00  3.2067e+00  2.6319e+00  2.6960e+00  5.4775e+00
-#>   2.5490e+00 -2.2545e+00  1.9085e+00  1.9497e+00 -5.6462e+00 -5.9350e+00
-#>  -7.9047e+00  2.6436e+00  2.0883e+00 -4.8206e+00  1.5255e-01  9.9853e-01
-#>  -6.8098e+00 -4.4973e+00 -9.4591e-01  4.4095e+00  6.2232e+00  4.2034e+00
-#>   3.6400e+00  4.5510e+00 -1.4693e+00 -1.9791e+00  1.4600e+01 -1.1596e+01
-#>   2.0117e+00 -7.4495e+00  1.9576e+00  1.4078e+01 -1.7327e+00 -7.9045e+00
-#>  -5.4675e+00 -8.1726e+00 -2.1059e+00  6.7968e+00 -4.1634e+00  7.1000e-01
-#>   3.3354e-01 -2.4415e-02  1.1163e+00  5.2675e+00  4.8768e+00 -1.1194e+01
-#>   5.9835e+00  8.5104e+00  4.7339e+00  1.0464e+01  4.9154e+00  2.1113e+00
-#>  -1.3915e+01 -1.9716e+00 -2.9983e+00 -1.9405e+00  2.1733e+00 -9.8436e-02
-#>   7.1030e+00 -2.7523e+00  3.8541e+00 -6.1061e+00  6.9082e-01  4.5209e+00
-#>   9.2541e+00 -1.7739e+00 -4.2750e-01 -9.3434e+00 -6.3315e-01  1.9368e+01
-#>  -3.5566e+00 -1.6541e+00  4.1912e+00  4.9319e+00  9.8950e+00 -1.2191e+01
-#>   1.1695e+01 -3.1335e+00 -3.6334e-01  5.4959e+00  1.1974e+01 -3.9265e+00
-#>   5.5305e+00 -2.7223e-01 -8.4738e+00  3.1758e+00 -1.2866e+00 -6.1654e+00
-#>  -5.3902e-01 -3.0807e+00 -9.3326e-01  7.4006e+00 -3.2220e+00  7.1673e+00
-#>  -5.6331e-01  4.7113e-01  1.0444e+00 -5.5636e+00  7.4316e-01 -1.0495e+01
-#>  -5.2943e+00 -3.6060e+00 -2.2940e+00 -2.6019e+00  1.6932e+01 -9.5823e+00
-#>   1.2445e+01  1.6621e+00  1.4975e+00 -4.6527e+00  3.0234e+00 -3.0627e-01
-#>   3.7138e+00 -7.4142e-01  6.6150e+00 -2.3159e-01  2.8262e+00  1.0662e+00
-#>   7.6823e+00  8.5604e+00  9.3044e+00  5.1059e+00 -6.4475e+00 -5.8448e+00
-#>  -3.1239e+00 -6.4697e+00  2.3946e+00  3.6733e-01 -7.1365e+00 -9.1770e+00
-#>   1.0079e+01  1.0610e+00  7.7564e+00  9.0786e+00  3.1420e+00 -1.1080e+01
-#>   3.1758e-02 -6.1169e+00 -2.4867e+00 -7.9838e+00  1.3619e+00 -9.4099e+00
-#>   2.5039e+00  9.3481e+00  6.4029e+00  3.7382e+00 -3.6803e+00 -1.0931e+01
-#>  -7.1225e+00 -5.6279e-03  5.9118e+00  1.0263e+01  2.1115e+00 -7.7262e+00
-#>   2.5962e+00 -4.0675e+00  1.1823e-01  4.6199e+00 -8.2061e+00  7.5710e+00
-#> 
-#> Columns 7 to 12  1.8408e+01  1.5652e+00 -1.9543e+00 -2.8085e+00 -6.8231e-01  4.1349e+00
-#>  -6.5164e+00  1.4820e+01 -1.1288e+00  4.5189e+00  8.7818e+00 -4.2647e+00
-#>  -7.7523e+00  7.2089e+00  1.1656e+00 -2.2606e+00  2.2263e+00 -8.8486e+00
-#>  -6.4213e+00 -7.8116e-01 -1.3705e+01 -1.4288e+00  7.0685e-01 -9.6210e+00
-#>  -5.2904e+00 -4.3724e+00 -4.5616e+00 -2.1462e+00 -1.6026e+00  2.4151e+00
-#>   7.6076e+00  1.4017e-01  7.1753e+00  1.6275e+00 -1.8538e+01 -6.3133e+00
-#>  -3.0048e-01  1.1206e+00 -2.7181e+00 -8.8819e+00  1.1768e+01 -2.3927e-01
-#>   3.9040e+00  1.5906e+00 -4.6165e+00  5.6143e+00 -2.3985e+00  3.8737e+00
-#>  -1.8581e+00  4.4859e+00 -8.5661e-01  9.1416e+00 -7.1321e+00  1.5359e+00
-#>   2.5870e+00  6.4013e+00 -3.5112e+00  3.0193e+00 -9.9813e-01  4.9914e-01
-#>  -5.6749e-01 -2.5634e+00 -2.0049e+01  7.6160e+00 -5.6121e+00  1.2002e+01
-#>  -5.1016e+00  4.5954e+00 -6.6194e+00  1.0558e+01  8.4648e+00  4.0876e+00
-#>   1.9074e+00 -4.4138e+00  1.6856e+01 -2.3015e+00  9.8041e+00  5.1201e+00
-#>   7.9012e+00 -7.8831e+00  6.1398e+00  3.3781e+00 -1.0034e+01  1.1867e+00
-#>   3.8663e+00 -8.9898e+00  8.8156e+00 -1.6206e+01  1.1986e-01 -2.6682e-02
-#>  -3.2041e+00 -1.4711e+01 -1.0443e+00  5.8869e-01 -9.3162e+00 -3.5679e+00
-#>   8.5277e-01 -6.4199e-01 -6.1192e+00 -1.5057e+01  7.4858e+00  1.3985e+00
-#>   2.5524e+00  1.4245e+01 -5.8290e+00 -2.5738e+00 -9.5414e+00 -2.3828e+00
-#>  -1.0419e+01 -1.1870e+01 -3.9730e+00  1.5484e+00  6.0266e+00  8.5218e+00
-#>  -3.8284e+00  1.5833e+00 -6.3376e-01 -1.2201e+01  5.7151e+00  1.7104e+00
-#>  -7.7268e+00  4.3423e+00 -1.8297e+00  1.2690e+01  6.7800e-02  8.8895e+00
-#>   2.4643e+00  2.1095e+00  2.8859e+00  4.3288e+00  6.2184e-01  4.8834e+00
-#>   1.5286e+00 -5.3391e-01  6.7303e+00  2.7903e+00 -2.8789e-01 -4.8311e+00
-#>   5.6804e+00 -1.5724e+00  4.5631e+00  1.4864e+00  1.0664e+01  7.5194e+00
-#>  -5.1199e+00 -6.2939e+00 -1.4950e+00 -2.8765e+00 -3.7088e-01 -3.6455e+00
-#>   9.5212e+00 -1.0249e+01  4.2535e+00 -1.1122e+00  1.0850e+01  5.5438e+00
-#>  -9.8939e+00 -7.7922e+00 -6.7318e+00 -3.9111e+00 -5.4714e-02 -1.3669e+00
-#>   4.4817e-01 -1.5928e+01  7.6097e-01 -9.1389e+00  8.9798e+00 -6.3461e+00
-#>   1.1088e+00 -5.1600e+00  1.5623e+00  4.1705e+00 -3.6206e+00 -2.3324e+00
-#>   2.9760e+00 -4.8346e-01  2.1871e+00 -7.7891e+00 -8.0293e+00 -6.5323e+00
-#>  -1.4006e+01 -1.1111e+01 -9.3578e+00 -1.0229e+01 -9.0496e+00 -5.3950e+00
-#>  -6.5811e+00 -4.4025e+00  7.0217e+00 -1.9153e+00  1.4510e+01 -6.2039e+00
-#>  -8.7036e+00 -6.6167e+00  7.2552e+00  8.6312e+00  3.9875e+00  7.1604e-01
-#> 
-#> Columns 13 to 18 -4.0327e+00  1.2063e-01  3.8963e-01  1.2082e+01  4.4529e+00 -4.0982e+00
-#>   7.4974e+00  1.8638e+00 -8.1714e+00 -2.7034e+00  5.9391e-01 -1.8888e+00
-#>  -1.9006e+00 -4.8901e+00 -5.2321e+00 -1.3496e+01  1.1133e+00 -1.3292e+00
-#>  -1.7714e+00 -6.2307e-01 -1.4108e+01  7.9459e+00  4.0040e+00  4.6520e+00
-#>  -5.9653e+00 -6.6982e+00  2.3026e+00 -4.1065e+00 -9.7532e-01 -4.1826e+00
-#>   9.5455e+00 -1.2973e+01  9.3023e+00  8.9305e+00 -2.2556e+00 -8.2025e+00
-#>   6.3662e+00  2.3125e+00  6.1421e+00 -2.8569e+00 -6.1378e+00  3.1209e+00
-#>   1.7458e+01  1.5194e+00  1.1149e+00 -1.0232e+00 -5.4569e+00  4.5391e+00
-#>  -8.9644e+00  5.6622e+00 -9.7496e+00  6.8962e+00  2.9958e+00 -5.5083e+00
-#>  -3.4634e+00 -4.3375e+00 -3.3078e+00 -1.6976e+00 -1.8537e+00 -8.7774e-01
-#>   6.9377e+00  3.8605e+00  6.0268e+00  2.3274e+00 -8.8593e+00 -2.4879e+00
-#>   6.8199e+00 -5.9747e+00 -7.2865e+00 -1.4146e+00  7.5257e+00 -2.5840e+00
-#>   5.0461e+00 -6.7386e-01 -8.0948e+00 -6.2207e+00 -4.3244e+00 -6.1133e+00
-#>  -2.8252e+00 -2.5870e+00  1.8028e+00 -3.6711e-01  2.0952e+00  3.6568e+00
-#>   2.3060e+00 -6.2086e+00  3.9923e+00 -1.4150e+00 -8.5676e-01  5.8699e+00
-#>  -1.3242e+01  6.3704e+00 -1.0038e+01  2.3710e+00 -1.4868e+01  6.5075e+00
-#>  -2.8410e+00 -6.0744e+00  9.4370e+00  1.7102e+00  5.2457e+00 -7.8648e+00
-#>   8.1169e+00 -9.1841e+00  8.2035e-01  4.0471e+00 -2.7658e-01 -3.2713e+00
-#>   7.9194e+00 -5.5048e-02 -3.5546e+00  2.4541e+00 -9.2249e+00 -5.8647e+00
-#>  -1.7885e+00  5.6227e+00  9.0222e+00 -1.3317e+01 -3.9801e-01  1.6702e+01
-#>  -3.9880e+00  8.6878e+00 -2.2664e-02 -7.4790e+00 -1.3203e+00  1.8476e+01
-#>  -4.4812e+00  5.0602e+00 -3.5708e+00  1.2414e+00 -2.9848e+00  1.2678e+01
-#>  -4.5570e+00 -2.6359e+00  1.4333e+01  9.2020e-01  4.0976e+00  4.4490e+00
-#>  -1.1185e+01  3.1190e+00 -5.5076e+00  4.0855e+00  6.6168e+00  1.6649e+00
-#>  -6.8375e-01 -2.9971e+00  3.8258e+00  5.9877e+00  6.4271e+00 -5.3766e+00
-#>   1.2921e+01 -1.8491e+00  1.1426e+00 -2.6788e+00 -1.8496e+01  7.4915e+00
-#>  -7.3414e+00  3.5322e+00  2.5229e+00  5.7388e+00  1.1160e+01 -3.6650e+00
-#>  -3.9620e+00 -4.8352e-02  7.4024e+00 -7.2069e-01 -9.0369e+00  1.8158e+01
-#>   1.4811e+00 -2.8406e+00  2.1601e+00 -6.3998e+00  8.4908e+00  7.1127e+00
-#>  -1.0033e+01 -8.8568e-01  4.8732e+00  1.6056e+00  6.6930e+00 -8.4509e+00
-#>  -3.1729e+00 -1.4420e+01  2.9933e+00  9.6449e+00  6.0507e+00  3.3586e+00
-#>  -5.9448e+00 -4.6698e+00  2.9528e+00 -4.8673e+00  3.8821e+00  2.8102e+00
-#>   2.5439e+00  5.1470e-01 -2.2484e+00 -7.2786e+00 -2.3403e+00 -1.0638e+00
-#> 
-#> Columns 19 to 24  9.0982e+00  5.1736e+00 -2.4757e+00  1.5844e-01 -6.7332e-01  5.6665e+00
-#>  -4.8000e+00 -8.6078e-01  9.5131e-01 -2.8858e+00  1.2703e+00  4.7499e-01
-#>  -7.5245e+00  3.5997e+00  5.3406e+00 -3.8828e+00  5.1932e-01  7.5683e-02
-#>  -8.4505e+00  1.1701e+01 -2.4831e+00 -1.4745e+01  5.4654e+00 -1.7338e+01
-#>   1.0567e+01  6.2607e+00 -3.6660e+00 -8.7202e+00 -1.5690e+01 -2.2930e+00
-#>   9.8490e+00  8.0237e+00 -3.6052e+00  3.3250e+00 -7.4190e+00  4.9683e+00
-#>   7.4106e+00 -6.2680e+00  5.1907e-02 -1.2005e+01 -6.8563e+00  4.7717e+00
-#>  -9.6498e+00 -3.7699e+00  1.3658e+00  4.0453e+00  6.3397e+00  2.1112e+00
-#>   3.6101e-01  2.1361e+00 -5.9951e-01 -7.0478e+00  3.9619e+00 -1.2897e+01
-#>   1.7499e+01  1.4941e+00 -1.6669e+00 -1.9513e+00 -1.1608e+01  6.2823e+00
-#>   5.6085e+00  3.7327e+00 -1.0657e+01  6.8685e+00 -2.5067e+00 -2.8697e+00
-#>   4.8413e+00  1.7147e+00  3.9855e+00 -2.3646e+00 -1.2719e+00  5.9345e-01
-#>  -1.1901e+01  2.1154e-01  1.2861e+01  6.5933e+00  5.5342e+00 -1.1736e+01
-#>  -4.1619e+00 -4.0502e+00  4.5874e+00 -5.9631e+00  6.2003e+00 -1.5919e+01
-#>   3.2160e+00  6.1556e+00  5.7512e+00 -2.4275e+00  1.1169e+00 -7.0630e+00
-#>   3.8809e+00 -5.3835e+00  5.9786e+00 -1.2241e+01 -1.6439e+00 -1.5627e+01
-#>   1.7291e+01  2.2233e+00 -5.2146e+00  6.4464e-01 -5.5866e+00  1.7714e+01
-#>   5.3964e+00  1.3962e+01  1.9331e+00 -1.3314e+00 -5.3025e+00 -2.3913e-01
-#>  -7.8175e+00 -7.1026e+00 -6.0704e+00 -8.0185e-02 -2.7631e-01 -3.9497e+00
-#>   2.5051e+00 -6.0993e+00 -5.4260e+00 -5.7393e+00 -3.3067e+00  9.7217e+00
-#>  -1.2314e+01 -1.6606e+01  1.5940e+00 -6.1850e+00  1.7046e+01 -4.5048e-01
-#>  -8.8948e+00 -5.6061e+00  5.1760e-01 -4.5070e+00  2.5624e+00 -1.1760e+01
-#>  -8.3118e+00 -4.8301e-01  2.4386e+00  1.1577e+00 -5.4625e+00  9.3602e+00
-#>   1.0364e+01 -1.7660e+00 -7.1697e+00  4.0364e+00  6.2778e+00  8.9278e+00
-#>   9.0088e+00  3.4713e+00 -1.4689e+01 -3.7752e+00 -2.6339e-01 -5.4922e+00
-#>  -6.4503e+00  7.0307e+00 -2.5707e+00 -4.4928e+00 -3.8815e+00  2.7975e+00
-#>  -4.7806e+00 -1.3645e+00 -2.0122e+00 -2.8309e+00  3.8261e-01  9.4296e+00
-#>   1.8947e+00 -7.1179e+00  2.2631e-02  3.1296e+00 -5.7349e-01  6.5689e+00
-#>   2.1592e+00 -2.2114e+00  1.0351e+00 -1.8950e+00 -2.5478e+00 -7.2731e+00
-#>   9.4142e+00  3.9147e+00 -3.7307e+00  6.2922e-01 -2.9651e+00  7.1303e+00
-#>  -2.8823e-01  7.4542e+00 -4.3850e-01 -7.6315e+00 -3.2843e+00 -6.0237e+00
-#>   9.5521e+00 -1.9974e+00  2.2915e+00  2.4075e+00 -7.1246e+00  3.7398e+00
-#>  -4.8636e+00  1.7748e+00  9.8526e-01 -3.3251e+00  3.6598e+00 -1.3092e+01
-#> 
-#> Columns 25 to 30  3.1280e+00  1.2788e+00 -5.9366e+00 -4.2194e+00 -1.0509e+01  1.7713e+00
-#>  -5.3974e+00 -4.3479e-01  4.8893e+00  4.0898e+00  5.8615e+00 -5.5451e+00
-#>  -7.2648e+00  1.4969e+00 -6.0060e+00 -3.7986e+00  4.8079e+00  6.1958e+00
-#>   7.6044e+00 -5.9375e+00 -1.1993e+01  3.3922e+00  5.0549e+00  9.1746e+00
-#>   5.0515e+00 -5.0973e+00  4.8219e+00  1.0619e+00  4.5165e+00 -8.5143e+00
-#>   2.8204e+00 -3.9292e+00  9.8225e-01 -6.2513e+00  3.4724e+00  5.7119e+00
-#>  -5.8743e+00 -4.5231e-01 -8.1410e+00 -4.4791e+00  1.2651e+01  1.2361e+00
-#>  -7.2702e+00 -2.2595e+00  6.5974e+00  3.6935e+00 -1.9022e+00  7.6575e+00
-#>   9.8234e+00  2.3628e-01  2.1644e+00  1.7129e+00 -6.3852e+00 -1.3977e-03
-#>   7.5595e+00 -2.3019e+00  9.2416e+00  2.9271e+00  4.4853e-01 -4.5047e+00
-#>  -2.7705e+00 -7.2757e+00 -2.2790e+00 -2.7667e+00 -2.1357e+00 -3.7666e+00
-#>  -1.5240e+00 -2.3198e+00 -1.0283e+01 -3.7943e+00 -9.4341e-01  6.0867e+00
-#>   2.9003e-01  1.1639e+01  5.3857e+00 -3.1820e+00 -1.7843e+00  1.6590e+00
-#>   1.1793e+01 -5.1962e-01 -1.2450e+01 -7.9430e+00 -1.1940e+00  2.2993e-01
-#>   3.3375e+00  1.0644e+01  5.7075e+00 -6.2523e+00  1.4935e+00 -8.8530e+00
-#>   7.4632e+00 -1.0253e+01 -5.3447e+00  2.2636e+00 -3.4865e+00 -2.5092e+00
-#>   6.9494e+00  6.0295e+00  2.3967e+00 -4.1855e+00 -8.0001e-01 -6.9067e+00
-#>   6.3739e+00  3.9918e-02 -5.2436e+00 -6.9196e+00  4.8604e+00  1.7641e+00
-#>  -8.2620e+00 -6.7295e+00 -1.7506e+00  4.9274e-01 -5.8497e+00  4.8315e+00
-#>  -8.1358e+00 -8.7697e+00 -1.4823e+00 -1.1388e+01 -4.1414e+00 -4.4856e-01
-#>  -2.1427e+00 -8.0484e+00  1.3658e+00 -4.8648e+00 -1.7214e+00  2.5082e+00
-#>   4.4764e-01 -1.6163e-01 -2.3081e+00 -3.4154e+00 -3.8190e+00 -2.5088e+00
-#>   6.5203e-01 -6.3120e+00 -2.4546e+00 -4.3488e+00 -4.5031e+00 -4.1094e+00
-#>   8.2619e+00  2.1131e-01  7.1995e+00  3.4129e+00 -4.1694e+00  6.2864e+00
-#>  -1.2033e+01 -1.0293e+01 -8.4255e+00 -1.1377e+00 -7.0015e+00  1.5763e+00
-#>   1.9668e+00  8.1584e-01  1.2777e+01  3.1784e+00 -6.6701e+00 -3.4124e-01
-#>  -3.6915e+00 -5.8549e+00 -4.4516e+00  4.3056e+00  4.6994e+00 -2.7244e+00
-#>  -1.6473e+00  1.9608e+00 -5.7647e+00 -7.5848e+00  4.9922e+00 -4.6011e+00
-#>  -8.8363e+00  3.1260e+00 -2.4390e+00  1.9239e+00 -1.3892e+00  3.3748e-01
-#>  -1.2075e+00  7.8508e+00 -3.4638e+00 -5.1220e+00 -1.4918e+01 -1.8710e+00
-#>   1.6721e+00 -1.1214e+00 -1.0962e+00  2.9899e+00 -4.2842e+00 -1.8938e-01
-#>  -2.8022e+00  4.7344e+00  4.4270e+00  6.5299e+00  1.0474e+01 -4.4890e+00
-#>  -5.0150e+00  2.8116e+00 -1.5807e+00 -4.7746e-01 -7.7342e+00  1.1266e+00
-#> 
-#> Columns 31 to 36  3.5927e-01 -5.8005e-04 -1.1270e+01  9.0515e+00 -8.1627e+00  6.4540e+00
-#>   3.4068e+00 -1.2006e+01  5.3405e+00  8.2016e-01  6.0324e+00  7.1262e+00
-#>   3.7822e+00 -2.6076e+00  2.8620e+00 -5.2815e+00 -2.5095e+00 -1.0144e+00
-#>  -8.2290e-02 -1.3884e+01  1.2595e+00 -3.4814e-02 -9.7531e+00  5.2547e-01
-#>  -1.4943e+01  5.3147e+00  5.4917e+00 -1.0074e+00  9.0445e-01 -1.3210e+01
-#>  -9.3631e+00 -3.5228e+00  4.4674e+00 -1.0704e+01  7.5457e+00 -6.0601e-01
-#>  -1.3818e+01  5.2119e+00 -1.1345e+01  3.5436e+00  1.3895e+00  1.4796e+00
-#>   2.4844e+00 -8.3415e+00  6.8261e+00  4.8637e+00  5.8557e+00  1.5788e+01
-#>  -6.6571e+00 -1.9480e-01 -5.0670e+00  5.2520e-02 -9.8149e+00  1.1303e+01
-#>   5.5721e+00 -6.1646e+00  1.1623e+01 -1.4157e+00 -5.3222e-01 -5.4600e+00
-#>  -6.5203e+00 -6.7455e+00  7.7968e+00 -9.9367e+00  1.3339e+01 -1.4284e+01
-#>  -1.4378e+00  8.9679e+00  9.4038e+00 -8.1249e-01  6.5240e-02  3.7578e+00
-#>   9.4793e+00 -4.9221e+00 -1.6958e+00  2.6819e+00  2.1806e+00 -3.3611e+00
-#>  -1.5861e+00  2.8447e-01 -1.2740e+01 -8.7952e+00  5.9439e+00 -3.9432e+00
-#>   4.3443e+00 -6.5303e+00 -7.6826e+00 -4.1726e+00  7.0762e+00 -1.4766e+01
-#>   1.7967e+00  1.0627e+01 -8.8136e+00  3.6239e+00 -9.1490e+00 -2.4560e+00
-#>  -1.2485e+01  8.5619e+00  2.9441e+00  1.5589e+01 -4.7064e+00  1.8462e+00
-#>  -4.0600e+00 -1.4710e+00  4.7904e+00 -1.8557e+00  8.3307e+00 -7.1752e+00
-#>  -3.9976e+00 -6.5843e+00  2.6554e+00 -3.1964e+00  1.0752e+00 -4.8778e-01
-#>   3.3116e+00 -1.7098e+00  3.2721e+00 -4.4763e+00 -2.2594e+00 -7.9010e+00
-#>   5.7161e+00  6.5650e+00 -1.6615e+00 -1.1369e+01  4.7180e+00  5.7694e+00
-#>   8.5136e+00 -5.9878e-01 -1.5651e+01  8.4650e-01 -2.4974e+00  6.7638e+00
-#>  -5.8394e+00  5.7140e+00 -1.1210e+01 -2.2551e+00  1.6443e+00 -6.3403e+00
-#>   4.9703e+00  1.8214e+01 -9.8342e+00 -3.6146e+00 -5.6450e-01  7.4347e-01
-#>  -7.1601e+00  8.6449e+00  1.8380e+00 -6.6168e+00 -9.3282e+00  4.5143e+00
-#>  -7.4665e+00 -4.2576e+00  1.4170e+01  3.6051e+00 -4.7992e+00  3.7910e-01
-#>  -1.4914e+01  3.7157e+00 -8.1508e+00  7.3911e+00  1.0948e+00  4.0697e+00
-#>  -1.0161e+01  3.9304e+00  3.7996e-01 -1.3344e+01  9.3577e+00 -1.5207e+01
-#>  -1.7293e+00 -2.0529e+00  1.0785e+01 -9.5662e+00 -1.9826e+00  5.8388e+00
-#>   2.3011e+00  4.4566e+00 -1.1964e+01 -7.5399e-01 -9.7352e+00 -6.9948e+00
-#>   2.0763e+00 -1.1806e+01 -4.8473e-01  5.3601e+00  2.5147e+00  8.4158e+00
-#>   7.8955e+00 -2.6015e+00  6.0200e+00 -2.1845e+00 -1.3762e+01 -6.8676e+00
-#>   4.4075e+00  1.2753e+01  1.1472e+00  2.4227e+00 -1.0693e+01  4.7613e+00
-#> 
-#> Columns 37 to 42 -1.1020e+01  6.6465e+00 -1.9290e+00 -4.8945e+00  1.8490e+00  1.5339e+00
-#>  -4.5310e+00 -1.0638e+00 -4.4853e+00  7.1065e+00 -8.2337e-01  6.9402e+00
-#>   1.7499e+00 -3.6581e+00  1.2350e+00  2.8491e+00  1.7408e+00 -1.7779e-01
-#>  -1.4229e-01  5.2913e-02 -2.4037e+00  8.6961e+00 -1.7492e+00  5.9939e+00
-#>  -5.8223e+00  1.6047e+00  6.7784e+00  2.6981e+00 -1.2013e+01 -1.1739e-01
-#>  -1.6281e+01 -1.5035e+00 -2.1397e+00 -6.4374e+00  6.2523e+00 -9.6294e+00
-#>   2.9919e+00  4.3445e+00  1.8871e+00  8.1929e+00 -1.2139e+01 -1.4848e+00
-#>   2.7343e+00  1.6345e+00 -3.4733e+00  3.3497e+00  6.7508e+00 -1.8764e+00
-#>  -1.9815e+00  4.4974e+00 -1.5160e-02 -2.7958e+00 -4.8445e-01 -1.2368e+00
-#>  -2.6236e+00 -1.4458e+01  5.1467e+00  4.9547e-01 -3.0636e+00 -5.2946e-02
-#>   1.2763e+01 -6.6685e+00  2.1419e+00 -2.5938e-01  1.0607e+01  9.4662e-02
-#>  -2.5186e-01  1.7765e+00  1.8338e+01  5.5510e+00 -1.2427e+00  3.7455e+00
-#>   1.2176e+01 -1.7097e+01 -1.3656e+00  5.5532e+00 -7.6164e-01 -8.6892e-01
-#>  -6.6048e-01 -4.9274e+00  1.1193e+01 -3.6997e+00 -6.0456e-01 -2.1174e+00
-#>  -2.6654e+00 -6.7153e+00  4.3796e-01  8.9276e-01  3.0882e-01  6.6746e+00
-#>  -3.7119e+00 -4.4686e+00  3.3810e+00  1.2998e+00  5.5979e+00  1.9030e+00
-#>  -2.1550e-01 -7.2725e+00  3.9841e+00  1.9504e+00 -1.3238e+00 -9.6834e+00
-#>  -1.7437e+01  7.4582e+00 -5.9427e+00  5.0374e-01 -2.0163e+00  6.8246e+00
-#>   1.0537e+01 -6.8816e+00  3.1621e+00  4.3694e+00  1.6787e+01  4.0453e+00
-#>   7.4132e+00  2.5091e+00  5.2410e+00  6.6752e+00 -7.1528e+00 -4.8371e+00
-#>   6.0238e+00  1.1466e+01  2.0985e-01  1.3284e+01 -6.6727e+00  9.7171e+00
-#>   1.6460e+00  1.2047e+01  1.4384e+00 -9.1026e-01 -3.0676e+00 -3.0724e-01
-#>  -2.9586e+00 -8.5117e+00  2.8998e+00 -7.8197e+00 -9.8610e+00  4.7346e+00
-#>   3.0021e+00 -6.0272e+00  2.6469e+00 -4.5248e+00 -1.1962e+01 -5.6412e-01
-#>  -2.9277e+00  1.6119e+01 -4.9798e-01  5.0180e+00  1.2726e+01  6.9309e+00
-#>  -1.4897e+00 -8.5421e+00 -9.3005e+00  6.3357e+00  2.9906e+00 -3.1236e+00
-#>   3.2158e+00  1.0030e+01  7.0613e+00 -3.3065e+00 -7.8101e+00  1.4496e+00
-#>  -3.6055e-01  2.4626e+00  1.1457e+01  8.1908e+00  9.5694e-01 -1.1264e+01
-#>  -6.4157e+00 -2.8063e+00  6.9518e+00  9.7039e+00  1.0435e+01  2.9756e+00
-#>  -2.5457e+00 -2.9342e+00 -1.4990e+00 -1.3760e+01  5.3558e+00 -1.0135e+01
-#>   2.4038e+00  8.8341e+00  1.1209e+01  1.9843e+00  3.2433e+00 -2.7260e+00
-#>   5.9133e+00 -8.8468e+00  3.7391e+00  3.1379e+00  7.2138e-01  4.3503e+00
-#>  -9.0470e+00  2.9551e+00 -7.0717e+00  4.9854e+00  4.3781e+00  4.0203e-01
-#> 
-#> Columns 43 to 48 -8.8934e+00 -5.8731e+00  5.3382e+00  3.8283e+00 -7.9933e+00 -6.6487e+00
-#>  -2.6201e+00  6.9511e+00 -1.7930e+00  1.0116e+01  3.7670e+00  9.1283e+00
-#>   1.2525e+00  7.8141e+00 -2.4204e+00 -5.5882e+00  6.7728e+00  1.0990e+01
-#>  -9.0517e+00  5.1123e+00  2.6250e+00 -3.6983e+00 -1.6602e+00  3.9326e+00
-#>  -1.8962e+00  2.7367e+00 -1.8428e+00 -2.0395e+00 -5.9638e+00  1.1027e+01
-#>  -4.4575e+00  1.3333e+01  3.1724e+00  7.7592e-01 -5.7508e+00  1.6969e+01
-#>   8.7714e+00  1.4718e+00 -6.6618e+00 -7.8995e+00  9.0566e+00  9.4957e+00
-#>   4.8238e+00  6.6510e+00 -8.4335e+00  1.5356e+00  2.9097e+00  5.2194e-02
-#>   8.9002e-01  3.0509e-01  3.3963e-01 -4.1054e+00 -4.2206e+00 -3.0510e+00
-#>  -1.1020e+00 -4.3733e-01 -3.6850e+00  7.9086e+00  4.6608e+00  6.0527e+00
-#>   7.2235e+00  2.0171e+00 -1.2581e+01 -1.5761e+01 -6.6077e-01  2.3741e+00
-#>   3.0645e+00  6.5877e+00  5.2796e+00 -7.6957e+00 -5.3312e+00  9.6305e+00
-#>   2.5498e+00 -3.3503e+00 -4.4238e+00 -1.2314e+01  7.3962e+00  1.7007e+00
-#>   3.7125e+00 -4.4944e+00 -3.4737e+00 -9.5028e+00 -3.9384e+00 -1.1472e+00
-#>   2.9937e+00  1.5265e+00  3.5508e+00  2.3970e+00  9.5820e+00  1.5473e+00
-#>  -1.3902e+00 -8.5131e+00 -1.1774e+01 -4.6225e+00  7.6096e+00 -7.9541e+00
-#>   6.0351e+00  5.2485e+00  7.6588e+00  4.2709e+00  4.7346e+00  6.0520e+00
-#>  -3.9830e+00  7.0588e+00  6.1215e+00  4.3871e+00 -1.1102e+01  8.4514e+00
-#>  -2.8928e+00 -7.0470e+00 -9.8818e+00  1.0352e+00  4.5459e+00 -8.3517e-01
-#>   7.1982e+00 -3.6810e+00 -1.4942e+00 -9.4427e+00  1.7525e+00  4.1326e+00
-#>   9.4196e+00  3.0754e+00  1.7681e-01 -1.2273e+00 -7.9381e+00 -1.5840e+00
-#>   9.9200e-01 -3.7385e+00 -4.5607e+00 -9.1025e+00 -1.7741e+00 -5.9780e+00
-#>   3.4331e+00  6.9655e+00  2.2767e-01  5.4729e+00 -1.9004e+00  1.0142e+01
-#>  -2.0826e+00 -6.2107e+00  3.6577e+00  2.0191e+00 -1.2671e+00  2.2451e+00
-#>   3.1745e+00 -1.8666e+00  9.5549e-01  1.1877e+00 -5.7058e+00 -1.1480e+01
-#>  -3.5149e+00  1.5049e+00 -5.9846e+00  5.1825e-01  1.1530e+01  7.2957e+00
-#>   1.3030e+00  2.6066e+00  1.7635e+00 -9.6033e+00 -4.6952e-01  6.4410e-01
-#>   1.0071e+01  1.2886e+00  9.9078e-02 -1.1346e+01  2.7033e+00 -1.6566e+01
-#>   3.3214e+00 -8.9271e+00 -1.5329e+00  1.2822e+01  7.3061e+00 -1.0610e+01
-#>  -5.5313e+00 -1.9182e+00  5.2385e-01  3.3351e+00 -2.3038e+00 -2.5890e+00
-#>  -9.6380e+00 -2.2434e+00  4.5545e-01 -2.4699e+00 -8.2808e+00 -7.3125e+00
-#>   5.7026e+00  2.7890e+00 -1.1418e+01 -2.5375e+00  6.9807e+00  4.0363e+00
-#>  -4.7068e+00 -5.1611e+00 -9.9217e+00  2.1581e-01  5.1793e-01  1.5048e+00
-#> 
-#> (6,.,.) = 
-#>  Columns 1 to 8  -0.1104   0.9378  -5.7425  -1.3886  -6.7944  -1.2199   2.5925  -9.2208
-#>   -9.2785   1.3463   2.8895   6.0180  -2.2107  -3.1544   9.8532  -1.1469
-#>   -2.0518 -13.8740  -2.3745   6.4815  -3.4840  -8.7533   2.0470  -1.2614
-#>    2.8974   1.6251  -6.4410  11.9340  -4.1402   5.6601   2.2437   1.0130
-#>    2.1063  -0.8892   6.5428   0.7713   1.8376   0.2168  -1.7869  -3.1655
-#>    5.9192  -6.5299   3.9429  -4.7417  -2.0345  -2.1108   1.4094  -1.7040
-#>   -3.1214  -9.4695   7.8207  -5.2906   3.8265  -3.1306   0.2918   8.0468
-#>   -2.8765  -5.4424   5.3099   5.6219  -2.0720  -3.4595   6.4237   9.0764
-#>   -6.5373   2.7311   5.2222   3.8423   3.1656   0.4355   8.0006  -2.0814
-#>    1.2809   2.4835   1.5944   4.1153  -2.1983  -3.9559  -3.0186   0.6905
-#>  -12.6721   7.8146  11.8049  -1.7205 -10.0410   8.1629   3.5770 -11.8054
-#>    5.8688  -5.7280   6.1080   3.1844   7.2086  -9.3712  -2.7592   6.2454
-#>   -5.4817   8.3479   0.4329  -4.7231  -3.7640  -0.2761  -4.6210   0.6497
-#>   -2.6736  -0.6957   4.6696   0.0148  -5.5836   0.8055   0.7237  -2.8764
-#>    5.1874   2.4803  -2.1401   4.0009   0.3659   0.4488   8.4825   6.3955
-#>    8.2776  -6.1439  -6.7428   0.6655   4.5106  -7.2390  -1.9144  -5.6041
-#>    2.6683   0.6469  -7.0735  -0.0103   9.7698  -1.6489  -1.4062   0.4159
-#>   -3.5536 -11.1788   8.0697  -5.3634   0.5343   5.5538   7.8198  -7.0964
-#>    7.5448   3.3799  -3.9270   0.0477   7.1459  -2.2052   0.3239   2.4638
-#>   -8.3648  11.6064  -1.8365  -2.8855  -3.7194  -9.8075 -13.9457   2.6624
-#>   -0.8695  -7.6880   2.9305   1.6563  -4.7491  -5.6331   7.2559   7.0108
-#>   -4.5930   8.6262  10.1692  -1.9337  -1.3028   1.9578   4.1001  -1.4597
-#>    5.8933   4.6195   0.6673  -3.3505   4.2870  -3.4126  -1.0499  -1.0670
-#>    3.0959  11.1196 -11.3394  -2.6640  -3.7993  -3.4703  -4.4409   4.4927
-#>    7.0263   1.0446  -5.2210  -0.0563   7.9781   3.2414   4.7680   0.0977
-#>    8.4570   2.5366 -12.1487   8.7612  -3.6473  -6.9408  -9.0835   3.5213
-#>    1.4681  12.0926   4.3367  -7.4203   5.1335  -1.2544   1.0279   2.6569
-#>   -1.6785 -21.8863  -1.3839   9.7845  -9.6162  -2.6612   3.2954  -1.9311
-#>    0.9169  -1.5492  -3.9720   3.8593  -0.8790   1.1036   1.5049   6.8214
-#>    0.2175 -10.2392  -8.1670  -1.4911  -2.5520 -10.8965  -1.0136  -5.1678
-#>    1.3053   0.3541  -2.9467   7.1714   4.1810   2.3582   4.0887   4.2044
-#>    5.4859  11.4342 -12.2243   6.8399   2.0426  -0.6062  -4.4571   6.7048
-#>   14.3603  -3.2200   3.9387   1.3679  -3.8398  -8.6160  -2.1590  -8.0406
-#> 
-#> Columns 9 to 16  -6.3904   5.2458  -4.8878   0.4777   9.6190  -4.0347   6.2520   0.1535
-#>    5.3624  -4.1868  -9.0603   6.5989  -2.7368  -0.5048  -8.6676   5.6638
-#>    5.2559   3.3481  -7.0247  -1.2221  11.6998   5.6450  -0.9032   6.0717
-#>    0.3988   8.7230   1.8028  14.9125   0.2944  -0.6893   1.6754  -1.2014
-#>    5.9161  -2.8328  11.3242  -0.0847   1.8416   2.2938  -4.5816   2.5918
-#>   -1.9162  -1.6693  12.8858  -7.4261  -4.7459  21.0233  -0.7926 -13.2537
-#>    0.4529  -1.3662  16.3296  -6.9726  11.5936  -2.3820 -12.0824  -0.4903
-#>   -3.9553   2.5013  -4.9634 -13.3355  -3.9944  -0.6187   6.8524  -4.1107
-#>    4.6945   4.5045  -8.6546  10.3891  -2.7178  -5.6847   4.6090   1.6181
-#>    9.8551  -1.0476  -7.6735   9.0922   5.5801   2.0157  -3.8528  14.7383
-#>   -1.8485   2.3732   5.4091  -0.7128   9.2158  -4.9523 -13.8347 -14.1544
-#>    1.9425  -4.4296  -0.9140 -20.1580   3.1129  -4.2815  -1.1541  -4.7674
-#>   -6.3371  -1.7931  -2.5522   3.0294  -5.5946   0.2522   2.1318   6.1523
-#>   -6.9545   4.2920   3.5247  12.1743  -8.3309   1.5324  -4.0184  -2.4786
-#>   -7.9861   0.6720   8.6008   8.4768  -5.7279   6.4261  -0.0801  -1.1222
-#>    2.8641  11.7266  -2.5167  11.6780  -3.7370   0.2652   7.3983   3.9098
-#>    9.1588  -3.0015  -3.0287   7.1194  -4.4060   0.8970  -6.1486   5.8799
-#>    1.1350   4.9105   5.6519  -7.8276   5.2783   3.6180  -5.6088  -4.1530
-#>   11.4123   0.2572  -2.4716  -1.5631  -4.3751  -8.8379  -9.4847 -10.3646
-#>   -2.2827  -5.8110   4.0167   5.5697  -1.9213  -6.4974  -6.3152   3.8903
-#>    0.8398   3.9075  -4.3331  -8.2620  -0.2703 -12.2609  -1.9502  -0.2135
-#>  -12.2079  -1.3780  -1.2939  10.9357  -1.0795 -15.5522   3.0758  10.4788
-#>   -1.6880  -4.5991  12.9073  -0.1889  -3.7366   3.0858  -3.7235  -6.3634
-#>   14.6158 -10.9498  -2.9490  14.3120  11.5878   8.4827  10.1251   7.4441
-#>   12.6866  -0.0641  -3.6279   3.0155  -9.3858 -11.3412 -11.1634  -9.6604
-#>   -1.7954   3.4595   3.4319  -3.6742  -8.5487  -3.0773   3.5025   1.9246
-#>   -2.6488   0.0993   6.1076  -3.1861   1.9818  -3.6518   6.4687  -0.0724
-#>   -1.3800  -9.8271   2.9700   5.3824  -0.3110   7.3517  -8.2606 -11.3658
-#>    7.2609   4.6560 -10.1995   8.5112  -9.4829 -10.5180  -6.1653  -0.7339
-#>    6.4446   3.1358   1.7575  11.6717  15.9693  -1.8292  -1.2986  -5.1633
-#>    5.2860   6.0090  -0.7773  -4.0574   2.2034   8.1937  10.7889   3.1794
-#>   -2.7170   0.8778   7.5877   0.2280 -13.0934  12.6423 -10.7424   2.5844
-#>   -3.7412   1.8666  -0.7106  -0.6447   2.4800  -1.3900   2.4915  -3.1562
-#> 
-#> Columns 17 to 24   5.4172  -8.8975   4.1241   4.2239  -9.0144   0.6235  -4.9852  -5.5359
-#>   -6.1860  14.5444  12.7085  -4.9095  -1.4238  -1.5653  -4.4326  -0.0120
-#>   -6.4332  15.0012   7.7659  -0.6413   3.6668   7.6917  -0.7941  -3.8065
-#>   -4.8550   0.6845  -5.9768   1.8772  -7.2331   7.2820   2.7222  -3.4189
-#>   -1.8828  -5.6188   3.9142 -15.6999   1.0825   3.6080   0.5859   0.2142
-#>  -10.4254  -7.3322   7.6011  12.1679   2.1166  -1.3719  -0.8404  -5.9731
-#>    1.6563  -8.6208   0.9193  -1.9948   4.1875   9.4993  -1.0747  -8.5283
-#>    3.6731   1.8565  -0.7910   9.2543   4.0691   0.0453  -3.9086  -7.4447
-#>    1.2494   1.7534   0.7568  -3.5535   5.5141   0.9877   0.9665   3.8092
-#>   -7.3750   5.0936   5.8954  -5.7369  -3.3362  -4.8037  -4.0630  -7.6349
-#>  -15.0747   6.5869  10.2412  -8.1852  12.6544  -6.4481 -17.7733   9.8440
-#>    2.3890   5.0567   2.5480  11.3571   6.9356   1.6431  -4.5357  -2.4146
-#>   11.5287   3.0665 -12.9552  10.2215 -10.5193  -0.5883   4.4301  -2.9194
-#>   -4.5341   4.6156 -11.1645   5.2721   4.7755  -5.4269   8.8919  -4.1058
-#>   -2.5422  -4.8962  -4.2533  -3.4713   0.4350  -0.3875   5.2587   6.7581
-#>   -2.2825  -1.3043  -5.7796   0.6123  -1.6119  -9.6504   6.3327  -4.8924
-#>   11.4267   3.9958  -5.8304  -0.8209  -9.3814  -1.0441   7.5002 -10.5179
-#>   -7.2332  -2.1754  21.7108  -6.8779   0.6091   2.9913  -5.5717  -9.2769
-#>   -8.4591   6.1532  -3.9296   0.5712  -5.5937   2.2535 -12.5900  -2.0844
-#>    4.4383   5.7097  -7.7355  -5.1943   5.3091   4.7351  -3.1911  -7.3453
-#>    2.7565  17.1407   7.5476   2.4632  18.5448  -0.6328   1.3705  -5.2688
-#>   21.0880  -6.0673 -15.0749  -6.9460  -0.4218 -10.5325   7.6761   7.2181
-#>  -13.9597  -5.9834 -13.9455   3.4727  13.5404   2.1127   6.0688   3.8601
-#>   -9.5446  -6.9907 -11.8360  -0.7782  -3.4148   0.4085  -7.5634   9.1145
-#>   -7.9267   4.0797  13.3777   0.9393   7.7659   7.1630  -9.1600  -2.5893
-#>    6.0249  -5.8716  -5.5973   0.0046  -3.0231   1.1144  -5.0819  -5.3707
-#>   -4.0046 -13.7106  -4.8585   4.0724   6.5528   8.7553   3.9177   6.3200
-#>   -0.9138   6.5573   8.3198   2.7524   4.8368  -0.0342   4.3763   0.9721
-#>   -4.3229  19.7568  11.2584   5.4611  -1.0099  -5.6638  -2.6794  -7.9756
-#>    3.7537   1.4670  -7.9778   1.0656 -12.9156   2.0157   6.9521   0.2425
-#>   -5.8054  -7.1049  -2.3444  -1.4620  -7.0817   3.2761   2.1241   0.6728
-#>   -4.7399   8.6810  -0.3137  12.5848  -2.7645  -4.2000  -7.7583  -4.0197
-#>    6.5134  -6.5004  -1.0067   3.1483  -7.6219  -9.4831   2.1260  -2.9960
-#> 
-#> Columns 25 to 32   0.9164   6.6364  -2.1096   7.6167   5.2051  -3.5309   5.7607  -3.6761
-#>    5.7481  -6.2620   5.5865  -7.7751   6.8103   4.0659  -5.4968   3.3758
-#>    8.8301   0.1947   1.3354 -10.1364   2.6818  -6.0935  -2.1317   5.1515
-#>    3.2282  10.2814  -1.9183  -5.0089   0.9938   0.0229 -14.7824  11.1888
-#>   -1.7901   0.0106   0.7380   3.1113   0.0016   3.8805   8.3862   7.7820
-#>   -5.4735   9.7038   0.6526   1.3334   2.2150 -16.1218  -2.6737  12.9847
-#>    7.7840  -3.3068  -1.8455   2.6265   0.7157  -3.4875   8.9484   1.1720
-#>   -5.0259  12.4743  -4.9562   4.6295   0.6454 -10.6554  -1.6643  11.3980
-#>    5.6431  -4.1585  -3.5017  -4.5217  -6.4074   2.7111  -0.2705   2.2997
-#>   -4.3305  -5.4521  13.0141  -1.0215  -2.0584   3.4156   4.8674  12.2027
-#>   -2.6645  -3.5395 -15.7281   3.9482  -7.8991   0.9436  -1.4279  -2.3085
-#>  -21.3975  -6.5419  -0.5706  10.4837   9.4136 -14.3615 -14.6530   9.4419
-#>   10.6879  -8.3461  -4.0221  -4.7406  -8.1651   0.4021   6.4712  -5.6879
-#>    3.2240  -2.9612   0.0767  -8.3518  -6.7247   3.3232  -3.6010  -3.9666
-#>   10.8131   6.4351   4.1059  -3.3593  -8.6990  -0.5441   8.8988  -9.9437
-#>  -11.7232  -2.4128   4.6083  -4.7411 -12.0841   8.8367  -2.9589   0.9392
-#>    6.9387 -11.6099  13.2340   0.9739   8.6156 -11.7220   7.7174   1.2421
-#>   -3.5701  -0.7452  -2.5017   6.0676   1.6873  -1.5639  -3.9503   9.2895
-#>   -1.7005   5.3688  -5.0744 -16.4163 -10.0591 -11.3082  -6.0288  -1.3911
-#>    3.5844  -5.8183   4.4966  -0.8732  11.8576   4.8436   1.3823  -1.7042
-#>   -6.3087   5.5004 -10.4076  -2.8330  -7.5559   0.0676 -10.2145  -0.1479
-#>    4.1106  -1.4924  -9.4611   4.5932  -3.8251  16.5333   1.7181   0.8679
-#>   -6.7011   4.7048   7.9716  -4.6916   3.7326  -8.1865 -15.0386  -4.2498
-#>    2.4939  -2.1954   7.4570  -0.5020   4.1974   1.0839  11.5044  -6.8840
-#>   -6.2699 -12.1483   2.7677  -7.6698   3.2706  -6.9778   1.7840 -10.8523
-#>   -8.1987   9.3442  -1.6294   1.6090  -3.3236 -11.7136  -0.4056  11.0062
-#>   10.5299  -1.8471   0.9151  -0.6181   4.6984  -2.4208  -5.0834  -1.4955
-#>   -3.2630   2.9769   0.9128  -4.3088 -10.9615   1.7562  10.8109  -7.8176
-#>   -2.5736 -10.3437  11.3399 -11.4009  -1.4814   0.4352  -0.8255  -4.6240
-#>   -9.0465  11.5223   0.9622  -0.6233   3.3622  -9.5351   8.7453   3.0839
-#>    0.4572   3.4590   7.2394   1.7829   4.6249  -0.4822  -1.4148  10.4267
-#>    2.5418  -7.5339   9.3177  -9.9249  -0.6547  -6.0251   2.0060  -3.2288
-#>   -9.2620   1.0335  -8.6599  -0.5788  -5.1153   1.5086   2.8250  -4.3265
-#> 
-#> Columns 33 to 40 -11.8986   2.4147  -4.4610  -0.6107   3.3662  -8.5069  -2.2273  -9.2873
-#>    8.6256  -1.8284   9.2563  -3.4672  -0.0532   1.5168  -5.4890   3.4163
-#>    4.2299  -1.9203   1.9084  -2.1578 -10.2429   7.3712   0.1574  -2.3494
-#>   -1.5205  -7.9155   7.4539 -18.9782  19.1242  -7.3024  -4.1140   5.9851
-#>   11.3259  -2.9690  -0.6773   6.6274  -1.2912   3.8586   5.5946   4.4924
-#>   -1.0683   9.5736 -10.4562  27.6726 -11.6011 -16.5427  14.6381   5.0497
-#>    7.0892  -9.6719  -9.7624   1.9728 -13.5009   7.0491   4.0457  -5.3395
-#>   -7.4001   3.7149  -4.0817  -8.0094  -7.2760  -3.4291   3.6974  -1.5636
-#>   -2.8440  -3.4581  -5.7945 -11.4628   6.9835  -9.2022   0.4153   0.5739
-#>   12.5440  -7.6764   0.5098   1.7730   9.1909   8.1671   2.5764  -7.0154
-#>  -15.5586   7.9803   6.0739 -10.7840 -12.0563  12.1422   7.7948  -3.1358
-#>    5.9472  -2.5652  -0.9360   5.3704   1.3411   0.7240  11.4107 -11.6393
-#>    8.1663  -5.4020   8.3506 -17.4386  -0.8662   2.8202   6.6108  -3.2091
-#>    1.4430  -6.1873   0.6227  -7.8972  -9.4365   0.6525  -3.3555  -0.8793
-#>    6.1050 -16.9247   6.6229  -5.1295 -18.5341   2.2928  -2.2324  -5.7622
-#>    9.0676  -5.8671   5.1432  -8.3789   8.0141   8.3037   7.0876   6.2444
-#>    9.7063   1.2566 -10.7937  -0.6233  -1.8329  -8.1201   1.5637   1.3777
-#>    2.8862   2.2535  -0.1103   8.3647  -7.7593   2.0481   5.5800   3.2890
-#>   -5.4787  -6.0615 -15.2475  -3.8781  -7.9978   9.5445  -4.6079  -0.9424
-#>    8.5528  -0.5881   4.5892   2.4927  -3.2778   8.8345  -3.8547   1.1981
-#>    0.0472  -3.2210  11.5313 -10.6498  -2.6684  -0.0521  -8.7467   0.4957
-#>   -3.0700  -6.1665   6.6928 -15.0433   3.9053   1.0258  -1.8824   0.8121
-#>    1.9634   3.3713  -0.2318   5.9410  -6.4410  -0.9862 -14.5837  18.1802
-#>    0.7374  -9.0851  -4.4673  12.4889   7.7687   6.9073  -7.1165  -0.6474
-#>   -1.6195   1.2593 -19.5939  21.1321  -5.2771   5.0347  -4.9280   4.1432
-#>    3.3800   2.1993 -10.3884  -0.5506  -3.8106   6.3740   1.3545   2.2678
-#>  -12.3134  -3.8762  -1.9338  -3.3150  -4.5496 -18.2740  -7.0708   5.3476
-#>    6.4340  -3.6236  11.0587  11.1597 -18.1282  13.3269 -13.9402  -2.0978
-#>    1.6286  -1.0577  -4.4468   7.5563   0.7859  11.6447 -11.9411  -3.2172
-#>    1.3110   6.0144   1.8548   4.5404  -7.4083   5.3819   3.6763  -4.2610
-#>   -7.4247  -1.7670  -4.6530  -8.1285 -10.1503  -3.5548   0.2605  -7.1283
-#>    0.1491  -3.1582   8.1519   0.3861   5.1820   2.5664   3.2590  -3.3111
-#>   -0.4734   2.3086   2.7129   1.1263   9.1693   9.1878  10.2963   6.6774
-#> 
-#> Columns 41 to 48   1.2782   3.0907   1.9709   2.3417  -4.3050   7.1534  11.8273   0.9176
-#>    2.4463  10.8948   1.6810  -8.5839  -0.7853  -4.7783 -14.4342 -11.8971
-#>    1.7757   5.7692   5.3072  -4.5191  -3.9519   6.7591  -0.8277  -3.8367
-#>    2.7664  12.2129 -13.4740  -5.2795  16.2868  -3.6188   4.1092  -1.3353
-#>    9.7374  -6.3342 -17.3494  -0.5017  -9.1257   4.6217   3.1513   3.2992
-#>    4.9675   4.2960 -21.1272  20.9616  -4.4320   1.8313   6.9209   4.1419
-#>   -0.6570   6.6272  -0.8664  10.3004  -7.1146  10.9799  -2.9945  -2.8342
-#>   -6.3293   3.1892   2.5886   5.4950   3.4890  -8.0250  -2.8254  -7.9372
-#>   -0.5784   3.8178   4.4441  -3.1310   7.4993   3.6207  -0.3728   2.2499
-#>    8.4700  -1.6866  -3.0571 -16.1363   5.3445  -0.7077  -9.9704  -6.9214
-#>    2.5221 -11.8837   8.1810   1.1480  -4.3493   8.9619  -4.5526   1.7608
-#>  -12.1878  21.2674  -6.3019  -5.0895 -14.0484  -0.5744   2.9402  -5.4836
-#>   -7.0631   5.2959   7.0089  -3.0844   3.8373  -6.6631   2.1493  -0.9894
-#>    2.0537  -0.0306  -1.7520  -3.4060  11.9724   1.3590   4.3287  12.5580
-#>   12.4126  -4.2870  -4.4063  -1.9916   7.4882   3.6379  -0.4937  10.1923
-#>    1.7027  -6.4505  -2.7387 -16.2455  12.9007   0.3193  -9.1811   9.0046
-#>    2.1747   0.5451 -11.6460 -13.3928   0.0783  -4.0526   3.3838 -17.6555
-#>    4.6698   2.2650 -13.3213   6.2734 -15.1414   3.1142   4.6771  -1.2094
-#>   -2.7839   0.2344   8.5908   4.0064   3.6063   3.4178  -3.9916  -4.0202
-#>  -10.4147 -11.0731   1.7277  -4.9056   6.9644  -3.1199   2.0394  -2.1820
-#>   -5.5745  -2.2343   5.6448   6.1782   7.3012  -7.1318 -10.1754  -5.7040
-#>   -4.8389   4.2046   7.1973  -1.9157   8.2896  -0.3394 -12.3002  10.6009
-#>    4.0387  -2.4189  -3.1629  -2.7025   5.4084   1.8913   2.7038   9.0425
-#>   -8.2393  12.7998  14.6245   0.2574   3.2928   4.0283  -3.1100 -17.5313
-#>   -9.2878  -1.9203  -6.5422   7.5714   3.6611   7.3589  -3.7316  -9.5962
-#>   -1.1382  -0.0783 -10.7222  -6.2943   9.0036   2.3743  -2.7191  -0.3087
-#>    0.0976   1.9307  -4.1910   5.6042   0.3211   4.8762   9.9717  11.4200
-#>    5.8096  -6.6802  -3.5446  -9.9357   3.0509  -1.8566  -7.1458   5.7246
-#>   -1.5739  -1.6134   0.8694 -13.7485   8.9703  -8.6950  -7.3309  -1.9867
-#>   -1.3322  -1.9676  -8.1164 -11.3705  -4.6452  10.2379   7.2234  -2.2219
-#>    0.1392   5.5978  -9.3507  -3.7589  -5.3242   2.3767   8.0046   2.3059
-#>   -1.6345 -10.6635   2.6171  -2.6985   4.7007  -0.0966   3.1772   1.3217
-#>   -9.4622   6.9922  -7.3082   4.2840   0.9404   0.6788  -3.7414   0.1027
-#> 
-#> (7,.,.) = 
-#>  Columns 1 to 8  -8.3868 -11.6401   7.4697 -10.9227  -1.2344  -0.8900  -8.8770  -5.4839
-#>   -9.1322   4.3897   0.3318  -1.2337   5.0459   2.4164  -1.0493   0.3225
-#>    0.1665   5.2499  -0.4661  -2.1035  -2.2735   1.1229   1.1841   1.3380
-#>   -2.3096   2.9836  -0.8848 -14.8167   6.2941  -7.0506  -2.1243  12.7500
-#>   10.8477  -7.4534  -3.2469   1.2711  -1.6800   2.5467   1.2383   5.5295
-#>    5.1895  -2.6360  -9.6760   9.0771 -11.6362   3.2688  -2.5761  -4.0012
-#>   -6.1945  -9.9513   2.7478 -14.0589  -5.7439  -4.8767  -6.6987  16.0320
-#>   -1.7465   2.0612   5.0697  -4.8202  -0.9129  -1.0709   5.3457  -3.9324
-#>    3.5950  -0.1859  -6.9557  -6.4294   1.4565  -4.4959  14.1353   2.4524
-#>  -12.4705   0.6877  -3.4191   6.5834   5.5183  -1.0149   1.1827   5.6798
-#>   -8.8835   2.0619  -3.5445   8.5120  -7.9080   6.9911   8.6304  -8.9595
-#>   -0.2742  -3.0854  -1.6339   9.4392  -2.5067   4.5785   0.2035  -5.0767
-#>   -1.1434  15.9596  -7.8063  -9.7865  10.5698  -4.6604   8.0900  -5.7061
-#>    4.6076   3.9945 -11.6087  -5.4445  -6.3514  -2.9826  -1.0471   3.7196
-#>   12.9841   5.3269  -6.8384   0.2756  -5.1947  -6.7814   2.5640  -7.7269
-#>    2.2016  -1.5726  -2.6084  -0.5966   8.7939   6.1295  -0.2765   0.4567
-#>    1.7501   2.1831   2.6268   4.4627   9.1659  -1.7995  -2.6477   0.7981
-#>    0.1504  -7.4290  -3.9983  -5.3382  -1.8106   0.6926  -6.7663  12.2016
-#>   -8.6294  10.0757  -7.5659   6.8896  -6.0096   6.9586  -4.1332   3.6935
-#>   -1.4324   4.3200   6.0882   6.4074  -4.5371 -13.4335   3.0922   2.4905
-#>    6.0976  -1.1818  10.5174  -4.9773  -5.7739   6.8962   0.0486   3.7851
-#>   -4.0463  -1.4940   1.7253 -16.4487   2.5028  -6.1684   4.3875   7.6539
-#>    7.5972  -0.5269  -2.3114   6.6299  -8.5260  -3.5701 -16.3837   0.6222
-#>   -8.7690  -5.4894  -4.1165   5.5410   4.2613   7.8740 -11.9896  -5.6817
-#>   -2.9779  -3.2116 -11.2902   7.1702  -9.1442   5.8912  -4.4738   3.3884
-#>    6.4202   2.3732  -0.1645  -4.8198  -0.4243  -1.2116   0.7092  -4.4674
-#>    5.3454   6.0369   2.3749  -5.2803  -0.7246  -2.2842   6.0546  -0.1393
-#>    1.7873   1.5501   9.4551  11.0980  -7.7700  14.2810  -6.7579  -0.5943
-#>   -3.9084   9.7229  -3.9989   3.4151  -2.5499   3.7245  -2.2462   1.4619
-#>   -3.2414   0.1511  12.3477   9.7800  -3.8685   3.8956 -13.8782  -1.4243
-#>    5.0264   3.0037  -7.3815   9.3482  10.0306   2.9168   0.5762  -5.2767
-#>    3.6499  -5.1904  -9.1577   5.1506   5.4749  -2.8899  16.6732  -6.3274
-#>    3.2116  -2.8429   3.4621  -5.2504   5.2777   8.3226  -5.6161 -11.3883
-#> 
-#> Columns 9 to 16   0.1044   0.9257  -1.7411 -12.9353  -1.1161  -2.5187   6.6271  -4.6543
-#>    6.4146   6.4546  -0.6852  -0.9331   3.4367  10.3163   1.5969   1.0194
-#>    9.9608   6.6149   4.0068   1.5514   4.0437   0.5026  -0.8534   5.0826
-#>    6.3817  -2.3573   1.7044   0.2486   8.7588   7.6900  -7.8460  -1.7487
-#>    6.5979   5.1956  -0.4842   7.8987   2.7213   0.9117  -0.3715  -2.7825
-#>    9.8294  -3.1727   8.8739  -1.6314  -3.7998   0.8782 -10.0484   8.0967
-#>   -6.6380   6.3176 -10.2232   2.1496   8.0068   6.6707  -4.9896 -10.9107
-#>  -19.6346   4.5560   4.6483 -10.3726  -4.4241  -0.3028  -3.2161  10.4890
-#>   10.0427   9.7625   0.8205  10.1058  -0.2017   2.8767   2.2373   2.5954
-#>    6.4168   2.5372   4.0594   2.1162   8.3249  14.2594   1.9229   2.2237
-#>   -1.2217 -11.7178  -4.6981  -7.9874  -4.3094  12.9896  -2.1611  -1.9043
-#>   -2.7255 -11.3130 -13.3637  -3.7082  -2.8801   4.3433 -12.1371  13.4229
-#>   -4.5561   3.4566   4.1829  10.1662   5.0826  -6.1936  -6.7400  -4.1677
-#>    4.8390  -1.7285  -2.7652  11.2970   4.4895  -6.7624  -8.2958  -6.0062
-#>    4.2228   2.8737   1.6859  15.0594  -4.6857  -5.1865   7.1145 -14.9673
-#>    3.8024   2.3836  -2.7731  14.1042  -3.3697   2.4403  -8.3130   2.2377
-#>    2.1825   9.9036  -8.2547 -19.5848  16.7161   6.8862  11.2340  -0.6450
-#>    1.9039   1.6240  -5.4858  -6.3526  -6.1712   3.0947   0.7988  12.7938
-#>   -3.8852 -13.2789  -1.2641  -3.7055   4.4524   7.2319  -9.9409  -2.3498
-#>  -15.0849   5.1506  -0.8828  -9.4088   8.0829  -0.0959   3.6027 -13.6473
-#>   -1.5873  12.1405  -3.7498  -0.2601   1.4899   6.2359  -9.1811  -1.0767
-#>   -5.0776  -0.6590  -1.7319   9.0051   4.7089   2.1270  -2.0193  -4.1000
-#>   -8.3452   5.3808  -5.5685  -2.0289  -0.4346  -1.4210  -2.9761   0.2022
-#>    6.6507  -1.0421  -5.2787   0.5100   9.9340  -4.4988   7.4089  -7.7939
-#>   11.7048 -10.1678  -6.2539  -5.6852   1.4961   0.2818   9.4267  -2.1771
-#>  -13.4982  10.8678   3.8737  -5.1592   6.8410   0.9065  -1.2240  -8.9981
-#>  -13.6598   8.9172   0.6429   0.0794  -3.5587  -6.8124  -9.3900  -2.4623
-#>   12.8951  -6.3400  -1.7720   6.4668   7.2949  -1.0787  -0.7375 -11.4979
-#>   -4.4058   3.9306   2.9124   0.6470  10.7702  -6.2025   3.5430  -2.4585
-#>   14.9334  -7.0435   9.1913  -5.1234   6.4600   6.1955   4.8577  -0.3465
-#>   -4.0835  -2.4654   6.2225  -0.5559  -9.6441 -11.2900   4.6662  17.0918
-#>   -4.6378   1.4535   4.4265  13.6624   9.1190  -7.6378  -0.3699  -2.8796
-#>    0.9298  -4.5409   0.4691   5.7132   2.0187  -0.3962  -6.2617  -4.4942
-#> 
-#> Columns 17 to 24  -7.5082   0.1230  -2.7170  -0.1290   1.2378   3.9310  -6.8861  -2.4757
-#>   -0.0840  -4.9533   6.8870  -7.8175  -6.2897  -1.5212  12.2053   1.7018
-#>   -6.1950  -5.3380   8.6784  -1.1482 -13.9410 -14.6168   7.9137   3.0913
-#>    2.9134  -8.0645   1.5240  -5.6552  -3.8008   4.0944  -0.5454  -8.5642
-#>   -2.7359  -4.8647  -4.7398   3.6420   4.1403  -0.2886  -4.6023  -1.1916
-#>   -4.4233   1.7021  -9.9913  12.8842   7.4480  -4.9905   0.0126   1.7723
-#>    0.5143  -6.9647   8.9175   2.7165  -0.2401   2.0856   1.3035  -3.8266
-#>   -1.7071  -3.9459   4.0166   1.7316   2.3844  -5.6508   0.4939   6.4315
-#>   -1.7874   2.9283  -1.7397  -0.0795   3.3628  -3.7478  -3.3556   2.6575
-#>    3.8874  -5.4597   1.9906  -2.7408  -3.4235   0.8202  16.5486   1.5903
-#>  -13.6912   7.5320   3.8591  -2.7932   2.2483  -5.1478   9.2281  11.0863
-#>   14.2475  11.0989  -1.1390  -4.1648 -10.9931  -2.8091  14.1621   8.3148
-#>    4.0171 -24.1954   7.6173   6.6532  -1.9288  -2.6925   2.4648   8.5419
-#>   -1.1056  -3.7104  -8.6558   7.8332   3.5572  -4.8852   5.6026  -7.8859
-#>  -17.2301 -14.5154  -2.0305   0.4276  -9.1197  -0.3856  -9.3708   3.1188
-#>    0.1942 -10.6688   1.5796  11.6256  -2.3149  -0.3170  -1.3611  -3.2544
-#>    8.5874   3.6442   1.8841  -5.7845   3.7858   3.3507   9.5682   5.7652
-#>   11.5020   7.4198   3.1556   7.7805   4.3205  -6.7041   3.4949  -0.9564
-#>  -20.2656  -5.5543   8.3369   6.5278  -4.8215  -6.9655   8.4572  -8.7750
-#>   -2.4090  -6.5655   0.6052  -7.9955  -2.2705   1.3170   5.0470  -6.7168
-#>    2.3647   9.7501   0.1719   1.5766   3.1177   1.5264   5.3542  -6.0455
-#>    5.8564  -6.1934  -3.3479  -0.8891  -4.3840  13.8145  -6.9400  -5.6010
-#>  -12.5772  -2.0369  -4.0395  -4.4490   6.7978   5.7791  -2.5945  -7.7926
-#>   -2.7811  12.2068   8.0313   8.7803  -3.9315   2.2293  16.3355  -9.4390
-#>   -4.9157  21.3081  -4.7641  -0.2722   0.8232  -7.7434   7.1377 -10.3087
-#>  -16.9968 -18.4087   6.2145   7.1472   1.6623   4.9139  -7.8575   2.1788
-#>   -4.1799 -10.8815  -1.5725 -13.0412   4.1585   2.2265 -13.8721   0.6302
-#>    9.3972   5.2940   2.4569   5.7679  -4.9076  12.6545   9.4319  -5.0808
-#>    1.1354   0.0392  -5.1914  -9.7269   0.4172  -2.1484  14.0104 -10.9378
-#>   -8.9589   5.7229  -7.1192   5.9937   2.2431  11.6666   5.2665   2.3037
-#>   -9.6188  -2.3606   8.3819  -5.6156  -7.0418  -2.6749   2.5972   0.8289
-#>   -0.9066 -14.1249   0.7172  -3.3330  -2.2977   3.1394  -2.7918   8.9389
-#>   -7.4333  -3.1721  -8.1599   4.1023  -1.1054  -1.7108  -0.0041   4.4734
-#> 
-#> Columns 25 to 32   6.2032   6.7606  -4.1842   4.8856   4.8326  -4.3465  12.9020  -1.9198
-#>   -4.9915   7.2119 -13.3811  12.4029   4.2098  -8.6987  -7.5963 -10.2318
-#>    3.2876   0.8499 -10.0451  10.1758   9.1603   4.5728   2.2774  -5.3308
-#>    6.9995  -2.8632 -17.0167   4.8331  13.0833  10.4276  -6.8767   9.1409
-#>  -12.4603  -7.5971   0.9850   3.2682  -1.8272  -9.1431  11.7883 -14.3354
-#>   -5.0676   0.1846  -2.6840   0.9372 -13.7782   6.9465 -10.2780  -4.0720
-#>   -2.2802  -7.5383   3.5250   2.1399  11.5853  -7.8419  -4.0121  -3.3568
-#>    0.4598  -6.3054  -5.5410  13.9453  -2.0206  -7.8521  -1.9166   4.1174
-#>   -4.2545  -2.3393   7.3020  12.9533   1.3055   3.4879   6.2869  -5.5537
-#>  -12.6416  -3.1648 -10.2565  -0.2521  -1.3447 -10.9668  -0.7903 -17.2056
-#>   -3.9141  -9.5369   3.0149  21.3584  -2.6780   1.7901   5.7324 -17.4045
-#>   10.1366   4.0803  -5.2090  22.8942  -9.7008  -9.1265  -3.6588  -1.8557
-#>   -4.3599 -13.6794   5.6573 -10.4187  -1.9024   0.6204   1.9504  -1.9618
-#>    0.9198  -9.5261   5.4805  -1.3540   2.4746   9.3641   7.0959  -0.9933
-#>   -8.8033 -11.0557  13.0802  -6.0954  12.3576   8.5847  16.7252   1.8883
-#>   -5.7972  -4.5999  -3.2640 -12.8288   3.2058   4.2547  -2.3899  -2.1646
-#>    7.3529  -7.6689   1.1825 -13.8196   8.5992  -0.3931  -5.4558   3.0841
-#>    3.8626   0.6250 -15.1615   5.8399  -9.6220   0.8828   1.3470  -3.3132
-#>   -8.2905  -6.0210  -6.0656  17.1575   8.8731   1.6172   1.9336  -2.0237
-#>    4.8286   1.5069  11.3829  -1.7471   5.2813   4.3474  -6.8825  -5.2249
-#>    8.4692  11.5821  -8.9529  11.3588  -1.6938  -5.6908  -3.0364  -2.1104
-#>    6.3942   4.8667  15.7654  -2.2110  -7.7429  18.1713  -8.2298   4.1817
-#>    5.3835  -2.9582  -5.8010  -4.3772   0.1724  -4.1213  14.8204  -4.8059
-#>    9.6845   0.8019 -10.6839 -13.3050 -10.2778  -5.5199   0.4439 -10.2108
-#>   -7.2615   6.5667   9.5359  18.8324  -0.5216   8.7538  -9.2066  -5.9030
-#>   -0.3073 -15.2456   2.1572  -3.2268  -5.6128  -1.0913  -2.9713  -4.1415
-#>    5.9096  -4.1621   1.9846   9.2107  14.1094   3.7912   8.5795  -0.3664
-#>    3.1452  -3.1311  11.1002  -6.0726  -3.6456   6.7602  -9.3967   9.3666
-#>  -10.4010   2.7401   8.1540  12.5843   3.1052 -10.2762  -4.5395   6.4634
-#>   -1.8610  -6.9201 -18.4132 -11.7961  -3.1464  -0.3100   4.7452   1.2053
-#>    0.6977 -13.1071 -13.5327  13.9274   8.3351   3.5292   7.3812   5.6499
-#>  -13.9419  -2.8361   9.5517 -16.0587   8.9735 -15.3313   1.8182   3.1891
-#>   -8.8529   4.9793   5.3233   2.8270 -13.7933   5.3210  -6.3498  -4.2234
-#> 
-#> Columns 33 to 40  -4.6260   8.8620   3.4329  -8.1481  -8.1702  -0.7852   2.0633  -2.5541
-#>    9.5929   2.5161  -5.7370   7.1015   2.3320  -6.9793  -8.9219   5.1713
-#>    6.1647   1.8937   4.1104  19.2877   1.8289 -11.5295  -3.7615   7.3228
-#>    2.2258  -5.6179   0.3055   9.3003 -11.9471  -4.2971  -6.7805  14.6903
-#>   -6.3973  -2.0035  -2.7854   0.8171   7.2534  -9.8019   3.8554  -7.0032
-#>    1.0929  -7.6663 -11.2568   4.7428  -4.0117  12.4513   5.8052   5.8733
-#>  -17.8153  -1.4327   5.2808   1.4281   0.3969 -17.7094   3.8365 -10.8317
-#>   -7.7704   4.2667  -4.6862   6.3226   0.9491  -1.5931  -0.6221   7.8181
-#>   -3.7231   5.6704   4.0881  -1.1873  -4.5026  -0.5250   2.7140   0.5003
-#>   12.6789   3.0519  -6.3219  -0.5452   1.5616  -9.3017 -15.5234   1.3124
-#>    5.1097   6.3707  -4.6902  -3.8234  -8.2346   6.2555   7.4859 -11.0259
-#>   10.0714   7.5715  -2.5473  13.3906  -2.7765  -7.9680   2.9459   6.5335
-#>   -1.5787 -12.5808  -0.2462   9.8006   0.4333   0.8598  -2.3843  -9.3571
-#>   -6.2396  -2.2068 -10.0107   7.9180  -2.8846  -2.9688   1.4483   2.2000
-#>   -2.5841   0.5950  -0.6526  -0.3271  -2.4743  -6.3197   9.3940  -4.1160
-#>    2.2653   3.4443  -8.9261  -0.0572   5.1695  -6.9099  -6.3007  -0.9485
-#>    2.9831   2.9335   1.6797 -10.6045   4.4246  -4.5346 -11.1331  -8.0911
-#>   -2.4504   7.1525   4.0060   9.7077   1.3683  -5.5369  -0.4538  -3.3585
-#>   -0.3564  -4.4546   4.0470  -3.3142  -4.0203 -10.6082  -5.8961  10.2849
-#>    0.5000  -3.4236  -2.3786  -2.6622   8.2442  -9.2194  -2.3547   1.3446
-#>    2.0190   0.3457  -8.0455  11.9630   5.4565  -7.7393   3.8689   6.4768
-#>    1.7497  -0.7774  -1.2803   3.1482  -8.3563   2.5110   0.5142  -6.7214
-#>   -7.3846  -3.3771  -8.7545  -6.7862   8.2003  -4.0938  15.2718   3.2306
-#>    8.8394  -4.8474  -1.2207  -3.2168   7.5263  -7.6474  -2.2813  -6.1993
-#>    4.1752   8.8815   5.9743  -9.0465  -5.3511  -6.1333  -7.7893   7.6450
-#>   -9.8636  -2.4300  -3.4376  -5.1739   8.7395  -9.5285  -2.1348  -0.2848
-#>   -6.7656   0.5918  -0.9239 -10.9065  -5.3489   3.6691  15.7931  -3.7872
-#>    5.8799 -10.8813  -7.1825   1.8036  10.5524  -7.3634  12.1371   2.8828
-#>    5.1392   5.2809  -7.0235  -8.9886   1.4891  -8.4972  -8.1288  17.9264
-#>    1.7187 -11.7695  -8.5988  -4.9949  -1.4106   0.4465  -3.4039  -0.1012
-#>    1.4442  -4.1300  -1.3585  -4.7286 -16.1419  -2.8185   0.3761   9.5014
-#>    5.2136  -3.7293   4.7115  -7.1455  -1.6220  -1.0989  -2.3889   5.3699
-#>    3.4604  -1.1786 -13.8256   4.2584   1.8335   6.6330  -3.3588   1.7650
-#> 
-#> Columns 41 to 48 -16.4893   5.8363   5.3839   6.3958  -1.4048   4.1891   5.3464   2.6790
-#>   -2.8933   2.2947   7.0668   1.9631   0.7939   2.5534   3.7270   0.8783
-#>   -3.1012   4.6196   1.3724  -5.0885   2.5820   3.7266   3.9187  -7.3649
-#>   -3.5867   4.6952   7.6381   4.0358  -9.3693  -0.2167  -3.8898   2.1728
-#>    3.5989   5.3581   0.7363  -6.6457  -6.0644  -3.5028  -3.2641   4.5286
-#>  -14.7376   2.8292   6.0339  -4.5955   0.7311  14.3921   0.4553  -9.5141
-#>    7.2329  11.5357  -6.6458 -10.2309  12.7271  -7.7142  -5.5970   2.3869
-#>   -9.5121  -4.3462   0.2946   5.5319  -0.1472   7.0475   4.4480   2.4542
-#>    1.9695  -4.8489  -0.9918   1.9768  -8.7223   0.4888   0.2278  -7.7381
-#>    7.7019 -10.5364   3.4470   1.3466  -1.5905 -14.6580   7.5356  -2.0517
-#>    5.6417  -0.9466  -9.8949   1.3275   3.2663  -1.9140  -2.8904   5.5347
-#>    3.7832  -2.9117  -5.5314   0.2180  14.6717   4.5803   9.6055  13.1461
-#>    7.8353 -18.9302   3.6607   1.4060   3.8057  -9.1981   0.9597   1.2560
-#>   -2.9988  -7.1067  -6.3133   7.0839   2.7961  -6.5958   9.6824  -2.1668
-#>   12.1370  -7.3829  -1.9034  -2.0261   7.5014  -1.3085   0.9310   4.8365
-#>   15.2301  -2.5347   1.4293   4.3834 -11.1265  -9.4680   8.2599   0.5567
-#>   -6.9354  -0.8273  12.9513 -16.3247   8.7733 -10.5149  -8.3016  -3.5463
-#>  -14.8789   6.2359   0.9929   5.7673  -0.0762   0.3267  -2.3356  -5.3080
-#>    4.9092   5.9725  10.4473  -7.8813  -2.5593  11.1572   8.9717  -0.6405
-#>    2.2686  -2.9832  -0.0737  -2.9151   3.4551   8.8344   8.4443   1.2575
-#>   -5.6279  -1.6709   0.0603   4.5958  -2.0800   9.7000   7.3730   1.3542
-#>    3.8505   2.0401  -9.5092  18.2622  -3.4618  -5.5624  -1.9886  -0.1771
-#>   -1.8987   5.4359   3.5942  -6.1446   0.4830   9.7549  10.8543   2.9409
-#>   -3.1502  11.0579  -4.4697 -10.3236  -4.4525  -3.1020 -20.5302  -8.3858
-#>    2.7279  21.5577  -0.4661   5.0657   2.4091  18.8487   3.7039   4.6378
-#>   -5.8267   4.8931  -3.0858   2.2141 -10.2913  12.2682   2.7239   7.4378
-#>    1.9541  -1.8613   0.1603  -3.9407  -2.8795   6.0980   3.9917  -1.0544
-#>    3.9225   4.6235 -17.5093  -8.6676   0.0359  -8.2765  -9.6504  -2.3546
-#>   -3.0591  -0.6578  -2.2810  14.9146   1.5137   4.6708  18.2630  -1.8310
-#>   -2.0140  -2.5775  -2.4864  -8.7478  -2.2091  -5.5142 -10.5906  -5.6048
-#>   -5.8611  -1.2948   3.3547   0.3666 -10.2013  -2.2231   5.5278  -1.7056
-#>    8.8016 -10.1636   6.4712  -8.4551  13.4827  -1.8375   6.0630   9.2747
-#>    1.3997   1.9604  -6.4808   8.4185  -0.9404   6.2133   0.5617  11.8576
-#> 
-#> (8,.,.) = 
-#>  Columns 1 to 8   9.7505  21.5211  -0.7218   3.5807   9.2035  -5.7990  -5.3427  -6.5380
-#>   -9.3461  -4.3623  -9.2135   0.1155 -17.1370  11.1844  -8.7337  -8.3448
-#>    4.5790  -3.9913  -6.5917   6.4134 -14.5408  -5.1644   1.6960  -7.1082
-#>   -0.1047  -7.5503  -1.7047   1.2399  -2.9983   4.1608  -5.1501   2.1459
-#>  -12.8589  -4.4785  -3.2548   0.9505   5.6102   1.1982  10.0071   6.0460
-#>  -11.4783   5.1726  -4.2252   4.2747   1.2827 -10.9742   0.3886   2.7719
-#>    4.9565  -1.0287  -9.1887  -7.2761  13.3099 -11.3184  -5.4476 -12.9089
-#>    0.8144   4.3780  -7.6663  -5.0387   8.2941  -7.1678  -3.1985  -3.6510
-#>    2.9263  -6.0486   2.0268   2.1189  -5.3773   0.8080  -6.3592   8.9286
-#>   -8.7668   2.5708  -1.3839   7.6679  -9.7390  10.7981  10.0152   3.3428
-#>    1.9171  -0.7832  -3.3217   9.5469   0.1392 -13.4776   6.0295  -2.9709
-#>    0.3317   2.2112  -6.3795  -2.9447   2.0840 -22.5732  -2.1504   6.8618
-#>   -6.5304  -0.3599  -4.6970   1.2913   2.6933   7.7431   1.0711   7.2043
-#>   -0.4512  -4.5796   7.8038   6.7893  -3.4706   0.2633  -1.9958  -5.2133
-#>    5.8139  -2.7947   0.3844   9.0265   6.0386   0.1709  -2.3162  -5.0494
-#>   -9.7443  -8.1242  10.9562  -1.5658  -3.0498  11.7880   6.9681   6.9782
-#>    2.8668   5.6868   7.3995   0.1941  -8.7889   8.4973  -0.6561   0.4459
-#>    4.1562   5.1469   0.3420   3.6547   5.1419  -9.6600   3.7330   3.8443
-#>   -2.3604   1.4771  -4.2629   0.4718   3.2103  -9.8736  -0.7177 -12.7919
-#>   12.6961   3.1390  -5.6916   0.6333  -0.3446   1.4370  -2.3489 -14.2977
-#>    0.3057  -4.6486  -2.6362  -7.3607  -1.4229  -4.7620  -1.7483  -6.2187
-#>    6.4337  -9.8663  -2.6658  -0.2098  -3.9033   6.9333 -10.1242  -6.0069
-#>  -14.3827  -8.0791   2.1936  -2.1635   7.4595  -9.9999  -4.5255 -15.1139
-#>   -6.5837   0.5889   9.7503   1.1785   2.1524  11.7576   8.8130  -2.7063
-#>   12.3878  -0.1523   1.3679   2.5950 -13.6549  -3.5532  -3.3741  -9.7953
-#>   -0.7170  -6.3100  -8.0999   9.8903  14.4547  -6.2736   9.8533  -5.8607
-#>    1.1360   1.0335  -0.0059   3.1830  -3.2109  -7.0617  -5.7204  -3.7372
-#>    5.6506  -5.1117  11.7300  -3.5025  -7.5426   7.8535   7.1664  -9.1174
-#>    5.7702  -0.2175   1.7750   6.0243 -21.9186   8.0276   9.3998  -4.4018
-#>  -10.6650  12.4759   5.2718  -1.5027  -2.9467   7.5737  15.0786   2.4842
-#>    6.5021   8.8091  -0.6611   4.7909   5.5022  -8.2502   3.1660   7.5191
-#>   -8.0627  -1.6115   0.2394   6.6535   2.8433  13.6172   2.0723  19.1200
-#>   -6.4064  -6.9318   2.5021   0.6963  -5.9183   5.2332   4.6153  12.9524
-#> 
-#> Columns 9 to 16  -7.4489  -0.8042   2.7854   8.4652  -6.6809  15.0909  19.8745   7.1479
-#>   -9.3199   9.4770  -3.4972   7.5095  -0.6291  -4.8227   2.6740  -6.6423
-#>   -2.7731   0.6078  -0.3694  -3.5848   4.8989   4.5288  -0.2155   2.2294
-#>    0.0430   7.7739   0.5964   5.9654  -5.0387  -8.3233   6.6414  -0.9961
-#>    4.0313  -1.3787   6.4081  -6.2994   2.1910  -5.4143  -3.6866  -1.5317
-#>   -0.4060   3.4386  -5.3304  -1.7402  -5.4494   8.4392  -2.5724  -2.6365
-#>    2.4761  -6.6946  -2.1128   2.0522   7.0601   7.0882  -7.5598  16.4945
-#>    1.7845  -5.1161  -4.2109   0.0422   2.5480   2.2563 -13.5602   6.9806
-#>  -10.9724   2.8955  -0.9919  -4.6993   3.9657   5.9168   2.8627  -3.5043
-#>   -2.7526   3.3810   0.4070   4.7812   2.5217 -14.3968  -4.0289   3.0694
-#>   11.0097  -4.5242  -2.8502  -7.9287  15.9853 -11.4852   8.3789 -10.0767
-#>    1.5480  -3.0409  -0.6347   7.7452   6.0101   2.0608  -2.8751   7.2602
-#>    0.0410  -5.5461   7.5252 -11.0345  12.0098 -14.8030  -7.9794   0.9225
-#>    4.2580 -11.4530   3.3565  -3.9571   5.1758  -4.5969   5.6352   9.2885
-#>   12.7585  -1.5618  -3.5641 -13.9890   5.7753  -1.2041  -3.7336   6.4951
-#>    1.9021   3.1302  -0.2939   3.6689  -5.5221  -9.6258  -4.1210   5.6672
-#>   -0.9260  -2.8063 -10.6438   5.8411   4.1513  -2.4985 -12.3546   3.7584
-#>   -2.3836   2.6476  -0.4543   4.2394   5.1216   6.6579   1.9311  -3.5211
-#>   -2.4128   7.1206  -3.8751 -13.5594   4.8275  -8.7876  -0.1497  -2.2992
-#>    0.6666  -1.5999  -1.9034  -4.0682  11.3346  -6.7277   5.9325  15.2344
-#>   -6.1739  -7.7141   1.4850   1.0927   4.3074  -3.4131  11.6982   5.0749
-#>   -2.6437   2.8348  -0.0386  16.8818  -1.2101  -2.0663   8.1696   7.1242
-#>    6.6301  -1.2115  -2.6788  -3.4775  -9.8312  -4.7657   6.6134  -4.9456
-#>   -3.8740  10.2621  12.2994  -6.8315   2.0823 -15.4459  13.4714 -16.5075
-#>   -6.1147   7.5286  -2.9784  -3.6033   4.8837  12.5250   1.7987  -0.8655
-#>    9.8514  -3.1028  -6.3037   1.3988  -3.0110   1.9040 -11.5565   9.3783
-#>    5.2401  -1.0755  -3.6053  -5.0613 -11.9937   5.4612   9.3271  -4.1031
-#>   13.2205  -8.3787  -7.4333   1.0424   3.7193  -8.2087   1.5639   6.6348
-#>    5.1053  -8.5397  -4.6884   5.3031  -1.3494   4.1826   0.5436  14.7424
-#>   -0.3656  -4.0147  -4.7661  -7.8632  -4.5603   1.3236  -5.4402  -3.0846
-#>    4.5304   0.9833  -2.5083  -9.7851 -14.0094  -0.4676   3.3590  -8.4788
-#>   10.5364   4.5215  -1.5188   5.0515  -7.1089  -6.8000  -7.6796   2.9325
-#>   -0.4371  -3.9301   3.3137   3.9294  -1.5344  -0.7612  -7.9348   8.6682
-#> 
-#> Columns 17 to 24  -0.1389   2.7833  -7.6417   1.0127   4.3739  15.8968  13.7240  -2.0858
-#>    1.2018  -1.3251  11.9279   4.4186  -5.6396  -6.8106   1.4250  -3.8938
-#>    7.1025   7.5194  15.7088  10.0171 -14.3076 -12.7381   3.2395   0.8369
-#>   -4.8720  -8.5710  12.2525   3.2695  -5.5304  -5.0498  15.3786  -3.1256
-#>    5.6980   6.7997  -3.1759   0.1066   2.2419   3.0356  -4.8155  -1.3508
-#>  -15.3042  -7.0760   4.9612 -14.5196   6.9446  11.3609   1.7151  -9.4168
-#>    2.4581  11.1489  -2.4932 -12.1783   8.0708  -0.7791   8.1304  -3.2390
-#>  -11.4518   5.1643   1.6748  -0.9467  -7.1317  -7.2802   3.5551  -1.4782
-#>    8.5132   6.0701   2.0084   2.9306  -7.6543   4.9825   2.7986  -1.4610
-#>    7.6029  -2.1964  -4.7003   4.6741  -2.4956   4.5839  -3.3663  -0.7649
-#>   16.4216   1.3188  -0.2868  -7.9091  -4.4317  11.5826  -4.2421   3.3804
-#>   -0.3521   1.5658  -3.3197  -8.8169  -8.6169  -7.3379   0.2026  -4.2922
-#>    4.6503   0.3193   4.8151   1.6662  -5.3378  -8.9674  -5.4164  10.2136
-#>    0.3513  -0.3407   2.1979  -3.5283  -2.5753   8.7386   1.5805   5.4534
-#>   10.4485  13.4950   1.1411  -0.7244  -8.2095  -4.7985  -5.4840  17.3438
-#>    5.4441  -7.1625   3.4126   6.5226  -3.5294  -6.1442  -2.3265   1.3708
-#>   -2.3677   4.9284 -11.7406   5.4634  -2.0317   7.2705   9.2657   1.4044
-#>  -11.1751   5.8955  -3.9999  -6.3525   7.5790   1.7701   2.9943  -8.7027
-#>    5.8696   2.9949  17.6991  -1.4768 -12.4445  -6.9235  -3.2380  -6.8725
-#>   12.3087   3.4907  -5.1019   8.4455   7.1078  -1.3708  -3.1946  10.9005
-#>   -7.0519  -0.9545   2.6750   5.3558   0.6328 -18.4314  -3.3503  -8.6130
-#>    8.1444   1.3283  -1.1925  -3.6192   8.7250   0.5334   0.0338  12.2328
-#>  -13.1485  -2.2758  -0.7310  -1.1975  -0.2313  -1.8796   8.2425  -5.9023
-#>   -0.4631 -21.9035  -2.7409   1.5576   2.6490   8.8296 -12.3949  -3.2970
-#>   16.1738  -3.6051   5.2913  -6.8463  -0.1715   3.8577  -4.4052  -7.1396
-#>    8.6622  -2.5606   4.2791   0.3756  -2.5184  -2.4997   1.7716   2.0096
-#>   -0.6146   6.1604   0.3380  -3.5578   5.9450  -6.0484   8.9132   2.9027
-#>   11.9040 -15.3711   4.8235  -6.3088  -3.0654   5.7914 -16.9265  10.7671
-#>    9.6258   0.8721   2.1063  -1.4997  -7.8480   0.8220  -1.0926  -5.7129
-#>   -0.8224  -4.4742  -5.6826   3.2623  -3.7203  13.1767   5.8564   0.3523
-#>   -8.5367   4.8810   4.6889  -6.7867 -10.1217  -1.6946  14.1697  10.3410
-#>   -3.1274  -7.0905 -12.2435   2.7620 -10.2159  -6.9943  -0.7448  -2.0076
-#>    6.4558  -4.3491   1.4251  -2.4677   2.4405  -4.2120   0.5065  -1.7806
-#> 
-#> Columns 25 to 32  17.1026   2.5812  -7.0203   9.4465   6.9103  -0.1044  -2.8706   1.0071
-#>   -0.1017  -4.5751  -2.9160 -13.9657  -7.6557  -3.4798  -5.2746   0.7955
-#>    2.6565  -3.7401   3.4362   1.4084  -8.9534   3.8798   0.9005  -5.1114
-#>  -13.6695   5.2867  12.6991   4.7499  -5.6753  -3.7951  -0.4789   4.1006
-#>   -3.4392  -0.5906  -1.6030   4.2723  -7.6491   0.5247  -3.4139  -9.0802
-#>   -8.7874  12.8729  -2.4180  -4.9332  -6.5361   0.6753  -5.9675   1.7622
-#>   -0.2850  -1.0839   6.2215   9.3789  -1.1318  -3.4875  -1.5527  -1.2908
-#>    3.6416  -1.7346   6.5360  -5.6006   3.7726   3.3410   6.3362  13.5934
-#>   -0.0879  -0.4969   1.7138  -9.7765  -9.0325   1.5765  -4.2677  -6.9798
-#>    0.4966 -10.6000  -5.1651  -9.7292  -4.8165   6.4431  -7.8703 -12.7564
-#>  -12.0424  -2.4013   8.9028  -3.3145   7.1346   3.8412   1.4704  -9.6413
-#>   -3.1423  -5.1933  -0.0150   1.9071  -0.2029  -0.1701   0.1602   1.7525
-#>   -1.8765   1.9230   1.4065   0.4553  -0.2087   8.1157   8.0254  -4.5533
-#>   -4.3565  10.7568   7.1821   9.0265   2.6125   0.1118  -1.6544  -0.7527
-#>   -0.7168  -2.0606  -8.3614  11.2642   3.2440   1.1178   4.9414  -2.3209
-#>   -4.8396  -6.3713   5.5836   5.3154  10.2439   3.4682  -1.7568  -1.7856
-#>    1.6700  -9.3760 -19.3531  -6.9326  -2.3024  -1.7312   2.0125   2.4404
-#>    1.9675  -0.2319  -0.9638  -6.1850   0.7932   3.1152   1.3602  -7.4149
-#>    3.1073  -2.1754  13.7664   0.9557  12.6638   4.6034   1.6815  -3.3305
-#>    0.4340  -7.9141   5.9485   2.6441   6.5036 -10.2815  -8.1310  -2.6895
-#>   -5.8532   1.4265   1.8267  -6.1927   3.4157  -7.9354   2.2704  10.0210
-#>    1.6743  -1.2345   0.3427   1.3384   1.5342  -8.3515  -8.5344   5.3275
-#>   -2.2453  -5.9218   2.3372   2.9031   9.8127 -13.8977   2.0153   9.5521
-#>   12.5302  -2.4257  -7.4401  -5.6927   5.5491   1.6091  -0.7882  -6.3543
-#>    5.6356  -1.7172   3.7151  -2.9179   3.5514   4.6399  -0.7576 -12.3475
-#>   -3.5529  -8.4877  11.6536   3.9503   0.1079  -2.8133   9.7080   0.9127
-#>    7.0334   5.4122   2.6707   0.3616   4.5236  -6.2855   5.1213  14.4611
-#>  -11.7232 -12.7253   4.7953   3.4767   5.8241  -8.2993   0.8080 -11.5194
-#>    1.3343  -6.4987   7.4096  -5.8047   7.1858   3.4141  -5.2520  -4.4160
-#>   -0.1721 -14.0198  -8.1386   7.9760  -0.4833   1.1973  -5.2975  -2.7722
-#>   10.6461   1.8665  -7.1903   5.6824   6.1101   2.6576   6.4654   1.1838
-#>   -6.6105   5.0036   2.2007  10.3305  -6.5373   0.0834  -0.2109   2.9936
-#>   -8.1512  -1.1948   3.9096  11.5425  -2.3290   4.5866   1.5263  -2.7111
-#> 
-#> Columns 33 to 40 -13.6757   3.2050  -1.7102   3.0423   3.4864  -6.0581  -7.0705   2.3436
-#>    2.8154   6.7909  11.3422  10.5838  -1.1585   0.3751  -6.7020   3.0085
-#>   -2.3170   5.7280   8.5315   5.9173  -2.9746  -7.2888   9.4413   7.2076
-#>   -5.7367   7.5299  -1.4259  -1.6888 -14.4085   4.8950   5.3013   8.0799
-#>    0.9883   0.7480  -2.2983  -8.1308  -4.4850  -8.3801  -8.7969   7.5343
-#>   -0.8695  -1.1404  -3.4372  -2.3562  -0.8389   6.6296  -5.0157   3.7476
-#>   -0.6605   1.1048  -7.0557  -4.1054   4.1530  -0.1704   9.4408  -5.8617
-#>   -0.5488   4.6426  -2.6103   6.9008   6.1783   6.9762   2.5179 -14.8938
-#>   -4.0633   8.2448  -3.4058  -0.3540  -8.4536  -6.8800   1.3375  -0.1555
-#>    1.3970  -0.2304   0.0156  -5.2144   3.5280  -1.1255 -12.1279  -0.2955
-#>   -2.0486 -10.7888   4.8500   2.6335   2.3910   5.6371  -9.2602  -6.2757
-#>   -2.1683  11.4737  14.6329  13.6383   3.6652  -0.8922   1.0977 -11.2232
-#>   -6.7400   3.4379  -0.9003   4.8029 -10.6919   0.7908   6.7403 -16.2157
-#>   -4.8868  -2.2131   0.3401   0.4005  -6.0264  -4.2342  10.5046  -2.7749
-#>   12.2537  -3.7335  -1.7123  -8.3265 -19.5502  -2.4142   1.6112   1.2166
-#>   -6.8644  -1.0912  -2.1300  -4.5600  -3.7915  -4.2678   6.2871  -1.8575
-#>   -1.6467  -3.3780  -2.6142  -6.3843   8.7063  -4.1567  14.1473  -0.9055
-#>   -4.3967  -1.3374   5.1231   2.8412  -0.3606   1.1262  -8.2442   9.5898
-#>   -1.0255   1.8714   5.5460  -3.0920   2.2492  -1.4507   3.2481  -4.1082
-#>   15.2278  -1.4644  -1.6724   7.7990   5.4575  -9.2883   1.3260  -0.2767
-#>    2.4734   1.0298   3.4018  19.7773   6.4603   0.6927  -2.3733  10.8856
-#>    7.5390   5.9991  -1.6665  -1.1839 -15.8801  -4.4179   0.7634  -3.5811
-#>    7.5150  -7.2280   4.7583  -7.6827   7.1725  -8.0871   6.3701   6.3065
-#>   -4.4429 -10.0434 -13.7058  -4.5672   2.3839  -7.5390   7.9516   3.9969
-#>    8.9826   8.9293   7.4789   6.6279  10.4749   2.4352  -2.6923   0.4486
-#>    0.9305  -0.8680  -7.8173  -8.4552  -0.5972  -2.2555   3.4722  -5.0586
-#>    0.5122  -4.1389  -4.9615   0.4793   3.6580  -3.4449   4.5336   3.1636
-#>    2.0379 -19.8241   2.8024   2.6805  10.5936   2.1072   1.4258 -11.4136
-#>    4.7889   6.2284  10.5768   4.5156   9.1442  -8.2460  -6.0661  -2.1584
-#>  -15.9749  -3.6706   0.0012  -7.2619  -0.1787  -9.4787  11.7275   4.4441
-#>   -3.1463   4.3559  -0.9214  -5.9368  -7.9699   3.1736  -0.3443  -1.0745
-#>    2.4498   1.2488   6.3804  -3.6066  -0.9166  -0.3392  -0.6077  -6.2136
-#>  -11.3278   5.6230  -1.1469   5.0627  -6.2094  -1.0932  -3.9888  -2.4802
-#> 
-#> Columns 41 to 48   7.0135   6.3294  -8.8584 -14.5510  10.0676  -0.6149  -4.1354   4.4232
-#>  -10.7895  -2.7615   2.1639  20.1588   2.0257   0.0967  -6.4809   2.2671
-#>   -4.5688  -2.0050   4.8668   6.7397 -11.2983   5.5228  -5.6060 -14.5368
-#>   -7.8524  -5.3228  12.4785  -5.4817   0.3444  -3.9921  -2.4454  -5.6591
-#>    2.3190  15.2425  -2.7544   8.7666 -13.2045  -6.8948  -1.2019  -8.1382
-#>    7.4866  11.4021  -1.9954   1.7636   0.1764   4.6639   5.1616  11.7051
-#>    5.7654   4.7622   5.9318   5.4125  -3.8339   5.5030   1.5205  -8.6833
-#>   -3.0645  -1.9771   6.8651  -6.9690   1.3674   6.4900   2.4330   0.6932
-#>    4.0131  14.1373   0.8940   3.5991  -1.6315   1.8919  -5.1338  -4.8808
-#>    0.9488  10.4619  -3.4536   5.8847  -6.6616   3.8535   3.3347  -8.1186
-#>   -1.3349  -1.6230  -8.1500   4.8047  16.3066   0.6186  -6.0828  -4.2537
-#>  -17.0976  -8.4809  10.3442  -6.5025  -4.5308   6.4224  -4.3393  -4.1076
-#>    1.9242   0.5845  19.1147   0.7265  -2.8166   2.5570  -3.9082  -3.4749
-#>    3.3098   7.0949   7.2359  12.9875  -7.0863  -0.1894   5.2748 -13.0611
-#>   12.1284  16.8619   8.8710   5.6396 -11.2881  -9.3478   1.6231  -0.1281
-#>    4.4235   0.4590 -10.8071   9.9598 -10.6359  -3.5016   4.7264   0.5766
-#>   -1.2458  -2.6800  -3.8881  -0.7222  -1.0022   1.7662   3.4330   0.8911
-#>    0.1136   5.5672   0.2567  -0.0802  -3.1427   5.4188   4.5744  -6.9384
-#>   -2.1668   1.9430  18.8833   9.1453   9.9037  11.1195   0.9507  -8.9267
-#>    1.1640 -10.1598   3.5957  -4.8968  -5.7853   0.4013  -3.8952  -8.5320
-#>  -14.0711 -19.9621   0.9685  11.0321  -2.2575   3.2575  10.8891  -0.8897
-#>    0.3868   7.2838   3.2818  -0.9068  -1.4344  -2.2705 -17.2004   6.3904
-#>   -1.4807   6.5627  12.2064  17.1711   0.4854   8.3901  14.3149   2.6802
-#>   -6.3829 -12.7508  -9.0151   0.1801   2.1498   3.4304   5.9517  10.1215
-#>   -1.7794   0.6457  -5.0495   2.3017  -2.0233  -6.5212   4.6659   5.2673
-#>    4.9225  10.4069   4.0690   2.3700   1.0188  -5.1772   4.6426   8.9672
-#>    3.3292   1.8687  -3.6283  -8.1369   5.5408  -5.3452 -12.8441  -0.9602
-#>    3.5477 -10.2707 -13.2258  10.0899  -0.9471   0.1878   5.2863  -0.4901
-#>    3.1693   2.1068  -3.6108   3.8335  -0.3474  -9.2534   2.9291  -8.8937
-#>   -1.5092   0.1917   3.0646   0.2466   6.9229  11.0598   3.4176   4.7661
-#>   -1.5282   1.3181   2.3549 -15.1336  -7.1468  -5.1242  -1.5188   4.6585
-#>   -1.7846   4.1737  -5.0727  -4.0856  -6.7048  -6.8882   0.4729   6.3599
-#>   -1.1021  -4.5408  -5.4687   5.1239  -2.5769  -4.7390  -4.1566   8.3170
-#> 
-#> (9,.,.) = 
-#>  Columns 1 to 6 -5.9772e-01 -9.8983e+00  1.0404e+01 -6.1356e+00  3.5001e+00 -8.9590e+00
-#>  -6.7052e+00 -2.4473e+00 -5.9757e+00 -6.9124e+00 -7.5695e+00  1.5501e+01
-#>  -5.1638e+00 -3.0836e+00 -8.2642e+00 -1.0065e+01 -2.8839e-01  1.8010e+01
-#>  -3.9167e-01 -4.5961e+00  2.2247e+00 -1.1997e+01  1.2477e+01 -5.6814e+00
-#>   6.0788e+00  1.2863e+01  3.9910e+00  2.3731e+00  7.4136e-01  4.5087e+00
-#>  -3.1761e+00  1.3578e+00 -4.6626e+00  2.6179e+00 -3.8575e+00  9.9712e+00
-#>  -1.1396e+01  3.3664e+00  1.0453e+00  6.6824e+00 -3.1272e+00  7.3237e+00
-#>   3.7370e-01 -8.0273e+00  6.5533e+00 -1.0999e+01 -9.4513e+00 -8.1806e-01
-#>  -1.0576e+01 -1.6506e+00  1.9676e+00 -8.0581e+00  7.0890e+00 -4.9045e+00
-#>  -4.7729e+00  1.8349e+00 -1.5519e+00  8.6583e+00 -7.6534e+00  9.1328e+00
-#>   8.7335e+00  1.4546e+01 -5.9599e+00 -5.1928e+00  2.0621e+00  9.8500e+00
-#>   1.0081e+01 -1.6942e+00 -6.2237e+00 -5.0624e+00  7.8544e+00  1.6970e+01
-#>   1.7356e+00 -9.4270e+00 -5.2761e+00 -6.2843e+00  2.3019e+00  1.8504e+00
-#>  -5.1167e+00  1.2433e+00 -1.0857e+01 -5.2793e+00  1.4291e+01 -7.3475e+00
-#>   1.5092e+00  5.9874e+00 -1.4614e+01 -1.3576e+01 -3.3374e+00  1.4458e+00
-#>  -5.6186e+00  3.3894e+00  2.7115e+00  8.4038e+00  1.0308e+01 -7.5663e+00
-#>  -2.6028e+00 -3.0251e+00 -1.3113e+01  8.9866e+00  2.3850e+00 -2.6730e+00
-#>  -8.7767e+00  1.3823e+01  4.7101e+00  6.0249e-02  1.1898e+00  8.5789e+00
-#>  -6.3649e+00 -7.8938e-01 -1.2153e+01 -5.6260e+00 -6.6550e+00  1.4229e+01
-#>   1.1580e+01  8.7932e+00 -2.5971e+00 -2.3929e+00 -6.2260e+00  1.0944e+00
-#>  -4.3348e-01 -4.3489e+00 -5.5791e+00 -1.3491e+01  8.0694e+00  1.1862e+00
-#>   1.6337e+00 -8.1348e+00 -3.0525e+00  4.3178e+00  1.4512e+01 -7.3191e+00
-#>  -4.4990e+00  1.0172e+00 -2.2629e+01  4.2001e+00 -7.2138e+00  1.2539e-01
-#>  -1.9345e+00 -2.1191e+00 -4.6567e+00  7.5494e+00  1.1774e+01 -6.5196e+00
-#>  -7.8636e+00  1.3880e+01  1.7210e+00 -3.0380e+00  3.4229e+00 -4.0000e-01
-#>   6.5259e+00 -4.9772e+00  1.7033e-01 -1.4080e+01 -6.3298e+00  3.6542e+00
-#>   6.0277e-01  1.8627e+00 -5.3594e+00 -4.0737e+00 -5.3710e+00 -3.8900e+00
-#>   6.8195e+00  1.4984e+01  5.6257e+00  1.4304e+01  7.8092e+00 -5.7403e-01
-#>  -4.1558e+00 -5.3392e-01  3.6684e+00 -9.8107e+00 -2.4841e+00  1.7410e+00
-#>   2.3968e+00 -1.2837e+01  7.0652e+00  1.3442e+01  5.5320e+00 -5.8472e+00
-#>   1.0226e+00 -3.8321e+00  7.9918e-01  1.0291e+00  6.0297e+00 -4.5766e+00
-#>   1.9976e+01 -9.2697e+00 -1.4449e+01  4.4617e+00 -1.1223e+01  4.3968e+00
-#>   4.3422e+00 -8.1300e+00  2.7784e+00 -8.8056e+00  9.0905e+00 -3.6160e+00
-#> 
-#> Columns 7 to 12 -7.9886e+00 -4.1789e-01  3.4889e+00 -6.5670e+00  1.7025e+00 -6.1554e+00
-#>   1.2431e+00 -3.3220e+00 -8.6634e+00  3.4699e+00  9.3183e+00  1.4846e+01
-#>   1.4251e+01 -3.7282e-01 -6.0053e+00 -1.1380e+00  3.1382e+00  7.6234e+00
-#>   2.7875e+00 -1.0526e+01  1.5817e+01  5.0829e+00 -1.1793e+01  3.4515e+00
-#>   3.1888e-01  4.2712e+00  5.5459e+00  1.3774e+00  4.5625e+00 -2.3585e+00
-#>  -3.6468e+00  9.7521e+00 -3.9350e+00 -2.4761e+00  2.9528e+00  1.9369e+00
-#>   8.7279e+00 -3.0858e+00 -4.8988e+00  1.0578e+01 -2.2710e+00 -8.5113e-01
-#>   6.3638e+00 -3.3849e+00 -1.4784e+01 -5.1161e+00 -2.4338e-01  5.7786e+00
-#>   1.1709e+01  7.0229e+00  1.0275e+01  2.0638e+00 -8.0311e-01  2.2046e+00
-#>  -1.9236e+00 -5.5674e+00  3.4492e+00  4.4238e+00  1.4859e+01  9.4732e-01
-#>   7.5186e+00 -7.2396e+00 -1.1684e+01 -8.1177e+00  1.1721e+01  9.8822e+00
-#>  -1.7116e+01 -2.7047e+01 -1.5828e+01  1.8199e+00  3.7760e+00  1.2151e+01
-#>   1.5202e+01 -1.6115e+00  4.3183e+00 -2.2170e-01 -1.3697e+00  6.1547e+00
-#>   1.0838e+01 -2.1337e+00  1.5393e+01 -3.0158e+00 -2.5116e+00 -1.3567e+00
-#>   9.8020e+00  3.9353e+00  8.0539e+00  1.0331e+00  8.9065e+00  4.4400e+00
-#>   1.1014e+01 -5.1721e-01  1.0056e+01 -1.8658e+00 -1.0587e-01 -1.0652e+00
-#>  -9.9327e+00 -7.8304e+00  6.8668e+00  5.4561e+00  1.7317e+01 -8.1515e+00
-#>  -3.6940e-02 -2.0545e-01 -4.2434e+00  2.7756e+00 -1.0222e+00  3.4477e+00
-#>   7.5637e+00  5.2881e+00  5.0657e+00  8.2493e-01  2.7979e+00  4.6146e+00
-#>   6.9088e+00 -1.3395e+01 -1.2399e+01  3.8654e+00  1.9648e+00  3.5629e+00
-#>   6.7725e+00 -7.7367e+00  4.9227e-01  8.8902e+00 -9.8244e+00  1.1019e+01
-#>  -5.6635e+00 -1.9914e+00  7.1919e+00  9.3059e+00 -3.8622e+00 -3.8176e-01
-#>  -9.7490e+00  1.1049e+01  1.6366e+01 -2.3207e+00  6.3493e+00 -1.8590e+00
-#>  -9.0104e+00 -1.3411e+01  1.2738e+01 -5.6306e+00 -3.5250e+00 -8.7249e+00
-#>   4.4496e-03 -2.9238e+00 -6.2142e+00 -1.1927e+00 -1.8179e+00  5.8272e-01
-#>   8.8736e+00  3.5496e+00  1.7175e+00  9.9175e+00  4.7218e+00  4.1414e+00
-#>  -6.9808e+00  5.4581e+00  1.1534e+00 -1.2803e+01 -9.3482e+00 -5.8145e-01
-#>   2.9285e+00 -5.5349e+00 -3.5046e+00  7.2675e+00  5.2552e+00 -3.8660e+00
-#>  -1.4056e+00 -7.3416e+00 -1.8322e+00  6.9944e+00  7.7574e+00  8.1254e+00
-#>  -1.3827e+01  1.4488e+01  1.4742e+01  9.4897e+00  1.2354e+01 -9.0142e+00
-#>  -1.9852e+01 -3.4075e+00 -1.7569e+00 -9.4174e+00 -3.2770e+00 -6.5168e-01
-#>  -1.2156e+01 -7.0697e-01 -7.5206e+00  1.0838e+00  9.1425e+00 -4.8583e+00
-#>   8.3659e-01  4.9569e-01 -8.0905e+00  3.2208e+00  2.9091e-01  1.9936e+00
-#> 
-#> Columns 13 to 18  1.4268e+00 -6.4643e-01 -1.7622e+01 -9.2782e-01 -6.9855e+00 -5.9646e+00
-#>   5.6406e+00  6.4896e+00  3.9732e+00  1.1957e+01  5.9071e+00 -3.4283e+00
-#>   1.4863e+00 -3.2103e+00  6.5280e+00  3.0830e+00  7.4875e+00  3.9739e+00
-#>  -1.3436e+01  1.1493e+01 -5.9182e+00  2.1436e+01 -3.7133e+00 -4.1458e-01
-#>   7.0292e+00  4.3810e+00  1.8445e+01  9.7100e-01 -1.0219e+00  9.3279e+00
-#>   1.1690e+01  9.6098e-01 -6.6718e-01 -8.3299e+00  1.1058e+01 -4.3912e+00
-#>  -8.2923e-01 -3.1380e+00  5.5529e-01 -4.2088e+00 -5.2010e-01  4.0494e+00
-#>  -4.9005e+00  9.1976e-01 -6.6567e+00  4.6711e+00  1.0360e-01  2.0231e+00
-#>   1.0765e+00  6.4593e+00  3.4552e+00 -9.2336e+00  4.4749e+00 -3.8992e+00
-#>   2.2649e+00 -1.8091e-01  1.3098e+01  2.2921e+00  6.5336e+00  6.9644e+00
-#>  -5.2512e+00 -9.9104e+00  6.5997e+00  2.8382e+00 -5.9939e+00  5.7500e+00
-#>  -7.6590e+00 -4.8979e+00  4.1078e+00 -9.0289e+00  2.2878e+00  6.8816e+00
-#>  -1.0736e+01  6.7032e-01  6.6919e+00  6.4399e+00 -2.3449e+00  9.0975e+00
-#>  -9.6398e+00 -2.0480e+00  4.3620e+00 -2.9848e+00 -2.7863e+00  3.0141e-02
-#>   7.9273e+00  4.9398e+00  4.2805e+00  1.4641e+01 -5.9008e+00 -8.4110e+00
-#>  -9.7235e+00  4.5998e+00  1.4957e-01  1.4007e+01  1.1557e+01  3.9815e+00
-#>   1.1669e+01 -1.4081e+01  7.0342e+00 -3.7801e+00 -3.2983e+00  4.9881e+00
-#>  -4.2155e-01 -8.0756e+00 -1.6138e+01 -3.7417e+00 -5.7215e+00 -6.8203e+00
-#>  -6.5037e+00  5.9370e+00  8.5020e+00  1.4392e+01  1.2422e+01  6.3810e+00
-#>   3.2395e+00  9.6114e+00  4.6214e+00 -2.9670e+00  6.5430e+00  8.2656e+00
-#>  -3.6188e+00  1.2400e+01 -1.9658e+01  1.3046e+00 -2.5884e-01 -4.3679e+00
-#>  -8.8569e+00  5.8866e+00 -1.1162e+01 -1.3357e+00 -2.4089e-02 -7.8688e+00
-#>   8.4758e+00 -4.6751e-01  6.0961e+00  3.6116e+00 -9.4077e+00  1.3468e+01
-#>  -5.6394e+00 -2.3949e+00 -1.3362e+01 -1.1423e+01  5.0500e+00 -6.3947e+00
-#>   5.6710e+00  9.3222e+00 -4.5345e+00 -1.2849e+01  1.2083e+01 -8.1289e+00
-#>  -1.7648e+00  1.4789e+01  3.0152e+00  1.3705e+01 -1.6920e+00  9.6765e+00
-#>   2.4847e+00  6.5077e+00  5.2258e+00  4.6694e+00 -1.9813e+00  4.2211e+00
-#>  -4.8027e+00 -8.0172e+00 -6.2700e+00  1.1320e+00 -9.2289e+00 -8.3512e+00
-#>  -2.1190e+00  1.2911e+01  1.1840e+01  6.4975e+00  4.7221e+00 -1.3022e+00
-#>  -2.8746e+00 -8.6969e+00  9.1544e+00  1.7319e+00  6.2875e+00 -3.9191e-02
-#>  -7.6621e+00 -4.1807e+00  9.5632e+00  1.3037e+01 -7.3126e-01  3.5519e+00
-#>   1.0035e+01 -9.1915e+00  2.2972e+01  8.9583e-02 -5.2422e+00  9.7728e+00
-#>  -4.1256e+00  9.7167e+00  1.2161e+01  5.9556e+00  9.9613e+00  6.0163e+00
-#> 
-#> Columns 19 to 24 -1.4788e+00  6.5384e-01  3.4991e+00 -5.0203e+00  1.1833e+01  6.5428e+00
-#>   1.0071e+01  2.6512e+00 -1.8944e+00 -8.1566e+00  3.5798e+00 -1.0335e+01
-#>   3.6254e+00 -6.5613e+00  3.1483e+00  6.8949e+00 -7.4044e+00 -1.5177e+01
-#>   9.3763e+00  8.3278e-01  2.8163e+00 -1.2261e+01  2.4854e+00 -6.6130e+00
-#>  -6.1210e-01 -5.5153e+00 -9.4884e+00 -8.2171e+00 -3.7525e+00 -5.8954e+00
-#>   8.1481e+00  5.1251e+00 -2.1194e+01  9.2614e+00  3.1435e+00 -6.1527e+00
-#>  -2.6929e+00  4.8587e+00  2.8694e+00  4.9911e-04 -1.0671e+01  1.7866e+01
-#>   9.1479e+00  2.6889e+00 -4.1593e+00  5.9204e+00  2.1084e+00  4.5870e+00
-#>  -6.2372e+00 -8.9761e+00 -4.5223e-01 -1.2422e+01  8.3073e+00 -1.0398e+01
-#>  -3.1561e+00 -7.1218e+00 -6.7626e+00 -7.2100e+00 -1.9478e+00 -1.4644e+01
-#>   1.2066e+01 -2.1823e+00  5.2041e-01  4.1326e+00 -2.5256e+00  9.1427e+00
-#>   5.0821e+00  5.6678e+00  3.3381e+00  1.1488e+01 -7.2279e+00  6.3522e+00
-#>  -7.3032e-01 -2.6469e+00  3.1833e+00  8.2736e+00 -5.3655e+00 -6.5288e+00
-#>   4.6056e+00 -2.7989e+00 -1.0808e+00  4.8777e+00  7.8457e+00 -8.4777e+00
-#>  -4.3655e+00 -8.9310e+00 -1.7927e+01 -5.9276e+00 -5.2484e+00 -6.5302e+00
-#>   7.2124e+00 -3.5134e+00  5.8129e+00 -8.7390e+00  1.4915e+01 -8.4247e+00
-#>  -2.9340e+00  1.6258e+00 -4.3462e-01 -7.5343e+00  3.9852e+00  4.6746e+00
-#>   6.7741e+00  1.7857e+00 -4.5787e+00  1.1741e+01  5.7908e+00 -5.6829e+00
-#>   1.2928e+01  5.4310e-02  3.4659e+00  1.9901e+01  2.8366e+00  2.5718e+00
-#>  -4.6991e+00 -3.8227e+00  3.3682e+00 -3.3209e+00 -1.0747e+01  9.3639e+00
-#>   4.2007e+00 -7.2732e+00  2.2640e+00  1.5743e+00  6.7326e+00  7.5268e+00
-#>  -4.4983e+00 -4.3638e+00  1.5736e+01 -1.3776e+01  4.9824e+00  3.0897e+00
-#>   8.9306e+00  3.4701e+00 -2.1963e+00  1.0783e+00  2.4437e+00  1.6603e+01
-#>  -1.1078e+01 -1.6464e+00  1.2810e+01 -6.5072e+00  2.1953e+00 -1.2031e+01
-#>   1.7704e+00  2.0420e+00 -2.6357e+00 -3.8983e+00  1.6743e+01 -1.0175e-01
-#>   5.4945e+00 -3.4841e+00 -5.5210e+00 -6.9099e+00  1.0233e+01  1.2634e+01
-#>  -5.5126e+00  4.0176e+00 -1.0951e+00 -1.7160e+01 -8.5082e+00  1.4385e+01
-#>   2.0308e+00 -8.7291e+00  5.6261e+00 -3.4511e+00 -9.2142e+00 -3.6996e+00
-#>  -5.1241e+00 -9.3991e+00 -7.1547e+00 -3.7527e+00  5.2667e+00 -7.4117e+00
-#>   2.3922e+00 -2.5907e+00  3.1423e+00  5.9142e+00 -2.4298e+00 -5.1382e+00
-#>   8.3085e+00  4.0955e+00 -1.1548e+01  2.3639e+00 -7.7381e+00 -8.7379e+00
-#>  -8.2212e+00 -5.8799e+00 -1.5753e+01  8.4185e-01 -1.7796e+01  2.3051e+00
-#>   4.0001e+00 -4.4698e+00  1.2708e+00 -1.2979e+00  1.1418e+01 -7.5451e+00
-#> 
-#> Columns 25 to 30  7.9131e+00  2.7154e+00  1.4872e+01 -3.7386e+00  1.0172e+01  5.7043e+00
-#>   1.2539e+01 -5.7977e+00  1.4523e+00 -9.3483e+00 -3.8128e+00  8.8916e+00
-#>   5.1914e+00  7.2358e+00 -1.7755e+00 -4.7946e+00 -7.9701e+00 -4.5566e+00
-#>   5.7365e+00 -4.7697e+00  3.2019e+00 -1.5064e+01 -3.9985e+00  9.4707e-01
-#>   1.3723e+00  6.2656e+00 -6.1616e+00  7.3707e+00  7.5894e+00 -2.7101e+00
-#>   3.7877e+00 -1.2307e+00 -1.3621e+00 -4.5134e+00 -1.5887e-01  5.9096e+00
-#>  -2.1808e+00  9.1400e+00 -1.8969e+01  1.0006e+01  4.8628e+00 -4.7325e+00
-#>   2.6560e+00 -7.3123e+00 -1.2357e+00 -7.4553e+00  1.0254e+01 -8.7145e+00
-#>   2.8662e+00  3.2610e+00 -6.6772e+00  7.8181e-01 -1.5148e+00 -2.0216e+00
-#>   4.2846e-01 -2.5294e+00  3.9553e+00 -7.1783e+00  4.7985e+00  1.1505e+01
-#>   9.3605e+00 -7.1987e+00 -8.8640e+00  4.7875e+00 -2.3975e+00 -7.0165e-01
-#>  -1.1350e+01  3.1453e+00 -1.0100e+00  1.1050e+00 -8.0596e+00 -5.7571e+00
-#>  -9.8132e+00  1.3579e+01 -2.7146e+00  9.5315e+00 -1.0039e+01  6.6940e+00
-#>   6.9763e+00  6.9958e+00  8.9224e-01  5.1560e+00  4.1708e+00 -9.1118e+00
-#>   8.2931e+00  2.2381e+00 -8.5325e-01  9.0331e+00  3.3555e+00 -4.0119e-01
-#>  -3.5723e+00  4.3296e-01 -9.3968e-01 -9.5574e+00  1.9474e+00 -2.2715e+00
-#>  -4.5663e+00  1.2040e+00 -7.5696e+00  6.6384e+00  4.2499e+00  8.5393e+00
-#>   5.1188e+00  5.2276e+00 -4.3429e-01 -7.5187e+00  4.4111e+00 -6.0097e+00
-#>  -3.9696e+00 -5.2015e+00 -1.2500e+01 -2.1139e+00 -1.9498e+00  3.8756e+00
-#>   1.5824e+01  1.9293e+00 -8.2049e+00  2.0788e+01  6.3813e+00 -5.7230e+00
-#>   2.1565e+00 -5.5108e+00 -1.5508e+00  1.4097e+00  2.1417e+00 -1.3263e+01
-#>   8.3929e+00 -6.8624e+00  6.7333e+00  2.3216e-01  6.8439e-02 -9.4227e+00
-#>  -2.0929e+00  2.5265e-01 -7.1508e+00  4.1465e+00  3.3456e+00 -7.6238e+00
-#>  -8.5652e+00 -7.6295e+00  3.7898e+00  7.8058e+00 -1.4628e+01  9.4347e+00
-#>   1.1126e+01 -2.1903e-01 -3.5481e+00 -1.7516e+00 -9.3764e+00  5.1781e+00
-#>   4.5579e-01 -9.3047e+00  1.5870e+00  2.4300e+00  1.7651e+01 -4.7332e+00
-#>  -7.6143e+00  1.1278e+01 -7.4274e+00  1.3487e+01 -6.3099e-01 -2.0147e-01
-#>  -1.1968e+00  8.2476e-02  5.4258e+00  1.4257e+00  9.6109e+00 -3.0527e+00
-#>   5.3096e+00  8.0046e-02  7.3033e+00 -9.9037e+00  1.3863e+01  1.9194e-02
-#>  -1.4189e+00  6.8598e+00  5.9213e+00 -2.9910e+00  4.5579e+00  8.9131e+00
-#>   3.3798e-02  8.6234e+00 -3.4579e+00 -6.6073e+00  2.8995e+00  8.0238e+00
-#>  -1.2174e+00  1.7211e-01 -7.2720e-02  5.1705e+00 -7.9263e+00  6.3274e+00
-#>  -1.0282e+01  5.6483e-01  5.9698e+00 -1.0672e+00 -4.2042e+00 -1.7796e-02
-#> 
-#> Columns 31 to 36  1.1844e+00 -7.2629e-01 -1.8941e+00 -7.6083e+00 -4.2147e+00  2.5961e+00
-#>  -4.1939e+00 -2.9817e+00 -4.4593e+00 -5.4455e+00 -5.9977e+00  1.0099e+01
-#>  -4.0233e+00  6.7645e+00  2.6826e+00 -5.2496e+00  3.4417e+00  2.0870e-01
-#>   4.8918e+00  1.9567e+00 -1.6991e+01  1.1052e+01 -2.3925e+00  1.4149e+01
-#>   3.3314e+00 -6.8083e+00  1.0376e+01 -3.6049e+00  9.1836e+00 -1.4545e+00
-#>   1.6061e-01 -1.3304e+01  9.7826e+00  8.3859e+00  8.1357e+00  9.6278e+00
-#>   1.5257e+00  2.2147e+00  8.2917e+00 -5.8503e+00  8.1073e+00 -6.5636e-02
-#>   4.5712e+00  1.0947e+00  2.4104e+00 -1.5979e+00 -1.6107e+01  1.4548e+00
-#>  -1.9434e-01 -2.3166e+00 -2.6052e+00  9.9696e+00  4.3325e+00  1.0414e+00
-#>  -1.3384e+00 -2.7895e+00  6.8319e-01  2.3454e+00 -1.4055e+00  2.0878e-01
-#>   5.9838e+00  1.0921e+00  1.9156e+00 -4.6902e+00  7.7425e+00 -5.7468e+00
-#>  -7.3102e+00  5.0646e+00 -3.5681e+00 -9.5191e+00 -6.9702e-01 -2.1993e+00
-#>   4.7431e-01  4.3941e+00  6.1900e+00  5.5946e-01  2.3139e+00  1.3793e+00
-#>   9.5830e+00 -2.6180e+00  2.3090e+00  1.0488e+01  3.5078e+00  7.5632e+00
-#>   2.1537e+00  7.1923e+00  7.6483e+00 -3.7928e+00  6.7952e+00 -3.0645e+00
-#>   5.4097e+00  2.5142e+00  1.7182e-01  6.4000e+00 -4.7737e-01 -1.1004e+00
-#>  -4.6148e+00  4.4991e-01 -6.9146e+00 -6.9988e+00 -3.4297e+00 -1.2070e+00
-#>   1.4174e+00 -4.9231e+00  6.5810e+00 -1.2322e+00 -3.7342e+00 -1.9426e+00
-#>  -2.0832e+00  1.0677e+00  3.1704e+00 -4.3507e+00  1.0039e+01 -2.5501e-02
-#>  -3.6884e+00  4.0975e+00  3.6172e+00 -4.0332e+00 -3.9415e+00  4.6806e+00
-#>  -6.6195e-01  1.4217e+00  3.0961e+00  2.9925e+00 -7.7697e+00  6.8150e+00
-#>   1.5677e+00  8.5009e-01 -1.0536e+01  5.7060e+00 -4.8438e+00  8.2975e+00
-#>   1.4551e+00 -4.0907e+00  3.4321e+00 -6.6505e+00  1.5629e+01  1.1222e+01
-#>  -4.7277e+00 -3.8640e+00  5.8621e+00 -3.9335e+00  1.0030e+01 -2.7991e+00
-#>  -5.8399e+00 -2.7366e+00  2.3711e-01  3.0909e+00  3.6587e+00 -8.1478e+00
-#>   1.2677e+01 -1.5665e-01 -1.6311e+00 -4.9779e+00  3.6424e+00  3.0476e+00
-#>   2.8489e-01 -4.8924e-02  7.1833e+00 -2.2264e+00  4.6149e+00  6.1171e+00
-#>  -3.6819e+00  5.9067e+00  3.2096e+00  1.0361e+00  7.6217e+00 -3.1803e+00
-#>   7.7033e+00  3.5124e-02  1.5344e-01  6.7063e+00 -1.2995e+01 -2.4705e+00
-#>  -7.7243e-01  5.8335e+00 -9.0640e-01  7.5684e-01  1.5670e+01 -1.5860e+00
-#>   2.1432e+00  6.0483e+00  4.0227e-01 -7.1577e+00 -2.8218e+00 -5.0067e+00
-#>   2.5484e-01  1.0097e+01  2.4836e+00 -1.1225e+00  5.1515e+00 -2.9331e+00
-#>   2.1140e+00  1.2718e+00 -6.7375e+00  3.4060e+00  1.5925e+00  6.5458e+00
-#> 
-#> Columns 37 to 42 -9.0413e+00  4.2641e+00  1.5076e+01 -2.2102e+00  5.7407e-03 -2.3147e+00
-#>  -2.5366e+00  9.2487e+00  5.6971e+00 -7.5793e-01  3.1941e+00  6.2136e+00
-#>  -1.0855e-01  5.7570e+00 -2.9134e+00  3.9281e+00  7.0567e+00  3.6867e+00
-#>  -5.7815e+00  6.1053e+00  2.5409e+00 -4.6347e+00  4.2671e+00  1.3293e+01
-#>   8.7166e+00 -1.9208e+00 -2.0736e+00  1.6139e+00  2.7751e+00  4.2373e+00
-#>  -7.0585e+00  8.1723e+00 -9.2192e+00 -5.7954e+00  1.1677e+01  1.4162e-02
-#>   9.3435e+00  1.7496e+00 -6.9971e+00  1.2072e+01  4.4855e-02  1.0169e+01
-#>  -5.4095e+00 -5.3696e+00 -1.0430e+01  5.7073e-01  1.6181e+00  3.0055e+00
-#>   3.1037e+00  2.6920e+00  1.0490e+01  9.2666e+00  2.1840e+00  5.4630e+00
-#>   4.2201e+00 -1.5488e+00  9.5045e+00  6.7580e+00  2.3942e+00  5.4924e+00
-#>   1.5608e+01 -1.2334e+01 -2.4745e+00  8.0635e+00  4.5486e+00  3.4762e+00
-#>  -5.2987e+00  3.9534e+00 -4.9090e+00 -5.6588e+00 -1.2606e+01 -2.5031e+00
-#>  -9.3156e-01  6.0539e-01 -2.3783e+00  2.9253e+00  8.3484e+00  9.2735e-01
-#>   7.2469e+00 -6.1220e+00 -1.6000e+00  5.0525e+00  3.5506e+00  9.3286e+00
-#>   1.0778e+01 -2.7546e+00 -5.9711e+00 -6.2599e+00  1.7342e+01 -1.2956e-01
-#>   8.0654e+00 -7.5368e+00  4.2084e+00 -2.8554e+00  5.2685e+00  5.8940e+00
-#>  -4.1574e+00  3.0891e+00 -1.7797e-02  2.6921e+00 -6.5087e+00  3.0199e+00
-#>  -1.3272e+01 -3.6118e-01 -1.0552e+01 -3.4381e-01 -5.9032e+00  2.5379e+00
-#>   7.1396e+00 -2.4519e-01  1.4729e+00  2.1580e+00  4.3612e+00  6.5780e+00
-#>   6.7803e+00 -6.1947e+00  5.5043e+00  1.2180e+01 -8.9490e+00 -3.8433e+00
-#>  -1.3721e+00 -9.1113e+00  5.5899e+00  3.0196e+00 -9.7791e+00  6.4865e+00
-#>   1.7351e+00  3.5879e+00  1.5976e+01  4.0530e+00  2.1135e+00 -5.7476e+00
-#>   1.5130e+01 -1.0406e+01 -6.5211e+00 -7.1649e+00 -3.4955e+00  1.1013e+00
-#>   3.6414e+00  5.7045e+00  2.1475e+01 -7.1721e+00 -7.4133e+00  8.6888e+00
-#>  -1.0062e+01  2.4678e+00 -4.0043e+00 -7.9186e-01 -3.4969e+00 -3.5037e+00
-#>   3.4416e+00 -3.2598e+00 -3.1747e+00 -4.2460e+00  1.4092e+01  4.3662e+00
-#>   1.3994e+00  1.1805e+00  7.6355e-01  8.1165e-01 -7.8432e+00  4.3089e+00
-#>   1.7457e+01 -3.9223e-01 -1.0589e+01 -5.4922e+00  1.0360e+01 -4.8000e+00
-#>  -6.0066e+00 -5.0413e-01 -3.5014e+00  7.0190e+00 -6.6348e+00 -4.3041e-01
-#>   5.7813e+00  9.0441e-01  8.9422e-01 -4.0966e+00  9.7021e-01 -6.7914e+00
-#>  -8.4177e+00  7.5051e+00 -8.2865e+00 -5.5693e+00  3.7791e+00  4.6787e+00
-#>   9.4031e-01 -5.7692e+00 -9.4047e+00 -2.7730e+00  1.0719e+01 -5.6055e+00
-#>  -2.0762e+00  7.6085e+00  4.4843e-01  4.3434e+00  8.3749e+00 -4.6087e+00
-#> 
-#> Columns 43 to 48  4.3737e+00 -2.5087e+00  6.3889e+00 -8.0037e+00  1.2070e+01  7.6759e+00
-#>  -2.0968e+00 -2.8082e+00 -1.3237e+00  1.1395e+01 -8.8094e+00 -2.2518e-01
-#>   4.4428e+00 -1.5217e+00  7.1631e+00  1.0258e+01  7.0296e+00 -6.6016e+00
-#>   9.3830e-01  1.0754e+00  6.3390e+00  6.4996e+00  1.1364e+00  6.5365e+00
-#>  -7.3669e+00  1.2879e+00 -3.6561e+00  6.0226e+00  1.0079e+00 -5.1102e+00
-#>  -3.1869e+00  6.5918e+00  2.0732e-01 -4.5766e+00 -6.3377e+00  2.8138e+00
-#>   2.0437e+00 -1.0918e+01  1.2007e+01 -9.5038e+00 -3.3561e+00 -5.4644e+00
-#>  -2.2200e-02 -2.5758e+00  2.5701e+00 -1.5186e+00 -1.5802e+00 -6.7054e+00
-#>  -1.1495e+00  7.3191e+00 -8.1472e-01  6.5960e+00 -4.5626e+00 -6.9297e+00
-#>  -3.1921e+00  1.1575e+00  1.1334e+00  1.0130e+01 -3.0875e+00  1.0981e+00
-#>  -1.3860e+01 -7.7124e+00 -8.0822e+00  4.3692e+00 -1.8890e+00 -1.1706e+01
-#>  -1.0236e+01 -1.4173e+01  6.1403e+00  1.3038e+00 -6.5673e+00 -4.4560e+00
-#>   1.6044e+00 -2.1522e+00 -3.1384e+00  4.5684e+00 -1.2475e-01  8.4231e-01
-#>  -9.8657e+00  6.5702e+00  4.0053e+00  8.1363e-01 -3.0906e+00 -8.5054e-01
-#>  -6.7798e-01  6.9365e+00  6.7617e+00  7.0552e+00  5.7285e+00 -7.3362e-01
-#>  -1.9092e+00 -1.2977e+00  1.3451e+00  2.6043e+00  7.1558e+00  2.2102e+00
-#>   1.3258e+01 -4.5233e+00  4.2634e+00  2.9977e+00  1.4320e+00  6.0799e+00
-#>  -9.4925e+00 -2.7318e-01 -5.4690e+00 -6.3278e+00  1.4818e+00  1.2148e+01
-#>  -1.5550e+00 -4.9477e+00 -3.7344e+00  6.9157e+00  5.1898e+00 -2.2928e+01
-#>  -4.9061e+00  2.7184e+00  2.0085e+00  1.0371e+01 -5.9143e+00  4.4246e-01
-#>  -1.1608e+01  6.4426e+00  2.3071e+00 -2.4539e-01 -1.0232e+01  4.0275e+00
-#>  -1.8309e+00  8.3980e+00  4.2186e+00 -3.7221e+00 -1.2211e+01  9.1400e+00
-#>  -4.2552e+00  2.3704e+00  1.4545e+00 -3.9296e+00  1.6062e+00 -1.3370e+01
-#>  -2.1842e+00  1.5637e+00 -5.2733e+00  1.9259e+00 -6.5270e+00  9.1398e-01
-#>  -5.7691e+00 -2.3866e+00 -3.4253e+00  3.6446e+00 -7.2987e-01 -9.9818e+00
-#>  -5.3362e-01 -4.7567e+00  2.5631e+00  5.8880e+00  3.7545e+00 -9.2315e+00
-#>   8.1852e+00  5.2404e+00  4.6403e+00 -5.1693e+00  5.6024e+00 -1.9305e+00
-#>  -1.9630e+00 -1.4028e+01  1.1234e+00 -4.9099e+00 -8.5770e+00  9.8347e+00
-#>  -2.3442e+00  8.7382e-01  6.1165e+00  9.1952e+00  3.3971e+00 -4.7489e+00
-#>   1.4446e+01 -5.3570e+00 -9.9253e-01  1.6825e+00  1.0498e+01 -7.8251e+00
-#>  -1.2283e+00  8.3363e+00  3.4820e+00  8.8028e+00  1.2035e+01 -5.4122e+00
-#>   3.3773e+00 -5.1518e+00  3.0077e+00  1.2355e+01  5.0393e+00  1.6614e+00
-#>   4.3089e-01 -4.3456e+00  1.1466e+00  5.3134e+00 -2.3118e+00  8.6921e-02
-#> 
-#> (10,.,.) = 
-#>  Columns 1 to 6 -9.5958e+00 -7.3723e+00  1.2047e+01  6.5237e-01 -4.7717e+00 -1.2016e+01
-#>   4.8334e+00  5.0307e+00  8.7751e+00 -4.1145e+00 -2.9787e+00 -7.0777e+00
-#>   5.3799e+00  2.5190e+00 -1.0327e+00 -3.6891e+00 -3.6897e+00 -2.2001e+00
-#>   3.0633e+00 -7.7809e+00  9.6613e+00 -4.0909e+00 -3.6797e+00 -5.8602e+00
-#>  -8.7687e+00  7.7281e+00  2.8332e+00 -9.3733e+00 -4.9154e+00  4.3284e+00
-#>   2.6050e+00  7.4308e+00  2.4147e+00 -1.8745e+00  5.2642e+00  7.2496e+00
-#>   6.1107e-01  3.7037e+00 -3.6763e-01 -3.7137e+00 -6.8335e+00 -2.2041e-02
-#>   6.3727e+00 -4.0470e+00  9.8372e+00 -9.1580e-02  1.2816e+01 -7.3477e+00
-#>   4.6817e+00  1.1897e+00 -4.7628e+00 -3.2549e+00 -5.2942e+00 -4.7634e+00
-#>   1.2118e+00  3.4150e+00  2.0200e+01  4.0394e+00 -6.6602e+00 -4.1004e+00
-#>  -1.8507e+01  1.0203e+01 -6.0837e+00  4.8159e+00 -1.6472e+00  2.0550e+00
-#>  -1.1630e+00  3.0891e+00  7.1104e+00 -2.4322e+00 -2.3919e+00  2.3690e+00
-#>   2.2192e+00  5.9340e+00 -4.8951e+00  1.4631e+01  3.8242e+00 -3.2868e+00
-#>  -3.2127e+00 -3.8830e+00 -6.4114e+00 -5.0048e-01  8.1078e+00 -5.9243e+00
-#>  -4.0585e+00 -2.5125e-01 -1.9762e+01 -5.1743e+00 -7.5492e+00  1.3552e+00
-#>   6.4482e+00 -4.5000e+00 -5.4551e+00  1.3725e+01 -3.2925e+00 -9.4615e-01
-#>   1.8685e-01 -7.3666e+00  1.3488e+01  1.1538e+01  2.1585e+00 -3.5440e+00
-#>  -8.2571e+00 -7.2357e+00  2.6185e+00 -5.7009e+00  1.0855e+01 -6.0345e+00
-#>  -1.0681e+01  7.8015e-01  1.1207e+01  2.2349e-01 -1.9994e+01 -4.4592e+00
-#>  -2.9950e+00  7.2179e+00  3.6860e+00 -3.2118e+00  3.0814e+00 -8.9734e+00
-#>   1.2245e+01  4.6379e+00  8.6471e-01 -1.1092e+01  1.4047e+01 -2.6863e+00
-#>   3.6473e+00  9.6788e-01 -1.5797e+01 -7.5575e-01  1.0347e+00 -1.4668e+01
-#>  -8.9120e+00 -7.8948e+00 -2.6262e+00 -2.0313e+00  4.8857e+00  5.1242e+00
-#>   5.4599e+00  2.0633e+01  1.3699e+01 -6.1316e+00 -7.5593e+00  6.0918e+00
-#>  -2.3579e+00  3.9159e+00 -2.1032e+00 -1.1156e+01 -1.1244e+01 -1.0482e+00
-#>  -1.1059e+01  3.7168e+00  1.0351e+01  3.6616e+00 -2.1056e+00 -4.0807e+00
-#>  -3.3395e+00  4.6382e+00 -5.7943e+00 -4.8561e-01 -4.6544e+00  2.2636e+00
-#>  -4.9313e+00  1.4905e+01 -1.6279e+01  1.7440e+00  1.5006e-01  1.0771e+01
-#>   1.8941e+00 -2.4229e+00  6.7721e+00 -1.3173e+01 -3.8212e+00 -9.3271e+00
-#>  -9.6208e+00 -3.8180e+00  9.4949e+00  1.4090e+01  2.8480e-01  8.8882e+00
-#>  -1.6255e-01 -3.3643e+00  1.2077e+01  3.0009e+00 -2.8539e-01 -2.1709e+00
-#>   1.0549e+01  4.7026e+00 -8.8984e-01  2.2968e+00 -1.7090e+00  1.1730e+01
-#>   5.7700e+00  5.7668e+00  8.5146e-01  1.1141e+00 -3.9964e-01 -4.2993e+00
-#> 
-#> Columns 7 to 12 -5.5264e+00  5.2635e+00 -4.5586e+00 -7.7724e+00 -6.7307e+00 -8.4080e+00
-#>   4.2303e+00 -3.7358e-01  7.6817e-01  6.5046e+00 -3.3298e+00  1.6328e+01
-#>   4.7472e+00 -1.0522e+00  1.4437e+01  6.2140e+00  3.2500e+00  7.5858e+00
-#>   1.5293e+01  5.4497e+00  1.0482e-01  8.9316e-01 -2.1978e-01  5.0263e+00
-#>   2.2517e+00  1.9998e+00 -2.8958e+00  8.6876e+00  5.9590e-01 -2.5562e+00
-#>   1.5858e+01  2.7522e+00 -4.7113e+00  6.5628e+00 -3.8360e+00 -6.7326e+00
-#>  -2.1941e+00 -6.8718e+00  3.7223e+00 -1.0419e+01 -1.1370e+00 -4.4845e-01
-#>  -1.1046e+01  3.7126e-01 -4.4773e+00 -3.0009e+00  4.1116e+00  3.8538e+00
-#>  -4.7099e+00 -1.8970e+00  1.4741e+00  7.1659e-01  3.7944e+00  6.2840e+00
-#>  -7.3374e+00  7.9661e+00  8.4182e+00  3.5170e+00 -6.3520e+00 -1.7288e-02
-#>  -1.7208e+01 -3.3698e+00  4.1292e+00 -2.0220e+00 -1.0347e+01  1.5438e+01
-#>  -1.0082e+01 -6.4995e+00  5.5515e-01  5.9078e+00  7.1912e+00  3.6817e+00
-#>  -2.3130e+00  8.0063e-01  1.1618e+01  9.1944e+00 -7.0368e-01  1.0089e-01
-#>   1.9452e+00  4.3857e-01  9.5861e+00 -5.9947e+00  3.2025e+00 -4.1489e+00
-#>   2.3898e+00  5.1869e+00  7.7287e+00  3.4755e+00  5.7937e+00  5.4252e-01
-#>  -5.9263e-01  9.0569e+00  1.5395e+01 -2.7766e+00  1.0821e+01 -1.7293e+00
-#>   4.5239e-01 -1.0123e+01 -3.0120e+00 -4.2275e+00 -5.5924e+00 -1.9935e+00
-#>   1.8789e+00 -5.4162e+00 -1.0534e+01  3.1018e+00 -6.8032e+00 -4.4391e+00
-#>  -1.0391e+00  7.1574e+00 -2.2047e+00  6.4773e+00  2.0316e+01  2.3733e+00
-#>  -3.7556e+00  6.4101e+00  4.5364e+00 -4.5714e+00 -1.0283e+01  3.7152e+00
-#>  -1.0023e+01  4.5924e+00 -7.0045e+00 -3.7151e+00  7.6043e+00  1.0022e+01
-#>  -4.4115e+00  4.4164e-02  3.7332e+00 -1.2023e+00 -6.7839e+00  3.3401e+00
-#>   4.4598e+00  7.0718e+00  2.9898e+00  3.5532e+00  1.2489e+01 -4.6912e+00
-#>   7.4195e+00 -3.9552e+00 -3.8079e+00 -6.0742e+00 -6.6667e+00 -1.4535e+01
-#>   3.6264e+00 -1.3222e+01 -6.8985e+00 -2.4302e+00  1.0476e+01  3.2049e+00
-#>   6.4970e+00  5.5345e+00  2.7960e+00  2.5680e+00  1.0286e+01  5.2805e-01
-#>   2.8223e+00  4.0463e-01 -3.0897e+00 -2.8653e+00 -4.7640e+00  3.1136e+00
-#>   1.0542e+00 -1.2056e+01  2.3570e+00 -1.0653e+01 -2.1960e+00 -5.1746e+00
-#>   2.5563e+00  4.3190e+00  6.0527e+00 -1.5861e+00  1.3050e+01  3.7657e+00
-#>  -2.7193e+00  8.2389e+00  4.5454e+00  1.9444e+00 -6.1626e+00 -1.5945e+01
-#>  -1.5180e+00 -3.8249e+00 -4.1468e+00 -2.9882e+00 -4.2244e+00 -2.6074e+00
-#>   1.0457e+01  1.1844e+00  1.2966e+01  7.0765e+00 -3.6259e+00  7.7815e+00
-#>   4.6665e+00  5.6338e+00  1.1487e+01  1.3988e+01  6.3386e+00 -8.3386e-01
-#> 
-#> Columns 13 to 18  2.0886e+00 -7.3745e-01  3.8596e+00  1.1250e+01  7.6722e+00  5.0116e+00
-#>   2.1444e+00 -1.6645e+00  6.8044e-01 -4.4825e+00 -2.3838e+00 -8.9263e+00
-#>   1.7066e+00  8.2733e-01  1.3864e+00 -5.2769e+00  2.1373e+00  2.8794e+00
-#>  -9.2881e-01  5.1343e+00 -4.6063e-01 -6.8650e+00  5.7070e+00 -3.9509e+00
-#>   8.9417e+00 -3.2170e+00 -7.5011e+00 -1.3436e+00  2.3125e+00 -1.0677e+01
-#>   2.6758e+00  2.1154e+00 -1.4393e+01  1.9364e+00 -1.8340e+00 -1.4569e+00
-#>   1.2783e+01 -2.6929e+00  3.6496e+00 -6.7124e+00 -3.2855e+00 -7.3697e+00
-#>   3.5376e+00 -1.2442e+00  6.6557e-01 -9.2502e+00 -4.3490e+00  5.2186e+00
-#>  -1.0287e+00  1.7489e+00 -6.3890e+00 -3.3085e+00  4.4065e+00  4.9332e+00
-#>  -6.0900e+00 -6.4055e+00 -2.4614e+00 -3.4166e+00  3.4514e-01 -1.0389e+01
-#>   1.3470e+01 -1.1791e+01 -2.7705e+00  8.8360e+00  8.9835e+00  4.3432e-02
-#>  -2.2199e+00  3.6928e+00  7.5354e+00  1.7304e+00 -6.0398e-02  5.4968e+00
-#>  -1.4242e+00 -8.0811e-01 -3.7795e+00 -1.4466e+01  1.3720e+01  3.5974e-01
-#>  -1.2734e+00  1.6379e+00 -1.8523e+00 -1.4098e+01  1.9150e+01  4.3712e+00
-#>   3.7641e+00  2.0794e+00 -4.8163e+00 -1.0482e+01  6.0374e+00 -6.2612e+00
-#>  -5.7567e+00 -3.5951e-01  3.5209e+00 -9.5553e+00  7.4145e+00 -1.5226e+00
-#>  -4.3966e+00 -8.0719e+00 -7.3956e-01  8.1789e-01 -5.3823e+00 -4.8919e+00
-#>   5.3892e+00 -5.2176e+00 -1.4142e+01  7.2664e+00  1.4347e+01  2.4555e-01
-#>   7.7653e+00 -5.8794e+00  1.2585e+01  2.1114e+00 -1.4813e+00 -7.0001e+00
-#>   3.9473e+00 -5.0185e+00  1.1463e+01  7.1674e+00 -1.6456e-01  5.4728e-03
-#>   2.4814e+00 -1.7935e-01  6.9283e+00 -1.1585e+01  1.4784e+00  2.3654e+00
-#>  -6.4205e+00  1.1133e+01  2.9250e+00  1.7329e+00  7.0688e-01  5.7559e+00
-#>   1.0456e+01 -6.8576e+00  4.8140e+00 -7.6468e+00 -2.7327e+00 -6.2993e+00
-#>   6.8432e-01 -7.0138e+00  1.2649e+01 -9.6007e+00 -7.8085e+00 -2.0163e+01
-#>   5.6327e-01 -9.2424e-01  7.8005e+00  1.6437e+01  4.0998e+00  1.4246e+00
-#>  -3.8699e-01 -3.5711e+00  4.4379e+00 -9.7836e+00 -6.2413e+00 -2.5627e+00
-#>   1.2638e+01  8.7734e-01 -3.8200e+00 -2.4395e-01 -3.4485e+00  1.2097e+00
-#>  -5.7193e-01  1.1901e+01  2.9845e+00 -1.4858e+01 -6.7063e-01  2.3071e+00
-#>  -9.6732e+00 -5.1042e-01  8.5809e+00 -3.6766e+00  4.3520e+00  6.9453e+00
-#>   1.8797e+00 -2.1659e+00  4.6082e-01 -3.9050e+00 -7.2183e+00  1.1659e+01
-#>   1.8875e-01  3.7510e+00 -4.7080e+00  2.4531e-01 -2.2246e+00 -9.3851e+00
-#>  -7.1921e+00 -8.4018e+00  1.4588e+00  2.4293e+00 -7.9774e+00 -1.2957e+00
-#>  -7.1650e+00  6.5995e+00 -8.2609e-01 -4.5824e+00  1.8581e+00  1.0594e+01
-#> 
-#> Columns 19 to 24 -1.6547e+01 -1.7455e-01 -1.3965e+01  1.1901e+01  1.1083e+01  7.0531e-01
-#>  -9.7378e+00  4.9298e+00 -3.4363e+00 -8.7591e-01 -2.4670e+00 -5.0297e+00
-#>   4.3643e+00 -1.5493e+00 -5.8622e+00 -7.1102e+00 -1.2048e+01  2.1534e+00
-#>  -1.5390e+01  2.9438e+00  3.5965e+00 -4.0425e+00  4.1766e+00 -2.8972e-01
-#>   2.0704e-01 -8.7539e-01 -6.3799e+00 -4.0346e+00 -1.2937e+01 -2.5051e+00
-#>   1.3951e+00 -1.5320e+00 -2.3383e+00  8.2027e+00 -4.7966e+00  2.8239e+00
-#>  -8.0676e+00 -6.4718e+00  5.0619e+00 -5.0724e+00 -6.6217e+00  8.5565e+00
-#>   3.2756e+00  7.5328e+00 -5.7972e+00  9.7473e-02  3.8714e+00 -7.3040e-01
-#>  -9.6483e+00 -2.0600e+00 -9.7193e+00 -4.9492e+00 -7.4333e-01 -8.6179e+00
-#>  -1.0411e+01 -6.1163e+00 -6.2946e+00 -6.3084e+00 -8.5870e+00 -4.3519e+00
-#>   1.3475e+01 -6.2340e+00  9.6245e-02  4.0572e+00  4.9656e+00  6.8442e+00
-#>   1.2770e+01  7.3423e+00  5.9753e+00 -3.9718e+00 -5.8074e+00  1.2654e+01
-#>   6.1425e+00 -1.5625e+00 -5.7367e-01 -8.6138e+00 -5.9765e+00 -2.7418e+00
-#>   2.2703e+00 -2.2491e+00  8.4373e-01 -6.4280e+00  3.2190e+00  3.5527e+00
-#>   1.1549e+01 -7.9122e+00 -1.8938e+01 -1.5693e+01 -4.1604e+00  1.1917e+01
-#>  -5.9029e+00  9.0893e+00  7.5842e-01 -5.2862e+00  8.7295e+00 -7.4853e+00
-#>  -1.2061e+01 -6.9287e+00 -3.7148e-01  5.6541e+00  1.5193e+00  3.5425e-01
-#>  -5.0123e+00 -4.6697e+00 -1.0465e+00  1.5650e+00 -1.0680e+00  2.7245e+00
-#>   1.0073e+01  7.4417e+00  9.5893e+00  4.2450e+00  4.2740e+00  6.7323e-01
-#>   4.4783e+00 -4.2850e+00  5.4657e+00 -3.8559e+00 -6.5410e+00  3.1018e+00
-#>   2.0051e+00  2.6213e+00  3.6959e+00  1.6965e+00  5.2516e+00 -1.1366e+00
-#>  -1.3355e+01 -2.0926e+00  1.9470e+00 -9.4491e+00  7.5002e+00 -6.4533e+00
-#>   1.8658e+01  4.7384e+00  1.5558e+00  2.8851e+00  6.7274e+00 -1.1672e+00
-#>  -3.9936e+00 -6.7976e-01  3.4615e+00  1.5546e-01 -4.7202e+00  3.0028e+00
-#>  -1.1919e+00 -1.3104e+00  1.5569e+00  1.2456e+01  3.1025e+00  2.3721e+00
-#>   1.1173e+00  4.5066e+00 -9.3090e+00 -8.5264e-03  1.7803e+00 -6.3240e+00
-#>   1.9434e+00  1.0001e+01 -4.6523e+00 -7.2178e+00  1.9133e+00  4.2573e+00
-#>   1.7640e+01  2.0178e+00  1.2119e+01 -1.2353e+00 -1.2177e+00  3.3461e+00
-#>  -1.1616e+01 -2.9746e+00 -7.5183e+00 -4.7508e-01  4.7900e+00  1.1289e+00
-#>  -4.1454e+00 -8.5355e+00 -4.8618e+00  7.4444e+00 -6.7522e-01  1.5182e+00
-#>   9.5614e+00  7.6493e-01 -1.7208e+01 -4.1176e+00  5.2517e+00  3.0220e+00
-#>   7.9180e+00 -8.7570e-01 -7.2966e+00 -8.8530e+00 -1.2356e+00  9.8385e+00
-#>   8.0671e-01 -7.2320e-01 -2.2440e+00  6.0880e+00  2.4059e+00 -2.9827e+00
-#> 
-#> Columns 25 to 30 -1.7654e+00  6.1573e+00 -4.7365e+00 -8.6320e+00 -1.0574e+01 -2.4142e+00
-#>   9.6550e+00 -1.3046e+01  3.9530e+00 -7.7652e+00 -7.8142e-03 -2.3785e+00
-#>   7.7095e+00 -4.5380e-02 -2.2185e+00 -2.1431e+00 -2.8480e+00 -4.1918e+00
-#>  -1.2611e+00 -6.1082e+00  8.4187e+00 -1.1930e+01 -1.8830e+00  9.4992e-01
-#>   2.9813e+00  1.7482e+00 -3.5632e+00  1.0422e+01  7.9552e+00 -9.4522e+00
-#>   2.3804e+00 -3.7486e+00 -7.8931e+00 -8.6517e-02  3.5921e+00  5.1656e+00
-#>  -1.0735e+01  2.3283e+00 -1.0351e+01  9.9058e+00 -1.6368e+01  1.7590e+01
-#>  -3.3455e+00  6.6997e+00  6.1150e+00 -1.3539e+01  3.1509e+00  9.2606e+00
-#>   9.9220e+00  2.6841e+00 -7.4650e+00 -3.8941e+00  4.9308e+00 -9.6910e+00
-#>   1.2039e+01 -5.5931e+00  3.8058e+00 -3.7076e-02  4.8538e+00 -1.3906e+01
-#>  -1.2222e+00 -5.8971e+00  5.7628e+00 -1.3399e+00  8.7426e+00  2.8881e-01
-#>  -3.1671e-01 -1.3056e+00 -8.4503e+00  6.6954e+00  4.4994e+00  7.4874e-03
-#>   5.8190e+00  1.4675e+01 -4.8289e+00  5.9821e+00  6.1915e+00 -1.3343e-01
-#>   3.3220e-04  4.6068e+00 -4.6546e+00  5.7364e+00 -2.2543e+00  2.7028e+00
-#>   9.3891e+00 -2.0363e+00 -8.9049e+00  4.3891e+00 -8.8804e+00 -9.8594e+00
-#>  -1.0224e+00  5.4304e+00  5.4160e+00  1.0815e+01 -9.0729e+00  3.5370e-01
-#>  -1.5242e+00 -5.8958e+00 -9.4318e+00  1.0289e+01 -2.9070e+00 -4.4505e+00
-#>  -2.7731e-01 -6.3541e+00  1.7985e+00  4.7676e+00  1.1100e+01  5.3108e+00
-#>  -4.6237e+00  5.7527e+00  1.9249e+00  2.4828e+00 -6.9288e+00  4.5745e+00
-#>   1.0163e+01  5.8311e-01 -1.0615e+00 -6.9244e+00 -7.4802e+00 -1.2862e+01
-#>  -2.9575e+00  2.7526e+00  3.0471e+00 -1.4789e+01  4.2150e-01  4.5949e+00
-#>   5.3139e+00 -8.4273e-01 -1.1009e+00 -1.0658e+01  2.4117e-01 -5.2637e+00
-#>  -1.8128e+00  3.4119e+00 -9.5389e+00  4.7784e+00  1.5970e+00  8.7575e+00
-#>   7.7266e+00 -1.3421e+00 -1.6817e+01  6.9556e+00  8.4550e-01 -3.6458e+00
-#>   2.7317e+00 -5.6457e+00 -4.8476e+00  4.2472e+00 -7.1554e+00 -4.5735e+00
-#>   2.0287e+00  1.2595e+01  5.9875e+00 -6.9651e+00 -3.8292e-01  4.1640e+00
-#>   4.7382e+00  1.5736e+00 -1.6172e+01  8.5885e-01 -4.5621e+00 -4.1614e+00
-#>  -1.3447e+01 -8.9498e+00  1.1139e+01  1.4416e+01 -6.6645e+00  1.1075e+01
-#>   1.9097e+00 -3.9173e+00  1.9079e+00 -7.1014e+00 -6.5555e+00 -1.1570e+01
-#>  -6.9789e+00  1.3138e+00  2.7922e+00  1.4033e+01 -3.5511e+00 -6.7887e+00
-#>   4.4467e+00  4.0878e-01 -4.3903e+00 -4.1820e+00 -1.0072e+00 -4.2013e+00
-#>   5.4239e+00 -5.3318e+00  1.1106e+01  8.8659e+00 -6.3158e+00 -8.8431e+00
-#>   1.7667e+00  7.0973e+00  1.4381e-01 -1.7409e+00 -1.4521e+00 -8.0895e+00
-#> 
-#> Columns 31 to 36  5.7877e+00 -4.6230e+00  7.2711e-01 -5.3029e+00 -1.0070e+01  1.5749e+00
-#>  -6.4950e+00 -8.0376e+00 -4.5445e+00 -6.8741e-01  7.7429e+00 -1.0083e+01
-#>  -1.5847e+01  1.7480e-01 -2.1960e+00 -3.0769e+00  1.8834e+00 -1.6263e+01
-#>  -6.5188e+00 -4.4961e+00 -7.4398e+00  4.9623e+00 -5.0504e+00 -1.0719e+01
-#>  -6.9759e+00 -2.7550e+00  3.8908e+00 -1.4866e+00  1.4092e+00  8.6068e+00
-#>   1.1555e+00 -7.9262e+00 -1.7627e+00  1.4443e+01  5.2027e+00  1.9195e+01
-#>  -1.1875e+01  1.7406e-01  3.8870e+00 -1.6582e+01 -7.6358e-01 -9.4129e+00
-#>  -5.6199e+00  2.9915e-01 -4.2304e+00  1.3446e+00 -8.7123e+00 -8.4327e+00
-#>   1.4075e+00  2.2520e+00 -5.4539e-01  1.2540e-01  2.7837e+00 -1.9737e+00
-#>  -1.0888e+01  2.6625e+00 -2.2844e-01 -5.8239e+00  9.2506e+00 -4.6154e+00
-#>   3.2156e-01 -4.3800e+00  1.0448e+01  1.1336e+01 -5.0925e-01  1.1347e+01
-#>  -8.9026e+00  8.4311e+00 -1.1829e+00  8.5838e+00  4.1165e+00 -2.6017e+00
-#>  -1.0618e+01  4.5853e+00 -8.2806e-01  3.4717e-01 -5.1240e+00 -7.8976e+00
-#>  -1.1902e+01  1.0810e+01  3.4999e+00  3.2599e+00  9.5764e-01 -8.7301e+00
-#>  -1.3804e+01  3.4591e+00  2.7214e-01  3.7915e+00  8.5312e-03 -5.0884e-01
-#>  -8.1244e+00  1.0172e+00  1.8569e+00 -2.3522e+00  6.7773e+00 -1.2270e+01
-#>   1.8376e+00  8.6732e-01  1.6401e+01 -7.6989e+00 -2.0114e-01  2.8492e+00
-#>   1.0577e+00 -1.9100e+00  3.3745e+00  1.2003e+01  3.8370e-01 -1.6340e-01
-#>  -2.0837e+00  8.0215e-01  4.6182e+00  2.0742e+00 -5.6774e+00  1.3857e+00
-#>  -6.0330e+00  1.0794e+01  5.6998e+00 -6.3962e+00  4.2862e+00 -2.6237e+00
-#>  -3.0855e+00  7.1994e+00 -2.1118e+00  8.3192e+00  1.5085e+00 -1.3735e+01
-#>   2.6148e-01  1.1909e+01 -1.5958e+00 -8.5097e+00 -1.8810e+00 -1.4911e+01
-#>  -8.3884e+00  3.3756e+00  1.4041e+01  9.2340e-01  8.0913e+00  2.0051e+01
-#>   7.7919e+00 -1.8161e+00 -5.1652e+00 -1.1685e+01  6.0734e+00  5.4655e+00
-#>   7.2264e+00  3.7018e+00 -1.2938e+00 -3.1015e+00  7.4691e+00  3.9491e+00
-#>  -1.2834e+01  9.6482e-01  2.5271e+00 -7.3225e+00 -1.5531e+00  3.0103e+00
-#>  -3.2939e+00 -7.1995e-01 -1.5961e-01 -8.3620e+00 -3.4409e+00  2.4556e+00
-#>   8.2753e+00 -4.5557e+00  9.8521e+00  1.1992e+01  1.5305e+01 -8.5115e-01
-#>  -7.4579e+00  8.0515e+00 -3.0819e+00 -1.4713e+00  9.2651e+00 -1.5457e+01
-#>   9.7496e+00  4.9703e+00  1.0001e+01  9.2289e+00  1.9981e+00  8.2831e+00
-#>  -1.0757e+01  3.1880e+00 -2.7738e+00  1.1525e+00 -1.5764e+01  1.4619e+00
-#>  -5.5567e+00 -7.2724e+00  4.9576e+00 -2.6230e+00  5.0559e+00  9.4046e+00
-#>  -7.6573e+00  3.0302e+00 -5.1165e+00  3.8154e-01  4.2957e+00 -2.9837e+00
-#> 
-#> Columns 37 to 42  1.4781e+01  5.8403e+00 -7.9739e+00  4.3284e-01  4.0315e+00 -1.8781e+00
-#>   1.0714e+01 -3.9545e+00  5.9812e+00 -1.0373e+01  4.7588e+00  8.2855e+00
-#>  -2.0618e+00 -2.1084e+00  4.1794e+00 -1.0581e+01  4.8777e+00  8.7927e+00
-#>   1.1532e+01 -1.2932e+00 -6.2592e+00 -2.0058e+00  1.2718e+01 -1.0143e+01
-#>  -6.9627e+00 -1.5297e+00 -4.0824e+00 -1.5353e+00 -6.1356e+00  3.2539e+00
-#>   1.0944e+01 -1.2924e+00 -8.0913e+00  8.3946e-01  5.9463e+00  4.7262e+00
-#>  -8.8214e-01 -7.6500e+00 -9.0881e+00  1.2230e+01  7.8738e+00  1.2728e+00
-#>  -3.6937e-01 -7.4623e-01  2.8593e+00  6.0178e+00 -4.1878e+00 -6.2481e+00
-#>   4.6512e+00 -3.8977e+00 -8.6991e+00 -7.2066e+00 -8.0469e+00  4.5942e+00
-#>  -1.1658e-01 -2.2086e+00  4.4043e+00 -8.7130e+00 -8.1418e+00  6.6675e+00
-#>   3.2866e+00 -1.4364e+01 -1.1202e+01  2.3180e+00  9.5690e-01 -2.2525e+00
-#>  -6.0650e+00  4.9470e+00 -8.6046e+00 -3.2993e+00  1.0456e+01 -8.4463e+00
-#>  -1.1800e+00  4.0888e+00  1.0231e+01 -6.0781e+00 -1.3315e+01  8.7547e+00
-#>   5.2642e+00 -5.8803e+00  2.3019e+00  7.4136e+00 -4.8173e+00  4.0485e+00
-#>  -2.2386e+00 -9.5951e+00 -1.3358e+00 -4.3168e+00 -5.1432e+00  1.0044e+01
-#>   4.5064e+00 -1.4719e+01  8.1893e+00 -3.3648e-01 -9.4532e+00  3.5265e+00
-#>  -8.1611e+00  4.5290e+00 -4.8454e+00  3.8065e+00  5.1539e-01 -3.5527e+00
-#>   5.1429e+00 -6.1683e+00 -7.7518e+00  2.2368e+00  3.3335e+00 -3.2178e+00
-#>  -7.6238e+00 -8.8446e+00  3.7186e+00 -4.0304e+00 -1.2876e+00  3.0657e+00
-#>  -9.6459e+00  7.1043e+00  1.1225e+00 -8.9580e-01  3.6940e-01  8.4879e+00
-#>   1.3031e+00 -6.8280e+00 -9.7464e-01 -1.7561e+00  4.3782e+00 -5.3408e+00
-#>   6.9679e+00 -5.8045e+00  1.6076e+00 -1.3174e+00 -2.8242e+00  3.5411e+00
-#>   4.8554e+00  5.4867e-01  4.6937e+00 -8.3519e-01  2.8432e+00  1.4558e+00
-#>  -4.0727e+00  5.3502e+00 -6.1120e+00 -1.2640e+01  6.2638e+00  9.2251e+00
-#>  -4.0730e+00 -4.3857e+00  4.1525e-03 -5.7744e+00  5.5562e+00  1.8900e+00
-#>  -1.4340e+00 -1.0864e+00  6.5575e+00  2.4515e+00 -8.7689e+00  1.5012e-01
-#>   7.8476e+00  3.2083e-01 -1.9992e+00  1.2308e+00  1.4226e+00  4.5515e+00
-#>   1.4021e+00 -6.5290e+00  3.1490e+00  1.2606e+00  1.7082e-01 -2.3939e+00
-#>  -6.8526e+00 -6.5598e+00  7.8596e+00 -2.0899e+00 -4.9527e+00  3.3671e+00
-#>  -7.9504e+00  1.3332e+01 -5.1713e+00 -4.8172e+00  3.8252e+00 -9.7357e+00
-#>   4.2879e-01  1.0015e-02 -3.9781e+00 -9.1234e+00  1.0227e+00 -1.5022e+00
-#>  -9.6010e+00  7.0668e+00  9.2475e-01  3.4143e+00 -3.9469e+00  2.5812e+00
-#>  -7.8083e+00  9.0833e-01  7.7153e+00 -7.5587e+00 -5.6300e-01  4.6345e+00
-#> 
-#> Columns 43 to 48  1.2810e+01 -1.3189e+01 -7.7055e+00 -6.3976e+00  1.5547e+01  8.8330e+00
-#>  -1.5065e+01  9.7159e+00 -6.3723e+00  3.3489e-01  2.0888e+00  3.7348e+00
-#>  -4.2313e+00  6.3053e+00  1.9464e+00 -8.2544e-01  8.5206e+00  5.7207e+00
-#>   2.0316e-01  1.0027e+01 -5.5466e+00  9.0533e+00  1.8833e+00  5.5861e+00
-#>   3.3048e+00  3.1319e+00  6.2988e+00  3.1838e+00 -2.9775e-01 -9.9813e+00
-#>   1.6776e+00 -1.1691e+00 -5.3201e+00  3.5656e+00 -4.7045e+00 -8.3086e+00
-#>  -4.5635e+00  2.0188e+00  9.2125e+00 -3.9032e+00 -3.8079e+00 -6.3385e+00
-#>   5.3327e+00  7.2008e+00  1.8978e+00 -5.8745e+00 -3.4445e+00  5.1775e+00
-#>   5.9848e+00  2.5756e+00  2.4851e+00 -4.3141e+00 -7.1245e+00 -4.7120e+00
-#>  -4.2353e+00  4.1360e+00  2.4754e+00 -3.3324e+00  6.6476e+00 -2.6378e+00
-#>  -3.8737e+00 -2.7430e+00 -4.2719e+00  6.1796e+00  3.7464e+00  3.1966e+00
-#>  -8.9031e+00  5.3667e-02 -2.0811e+00  4.1157e+00  7.7390e+00  4.8121e+00
-#>   2.6389e+00  4.2188e+00 -3.3962e+00  4.2881e+00 -2.4315e+00  9.4363e+00
-#>   8.9556e+00  1.2537e+00 -2.8011e+00 -7.3613e+00  1.4005e+00 -3.8574e+00
-#>   1.2019e+01  1.4726e+01  6.9024e+00 -2.1664e+00  5.1618e+00 -1.1799e+00
-#>  -3.3054e+00  3.7320e-01  1.0245e+01 -1.4051e+00  1.6680e+00 -8.1630e-02
-#>   2.8422e-01 -7.4191e+00  2.8573e+00  1.6843e+00  1.4833e+01 -2.8418e+00
-#>  -4.4291e+00 -2.8135e+00 -1.5023e+01 -3.1354e+00  2.7697e+00 -1.9877e+00
-#>  -1.1016e+00  7.1654e+00  1.2580e+00  7.7710e+00  4.1162e+00  5.1481e+00
-#>  -6.9081e+00  5.2128e+00  2.9860e+00 -1.4196e+00 -1.7435e+00  8.4676e+00
-#>  -5.3983e+00  1.0983e+01 -3.1357e+00 -1.0623e+01 -4.6322e+00  7.8922e-01
-#>   8.3450e-01 -4.0142e-01 -7.6967e+00 -1.4488e+00 -8.4348e+00  2.2065e+00
-#>   5.7479e+00  3.1151e+00  1.8005e+00 -5.7872e+00  3.0122e+00 -9.6315e+00
-#>  -7.8079e+00 -7.6238e+00 -1.7575e+00  1.8205e+00  7.2539e+00  3.8008e+00
-#>  -1.0109e+01 -3.5757e+00  8.2105e-01 -4.4458e+00 -5.1365e+00 -1.5818e+01
-#>   4.6419e+00  8.5954e+00  8.1159e+00 -1.8950e+00  1.0888e+00  7.3860e+00
-#>   1.0279e+01  4.0486e+00  3.8668e+00  6.8263e+00  4.4360e+00  1.7308e+00
-#>  -1.8266e+01  3.0640e+00  4.1436e+00  8.5667e-01  6.0407e-01 -1.2738e+01
-#>   7.3158e-01  8.1287e+00  8.3764e+00 -5.1014e+00  2.8116e-01 -7.9374e+00
-#>   3.0224e-01 -5.2574e+00  4.2073e+00  6.6237e+00  5.8265e+00  1.7484e+00
-#>   5.1284e+00  5.3158e+00  4.4600e+00  1.2923e+01  1.3637e+01 -3.8000e+00
-#>   2.7022e-01 -4.0573e+00  9.2956e+00  1.5066e+00 -9.9581e+00 -8.9386e+00
-#>   5.2094e+00  1.3030e+00  2.6377e+00  3.6572e+00 -1.2780e+00  1.9778e+00
-#> 
-#> (11,.,.) = 
-#>  Columns 1 to 8   2.8781   2.2097  -1.0166   8.1775  -0.7718   8.1902  -0.9899 -11.8470
-#>   -6.2581   5.5743   7.7125 -12.8765  -3.1623   0.1986  -5.9956  -9.2363
-#>   -0.2172  -0.8420   9.4055 -11.2229  -1.8000  -1.2664  -1.7218  -1.0896
-#>   11.4138   1.5981   0.7542   1.7948  -1.1702  -1.7719  -9.2642  -9.8508
-#>   -3.8479   8.2731   3.5893  -6.5526   3.9244  -7.8203  -9.8070   3.4631
-#>    9.6756   1.5901 -10.3462 -10.4983   5.3770  -0.4591  -2.7411   3.5073
-#>    5.7282  -6.6532   7.0050  -3.8995  11.4720  -1.5143  -3.0646   7.9134
-#>  -10.4324   2.0450   1.2011   5.9723  -2.9955   5.4474   7.2957  -3.6325
-#>    8.0859   1.0551   0.5931   0.0603  -6.0898  -3.1183   5.7426  -6.1302
-#>  -12.6554  11.6160   9.1281  -8.6554  -0.7018   3.1528  -6.0132  -5.0604
-#>  -14.8811   9.6235  -2.2084   0.9378   5.9259   2.6533  -0.6158  -0.3892
-#>   -7.6016  11.6173  -4.0455  -9.1039   7.3522   1.6756   1.2683   7.6569
-#>   -4.3699  -5.2974   5.9473   7.1043  -3.2537   5.4743  -1.0449  -5.1495
-#>    5.2138  -2.3339  -1.0099   0.8368   2.8119   2.7774   3.6714   0.0031
-#>    8.2720  -6.8858   7.2203  -5.7084 -10.2222  -5.5913  -9.6192   0.9030
-#>   -2.1772  -4.7607  14.7243   3.4797  -0.6596  -1.4458   2.4896  -5.4278
-#>   -7.1641  -0.1249  -7.9006  -3.6037  -3.3484   8.6855  -5.7696   2.6918
-#>   -2.5526  -4.4526  -2.4355  -3.2067  14.2889   3.3216   1.6071   0.7929
-#>  -15.2051  14.2628  12.3325   3.1186  -1.8251  -7.4071  -2.9547  -1.7397
-#>   -5.2387   9.3243  -7.0639  -0.8562  -4.1239   3.8398   1.4057  -1.6789
-#>    0.5528  -0.8265 -10.4466  -4.1306   6.3734   1.5282  12.3649   4.0504
-#>   11.1108  -3.0633  -3.6177   5.7682   5.1008  -2.4357   7.9087  -9.6357
-#>  -10.3575  15.5950  -6.8540 -12.3639  -0.0412  -8.0590  -2.7639   6.2915
-#>   -7.8598  14.0037  -4.7523   2.2997   6.1635   1.7026  -6.2875  -1.1226
-#>    3.5824  -5.8863   4.7592  -3.0903   2.6901  -1.9886   2.6103   5.2841
-#>   -1.5312   4.2905   8.7163   1.5517  -6.8520   1.7236  -6.1933  -7.1499
-#>    9.4862   2.0688  -4.7118  -1.9646  -3.7517  -5.6849  -2.6916  -0.4818
-#>   -6.3272  -8.3648   1.5437 -12.2026   4.5188  11.4448  -0.7743  21.8454
-#>   -4.0406   3.5181   4.3892  -3.6517 -10.6121   1.8866   2.7786  -0.3579
-#>  -14.2391   7.8265 -13.1909   0.1867  -0.9296   1.5074   5.8571  -1.2301
-#>   -0.2945   6.9921   6.2890   1.7168  -1.7118  -6.4731  -5.1135  -1.5401
-#>    2.0167  -0.0208   2.3058   4.0023  -9.0107   0.2120  -7.0342   6.9787
-#>    7.1531   0.8605   9.0946  -3.1487   0.0899  -3.9209   2.6381  -3.0970
-#> 
-#> Columns 9 to 16  -6.1915  -8.2626  -9.0006  -4.0418  -5.2003  -5.3085  -4.1699  16.7122
-#>   19.5822   3.5655  -0.5455   2.5924   2.8166  -7.1293   2.1717   0.6939
-#>   -0.0666  -1.4415   2.4082   7.5363   0.5220  -1.4166   4.4682  -3.4278
-#>    4.9616   0.3099  -4.0076  11.1014  -5.7296   2.0147  -7.3795   9.0969
-#>   -8.2887  -3.4343   9.5883  -2.1146   0.5618  -2.2804   5.8889  -7.3778
-#>   14.1456   5.9190  -2.0328  -2.6687   2.2124   0.8063   6.1982   2.2195
-#>   -1.5928   0.4058   8.9747   1.6188   3.1345  -9.7028  -7.3274   6.0963
-#>   10.4677   2.9256   0.9422  11.0137  -2.2644 -11.5415   7.8916   2.9541
-#>   -6.0708   4.4333   3.1589  -5.0676  -7.9851   2.3739  -6.0588  -2.1832
-#>   10.8193   4.0739   0.8660  -2.5729   0.8408  -8.6158   4.2908  -6.9327
-#>   -6.8986   5.9385   4.4483 -11.7290 -14.5286   0.4827  17.4183  -2.5515
-#>    5.1375   4.0198   8.6159  -4.3389 -13.8087   0.7683   8.3413   2.9647
-#>   -6.5967  -1.5645  -1.2306  12.1490  -6.1894  14.5564   8.7421  -4.9975
-#>  -11.2655   0.8737  -6.1851   0.2204  -5.8681   4.7180   5.9456   1.9183
-#>  -12.2213 -15.1589  -2.7198   2.5953  -6.5069 -10.0845   9.1320 -10.0574
-#>   -0.7626   1.8518  -2.9269   1.0017  12.3075   5.7738  -5.0093   9.1991
-#>    3.9567  -2.3733   5.1063   4.6667  -0.3354 -10.5436 -11.7094   8.5977
-#>    2.9944  -6.6322   4.3978  -6.5010  -0.0759 -10.2965   4.9730   2.9660
-#>    8.9919   2.6644  -4.4370   7.2588   4.9745   1.3050  19.2494  10.3855
-#>   -1.0392  -2.9545  -5.6787   3.8888  -1.4883  -5.5207   6.1828  -3.4936
-#>   10.7327   4.6214  -0.5641   0.3687  -1.4311  -4.9733   3.9833   0.7176
-#>   -8.3585  -5.7510  -4.0912  -2.6170  -1.7727   7.6623  -6.1395  -9.1816
-#>    2.2889  -0.2266   3.1189  -2.6195   4.2082  -5.6330   9.8348   7.9776
-#>    8.7933  12.3731  -3.5023   2.7876  -0.1834   2.7520 -10.9274  -2.5809
-#>   11.7923   6.6071  -4.3929 -12.0979  -4.8559  -3.5806  -1.5844  13.4595
-#>    0.7568  -5.8900   2.0718  14.9415   2.2399  -6.6502  14.8196   8.2244
-#>   -3.6494  -1.7169   6.9579  -0.4423  -3.3180  -1.1368 -11.8920   6.4290
-#>    6.7029 -10.1809  -1.9819  -5.6661  -2.7719  -2.5286  -3.1997  -9.4372
-#>    8.7309  -4.0389 -11.5737  -8.1458  -3.5622  -0.5749   8.0254  -1.8079
-#>  -10.1502  -2.2120  -3.8260   6.5190   6.1603   0.6438   0.1826   1.3515
-#>   -3.4241  -1.1157   6.5079  11.2266  -4.7174  -7.4841   2.2192  -3.6866
-#>   -1.7339  -2.9707  -5.3129  -6.4849  -7.4874   8.1912   9.1298  -6.6721
-#>   -3.9521   1.4523   0.2940  -1.9947  -3.2829  16.9990   4.8489   6.0814
-#> 
-#> Columns 17 to 24   5.6664  11.0845 -10.2942  -4.7752  -6.0080   8.1106  -3.5690   0.9122
-#>   -7.6126 -19.9690   4.6329  -8.9806  -0.4967   2.6903   7.8598  -4.5414
-#>  -11.8706  -5.2885  -0.0619  -1.4086   3.6219   0.9005   1.2591  -2.8981
-#>   -5.7637 -11.8687  -0.4377  -7.7378   4.4512  -7.9747  -8.7335   0.4574
-#>    3.1455  -1.0820   2.9134   0.2344   3.9357 -11.2709   0.1974  -3.3359
-#>  -12.8174  -2.0394  -9.5841   2.4451   7.2319  -0.3387   0.5652  -0.1930
-#>  -13.7959   8.0847   3.8370  -1.3418 -10.4121   5.0823  -8.9951  -1.7491
-#>   -6.6635   3.5142   1.4026 -11.0846  -2.7440   9.2802   3.5806   5.7885
-#>   -1.1767  -0.9026  -8.0627  -2.0746  12.1456  -3.9612  -5.6939   3.9885
-#>   -1.7274 -16.3384   7.3582  -6.4908  -0.4155  -1.4168  10.5900  -8.3264
-#>   22.9371  -2.6684   7.5032  -5.1639  -0.7731 -15.6504   2.5496 -25.6584
-#>   -9.9856  13.7973   0.9767 -14.3910  -3.7744   0.1570  -6.9156  -0.4604
-#>   -1.7880   3.5345   1.4397   1.9685   8.2998  -7.1436   2.2520  -1.8548
-#>    2.6198   6.9447   1.9977  -4.7944   7.8972  -9.6881 -14.5384   2.0402
-#>    2.4234   3.0974  -5.9777  12.3375   2.4437  -5.6257   0.3055   3.5800
-#>   -2.0321  -4.9167   1.8321  -1.6269  -7.0493  -7.4840  -6.6248   8.4155
-#>   -6.3536  -5.2497  -0.8554  11.0275   0.3792   2.4790   6.4480  -4.4525
-#>    0.3469   6.9205   4.5552  -4.9644   9.5360  -1.3360  -9.3090  -4.5213
-#>   -6.4502 -11.7928  -6.1335  -8.0252   0.8029  -9.4381   5.9685  -4.3368
-#>    8.8216   0.8329  -0.2735  -7.1837  -8.0822  -9.6586   1.2252   0.6287
-#>   -1.3171  -6.6742   6.8636 -18.5117   5.8795   5.2577   0.1714   3.6908
-#>    1.8756   8.8235  -7.2492   1.8759  -0.0843  -4.4299 -16.8106   9.7002
-#>    5.3941  -1.8122  -2.9792 -10.6169   3.2304  -1.0800  -0.9455  -3.4328
-#>   -7.0644  -8.4130   4.7494   5.3553   7.0956  -8.0271  20.0530  -7.7643
-#>   -2.8851  -2.7643  -9.6940   0.6988 -13.5548 -11.2170  -6.3717   6.5294
-#>    1.4785  -1.5019 -13.8826   2.5866  -6.0112   1.4736   5.9116  13.5663
-#>    4.4456   3.7312  -3.7568  -0.6211   3.6518   0.4688  -5.2054   4.7016
-#>    4.1419   7.2961  22.8483  18.7562 -13.6192  10.4419   5.3509 -19.8762
-#>   -1.3481 -10.2438  -1.7149 -11.4341 -10.5016   4.5516  -6.6300  10.4442
-#>    4.8445   8.3227  -5.4541   7.5567   0.8922  -0.4789   0.3967  -5.8435
-#>   -4.6249  13.5624  -6.0266  -7.0758   0.9136  -2.8116   0.4097   5.0527
-#>    2.1420  -5.8441   1.0169   3.2376 -15.4424 -10.0194   7.6189   0.4871
-#>   -6.5184  -1.2838 -10.3528  -3.5135  -7.1717  -4.0199 -10.5834   9.9731
-#> 
-#> Columns 25 to 32 -10.3515   3.4975  -3.6517   7.7534   0.3283   0.2941  -7.0501  -6.8274
-#>    1.4270  12.2531   9.8292  -0.3401   3.0283   1.5113   2.4921   3.2593
-#>   -7.5005   7.8531   8.1607   1.5169   3.4709 -12.8539  -1.9708  12.2384
-#>    2.4944   3.9962   0.7539   3.6741  -9.7667  -4.9758   3.8036   2.6912
-#>    5.5318   2.7070   1.6485  -5.0410  -0.7996  -4.4982   4.1551  15.4612
-#>  -10.5261  -5.4848   4.7405  -1.1069   3.9326   5.6820  -5.2473  -8.3052
-#>   -1.1329  -1.5255  12.7872  -3.1154  -0.9708   2.8787   1.7427  14.3937
-#>  -12.2924  -4.7862   7.1931   0.5332   7.4366   7.9451  -9.8216  -8.7823
-#>   -0.7814   4.8600   0.4755  11.8892  -3.6985  -3.3411   5.5341   0.2644
-#>    3.4088   5.4109   8.5450  -2.0580  -0.5300  -3.7219   7.3510   7.8470
-#>    9.0335   9.4278  11.0499  -3.8728  -1.4293 -12.1467   1.2945  -5.4632
-#>    6.8743  11.7608   1.6648  -4.8751  13.1060  -2.2418   4.4712   3.7576
-#>    6.7995   3.0815   1.0554  -3.7866   1.1449 -10.6269  -4.1921  -1.4030
-#>   -0.4489   3.0557   1.8424   2.0436  -6.4865  -3.8906   0.9938  -2.0567
-#>  -10.4877  -4.4549   1.0167   0.4971  -7.5357  -6.5673 -10.2117   4.3548
-#>   -0.8417   2.0043  -0.5281  -1.6514 -12.2684  -1.2485  -2.1550   2.2551
-#>  -12.7666  -6.6186   0.8067   5.8773  -3.6131  -2.2668  11.4308   7.8232
-#>   -9.4452  -1.5626   6.0157  -1.6627   4.5507  -0.6084  -3.8642   1.6012
-#>    2.9794   0.1325   3.8421  -8.8259  -0.4939 -18.2918 -11.1140  -4.3855
-#>    6.2471   4.9542   9.4580   0.6201  -2.4679   0.5176   9.5204   6.5565
-#>   -0.3907   4.6630  12.7648  -0.0060   7.7318   9.3784  -0.8921   5.6945
-#>   12.5322  13.0636  -1.7965   4.8947  -6.6470   2.9905   9.2692  -3.8457
-#>   -0.9241 -11.4957  -1.6680  -5.9663   9.7777  -4.1905  -8.1391  -2.7973
-#>    5.2600  -4.3981  -7.8513  -1.1895   4.0581  -2.9813  10.5569   5.8662
-#>   -6.3160   9.7658   2.4030  10.7301  -2.1206   0.5316   2.3882   3.0174
-#>   -9.0679  -6.4728   0.3893   0.7980  -4.6750  -4.5644 -10.7221   4.0892
-#>    1.4679  -5.2258  -3.4626  -0.5695   2.0237  -1.4349   2.7530  -4.8818
-#>   -7.9846   6.8764   4.2227   2.3961   0.2227  10.2062   9.4042   9.6672
-#>    0.3163  12.4046  -0.8354   0.3742  -5.6021  -1.8028  -0.4147   4.0197
-#>   -7.1034  -3.1581 -15.2460   3.4164   1.7271 -13.3808   8.1067   5.0651
-#>   -5.3282  -7.1169 -14.3244  -2.9758   9.1079  -3.9552  -3.5461  -5.6140
-#>   15.6531   4.8156   1.1635  -6.2082  -6.7749   4.0234  -1.0711  -5.8332
-#>    4.3762  14.6048  -1.9513   1.7074  -6.0273  -8.7757  -5.1166   8.4456
-#> 
-#> Columns 33 to 40 -10.1023  -3.9219  -9.1799   9.5749   8.4975 -11.1930   9.7151   7.1257
-#>   -4.6959  -0.7548  12.8207  10.9396   0.5109  -2.8755  13.5778  -6.5760
-#>   -5.4996  -7.8202   4.7448   6.0284  -1.3823   8.4791  13.4582   9.2748
-#>   -6.7925   3.9432   4.9907   6.8099   3.3344   2.6087   3.8572 -13.1837
-#>    4.0631   2.9278  -2.1702   7.0277  -5.9275  -6.6336 -14.0380  -0.8810
-#>   13.7508   7.2422  10.8456   3.6631  -9.4903  -0.0949  -9.4799 -13.1683
-#>   -6.4126  -4.4764  -3.1920   3.6200   9.2765  -0.6455  -2.4736  -3.5236
-#>    5.1701  -2.2355  -4.0393  -2.0168   9.7074  -5.0182   5.5302   0.5364
-#>   -4.7535   2.4163  -7.0061  14.8368  -6.1606   2.4834  -2.1907   0.2419
-#>    1.8371  -6.5680   2.0259   6.1676   1.0617  -9.2078   1.2528   6.3787
-#>    9.4472  -6.5630   1.2068  10.8190  -6.5610   3.5445  -2.8692   2.9642
-#>   -7.2891   6.0978  -3.3751   4.1682   2.6300   1.0304   6.1595 -13.8636
-#>    0.6786  -0.7477  -7.2634  -1.8376   3.1016   5.0136   3.9515   1.5174
-#>  -12.6435   4.9984   1.0216   3.7301 -11.5680   9.5201  -1.4775  -8.6870
-#>   -3.6625   4.2057   5.5981   4.0501  -3.0935   9.0510  -4.4381  -1.4277
-#>   -7.8689  12.5444  -3.1417  10.3896  -9.9695   3.2747   2.8322   1.7519
-#>    1.8620 -12.7264   2.2756  -1.6458   1.6316 -13.8687  -4.1662  16.7878
-#>    0.3420   3.7973  -1.1359  -1.4965  -4.0083  -4.1914   1.7148   2.4032
-#>    6.3714   2.4896   7.8242   6.8193   7.0321   3.2046   6.8605  -4.6502
-#>    2.8786 -16.2526   2.4352  -3.8882   2.2542  -2.4454  -1.5951   4.7389
-#>  -23.1875  -4.1333   6.3962   6.6887  -6.1362   0.8058  15.6956   7.2013
-#>   -8.7329   7.1801  -5.0613   1.1381  -2.0916  10.0014  -3.2677 -12.7174
-#>   -4.5880  11.6920  14.5654   1.7693   4.4473  -4.4503  -6.5495  -8.7196
-#>  -12.2589  -7.2170   7.9125   3.5010  -9.2617  -7.6637   7.5840   8.4875
-#>   -5.3479  -5.0334   5.0921   6.9245  -8.3232   7.1854   4.1576  -1.5226
-#>    8.1612 -11.9883  -3.6214  13.7768   3.6118  -6.4589  -4.7312  -0.0460
-#>   -4.4050   2.7748  -5.1623   7.4096   5.4151  -9.0273  -6.8535  -1.7368
-#>   -2.0037  -5.8586   4.3548  -7.4482 -17.8355   7.5988  -5.9774   3.9989
-#>   -4.5390  -2.9569   6.4785   2.0471   4.1070  -0.4610  10.3860  -6.3458
-#>    2.7225   0.7216  -7.2652  -4.7884   1.4376  -6.6834  -0.9750   8.7055
-#>    6.1952   6.8069  -4.0349   3.0092  -4.8960   1.1997  -1.7488  -7.0332
-#>    3.4292  -9.9456   6.0187  -7.4573   6.3376  -2.4621  -2.0052   0.2439
-#>   -2.6957   6.6021  -1.6683   9.6269  -5.0222   3.0561  -0.4602  -3.9711
-#> 
-#> Columns 41 to 48  -2.3662  -4.1178   6.0542 -17.2736   1.1544 -14.6532  -4.2327  -1.6723
-#>   -9.6362  -4.0162   8.9831   0.0999   0.5063  -5.7267  -0.4100  -3.6152
-#>    4.8958 -15.0266  -0.8540  -2.6382   2.8027   3.7457   6.4793  -3.2880
-#>    0.2428 -20.9852  -2.8938  -5.1603  11.2823  -5.0218   3.5444   2.0790
-#>   -4.0056   2.6767 -14.2301  -2.8351  -1.5785   9.4275  -3.2266   0.2698
-#>   13.7052  -6.2582 -17.5901  13.6814   2.5738   7.9126  -6.7576   3.7483
-#>   -0.4129  -9.1739  -1.6229   1.1630   0.6235   4.9927   0.3573  -0.4206
-#>    9.1233   3.1910  10.6905   9.6390   2.7398  -2.4940   0.5708  -2.8679
-#>   -7.8422  -9.4731  -9.4229  -5.5931   0.8138   8.7929  -2.3753  -1.6864
-#>   -3.2461   3.6471   0.5257 -10.9632   3.6299  -0.0675   3.1798 -15.2807
-#>  -14.9918  -3.9584  -9.7075  14.5392  -5.4990  -1.5539 -12.2078   8.0035
-#>    9.5730  11.4291  -7.0751   0.0573 -15.4524   8.8957   3.7455   2.1895
-#>    7.8082  -3.8102   6.6591  -1.2311  -4.3900   0.9965   9.2195 -10.1490
-#>    3.8526 -15.6402  -5.1694  -3.2650   5.3320   0.6164   3.4690  -5.3112
-#>   -1.2557 -12.2737  -7.2549  -3.3846   7.4051   1.0831  -4.2061  -6.6609
-#>    0.8970 -14.5638   8.9470  -8.6792  -1.5913  10.7282   3.4311  -6.1475
-#>    4.0533   3.6346  22.0376 -11.0145  -3.3696 -10.2912   1.8133  -4.4615
-#>    6.9292   1.4592 -10.4186   9.6825   1.6393   7.4733  -6.5915   1.2002
-#>   -9.6357  -5.2183   6.0034   6.9522  -8.2266   2.9211  -1.4970   7.7575
-#>   -4.3216  -1.2334  -8.8963  -0.6198   2.8602  -9.6112 -12.6712  -1.2095
-#>   -0.2654  -4.1480   0.0734   8.8209   2.3408   0.8615  -4.3848   7.7182
-#>   -7.8335 -11.9261  -1.9578  -4.8017  -4.1270  -2.7510  -7.9025   3.6308
-#>   -5.1774   1.1036  -3.9699  -7.1527   4.0851   8.1275   0.2665   4.3704
-#>  -10.5100   8.6654   3.9859 -11.6839  -6.7456  -1.7912   4.1879   6.8301
-#>   -8.2875  -7.6335  -5.0641   3.4509  -0.7044   5.7884  -1.5903   5.4270
-#>   14.7860   1.6551   6.0810  -0.1190  -0.6695  -3.8393  -2.5997  -2.9030
-#>    3.0867  -8.2454  -2.4351   3.7962  -2.4292   0.2244  -0.2360   6.7647
-#>    2.3410   2.5302  -3.1083  -6.5420 -13.2832   6.7880   2.6604   2.6548
-#>   -0.9989  -4.2706   7.0629  -3.4784   6.5034  -6.2423   9.9963 -14.3233
-#>    4.2896  -4.0390  -1.4066 -14.4308  -2.8039   9.3129  -0.1018   2.7691
-#>   -2.0399   6.2666   2.7508  -5.6485  -1.2999   4.8603   2.6266  -1.8459
-#>   -3.3496  -4.6115   1.7148  -2.4124  11.2194  -6.3833  11.5255  -9.6916
-#>    3.6975  -7.3336  -0.8093  -4.4650  -9.5633   5.6365   0.4674  -2.7346
-#> 
-#> (12,.,.) = 
-#>  Columns 1 to 8  -1.6277   1.2656  -1.4825  11.3328  10.6316   3.1802   2.7966   7.3602
-#>    0.7563  11.4658   3.0252  -5.1515  -9.5619  -2.4433  -5.7716   3.6334
-#>  -10.5882  12.0208  10.2176   0.2408  -6.5390  -8.0264  -0.8759  -1.1588
-#>   -0.7622   4.3897  -1.1896  -1.7668  -8.9001   1.3332  -7.5461   8.9010
-#>   -3.4753  -0.1087   8.2465  -6.7310  -5.2670  -7.9490  10.5989   5.9618
-#>   -5.2578  -9.5628   3.8548  -2.9971  -7.3184  -3.6002  -3.0816 -13.1817
-#>   -1.4256   1.5171  -0.5876 -11.1181  -4.1151  -5.4291   6.7526   8.3762
-#>   11.6134  -3.6160   3.2140  -0.5094   0.0399  -2.3758  -3.6816   5.9869
-#>   -3.0449  -2.3651  -3.8286   4.9898  -7.6807   3.3386   0.9428  -0.6329
-#>   -8.3678   1.8662   0.6236  -3.0013  -9.7194  -8.1617  -1.3421   4.1308
-#>   15.7022   3.1097  -0.6749 -15.2644  -1.2098  -6.0772   6.4032  -5.7096
-#>    1.1139  12.4873   7.5104  -6.8317   3.5047   0.8986   8.7020   5.2628
-#>   10.1008   0.3333   1.9349  -2.2046 -10.1011  -1.9751  -6.3534   0.1331
-#>   -6.7504   4.4257  -8.3930   4.4600  -6.5332   1.4475   0.3728  -2.6905
-#>    2.4878   5.3327  -1.4330  -2.6156  -6.1966   0.1642   9.1931   0.4174
-#>   -7.9441  -2.8109 -10.7725   1.8952 -18.0593   0.0408  -9.0493  -2.1951
-#>  -15.9260   9.6935   2.5494  -2.1085  -0.7622  -8.1082   2.7542  10.5612
-#>  -15.6643  -9.0980   9.7987  -4.7460  -1.0128  -5.6632   4.0524  -1.4456
-#>   -0.5765  -0.7434  -8.1518 -23.3519 -10.5594  -6.9221  -8.8655   1.9328
-#>   -1.7641   8.7819  11.6229   2.5808   6.2782  -1.7949   8.3206  13.0015
-#>    7.5008   1.6181   3.6726  -4.3403   0.8462  -0.1154  -2.3327   4.6810
-#>   14.0324   2.9959 -11.2916   8.4420  -1.3429  10.1206   6.9614  -1.7276
-#>   -9.8757  -3.8125  -9.9773  -9.4471   5.8427  -8.9117  -6.0019  -4.9530
-#>   -7.5694  13.4387 -14.0810 -13.3506  -0.1395  -1.3370   4.0942   3.1384
-#>  -15.3091   1.5466  -7.9623  -5.0435   0.9694   9.4680   9.3944  -0.9201
-#>   10.9647  -3.0660  -0.2466  -3.3298 -10.6204 -13.0028  -1.8922  10.1039
-#>    1.9402   5.1355  11.9231   5.3982   4.5491   2.5113   4.7216   5.9898
-#>    3.4196   5.3555  -3.5012   5.1005   2.0839   0.7908  10.2228  -5.0219
-#>  -12.1527   0.5536  -4.1541   5.5112  -1.1533   6.0899  -1.1417   5.2220
-#>   -5.5688  -0.8625  -9.4234  -4.4074  -1.7161  -8.4068  -7.6863  -3.9451
-#>    7.5764   6.6458  -1.9726  -7.0190   4.9921   6.4549   1.7532   6.0018
-#>    7.9313  12.2998  -4.0938  -7.3600  -3.4463   5.2630  -3.4504  -4.2298
-#>    5.3973  -4.2705  -3.6850   1.1945  -8.5218  -1.3317  -2.4017   0.3735
-#> 
-#> Columns 9 to 16  -9.1700  -5.4425  -9.0628   2.4983   4.2815  11.4832   3.8628 -12.8614
-#>   -2.9090  -0.2501  -6.0506  14.9251   0.4494  -0.0867   5.6211   1.6150
-#>   -8.0287  -5.9114  -0.7751   4.1525   0.1630   7.7034   5.3568   3.9979
-#>  -13.7131  -4.1252   9.3736  11.7485   9.8657  -4.1714   1.9415  -9.1430
-#>   -0.1387   3.0135   0.7491  -1.8394   0.1614   2.2556   8.3285  -0.2316
-#>   -7.1668   2.9922   2.4090  15.7913  -6.2054   1.9636  -3.4500   7.7883
-#>   -4.2918  -4.8643   9.0532  -4.6586   1.6853  11.3118  -0.0763  12.0158
-#>   -3.6470   2.7414   1.4341   3.2723  -2.6892  -4.6365   0.6876   2.2663
-#>   -1.0103  11.8353   2.4095   3.1762   3.4075   2.2852  10.2877  -2.9286
-#>    0.0213  -3.4105  -5.0831  -0.0772   4.8501   1.4105   6.8952   0.7864
-#>   20.3048   2.1310  -2.1970   0.2460   3.9726  -6.4222  -8.8292  -4.2873
-#>   -9.2621  -9.3889  -2.6775   3.2402 -10.3275   6.7707  -0.3343  -5.5113
-#>   10.8026   8.6806   0.4268  -7.1872  -6.6650   8.9344   7.0174  -5.5875
-#>    1.4095   2.6411   5.9594  -5.0065   8.6666   0.3373  11.5285  -3.7100
-#>    8.7947   7.9955   5.4419  -9.5228  12.7298   8.9183   5.0018   4.0518
-#>    0.5228  -2.5511  -0.3390   1.5011   7.9529  -9.4144  -2.0756  -8.6872
-#>   -7.0986  -6.2085  -1.4724  -1.9024  -6.0415   5.1373  -8.1891  -2.0590
-#>   -8.7808  -0.9564 -12.4611  -8.2841  -4.8299  -0.9901  -2.5113  -4.2294
-#>    5.1060   3.8005  10.3105  18.3401  -9.3370  -6.2471  -4.2201  -1.0089
-#>   -4.3429 -12.8005  -3.6652  -3.7718   6.2987  10.2018   4.2139  10.7305
-#>  -17.5902  -7.8866  12.9877   4.4011   2.7936  -7.6180  13.9074   1.7445
-#>    4.2564   8.6423   2.3472  -9.2550   8.6073   9.5538   6.6635  -6.1956
-#>  -13.6064  -5.4308   6.6853   3.2149  -1.3079  -3.1043  -5.8836   0.8049
-#>   -1.6895   4.3386   8.2895   3.9100 -12.1941   5.3036   6.2939 -16.0732
-#>   -8.1796  -5.9860  -5.0674   9.5823  -5.6035   5.2585  -8.3461   4.2852
-#>   -7.3154  -9.5797   6.5789   5.3402  -3.7686   1.3971  -2.6134   1.4748
-#>    0.2336   9.6401   6.4530   8.9854   9.1470  12.0268  -3.3962   2.6446
-#>   10.4801  -7.9177   8.4949  -1.7847  -3.1570   4.6640  -3.5705  11.5037
-#>   -3.3292  -7.2115  -7.2814   5.5860   2.5205  -4.7651   2.1381  11.7788
-#>    1.8813  -1.8615  -7.5800   2.1251 -14.1831  -0.7501  -3.1613  -4.1596
-#>    7.9042   5.9622   5.9364   7.8909   1.2282   4.5489 -10.1794 -10.3223
-#>    1.7342   0.1356   1.8621   8.5985  10.3165   1.0473  -3.2761   5.2590
-#>    2.2492   2.5793  -1.9641   3.2908  -4.4731  -0.2186   0.3898   0.2026
-#> 
-#> Columns 17 to 24  -4.2233   6.4903   6.4824  -1.4196   4.8213   6.1748   6.0226   2.4244
-#>    3.0449   3.7639  -2.5759   6.9809  -0.7800   1.7511   3.8831   4.2390
-#>    1.0488  -2.6044  -4.1870   0.7029   5.0582  13.9877   5.3120   3.5888
-#>    6.0070   6.5205  -8.5075  13.3583  -0.2409   6.9363   0.4215   1.7057
-#>    4.8480   3.5007  -1.8699 -10.8386   5.1291  -4.9043  -2.1371  -5.0365
-#>   19.0496 -13.6680  -6.3532   9.1346  -4.0195  -3.4128   8.0442 -18.1827
-#>  -10.8550 -11.0827   8.8548  -1.0606  -3.7950   6.9387   1.0117   4.5115
-#>   -1.6574  -3.8386   1.0533  -3.1069  -7.0465   2.9287   9.3957   2.9272
-#>    4.9568   3.9067  -6.9688  -5.0304   6.6911  -2.0881   1.5644   4.6007
-#>    1.8367   7.9528   0.6834  -2.0801   4.4093  -1.0517   3.4551  12.8767
-#>   -2.4516   1.4194  -6.7113   2.9893  11.2839  -8.8115 -12.6606  -2.3365
-#>   -5.6027   4.1230  -0.2919 -10.3406   8.2155   1.4383  -3.7078 -15.4252
-#>    6.5895   2.5759  -1.3891   0.8086 -11.1012   9.8928 -10.9215  15.6942
-#>    1.6642  -1.2642  -0.4765   2.7864  -2.2443   6.1590   1.0146   2.5986
-#>    1.0419   1.3659   0.6039  -5.0359  -2.2402  14.3927   9.0557   5.2491
-#>    6.4229   3.5484  -1.2524  -0.4892  -3.4019   3.0809   3.7813   4.4377
-#>   -5.1540   0.9143  14.0819  -1.7899   0.6947   1.4108  -3.0455   4.5929
-#>  -10.6781  -6.3872  -4.5548  -6.6081  -0.5025   4.2739   5.1464  -5.9072
-#>    8.7503  -5.9570   2.4831  -4.3309   4.5570   6.4887   7.9486   3.6529
-#>   -6.3005   4.0479   9.9377   3.0526  -0.2363  -7.0282  -0.0508   4.9989
-#>   -6.6149   4.3750  -2.5539   0.0184  -6.1014  -5.4061   6.5405   3.4628
-#>   -9.9721   8.5501  -2.9886   9.5830  -8.8987  -0.2068   2.3084  -0.8445
-#>    7.9700  -2.0417   7.5790 -15.0848   2.8634  -5.3935   2.9578  -3.5884
-#>    3.0857  -0.8034  -2.5963  -4.5656   2.6836  -8.8144   0.8256   4.5884
-#>    1.8981  -7.6407   0.2676   2.9430   9.5955  -6.7089   7.3951  -6.9108
-#>    9.6305   4.2658   1.6670  -3.3669  -5.1492  -0.5657  11.6158   5.5527
-#>    1.0008   7.9193   9.3322  -5.0705   4.1512   6.1594  -3.7197  -6.7994
-#>   -2.6899  -4.9149  -2.1636   7.0355  -8.7861  -5.2892 -10.5869 -17.6919
-#>    0.6838   0.8199   7.2066   2.8384   4.5133   5.0984   3.4396   3.6857
-#>    4.4714   2.8224   7.5290  -7.9173   3.3588  -8.8742 -11.3356  -7.5563
-#>    8.5606   6.7477   3.0738  -9.4245   6.5681  11.2474  -3.1492  -0.1624
-#>    8.2534  -2.3553   5.9498   1.4656   7.2768  -0.2787  -7.3997   6.9294
-#>    9.2180   6.3995  -3.5352   4.6713   2.0107   1.4465  -1.4998 -13.3744
-#> 
-#> Columns 25 to 32   1.2006 -21.4737   0.9001   5.9005   6.3590  13.1644  12.8056  -2.2806
-#>    1.8015   1.9923   7.9683  -2.1466   1.5827   1.7011  -0.8395  12.8080
-#>    5.4818  -0.8662   3.4282   3.0912   0.0991 -15.9086   4.2903   9.6336
-#>   -4.9352   4.1994  -1.9150   3.4635  -0.2113  -2.8963   2.8749  -2.0883
-#>    9.9119  -2.3160   8.6780 -10.4556  -3.6645  -2.5613  -7.9138   1.2894
-#>    7.8871 -15.3103  -8.5266  -3.0464   4.2798   2.4715  -6.7049  17.0776
-#>    4.4950 -10.8351   3.4407  19.0463  -7.8943  -0.6938   4.3225  -0.0173
-#>    3.2397  -3.7939  -4.6765   8.3715   4.3350   2.6491   5.8642  -4.2448
-#>    3.7390   0.5699   1.5634   1.3752  -8.9437  -1.8978  -5.1261  -3.9542
-#>   -4.8704   5.9890  -1.4254 -10.6813   6.4396   0.1326  11.2238   8.7575
-#>  -10.8583  -4.5291  11.5939  -4.8853  -2.0942  -8.0907  -3.6970   4.8505
-#>   10.9864   5.7334 -10.6233   7.9893  -1.8921 -14.8004   7.8390  10.5799
-#>   -0.9188  11.9645   0.7103  -1.0071  -5.8439 -12.7488   4.8880  -0.4999
-#>   -9.1022   1.4576   4.3436  -4.2208  -4.4709  -6.3411  -0.1393   9.1185
-#>   -7.6898  -9.3494   9.6089 -16.1926  -1.2733   9.3045  -3.8841  -5.5536
-#>   -0.3512  -3.1724   1.7844  -1.9881 -15.5762   2.9981  -0.3080  -6.5510
-#>   -7.1989  -0.4827  -6.9958   6.3886  11.6115   4.9651  14.6168   3.8082
-#>    8.4152  -8.2617   4.9626  -1.7486  -6.7048   4.1693   2.4776  20.8345
-#>   -9.3380  -1.1969  -0.6511   2.3570  -4.3794  -1.5527  -4.2422 -17.0752
-#>  -13.8299   6.4868  -8.9810   0.4807  13.4931   4.3804  10.0231   1.8395
-#>    4.7416  -5.4565   2.1269   8.8037   2.9015  -2.4739 -10.3819   3.0256
-#>    4.5900   1.1081   7.7875   4.9756  -5.1406   9.5396   0.5284  -7.9706
-#>  -10.6671  -1.8440  -5.7507  -9.6134   0.4597   2.5786 -10.8169   6.2005
-#>   -2.9426  -5.4963   0.2590   6.3128   4.9737   0.8832  -0.7476  -6.4966
-#>    6.2490  -8.2589   2.1780  -4.8646  -2.6697  14.5472  -7.1675  -2.4236
-#>    6.2239  -4.4174  -3.0557  -0.4300  -0.0547  10.7877   2.4003 -14.0559
-#>   -6.4656  -0.9594  -4.6296  10.1206  -4.0693   0.3593  -6.3911  -5.4361
-#>   -7.3114 -12.1247   3.1014  10.2935  -7.8968 -12.6844  -6.3857  -8.1799
-#>   -5.6425   8.9205  -3.6614  -5.8458   3.4007   3.4536   7.0519  -0.3873
-#>    9.2386 -12.7774  -2.1179  -5.5670   1.9680  -9.7900  10.5361   0.4400
-#>   -7.3526  -4.2630  -2.5834  -8.1674   3.0012   9.5988  -1.7162  -3.8316
-#>   -8.0502  21.1164 -10.2557 -11.3802   5.5937 -13.6895   4.5989   6.8188
-#>   16.1440  -3.9049   5.8081  -1.8903  -4.4917   0.1119  -8.0698   3.1317
-#> 
-#> Columns 33 to 40  13.8945   5.1376  11.2892   4.7304   5.2678  -3.4176  -3.9634   6.5021
-#>   -8.8483  -1.0424  -7.2540  -1.0065  -9.8846  -5.2708  -4.0247   8.4387
-#>  -10.8568   7.6129   8.4755  -3.1647  -4.1767   1.8637  -5.9465   6.6877
-#>   -4.7889   2.6010 -11.0272  -0.9510  -7.6316   5.0427  -2.6191   1.1624
-#>   -8.7421 -19.1770  -4.6337 -12.8940  -2.2656   1.9619   0.7837  -0.8762
-#>   12.0341  -4.1220  -0.8017   8.1249   3.1926   1.6903 -11.3236   9.4815
-#>   -9.4582  -4.6722   1.3877   2.0304   5.5673   7.5023  -1.9270  13.0872
-#>   -4.0317   0.9335   8.0035   4.8700   6.0325  -2.9948  -8.9064  -1.1722
-#>    0.2323   8.4465  -1.6160  -7.1798  -0.7423   1.5307   1.2431   6.8303
-#>   -7.3312  -0.3445  -5.3169 -14.5565  -6.2619 -10.5918   4.3772   5.1601
-#>    2.6516 -13.7986  -4.7847  -6.2661  10.1320  -5.9332   4.6941  -3.3675
-#>   -7.1772  -1.6759   7.9337   0.4910   1.9311  -5.4967  -6.4506  -0.9066
-#>   -1.4451   8.3609  -1.1478  -4.2416  -2.5835   3.1141   4.8414  -4.9543
-#>    2.4346   2.4627   6.9711   2.8532  -1.3404  18.3892   5.8931   1.3370
-#>   -0.6633  -8.7064   0.6492  -5.9467  -6.5899  10.7705  14.2726  -1.0755
-#>   -8.1477  -5.0272  -2.3789  -7.4334  -8.4012   4.5336  -5.6346   1.4006
-#>   -2.9836   4.9160  -1.7135   2.6437 -10.8257  -3.6766   5.8261   1.5997
-#>    1.9477   1.5791   0.3320  -3.6651  -1.7538  -9.1123  -4.8502   5.0894
-#>  -11.1569  -1.6841  -2.3055  -0.9117  -7.9841  -3.5817  14.3205  12.3716
-#>  -10.0935  -2.0168  -5.7619   7.2712  13.5324   6.1849   6.3196 -13.0108
-#>  -10.8633   2.1813  13.8934   7.0051   9.0097  -9.0596  -5.2719   4.5808
-#>   -3.4282   5.9018  -7.4001  -6.6291  -0.2669  13.3171   4.1376  -2.6469
-#>    6.5581  -1.5709   9.2863   6.6821  -1.6363   8.3377   6.7423  -1.2998
-#>    2.4276   3.2596  -1.8186   2.4292 -10.6535 -12.4743   0.6836  12.4658
-#>   -2.0732  -0.9243  -3.3457   5.6381   0.9751   2.0973  -2.0401   7.6741
-#>   -3.1224  -5.8254   2.7578   5.8531  -4.7348   2.8611  -9.0410  -2.6366
-#>    2.3521  -2.6690   1.4861   8.4215  12.6360   9.4422   4.0272  -4.4723
-#>   -4.9759  -4.6801   3.2078   6.0508  -0.4729  12.5741   7.1624   0.3861
-#>   -3.6352  -1.5695  -2.0715   1.1457  -3.7931   1.3690   1.7022  -1.2421
-#>    3.2337   3.5467   6.6668  -9.6162  -6.5442   1.8203   8.3872   8.9253
-#>   -6.3699  -3.4739   0.2831  -2.6326  -3.4428  -0.5782   3.6256  -2.2742
-#>   -2.2679  -5.6235  -3.2640   5.4520  -7.1208   5.1629  -5.4996 -18.7610
-#>   -4.0078  -3.6185   1.1123 -10.8744  -4.2643  10.0603 -11.6839  -1.0615
-#> 
-#> Columns 41 to 48  -5.8828   2.6631  -3.0110  -6.7809   0.0293  -9.6178  -4.4755  -2.6119
-#>    4.1385  -0.2211  -5.7847  -0.0816   4.6601  -1.8591   1.6024  -4.3605
-#>    4.1862   1.2995 -10.7519  -5.6809   1.6123  -1.3902   2.0055  -6.9491
-#>    7.3520  -4.4029  -4.7040  13.0503 -17.7398  -1.9633   4.4114   3.0867
-#>   -6.6644   3.7429   8.4155 -15.3608   9.3341   0.3042   6.8965  -2.5737
-#>    3.2898   6.2717  12.4690   8.1115  -9.0811   1.5432   2.7472  -3.1159
-#>   -3.8740  -6.8035   9.4345  -3.9589   0.1824   4.4107   5.2822   4.0166
-#>    2.5250  -2.8588   4.2052  -0.8799  -2.6479  14.9133  -5.0021  -3.4389
-#>    3.1106  -1.1804  -7.3940   4.4292  -1.1075  -1.8306   8.6412   3.1824
-#>    5.9995   8.5145   9.8750 -12.1270   4.5127   2.0419   2.5088   5.1208
-#>    2.3775   1.5137  -4.6429 -16.8772  17.8410   8.7111  -9.2237   4.7237
-#>   -6.3879 -10.6307 -10.1143  -0.3322   7.1997   4.7498 -13.2150 -12.5384
-#>   -1.1561   5.3692   5.9235  14.8841 -11.4622   7.4580  -0.6476   7.9789
-#>   -2.6277   0.0278   4.4945   5.5632  -3.9244 -11.6345  13.3383  -2.1119
-#>  -13.9300  -0.8732   8.3611 -12.4894  -7.5368   4.1963   4.3556  -8.3775
-#>    2.0822  12.0235   5.5337  -4.5093  -0.8370  -1.8867  12.7920  -2.3058
-#>    5.4194   3.3481   8.3929  -4.1561  -2.4016  -0.4821  -2.4102   5.9310
-#>   -2.7429   2.2809  13.0835 -13.3393  -4.6388  -2.4268  -4.5999  -6.9089
-#>   11.8154  -0.0086  -8.7581  -1.8602   6.0035   7.5693  -1.2043   9.0918
-#>    3.5296  -8.8532  -8.3397  -6.5962  -0.2161   4.7971  -1.8156  -0.7327
-#>    3.9167  -5.9942  -4.0317  -1.1674  15.1550  -5.2999  -0.7470  -2.4903
-#>  -13.4262  -8.0015  -4.5641  11.9610  -6.9310 -14.0014   5.2533   7.4856
-#>   -0.4048  10.0374  12.3274  -7.3601  13.5395 -12.6968  -3.3714  -0.7631
-#>    8.6631   5.0238   5.7300  13.5159   5.2352 -13.0944   8.8396   3.8043
-#>   -2.9619 -11.5246 -16.4590  -2.5229  -0.7031  -3.5418  11.5896 -11.8498
-#>    6.3225   4.4412  10.7698   5.8504  -2.5050   5.8819  -1.0052  -3.7116
-#>   -9.8836  -0.2882  -1.3196 -10.6948  -0.0785   9.0144  11.0455   5.9665
-#>   -0.6816  -3.5955   4.0003  -4.2055   6.3928   3.0317   0.4626  -1.8370
-#>    1.2728 -12.7381 -11.3945  -3.2397   9.7050  -1.7765   6.0116 -12.0875
-#>    9.3059  15.8616  -3.6912  11.0371   6.4374  -8.0325  -1.6756   7.2352
-#>   -1.4773   9.8128  -8.6768  -7.0641  -0.5934  10.5564   7.9960   4.2665
-#>    1.8995   6.3863  -1.6942   4.8941   0.6913  12.0428  -9.7984  -1.0175
-#>   -2.9466   4.8298  -9.6127  13.0258   4.2701  -3.4983   3.2971  -9.5837
-#> 
-#> (13,.,.) = 
-#>  Columns 1 to 8 -13.7181  -0.3447   5.8356   1.5973   6.2881   7.5921  -5.4251   7.5284
-#>    6.1287   1.0172   4.8493 -11.8992   0.0052   0.0865   8.0248  -0.1148
-#>   -2.7358   6.5331   9.9707  -0.5685  -7.6053  -2.8306   4.6883  -3.0714
-#>   10.5341   0.0862   2.1901   0.7360   5.3057  -1.7246  10.2474  -0.4840
-#>   -5.9171  -1.9733  -0.1388 -11.1198   6.1604   1.0356   0.4460   0.5814
-#>   -1.5607  -0.5406   7.0331   7.1030   5.0921   4.7854   3.2406  -1.8484
-#>   -5.0085   2.8199   4.1907  -1.4205  -1.0750  11.5682   3.4545  10.3755
-#>    3.5391   0.1704   0.2633   0.0874 -16.5464   4.8874   3.8234   0.3897
-#>    2.8643  -0.8036  10.4355   0.0936   3.7378  -3.9538   3.5668  -6.0208
-#>   -0.3664   0.5333   2.8271  -6.5794   7.8843   2.2596  12.7841   1.2995
-#>    1.6696  17.1502 -12.6774 -21.5333  -0.3590  -2.8230   0.7653  -5.5555
-#>    0.3097  -3.6061  -5.9365 -14.6621  -9.4706  -1.2509  -8.0828  16.4437
-#>   -0.9067  -5.4808  -5.9482   3.4010 -11.3237  -5.6148   0.5647   2.0937
-#>   -2.1402  -2.1574   1.6820   1.7839   9.6963  -5.1659  10.5187 -17.2321
-#>  -12.0609   8.6689   7.5419  -5.2172  -1.5569   1.8496   9.3615 -19.1509
-#>   -7.8590  -8.8998   3.7620  -0.8961   1.9522  -4.9404  13.7316  -8.6320
-#>   -1.3495   1.6034   4.7689   7.2803   3.5203  12.2188  -7.4717  19.8181
-#>   -5.8713   0.0045  16.6472  -7.4291   2.1585   1.1381  -5.0502  -6.4395
-#>   -8.0780  10.4833  -7.7087  -4.3378  -6.3973   7.0898  -0.6432  -5.0435
-#>    1.3963   3.7577 -13.2720   2.1819  -3.5963   3.7020   4.4230  11.6741
-#>   15.3527  -7.6608   6.6919  -3.0026   5.1196  -7.1289   2.5166  -8.8008
-#>   12.4373 -10.7005 -10.4661  -0.0378   5.0841  -9.1134   6.2372  -1.9012
-#>   -3.8107  -2.2387  -2.3170   6.6957   7.6508  11.8243  -6.0962  -4.2987
-#>   -6.0589  -6.3465  -3.5900   8.8230  10.4383   0.5384  -1.9179  11.7249
-#>   -8.0175   8.1418   4.3234  -0.7016  -1.8186   4.3396   1.3985   3.0427
-#>   -9.0953   0.0205 -16.0239   7.2141  -6.1833   5.6397  12.4842  -7.2513
-#>    1.8304  -2.2553   3.7247   4.4194  -2.4612   5.6431 -11.8009  13.6524
-#>   -1.0657  13.1455  -6.4824   2.7998  -2.0011   2.4593  -0.1380 -18.9450
-#>    2.1227   1.4994  -2.0051   1.0570  -2.4858   9.9079  11.9125  -8.3056
-#>  -18.7767   7.6160   2.8001  13.6778   9.9759  11.7203  -7.3760  14.7773
-#>   -9.2775   3.5073   5.8065  -3.2242  -0.1886   1.7389  10.1169   5.1351
-#>    2.8881   1.9169 -14.5476   0.0994  -0.6918   1.7538   6.6907  -2.9264
-#>   -1.5610 -15.5603  -9.3431  -5.6707  -2.7868  -0.6588   0.4745   7.5594
-#> 
-#> Columns 9 to 16  -0.4537  13.3681 -18.0402   5.3283   7.8392  -6.5154   2.1867  -0.0730
-#>    0.4234  -2.8830   8.3738   5.2371  -5.4255  -9.4456   7.8477  -3.3967
-#>    0.8610  -4.0427   7.0516   8.8728  -8.9026  -6.1186   0.6182  -0.7321
-#>  -12.4426  18.5727   2.2000  -3.9517   1.9837  -4.6114  12.5401  -3.7230
-#>   11.1755 -13.6765   2.2766   7.0844  -5.9924   2.1159  -2.8463  -9.8425
-#>    3.4147  -0.2241   1.2905   2.3795  -0.3784   1.7020  -1.0787   2.1012
-#>  -12.1654   3.6320   0.2117  13.0024 -15.6153   0.4192   0.7902  11.0978
-#>   -3.3314  11.4002   1.2202  11.2767  -9.8292   1.9364  -3.5906   0.8670
-#>    1.3514  -4.4492  -4.2492 -10.7704   8.9424  -7.2758   0.2141  -8.3028
-#>    3.5126 -18.3866  -2.6702   9.4522  -6.4155 -10.0933  14.7170  -5.0721
-#>    7.3894  -9.3259  19.8691  -1.6937  -5.9989   5.7458  -7.6616   4.0485
-#>   -2.3914  -0.6550  -3.2121  10.7993  -6.6451  -0.9181  -8.1194   8.8540
-#>   -5.0520  11.2931   4.6047   2.6079  -2.1932   1.5243   0.4934  -4.4654
-#>    3.8567  13.9962   9.6761  -5.5595   0.2366  -1.1271   9.6884  -5.6431
-#>    4.9715  10.0399  18.6660   6.2897  -8.3365  -3.5119   1.8689 -19.4082
-#>    1.4753  -2.3583   1.3196   4.5634   5.9071  -7.1842  11.7466   3.7223
-#>   -1.7783  -7.2138 -12.7813  -4.8437  -0.8411 -10.0547  -6.0221  -7.1953
-#>    3.2388  -6.6003  -9.2452   5.9463  -6.5550   6.6169  -1.9871  -0.7962
-#>    0.6546   8.8318  12.1956  20.1071  -6.5240   8.5515  -3.0849   4.9281
-#>    2.2310  -1.9946  14.7048   2.0993 -13.3510   1.7606   4.6692   0.4065
-#>   -7.1707   0.6846   3.2662  -1.9376  -3.8787  -3.6412   4.8520   2.4025
-#>    2.3054   7.0808  10.2049 -20.7992  -2.9452  -0.6283   9.2520   1.4946
-#>    9.4116  -2.0544  11.1145   8.0121  -7.4675  15.5080  -6.3952   9.1342
-#>   -4.9404 -15.8663  -9.9113   0.3530   3.1372  -6.2037   3.0748   9.5932
-#>    4.7657  -7.6916  -1.1407   2.0595   8.4072  -2.1366   2.2217   1.0098
-#>    4.1171   3.3204   5.5477  16.9107 -11.5887   2.8421   1.1560  -8.0653
-#>    6.5559   9.8319   0.0996  -2.4849  -0.1135  10.7154 -12.6500  -6.1913
-#>   -1.2950 -24.7869  11.4024   2.9801 -18.1488  -3.0154   3.3089   2.1303
-#>    1.2271   3.9828  -0.3498   6.1050  -2.0436  -0.7848   4.1063 -10.9277
-#>  -10.3081 -13.2193  -5.8505  -0.1730   4.0578   2.0895  -1.7765  10.1179
-#>   14.2637  11.8619  -5.1396   0.1210  -2.3210   0.0329  -7.7430 -10.3766
-#>   -6.8249   6.6138  12.0710  -1.9869   5.8455  -0.9538   2.8660   5.0700
-#>    1.6378   4.3483   8.2711  -2.6529   7.7409  -0.4693  -0.2296   3.8611
-#> 
-#> Columns 17 to 24 -18.2529  -0.7843  11.8839  -4.1371  -7.5558   7.9947  11.7894   2.7651
-#>   12.3867   4.1103  -4.3933  -6.1404  11.4483  -3.2177  -6.0363  -4.0282
-#>   13.4974   2.2192   5.8247   3.9458   6.4274   4.3778  -9.2454  -6.9374
-#>   10.8095  -0.0647   6.7780  -4.2264  10.1764  -9.3652   8.2440   5.3695
-#>    1.5547   1.4251 -10.5475   3.5811   0.9859   4.3994   2.8293  -1.6326
-#>    7.2752  -7.2462  -9.2140   1.8691 -10.8841   9.8740   4.3779 -14.9532
-#>  -10.0497  -4.1462   5.6859  -1.9422  -7.2311  -2.2039  -6.7706  -7.7515
-#>   -7.2611   0.3434  10.8226  -2.5999 -11.7364  -1.3330 -11.1642  -6.6722
-#>   -0.5093  -2.7057   1.1337   5.9100  -0.0395   8.2051   8.4493   2.2405
-#>    3.0575   8.8751  -8.7246  -5.4412   6.1884   3.0971  -1.2062  15.2766
-#>   -9.0075   6.7520 -11.1594  -3.4083  11.5826  10.3835 -13.9912   5.7969
-#>   11.1581   3.9730   1.6344   9.1667  -8.0174  -3.3163   1.8599 -14.1742
-#>    8.8165 -10.4774   5.9238  -7.9778  15.0429  -2.0832  -8.3252  -0.3596
-#>    7.0435 -16.1194   0.4444   3.5330   2.8708   7.6799  -3.6777   4.3081
-#>  -11.0742 -13.8376   2.3848  -3.8527  10.4867   1.3087 -13.1818   8.7516
-#>    9.0784   2.0644   4.0886   4.4662   0.3837   4.4890   3.2950   7.3878
-#>   -3.0825   2.8900   1.5499  -1.8036  -6.2011  -5.9667   3.2099  18.0180
-#>   -6.6086  -0.4012  -0.6056  -1.2367  -2.0511   6.0924  -0.1952  -1.4324
-#>   -5.3393   5.1967  -0.9222  -0.1756  -2.3408   2.1642  -3.2054   3.4058
-#>   -2.7763  -1.9330   5.2968   6.1661  11.7368  -1.3482 -13.2555  10.1649
-#>    7.7419  -1.5774   6.6621   4.8569  -2.8487  -1.1811  -1.2904  -3.5283
-#>   -2.1136  -3.8623  10.4048   5.8220  10.9268  -1.0691  -0.8613   6.4316
-#>   -5.7798   1.2923 -15.6228   1.4064  -8.7560  -1.9435   0.4758  -2.0679
-#>    9.5343   4.2179  -6.6049  -5.6621  -1.8436   2.4079   3.7554  -6.3271
-#>    4.9440   5.6170  -2.8385   6.9373  -4.0377   1.8346   3.6950   0.9082
-#>   -9.6127   7.4395   3.0402  -2.2246  -7.6659  -1.1280  -1.8699   9.9940
-#>  -12.7051   4.4045  -3.1659  -2.2974  -1.9191  -4.9103   1.0011 -13.5765
-#>    1.8682  -9.5825 -10.4569 -11.7557   4.8390   6.9839  -4.2444   9.4414
-#>    6.5605   1.6008  -2.1709   3.3722   1.2255  -0.9195   0.8182  14.2683
-#>   -5.5613 -12.4914  -7.0133  -0.1660  -9.0405  12.4403   5.1551   7.0832
-#>   -7.8657  -4.8879   2.4015  -0.1641  -8.1200  -4.2953  -0.9813  -2.0122
-#>   13.1090  -1.1549  -9.9288   3.4933  10.4903  -6.2657   4.1032  11.1627
-#>   22.9732   5.6470   3.7499   6.1426   6.4469   4.5686   8.4451 -10.3352
-#> 
-#> Columns 25 to 32   5.7054  -5.3441  -6.0978   6.0447 -10.4055   8.0234  -0.7850  -5.8711
-#>    4.4482   5.9026   8.5244  -6.5094  -4.0751   6.0330  -6.4294   0.0522
-#>    9.7296   3.1358   8.8016  -8.0296   5.5832  -3.2571   5.4091   4.3074
-#>   -5.8150   4.7967  -0.4531   0.9397  -2.1079   3.9001  -4.1233   0.6805
-#>    1.8793   6.4777  -0.0286  10.9774  -0.6168  -2.8688  -0.0493  11.3595
-#>   16.6737   1.7602 -10.4666 -16.6184   8.4079  11.1176  -7.8161  12.0327
-#>   13.1589 -15.4014   3.1226   7.8401  11.9626 -20.9714   6.7551   3.3441
-#>    5.5559  -8.3731 -10.7067   2.6777  -6.6518   6.9165   5.1903  -0.0387
-#>    0.6119   9.3752   1.1777  10.1055  -7.7612   2.4682  -2.6647  -3.2515
-#>    1.4933   0.8544   6.1785   1.2085   0.6575  -1.3207   0.0227   5.0657
-#>    3.8068  -3.8701   6.7254   6.9575  -7.4438   5.2901   1.0952  -7.0580
-#>   10.7262   7.8740   0.3326 -21.5471  -7.1646  -4.9007   9.6586   9.2646
-#>    0.9556  -0.0027  11.4575  -1.3609  -1.2580  -5.2391   2.3976   8.6294
-#>   -0.6346   4.0469   3.8204  -2.8324  -1.9279  -6.9823   0.8844   9.6346
-#>   10.8041  -2.5132   4.4946   3.0518  -0.3110   0.5100   2.4100  -2.7544
-#>    1.7201  -0.0440  -3.2218   2.4878   0.9819   0.4591  -1.7396   4.6635
-#>    3.1439  -8.0099  -2.2478   2.0788  19.5492 -13.2788  -6.1485  10.1090
-#>   17.1915   1.1694  -0.3155  -3.8923  -4.0340  -0.6389   0.5847  10.7342
-#>   -3.1465  -5.3648   4.2524  -2.8767   4.0003   2.2823   3.7417  -0.6386
-#>   -7.0934  -3.8575   1.9482   8.5610  -4.5604 -11.2780   8.2424  -1.5578
-#>    1.1857  -1.3874   1.4302   2.4410 -17.7996  -2.9959  -4.0117  -7.0299
-#>   -4.5234  -1.2063   1.6614   6.9609  -5.7689  -0.8851  -1.3269  -8.5309
-#>   -1.9256   3.0458  -3.4664 -10.7231   5.4695  -2.0249   1.7123   5.4240
-#>  -15.2104   4.3967  -0.5276   2.4381  -4.1284   6.0441   3.7916  -6.4473
-#>    7.1233   0.2088   0.9726  -8.5699  -3.5048  -4.0555 -10.4901  -1.2846
-#>    1.7306   0.8744  -4.3449  13.0051  -0.1197   2.0478  -3.5184   5.3571
-#>   -3.3896  -4.1441  -3.2758  -1.9248   3.6815  -1.8899   4.8495   0.0962
-#>   -0.2889  -3.1917   0.6236  -3.3692  14.2575  -8.5865 -10.0056  -3.6139
-#>   -3.2592   8.1670   2.5470  -6.6863  -1.6065  -5.4853  -9.3110  -1.9122
-#>    6.8977  -4.4494  -8.8062  11.3942  14.2317  -5.9056  -6.1580   0.3269
-#>    1.0336  -5.3697 -12.3649 -15.0611   5.9503  13.7932  10.8067   4.7749
-#>   -2.3155  12.2920   2.8133 -11.2705  -3.1521  -5.7135   4.6695  -6.1455
-#>    2.0441  -0.5978   2.9946   2.2039   1.6005   0.5476 -10.1008   5.1450
-#> 
-#> Columns 33 to 40  13.9888   1.6621  -6.2028  -5.8881 -15.0765  -7.4038  -6.6962  -5.2960
-#>   -2.1251  -0.9159   0.2313   6.4890   5.8609   1.1657  -1.4320   1.8357
-#>  -13.0969  -3.3009   4.5870   0.1656   6.5823   4.0142   0.2058   5.6121
-#>    4.0445  -7.3677  -3.3419   6.4198  -2.8480   5.5827  13.7324   2.4249
-#>  -18.3887 -10.2412  13.6661   7.2739   0.7158   3.9746  -4.5782   9.0107
-#>    1.4559 -11.6066  -6.2290  -2.9388  -5.0435  -1.1411  -1.3263  -4.6638
-#>   -9.8511  -7.0693  11.5878   9.9078  -6.7654  -3.8763   0.2616   1.9187
-#>    6.3136  -5.5531  -0.5147   2.4884   5.5770   0.8150  -1.4526  -5.0775
-#>  -13.1313   3.6136  -6.0809   1.1632   6.8753   4.5192   2.9325  -4.9329
-#>  -16.4135  -5.5086   5.1798   0.9344   4.7721  -5.9660  -6.8268  12.8024
-#>   -7.5546   8.2615   3.2973  -7.1439   6.9062  -3.2143   3.7799  -3.6713
-#>  -10.0234  -4.3302   6.5380  -3.5359  -0.6433 -12.9074   0.1519   6.9613
-#>    1.6368  -3.8118   7.6408  -0.7032   9.1176   7.9364  -6.1997   0.0139
-#>  -11.1897   8.9903   6.2937  -8.6550   0.1846   3.1229   7.4796  -9.4778
-#>  -13.8819 -10.0532  17.6174  -8.0613  -1.6298   9.8393  -9.3358  -9.0107
-#>   -1.8548   1.0385  -0.4659   6.4678  10.0948  -3.8695  14.7695   8.0527
-#>   -8.6322  -9.9610  -2.1486   5.3907  -7.8803  -8.2907 -17.0719   9.2418
-#>   -5.7645  -4.1250  -0.8757  -5.3394  -1.9818   0.1242   1.4888   1.3666
-#>    4.2515  -3.5361   3.5730   9.5375   8.4103   2.8748  11.2373   2.8970
-#>   -7.1270   7.0362   6.7197   0.2017  -9.1090  -3.3480 -11.7893  -5.8566
-#>   -0.3841   5.4366  -0.9445  -2.6105  -5.0244   3.3680  13.2543  -1.5195
-#>    1.0747  14.1734   1.1987  -1.7408   4.8632   1.5659   5.5709 -19.8471
-#>    1.6983  -1.2366   4.8798  -2.5299  -9.1657   0.9159   2.1604  -8.2412
-#>   -1.0699  -3.8091  -0.6495   0.3911 -17.1536  -1.6623   1.0929   8.2718
-#>   -1.9780   0.0814   0.5713   5.0296  -6.1497   2.7823   4.6570   2.8901
-#>    9.9274 -15.5823  11.1468  13.4251  -6.8414  -1.5312  -4.1873  -3.8815
-#>   -3.2455  -0.2084  -6.4184   2.6211  -4.3341  -1.4415  -4.0072 -10.8088
-#>   -5.4387  -5.7921   0.6411  -5.3342  -2.9096 -17.0201   8.5314   9.1331
-#>   -9.3609   2.5416  -0.6532   0.8389   3.4634  -5.3714   1.6504   6.9934
-#>    1.2955  -0.9298   0.3603  -5.5105  -4.1873  -4.4538   2.8055  14.2132
-#>  -10.8144  -7.4770  -1.9756  -0.7660   0.2557   5.3072   3.9560  -1.3456
-#>  -12.0214   2.5167   8.1731  -1.4205  -1.2927   8.0969  -3.6897  16.6704
-#>    1.7988   0.0107   8.2771   4.0196   2.4219  -0.4908   1.5162   0.4107
-#> 
-#> Columns 41 to 48   6.8223  -1.1953 -16.5700  -0.2202  -1.8059  -4.1714   2.2586   3.1392
-#>   -9.7408  -4.4622   9.2715   9.7497  -9.3702  -6.9846   1.4185   0.2367
-#>   -1.3460  -4.0343   6.5043   8.9333  -2.8347  -0.7131  -4.1512  -1.6062
-#>    0.1806   3.0887  -2.6297  -1.5396   3.7892  -3.0346  -1.3348   9.8749
-#>   -6.5430   1.0502   4.3606   7.9445   2.2091  -2.4708  -2.1457 -11.0325
-#>   -0.3296  -1.0538  19.1806  -0.6681   7.7155   1.0968  14.9470   9.6190
-#>  -13.6597   1.3308   4.1055  18.2011   5.4537   1.0127  -9.1060  -0.3762
-#>   -0.2162  -1.0438   5.0688  -8.3406   2.6659  -4.0165  -0.4390  -0.7727
-#>    0.0441  -5.9841  -4.3727  12.3835  -6.2386  11.5710  -3.6306   8.3481
-#>   -1.1854   2.9303  -2.0192   6.8946  -1.6485  -7.4601  -0.4546  -1.8196
-#>  -12.3307  -2.6553  18.9656  -0.5494   0.6465  -8.9811  -3.0752 -17.2511
-#>   -5.0318   0.9506  13.3649  -5.5659  -9.9188 -11.2742 -10.3484 -13.8310
-#>   -7.5226   8.9456  -1.2620 -12.0812   6.8642   5.7276 -10.8331   1.1360
-#>  -10.2075  10.5311   7.0963  -2.4166  -4.3044   9.0821  -1.8509   4.5489
-#>  -10.6392   2.3903   7.6245  -5.2061   1.1225   0.5899   0.8800  -0.8110
-#>   14.0755   5.1738  -7.7262   3.4819 -10.6563  11.5738 -15.0048  10.6674
-#>    4.9958   8.7628 -13.1731  -2.1885   5.2119  -3.2639 -12.4037   3.4309
-#>    6.2419  10.6203  11.5762   3.5668  -7.6028  -9.1614   4.8107  -7.8572
-#>   -0.7675 -12.4405   2.2323  -6.8869   3.5631  -2.5244  -6.3250   8.6155
-#>   -4.1113   2.0419   1.0660  -4.4972   4.0218  -6.5370  -6.7348  -5.9009
-#>   -0.9550   8.0446   7.0813   0.4395 -12.6257  -4.2030   3.9091  -0.8361
-#>   -0.6885   7.8596 -12.6022  -7.9810  -2.3373  11.8033   1.6770  -1.6105
-#>   -6.5064   4.1790   4.0999   1.1366   7.9675  11.6719   3.3407   6.3710
-#>   -1.5531 -11.0727  -1.7039  10.3451   7.2884   6.7369   0.9745  10.8481
-#>   17.5924  -8.7264  11.3075   2.8726 -14.5478 -11.4686  -3.0124  10.2257
-#>    7.9230  -6.6112   1.0433  -3.9745   6.6238  -8.8205 -12.9009   0.8202
-#>   -4.9997  -3.1634 -10.4045   6.3738   4.9820  12.5160  -2.0337  -2.6327
-#>   -5.0487  -8.2076  19.7135   2.4629  -0.8365   2.6996  -0.6732  -1.3441
-#>    7.2947  -0.0056   5.3458  -0.1922 -20.1747 -14.5637  -0.0136  -0.7484
-#>    5.6158  -6.5718   1.9386  -6.6233  14.2731   6.9216  -3.8060   3.3327
-#>    4.9116  -5.7868 -13.6616 -10.8264   3.4249   4.6447   6.1558   2.3811
-#>   -0.3448  12.0279  -1.1617  -2.9150  -6.2155 -14.8863  -4.9204  10.1638
-#>    2.2497  -1.4470   0.6458  -3.5691  -0.2670  -2.3919  -8.6552  -3.1209
-#> 
-#> (14,.,.) = 
-#>  Columns 1 to 8  -2.1444   0.6275  -5.3745   7.5895  16.7731 -13.2129 -10.5422   9.0688
-#>   -8.6767 -10.2206   9.7955   6.8703   2.7551 -14.1020  -4.8347 -11.2143
-#>   -7.6052 -18.8394   2.9669   5.0451   4.2223  -1.5257   7.6646  -7.3693
-#>  -10.0294  -4.8756  15.5625   8.1806   3.2329  -9.9985   2.3995   2.0965
-#>    4.9436   1.4775  -3.2586  -7.1490 -10.9452   7.2870   3.4860   0.2213
-#>    6.0503 -10.1194  16.5172   0.6114 -16.4727   1.2303   4.3059  -0.2589
-#>   -1.6701   3.0303   2.6328   1.9484  -4.5294   1.2428  -4.5693  -9.4682
-#>    5.1566  -4.0968  -5.4200   5.1647  -1.7696  -1.5406  -3.8415  -1.7984
-#>    3.6765   3.4669  -5.7077   0.1770  -7.3732  -7.5296  -0.5793  -2.3397
-#>   -5.7430  -8.4634  -5.5900   1.6437   6.9694 -11.7347  -6.2177  -4.6205
-#>    0.6710  -4.0882   9.8013  -1.7805   1.2103  15.6890  -3.8652  -8.9470
-#>   12.5851   4.4384  10.4783   1.7717   1.1894  14.1159 -11.6373   0.3138
-#>    0.3266  -3.3944 -12.8845  -4.9403  -1.4425  -2.2774   4.4732  -4.2001
-#>    7.7831  -0.6257  12.6476  -4.7705  -1.3084   3.2592  -4.7172   3.3935
-#>    0.2951 -13.8237  -0.6221  -3.9925  -6.7304   4.5764  -0.5526  -9.1893
-#>   -3.1852  -3.4848  -5.6748  -3.6255  12.7578   2.8069   1.2936   9.8560
-#>   -8.7286  10.1477   3.0263  -0.5387   2.2513  -3.9209  -9.0792  -0.1861
-#>    8.2871  -9.3489   6.1300   5.8867   0.3383  16.0642  13.6681   5.7864
-#>   -2.4832 -11.1671   4.5390  16.8200   1.3044  -0.3483  -5.8973 -11.7801
-#>    1.2391  -1.0048   2.2145  11.8020  -6.3125  -1.3823  -4.2399 -10.7427
-#>    6.6959  -6.8043   7.1455   3.5484  -2.3540  -6.0205  -8.9290  -1.8643
-#>   -0.3061  12.4510  10.5427  -7.4929   0.3782 -12.1543  -9.1551   6.2211
-#>    9.7317   4.5159  10.3419  -2.1879  -2.6870   7.7015  -1.9451   4.4211
-#>    1.7871  -0.0642 -10.1845   0.0613  -3.8851  -5.8144  -6.2688   1.7174
-#>   13.4599  -2.8482   8.3512  16.3706   3.3594   5.3914   1.4781 -11.9550
-#>   -4.2157 -11.1060 -12.5199   8.8629  -1.2847  -3.2273  -1.1830  -2.4803
-#>    2.6273  15.6500   5.7461 -16.2158 -15.0618  -6.3968  -4.2644  -2.5628
-#>   -4.7286   5.6862  14.9851  -0.5636  -7.2220  -2.7243  -2.4637   2.2551
-#>   -3.3462  -7.6389   1.9538   5.6908   4.4791  -4.6541  -5.4730 -10.1804
-#>    2.2027   2.9326  -3.4673  -5.2041  11.2478   4.3450  -5.5164  13.3213
-#>   13.4535   6.0531   0.5279  -0.7927  -3.0388  -6.7141  -5.2771   0.3138
-#>   -9.7670   6.7562  -5.5766  -8.8379   5.7746   2.1539   0.0484 -15.2464
-#>    0.5020  -6.4378  -6.9533  -6.7794   6.2663  -0.8423  -2.0734   5.1950
-#> 
-#> Columns 9 to 16  -0.9762   3.0234   1.0104   7.0104   6.8566 -10.5834  16.1784  -4.9472
-#>   -3.3504  -8.0560   1.4816   4.3687  -7.1903   3.3486   1.2851  -8.6248
-#>   -5.5563  -8.5923   5.3647   3.1766  -4.4003  -2.6062  -1.3784 -10.4659
-#>  -10.4741  -5.5723  -0.6460  13.2420   3.4350 -11.2556   0.8239 -15.1732
-#>   -4.9091 -10.6420  12.9322  -3.6824  -3.0656   1.6655  -5.3298   4.0569
-#>  -13.2547   6.9211   6.5194 -13.3752  -5.8592   6.8385 -10.8662   6.6610
-#>   -6.7226  -2.5321   0.3613   3.7425   0.5684  -2.5713  -4.4943  10.9557
-#>   -3.2104   1.4335  -3.7294   3.8372  -5.0969   5.9416   8.7966  -0.9288
-#>   -7.9040   5.0802   1.7175   9.3299 -16.1795  -0.6950  -4.9105  -7.8795
-#>   -1.7059 -23.6254   8.1958  13.6085  -8.8340   5.5690   3.1202  -6.7429
-#>    6.1610   0.7591  -0.4275 -16.4001  -3.7464  14.8188   2.7395  -6.1317
-#>   -4.6173 -10.0567   8.0694  -2.3891  -5.2332   2.1577  -2.2042   5.9672
-#>    5.1777  -7.5451  -6.8466  -0.0412   4.9360   5.7510   4.0687  -7.6527
-#>   -3.6797  -2.3772   2.3120  -1.6865  -4.3676   8.5093  -5.7215  -7.1319
-#>    0.6331  -0.7750   5.7840  -3.2438   2.5939  -0.9735   1.9388  -7.4345
-#>   -3.6778  -6.0983 -16.4988  10.7501  -8.7596   6.7864  -1.5069  -5.2718
-#>   12.5570 -12.5301   3.2381  10.6822  -0.4238  -1.2423   1.9058   0.9420
-#>   -8.1062  -3.3461   1.2314  -5.4680  -1.8716  14.2853   2.3487   4.5719
-#>   -7.1079 -11.9893   0.3655   4.2615   4.4804   6.5035  -8.9245  -4.1921
-#>    2.1747  -8.1742   7.0437   1.0222   2.6713  -3.7678  -6.9374   9.9402
-#>   -1.1591  -3.6203  -0.1846  -5.2856  -8.4578  -5.6804  -3.7338   7.4090
-#>   -4.8591  16.9304  -8.2518   6.5114   2.1960  -4.1031  -0.9657  -2.2481
-#>    5.8799 -13.4325   1.9374  -7.1436  -4.0939  -1.5348  -4.6633   2.8750
-#>   11.9135  -5.9931  10.6113  -0.2672   7.9714  -6.8968   5.2002  14.0565
-#>   -5.4820  -0.7658   0.9432  -0.8519  -2.5855  -5.5044 -12.3018   1.7075
-#>   -6.5375  -9.9194 -10.3241  11.1755   3.7264   0.8428   6.9324   4.6708
-#>    1.6639  13.2267  11.1546   1.4382 -10.2988 -11.9187  -4.2637  -0.9742
-#>    7.6500   3.4899   4.6013  -9.5289  -8.5190   1.7298  -7.5066   1.5496
-#>   -4.4808  -9.7392   1.8155   3.2473  -9.2740   2.8275  -5.9945 -12.2909
-#>    4.8999  -8.4155  -6.2310  -7.1038   6.0395  -7.5361   8.3684  -1.2237
-#>   -0.3172   2.4322  10.8408   4.7848  -1.3948  -2.0308  -3.4644  -3.6158
-#>    1.2032  -0.9063  -0.4967  -4.0826   1.0183  -3.8041   1.0185  -9.7255
-#>   -4.6066   0.2925 -11.5063  -3.3608   3.6180   1.6864   2.4220  -4.2550
-#> 
-#> Columns 17 to 24   3.7132   2.7026  -1.0514  -6.3857   5.8852   9.2122  -7.7985 -10.9549
-#>   -3.5542 -10.4813 -10.9183   5.2912   0.7644  -1.3522   5.8959   4.7606
-#>   -1.8724  -4.6788  -9.0386  -0.9991   8.7160   1.1418   3.4454   0.1548
-#>    0.3223  -5.1287  -7.9411   2.5696  13.8285 -13.6017 -11.8921  -1.6590
-#>   -7.4142   8.6525  -8.4901  11.6494  -4.1533   0.5475  -0.9908  -3.4346
-#>   -4.0823  -2.7602  -2.7770   7.8444  -8.3679  -1.6173  -2.7454  -4.3434
-#>   -8.7138  -5.5199   5.0951  -9.3132  10.9098  -0.3270  -5.0873 -10.6834
-#>    1.7729   0.1606   3.5235  -3.2069  -1.4126   5.3862   1.6555   6.0412
-#>   -2.3556  -3.7612  -5.9476  -0.6006   8.3621  -6.4094   0.4973   8.1502
-#>    5.8877  -8.2092 -14.1709   4.3547  -1.2038   5.5549   6.8928   1.8750
-#>    2.7522  11.6348 -15.4113  14.9461  -7.2504   3.8405  -1.0440   1.2750
-#>   -2.4977   6.1348  -1.9506 -13.9133   4.9634   8.0606  10.5353  -9.5552
-#>    3.4657   0.2648  -3.3996  -1.7110   5.0830  -2.7579   0.3588   4.0522
-#>   -2.9026  -0.4903   4.1745   1.7057  12.6265 -10.0762  -9.1472  -2.8776
-#>  -24.6963   4.9028   2.2497  12.7402   6.2606   0.9909   5.0337   3.1554
-#>    2.6343  -1.5731  -0.3098   1.5872   8.9305  -9.5032  -3.1197  -4.7093
-#>   11.4809 -10.5834   7.0071  -0.9855   0.1514  11.3191  16.7707  -4.3523
-#>   -5.0535  12.5095  -3.0928  -3.5276   3.5058  -5.5731  -6.5917  -3.1749
-#>   -4.4402  -4.3286  -2.5488   4.6506  -4.4229  -2.9584  -2.2505   0.4066
-#>   -1.5600  -3.5898   2.8236  -0.6024   4.6846   4.3762   5.7524   1.0433
-#>  -14.8293  -1.8766   0.7403  -6.9910  16.1613   1.9846  -0.6524   2.4959
-#>   -5.4296   0.8858  -2.0244   0.4708  10.3446  -7.2269 -11.1847   6.7621
-#>   -6.3072  -7.2304   5.8156   6.9135  -6.5860  -9.2921  -0.1708  -7.5872
-#>   -1.2483 -13.9717  -4.7192  -0.4497  -5.7567   0.0720  -3.2322  -5.1351
-#>   -3.1657  -4.7625   8.0419  -5.4016  -1.2495  -8.2174   9.3439  -3.0667
-#>   -1.3187   1.6499  -6.5667   4.9880   1.5150   0.8097  -3.4620  -2.6438
-#>   -2.3831  -2.7993   7.8687   7.6837   5.1708  -8.4030  16.2167   2.6972
-#>   -1.5868   2.9036   0.2707   3.7706   8.1882  14.5434   5.2464  -5.5609
-#>    5.3385 -10.0304   7.2175  -3.0437  11.3225   2.2313  12.7802   2.7522
-#>    2.7248   4.7965  -6.9295   1.4670  -5.0119  18.2201  -6.2492 -13.9647
-#>   -0.5215   6.2616   6.8941   3.0859   5.0107   0.5953   7.2855   2.0261
-#>    4.9730 -10.6661   2.0325   5.6798 -10.4148   6.9674  -0.3137  -1.7415
-#>    1.3191   1.6653  -0.9755  -6.9042   1.9775   1.5980  -2.9887  -7.3644
-#> 
-#> Columns 25 to 32  -2.2584  -3.1411  -1.7190   5.6135  -0.7605  -1.8872   8.8467   0.3317
-#>    1.8911   2.5071   4.7714   2.8800 -11.7468  10.1048  -1.3142  -1.2634
-#>   -0.3020  -6.2638   0.6670   0.2206   1.6318  -0.1777   3.8507   1.0090
-#>    3.9061  11.3403  -0.1531   2.3443  -0.2155  13.3499 -17.7884   5.8076
-#>    4.6891   4.9715   1.8122  -3.1096  15.1079  -8.8061   5.2626  -1.7620
-#>   11.8195  13.2659  -0.9978  -3.7929  10.2876 -14.6448   0.8412   5.7287
-#>    4.9830  -6.6374   0.5644  -1.2754  -0.3460  -2.1989 -15.5581   7.8374
-#>    3.7161  -4.0108  -5.7117   0.2989  -4.1622  -9.0537  -7.2892  12.5500
-#>   -4.2194  12.9871  -8.7733  -1.4598  -6.5197  -3.6219  -2.2843  -3.0821
-#>    5.8795   0.5183   3.4822   1.5533  -7.1084  -0.2438   8.3458   1.0638
-#>   -4.5325 -12.4497  -9.7003  13.0726   2.2119 -18.1307  11.2652  -3.5084
-#>  -12.6648  -0.2436   6.2022  -5.6211  12.8947  -5.3681 -11.4360   6.7527
-#>  -10.3917  -4.2899  -4.2763   3.9326  -9.5481   8.7687  -0.3994   1.4870
-#>   -1.9819  -3.9799   0.4570   0.6859   8.2083  -2.0543  -8.9140   3.7974
-#>    7.5153  -4.8186  -5.2606  -6.9110  19.4118  -2.6405   1.9254  -4.9273
-#>   -7.9555  12.0409  -8.3323  -0.7393  -3.4030  11.7891  -4.2925   2.8215
-#>   10.9420   6.2364   4.0612  -0.0166  -4.1987   6.9281  -6.5285  -7.9298
-#>    8.7943   1.2502   4.2291  -1.3830  10.3950  -2.8952  -1.2324   7.2852
-#>    5.0857  -6.5046 -11.5604   3.5139  -9.5032   1.5154  -8.9062   1.5487
-#>   -4.3588 -12.7515   4.9994   7.2508  -1.5145   3.3324  -0.5210  -0.6055
-#>    3.6695  -6.7156  -2.2916  -3.3440  -8.8127  -3.9514  -8.6936   5.4232
-#>  -17.4519   0.3753  -0.1577   0.2455  -3.8726   2.1221   3.0209  -8.5284
-#>    7.7403   3.7682  11.5437  -1.9562  11.3489   2.4882 -14.7405   5.0131
-#>   -3.4348  -6.1867  -2.1967  -0.9979 -15.9601  12.7055   7.6482  -5.3109
-#>    7.6323   3.3177   1.1548  -1.6291  -1.5787  -4.0459  -5.3410  -8.6709
-#>    1.7209   5.6033  -4.2626  -4.5748   5.7546  -4.3819  -3.5714   9.4888
-#>   -7.2977   4.9754   0.1989  -1.4958  -2.5134   2.0138  -8.3286  -1.0149
-#>   -5.3932  -8.4312  -1.7728  -1.3764  -1.8622  -2.0587   1.6540 -13.3436
-#>   -0.6438  -4.0675   9.3477  -2.1537  -6.8739   6.6785  -3.6679   2.0648
-#>    3.0280  14.1391  -0.7212   6.4096   9.0701  -3.9991  10.6811 -11.6501
-#>    7.7576   3.7023   2.2892  -3.6257  11.5897  -3.1216 -11.6945   3.9960
-#>    0.0091   1.1377   5.4606   3.3294   2.3344   0.6012  -3.5069   1.8844
-#>  -14.2707  -0.6768  -4.0255  -3.9383   3.2496  -0.0379   8.5746   0.7396
-#> 
-#> Columns 33 to 40  -7.0296 -12.8663  14.3987   3.2322   9.2232   0.7883  -1.6867   7.8755
-#>   -1.5344   4.4558   8.8551   0.2860   3.9697   4.3488   3.3198  -2.3698
-#>    1.2647   5.6437   5.3609   1.3164  -0.4757   2.6660   5.7626   0.0191
-#>   -6.5931   5.5725   7.9819  -2.8026  -7.9575  -0.1073   0.7064   6.7801
-#>   -6.1830  -5.9598  -9.5774 -15.6217  -0.8016   4.2489   1.4874   1.1143
-#>    5.8825   9.6267  -5.7148   1.8366  -3.2282   0.2424  -7.8084   9.1993
-#>   -3.0657  -5.0323   7.8977   0.8028 -12.1188  -3.9341  -4.0435   5.0439
-#>    2.6450   6.5642   4.8798   1.0387  -9.5429   0.2942   2.1092  -2.6703
-#>   -2.1335   3.0644  -8.9395  -4.4608   3.8032   9.3958  14.5904   2.0776
-#>   -4.7665   0.4154  -1.4496   1.7975   9.1528  -1.9465   6.8242   2.5062
-#>   -6.3660  -3.0849  -4.3183 -17.0690   5.4184   8.2195  -5.6109  -3.5211
-#>   -6.4113   2.3761   4.4897   7.6312  -2.9152  -0.5913  -7.5612  10.6324
-#>    7.5895  10.0746 -10.9153   0.2363  -1.3659   4.2210   4.9896  -9.9209
-#>   -5.0598  12.0518  -9.0581  -0.0027  -3.1009   6.3472  -0.5132  -1.0659
-#>    0.3511  -1.0510  -7.4713  -7.5874   8.9896  19.5600  10.1285   4.5457
-#>    7.4178   3.2575  -6.0450   1.1417  -5.1602 -17.7806  -3.1945  -1.6117
-#>   -4.3079  -1.4211   0.9433  25.7182  16.7786  -0.5493  -1.7191   3.3322
-#>   -8.1885   1.3356   1.4096  -8.2148  -2.4566  -1.6299  -9.5060  -3.4289
-#>    3.3245   5.9569   5.9231  -0.3552 -18.8013  -5.1879   2.9843  -2.1808
-#>   -8.7187  -7.0404  -4.1315  13.2902   6.9334   6.2543   1.4562  -8.1287
-#>   -6.5858  10.9018  20.6336  10.7662   0.3862  -2.1395  -6.7924  -5.6453
-#>   -2.4868  -2.3452  -6.7193  -4.8742   9.7127  -1.3753   4.8112   3.5227
-#>    0.2052   6.9031   5.0824  -2.2491  -0.1196   0.8441  -9.4147   1.7011
-#>   -8.9176  -3.0189   9.5077   4.9989  -1.1380  -5.3146  -5.5733  -6.7260
-#>   -0.4082   4.1821   5.2904   2.3013   1.2743  -2.9406  -6.0362  -3.0038
-#>   -2.0832  -1.8657   4.9447   1.4180   0.1416  -0.1899   4.1653  -3.4298
-#>   -4.8597   0.9150   0.4392  -4.8070   7.4141  14.8686   3.7364  10.1229
-#>    8.3424  -6.1335   4.3202  -2.1732 -11.5991 -13.4142   1.2317  -8.0982
-#>   -0.2153   6.0834   3.4220  10.8916   4.3969   8.2267  10.9871  -7.5181
-#>    2.3822  -8.7823   4.6057  10.1561  -1.5710 -10.0309  -2.0746  -2.2866
-#>   -5.8263  -0.6575  -6.0233  -4.0320  -7.1885   4.3454   9.4840   7.2089
-#>   13.3708   2.2496  -7.7976   8.7236   5.5935   5.6348   4.9756   5.5582
-#>    5.9179   1.3443  -3.3823  -1.2847   8.5173   0.9727  -0.8258  -1.4907
-#> 
-#> Columns 41 to 48  -4.8874   4.2648   1.4254 -11.1819  -7.5432 -11.3109  -9.2888   0.4967
-#>    9.5153  11.7349  -2.2791   0.8585  -2.8558   0.2460   1.0491   1.4689
-#>    9.3801  15.6711   2.0565   4.8049   3.5439   4.0364  15.1548  -3.6749
-#>   16.3325  -9.3679   2.0192  -3.3858  -9.8849   2.9303   1.8756   4.9476
-#>   -0.8684   4.5410  -9.3713  -3.4632  12.9519   2.7012  12.1265  -1.4925
-#>  -10.3416  -2.8101  -0.1286  -2.5805  10.9838   4.5101  -2.4895  11.1958
-#>   -2.4855  -0.2830  -1.4296  -3.1296  -0.3691  11.4104  -3.1588 -15.5533
-#>   -8.7580  -3.4773  10.9539  -2.5102   3.5234   5.4549  -5.1429   0.9529
-#>    8.7106   8.7735  -1.1035  -5.3187 -10.2647   3.4108 -11.0404  -0.5582
-#>    1.5927  12.1160  -0.9894  -0.0483  -0.4213 -11.2137   3.4887  -5.2136
-#>    8.0233  -7.5385  -1.8384  -3.2708  12.0102 -10.5743   9.0551  -0.5432
-#>    2.3194  -5.6273  -8.4390   9.7349   4.6735   9.8609  -3.3499  -5.7117
-#>    8.2240   9.1067   0.2126   0.6903  -3.8826   2.3915   8.0595  -2.0850
-#>    8.5066   5.3333  -5.2188 -13.0931  -3.6299   0.0899  -1.9622  -4.6138
-#>    8.9595   7.7200 -12.6849 -11.6411  11.2263  -0.6814  -1.3068   4.1395
-#>   -3.7121  -0.5109   7.3844   4.4809  -0.1424  12.3762   1.8442   3.6132
-#>    3.2045  -0.0386  -0.3754   5.7350  -9.9900 -16.1272   2.7240 -17.6808
-#>   -2.4684   3.9191  -5.6213  -6.9191   7.2749  -3.8292 -12.5974  10.8177
-#>    0.0444   1.4745  -0.4502  -0.5448   3.1292   4.7605  12.5336  -0.3326
-#>   15.8767   2.8549  -9.2265  -2.5690  -2.1676   1.7361   7.1797  -3.3801
-#>   -4.3841   5.1055   6.1935   1.2127   0.0729   3.1731  -5.4489   2.8111
-#>   14.3846   1.3005  -4.7567 -11.0473 -13.8530   3.5960 -16.5615  -2.5091
-#>   -2.9694  -2.3522  -5.3276  -5.7980   4.1376  -3.9642  14.9809  -3.5241
-#>    5.5171  -2.1734   0.9172   4.5474   1.9186  -9.3068   0.5903 -19.9337
-#>   -9.6287  -6.7558 -12.1460  -1.5988  -3.5005   0.7460 -12.0347   4.5429
-#>   -3.4691  -4.8915   8.4898   1.0439   6.9208   8.3621   3.7009   2.0818
-#>    2.2342  -6.7275 -13.9861  -5.2344  -2.4440   7.9633   3.3043   1.9516
-#>    3.4051  -5.0082  12.2827  14.6407   3.5717   1.9640   6.0396  -9.4632
-#>   -9.2561   4.1927  -4.7531  -2.0654  -6.8197   1.7003  -2.1707  -0.6996
-#>   -6.5002   9.6357   6.3987   5.9241  -6.2198 -14.6919   6.5156  -6.7869
-#>   -4.5410  -3.4889  -8.7651  -1.1678   3.2435  -0.3625  -6.3166   3.1699
-#>   -1.0508 -10.6551  -7.6276  12.8458   1.8464  -5.5304  10.2748 -19.5096
-#>   -7.3590   3.5982   3.9005   4.6067  -1.3096  10.7647   1.2693   2.7367
-#> 
-#> (15,.,.) = 
-#>  Columns 1 to 8   7.1115  -4.8512   6.6240  -2.8056  -3.3284  -8.8784  -1.8778   2.6504
-#>    2.0700   2.2446   3.5921  -8.6059  11.0003   4.2419  -2.6831 -13.7168
-#>   -3.0170  -4.9289  -6.9572  -6.9360   3.4179  10.0614  -1.7287  -5.8838
-#>    6.8548 -18.6215   4.7617   1.7003   5.2943 -13.6111   7.5037   7.0371
-#>    0.2961   6.8685  -6.2783   5.1014   0.8988   2.8195   3.8814   6.8040
-#>   -4.8547   5.2219   4.0477  16.4903 -12.9067   4.1274  18.0589   2.3969
-#>   -3.1119  -5.7625   2.6623   1.4139   2.1222  -0.2198  10.5954   7.2307
-#>   -7.6110   2.3029  -1.1292  -7.6671  -3.8505   7.4110   3.5311 -16.9414
-#>    1.3999  -9.8199   3.7912   5.3425   5.5118   5.7815   1.5328   8.6705
-#>    3.1219   5.7515   1.3880  -0.1989   5.4088   0.5510 -11.7477  -0.2784
-#>  -14.2673  10.1715  -8.1456   1.4596  -2.2529  16.0602   3.6823 -10.3038
-#>   11.0010  -7.4895 -21.9005   7.8930   0.6953  -2.4494  -1.8775  -1.9337
-#>    8.0189  -9.5857   0.1912  -0.3771  13.6362   1.4489  -6.2672  -7.9932
-#>   -2.5327  -3.8656  -7.2625  12.7074   0.4264   1.5368   5.6500  -3.9857
-#>    1.8156   1.5734   0.2571   1.3652   5.4486   9.7826   3.4503 -10.3677
-#>   -3.8268  -7.5919   8.7224  -7.5668  -4.8322   2.4466  -1.4175  -2.6361
-#>    8.6276  -4.3907   2.3532 -11.3650   0.8983  -8.9388 -21.6246  -0.2398
-#>    5.3831   5.5249   1.9599   0.7754 -11.9672  -3.6293   0.7607  -1.5836
-#>  -15.1287  -0.2102  -8.7870  -9.7320   3.5318   3.8834   3.2313  13.0888
-#>    2.2043   1.2443  -6.2095  -0.5036   8.3035  -2.9883 -11.5146  -0.9311
-#>   -2.2014  -4.3662  -9.8363  -0.0592   2.2272   7.1386  -2.3986  -9.8353
-#>    7.3829 -15.6095   4.8038  13.5743   8.6284  -4.1394  -8.4229  -2.4527
-#>  -13.6528   4.5778  -9.1974  10.2745   5.6171   1.3481   0.0050   2.3714
-#>    5.1346   5.7869  -3.6878   5.2730  12.8479  -8.4027  -0.2686  12.3093
-#>   -1.9531   1.3696  -5.5243  -0.3543  -4.0368   0.4145   1.2231   6.5016
-#>   -4.4855   3.5932   0.2520  -5.4919   1.7162  -2.1047   3.5553 -10.0349
-#>   -0.7338 -12.2822   3.8291   1.8916  -5.6240   4.1292  13.4338   8.6450
-#>    4.1223   8.7543  -3.3275 -10.5468   0.5172  11.4885   8.5203   4.6293
-#>    0.8531   9.3552  -4.1095  -4.7907   0.7956  -1.3426  -3.6576  -5.8040
-#>    3.3943  -0.0591  -5.4711   1.9219   1.3790  -3.3002 -14.7059   9.0316
-#>    0.1388  -3.6049 -10.5187  -4.0452  -4.5910   0.0103   2.7861  -6.4096
-#>   -6.2126  -1.2538   5.6032   1.9384   8.2004   3.6508   5.4220  -2.7547
-#>    1.4816  -4.9908  -3.5770   5.7046   5.3249   7.0368  -4.7861  -8.5862
-#> 
-#> Columns 9 to 16   7.3293   0.7758  11.2714   0.4524  10.0081  -1.8373  -6.1661  -5.1543
-#>   -5.8472  -0.0340   6.9379  -9.0681   0.1452  -5.2057 -12.2126  -1.6486
-#>    2.3437  -8.1255   4.6813   4.4023  -1.8926  -3.2216   1.2384  -9.5795
-#>   11.8843 -12.4493  13.3344  -0.9240 -10.9565   4.5401  -3.0791   7.8568
-#>   -9.3088   5.4911  -0.1078   0.6599   0.1283   2.3134   8.2952   5.1707
-#>    4.1178   8.3648  -9.3887  -6.1874  10.2946  -2.3948  -1.2944   1.9575
-#>    3.9512  -2.3895  -3.5280   6.1398   4.6451  -8.5465  -1.5480  -6.9995
-#>    2.2374   3.9606  -5.4656   4.2810   4.2215  -8.9381  -2.8852  -8.8617
-#>   -3.6895  -4.0458   6.1441 -12.8404  -8.1936   1.2354  -1.3595   1.0117
-#>   -0.8024  -6.1211   2.9179   0.7460   3.9652   0.3023   0.6151   5.0391
-#>  -11.5725  28.7443 -21.9473  12.8849  -7.2069  -3.0946 -10.9281  -3.5617
-#>   -5.4936  -0.1804  -4.5155   5.1270  -0.6444  -8.3817  -5.6771 -16.6725
-#>    2.3760  -7.0744  -3.9384  -0.7086  -7.3069   4.5342   2.1592   2.9307
-#>   18.0141  -6.5316   8.1737  -1.6791  -7.9068  -0.4408  -1.8226   5.0003
-#>    7.7288  10.7306   6.2264   3.0890   7.9790  -2.7280   5.4462  15.1681
-#>    8.5514  -4.4508   5.4930  -0.7188   3.4840   1.9186   1.2016  11.9940
-#>    4.5466  -9.5580  14.4330  -8.1170   8.1214   3.3552  -7.4451   0.8764
-#>   -6.6361  -2.7121   0.2718   2.8905   5.6385   1.1932   0.1248  -9.6453
-#>    4.6519  10.0845 -11.9125   7.3490 -12.6394  -5.6982  -3.9589  -5.8524
-#>    7.5585   4.5320   4.8395   8.3544  -6.1310   1.5636  -1.5205   3.5457
-#>    8.8557  -3.6722  -4.3582  11.1995 -13.1770  -5.3377   0.8860  -7.7558
-#>    9.9494  -9.0174   6.8326  -8.1302 -15.9986  -1.7770  -6.3664  14.5632
-#>   12.8819  16.6322   0.9788   0.6587  -9.5508  -0.4219   1.1572   1.0094
-#>  -13.2621  -4.8329  -3.3790   0.6607  -3.8674  19.4960  -2.4050   1.7530
-#>    8.2321   3.4733   4.7779  -5.3348   0.2874   1.8593  -7.6614  -6.7333
-#>    9.0961   5.4900  -4.4653   6.5680   0.2827  -4.5207   3.0002   8.9991
-#>   -4.3711   8.0910  14.0509  -7.3617  -4.7490   9.0630  -1.7344  -2.7312
-#>   -1.7179   1.9318  -9.6681  16.6352   4.7708  -6.8043  -6.1491  -0.2504
-#>   14.4281 -10.5506  18.0719   0.6034   2.2336   0.7469   4.2527   3.9240
-#>    3.4098   3.1782  -1.2625   5.2707   3.1843   3.9704   6.9703   3.3117
-#>    1.1391   6.7281   8.8927   5.2655   5.8992  -1.3787   1.0948  -1.4335
-#>   -7.3976   0.4969  -0.9509  -7.0835  14.7301  -5.6787   4.1450  15.2954
-#>   12.4407  -4.6198  -0.9390  -2.1177  -4.9047  -1.7441   7.3410  10.5900
-#> 
-#> Columns 17 to 24  -7.8632  -2.9569  -7.4812 -13.7157   8.0637   2.3197 -11.0591   6.0891
-#>    5.2080  13.6162   9.5811   9.7852   5.6479  -0.6554  -1.9055   6.7459
-#>    0.0767   2.2985  -3.4298   3.6582   3.5657   1.8876   4.9415  -0.0905
-#>   -0.8436  -8.1971   5.6436  -0.8189  -2.7185  -6.8124   1.8173   1.5997
-#>   16.2083  16.5765  -9.1467   1.2284 -12.6130  -4.5548   2.0418   1.2082
-#>   -0.3672  -0.1228  -0.0400  -4.1638   0.4942  -6.9269   7.8788  -6.5652
-#>   13.4752  -1.3951  -7.7839  -2.8148  10.6688   3.2956   5.4087  -6.8197
-#>   -5.1719   3.6068   8.4387   2.3832   9.2946   7.0327  -3.0611  -2.2567
-#>    1.8095  11.6708  -3.7466   6.6039   0.9131  -5.9405  12.7895  -1.3867
-#>   16.9034  14.6173  -1.4816  12.0900  -9.7003  -8.1873 -10.4125   3.2021
-#>   18.8224  -3.7732  -8.4614  17.2741 -12.3936  11.3341  -9.2802   9.3729
-#>    2.1984   4.4343  -2.0045  17.9349  12.9631 -10.8768   5.0915  -1.7478
-#>   -1.1283  10.2250  -0.7795   9.2527   2.0497  -6.3417   6.1346  -4.9914
-#>    3.7214 -17.4149  -2.9213  -3.1648  -9.2139  -1.9090  16.3691  -1.6833
-#>   17.7869   1.2456  -8.1943  -6.7983 -12.6561   1.0241   8.7451   1.9875
-#>    4.7919  -7.9875   5.3562  -4.6082 -12.4467  -9.7171   4.8647  -3.7000
-#>    8.2106   7.4021  -7.2296  14.3541   0.3103  -1.5755   0.0523 -11.7865
-#>    4.0221  -0.2243  -7.8152   2.0820   3.3237  -8.2121  -3.1208   0.4177
-#>    2.1557   6.9841   3.9537   3.1003  -4.4127  13.4479   6.2930   9.3346
-#>    6.3638  -7.4629 -22.5791   1.1678  -7.8419  11.4958   5.5791  -2.9846
-#>   -8.2702 -13.1500   3.4012   4.5281  11.8472   9.1611  -0.4623   4.2150
-#>    5.4411 -12.5264   6.7563  -2.7371   5.6250  -2.9614   7.0123   4.2767
-#>    5.8710  -6.3890   1.2708 -12.6806 -11.1545   2.2729   2.5553  14.9886
-#>    3.2095   8.6801  -0.5554   8.1789  -1.8506   0.1934 -27.5572  -1.5930
-#>   -6.8670  -1.6240 -12.6488  -5.6393   6.8329  -1.8703   6.1925  -2.0215
-#>    6.8801   3.9142  -2.3834  -8.1555   2.7157   7.8278   2.2930  -6.3260
-#>   -2.3847  -1.3235  -3.3893  -0.3491   4.1305  -3.4685  10.7051   2.8873
-#>    8.2645 -10.5847  -0.8847   1.2763 -10.3263   7.4525  -9.3715   3.6793
-#>   -2.3518  -0.1390   1.0705  -5.8702  -6.4483  -4.0465   8.3043   5.2560
-#>    3.2468   8.2362 -10.9262  -6.5290 -12.8279  -5.7303  -1.3841  -0.0960
-#>   -3.0723  10.6398  12.0713   0.7000  -3.5167 -14.5697   3.6254  -2.7057
-#>    0.0036  -1.4688   3.9714  -0.6494 -15.3354  -3.4704   3.0982  -7.4453
-#>    0.4233   3.8652   1.7919 -11.7475   5.7635 -13.0052   8.0559  -2.3109
-#> 
-#> Columns 25 to 32   1.6111   9.8913  -5.1667   1.7823   4.4968  11.4306   4.7141   4.1485
-#>   11.6637  -6.4701   7.3143  -4.6603   5.1235  -7.4082   8.3548  -0.5569
-#>    0.6283  -6.8813   2.5591  -1.5133   4.7381  -2.9992   9.0115  -5.5502
-#>   -5.2239  -4.3276   5.1755  -0.0235   4.7770  -7.2770  -5.2854   5.8824
-#>   -8.7289  -0.1826   4.4126  -2.3563  -3.5984  -9.1043   9.6573  -0.8086
-#>    4.8235   5.1700   1.8015   4.0285   7.1082  -7.7678   5.7203 -16.4164
-#>    4.0143   7.2377   4.8562   9.9579  -4.2431   0.6437  -3.0818  -3.3971
-#>   -1.1889   6.8866   4.1312   3.2933  -1.7267   1.8717  -1.0299  -4.4844
-#>    7.9196   5.0489  -2.6998  -6.7225  -5.8232  -7.8611  -8.9995   8.6625
-#>    6.5469  -5.5016   5.5969  -7.3770   1.7542 -13.9674   6.6170  -3.4024
-#>   -3.2858  -0.3681  14.5794  -3.3098  12.1049  -8.3324  -0.8725 -12.8950
-#>   -4.5755   8.1768  10.8769  -4.6412  -1.5418   6.8211   5.3414  -1.7760
-#>    4.8843   2.1762  -8.7745  -0.1388   2.1083   1.3516 -11.3251  10.4455
-#>    3.3188   0.7462   2.9958   3.4060   2.7944  -4.2444   4.0027  -0.6496
-#>    3.7560 -13.5172   1.9928  10.8493  -1.9686   0.5085   3.2974   7.6224
-#>    0.7598  -7.6283  -2.8274   4.6157   4.2587  -4.9954  -2.3554   0.9759
-#>   -3.5939   7.8641   0.4557   9.6153 -11.5511   0.3652   5.8262   1.3749
-#>   -1.5936  -5.9849  -0.1979  -1.2771  -2.1348 -12.1289   1.5487  -8.8455
-#>    7.6842   4.1295   8.3468  10.5882   5.1853 -11.4518   6.2370  -5.1755
-#>    1.1376  -0.9603  11.4751   7.1682   0.3050  13.5715  11.1495   2.8284
-#>    8.3730  10.5045  14.0180 -11.2211  -1.3625   2.3138   3.8882  -6.8152
-#>    3.7236  -1.7148  -7.6170 -10.5873  -3.2708  10.6142  -7.5766   8.5804
-#>   -7.0491   2.5127   8.0336   9.0789   0.4238   6.1985  12.7871 -13.7677
-#>    2.8779  10.3715 -17.5331 -10.4388  -4.2289  -2.8544   3.5366 -13.0072
-#>   14.9275   8.7207   3.2939   4.4447   3.7926  -9.0021   9.4168  -1.2667
-#>   -5.4648   7.6661  13.5957  10.1384   1.0440  -2.1800   3.8088   2.7490
-#>   -6.1931   2.9256   6.1095   2.7975   6.8424  10.0426   6.3487   9.9140
-#>    5.5017  -2.1072   6.9378   2.6380  -1.2445  -0.7814  -0.1116 -12.2251
-#>    7.5641  -1.5218  12.6226   3.0393   4.7455 -10.6471   8.8348   8.3849
-#>  -10.8081   6.4331  -0.9774   6.6743   2.1463   2.1770  -8.2339 -16.4321
-#>   -4.9075  -7.1971   2.3138   7.9496   2.5724   2.2093   3.0592  -2.0602
-#>    2.3171  -3.5195   3.2429   3.8005  -9.6169   5.8675  -6.2395   5.4772
-#>   -4.0414   7.8393  -3.1173  -0.2646   2.4630   4.9677  -1.7572   7.2229
-#> 
-#> Columns 33 to 40  -1.1876 -10.1433  -6.3296  -0.4366  -8.4276   4.5743  10.7330   9.1425
-#>    5.0169   1.3334  11.1390  -3.0555  -5.2267   2.4215   3.0188 -13.6635
-#>   -2.9091   7.9281   9.3381   8.1316  -7.7611  -3.3668   7.0043   7.1997
-#>   -3.4941  -8.8710  22.0236   2.1432 -11.5798  13.7316  10.7663  -0.7452
-#>    0.7552  17.7075  -3.7739  -8.6597   7.8503  -3.0094  -2.7635   0.9666
-#>  -10.9439  -3.6854   1.2291 -11.6378  14.9099   0.2285 -15.1566  -4.2762
-#>   -0.5783  15.5054   3.0954  -3.3504 -13.4509  -2.3075   9.3905   1.3299
-#>    2.8292  -1.6403   8.7995  -4.3770  -1.7240  -1.7656  -8.9716  -2.7895
-#>    2.3974  13.9734   1.0183  -8.1300   0.0174   6.9550   8.2570  -8.0587
-#>    3.0301  11.8596   8.8648  -3.7870  -8.1850  -2.5475  -4.2095  -0.0444
-#>   10.4876  10.5542  -4.7367   4.3377 -11.2366  -0.7845   0.3486  -2.2428
-#>   -5.8378   2.4098  -0.9600  -4.4779   0.1891  -6.8651  -9.0412  -1.2870
-#>    0.5132   3.5568   7.9932   3.6603  -3.2506  -9.6204  -3.2829  11.5460
-#>    4.3603  -4.4309   2.1633  -4.6632  -2.0197  10.3826 -10.8853  18.2003
-#>    7.5312   9.4417 -14.0978 -10.5704   7.4753   2.4065 -12.0700   8.9292
-#>   -5.9442   5.7016  10.2508   5.5452  -4.8535  -2.5900  -5.1088   4.2898
-#>  -18.8976   5.1532  -1.7340   8.3101   5.6807  -2.8783  -3.3924   0.9158
-#>   -4.6655  -2.7845   2.2621  -5.1463  11.9983   5.4542   5.0857   3.3174
-#>    4.7172   8.5207   8.1565   2.4791  -7.0028  -0.8125  -4.5575  -3.3890
-#>    4.9781   3.8925  -6.7012  -3.3485  -9.6601  -9.7996   0.6632   4.9651
-#>    6.2889  -6.5221  -0.1579   0.8559  -3.5223  -2.9480  -8.2845  -3.6279
-#>   16.2025  -4.6872   0.9619  -2.2647  -0.9014  -1.9281  10.6382   1.3064
-#>  -11.7320  -8.8520  -8.2607  -1.3817   9.6871   3.5138 -12.0845   0.8755
-#>   -1.4128  -0.8471  -8.2075   6.5583  -9.8319   0.0534  -2.4484   3.2296
-#>    0.1093   5.4801  -5.7720  -4.4883   1.0138  11.8541  -5.7959 -13.5700
-#>   -2.7510   3.9824   7.7852  -8.5101  -0.6763  -0.6188  -4.2763  -4.1366
-#>   -7.6959   1.4529  -2.4865 -10.2923   0.1622   3.2543   0.6042  -8.5631
-#>   -5.8255   3.2380  -5.6855  10.0065 -13.5559 -18.4401  -6.9620   6.1546
-#>   14.2976  -3.6410   2.4764 -14.9836  -9.5727  12.6544 -12.3706  -4.0015
-#>   -4.1990   1.5840  -2.1817  14.1879  -4.8348 -12.4974   6.0161  17.1821
-#>   -0.7287   3.5166   6.5444  -5.4296   2.1586  -1.0379  -4.0262   1.5960
-#>   -3.1356   4.7152  -3.5899  -3.5598   4.9465 -12.6657  -0.7683  -1.2444
-#>    4.7171   0.5994   0.7119  -1.2080  -0.6086 -13.2839  -5.4848   2.1458
-#> 
-#> Columns 41 to 48  12.1076   4.1011  -2.1738 -12.9176   6.0466  -1.2080   4.1916 -14.2161
-#>  -11.0032  -2.0739  -9.6891   9.1633  -6.7289  -0.9261 -11.6838  -9.1522
-#>   -2.8997  -6.1992   0.2605  -0.8266   3.4657   6.5743  -7.7402  -0.3860
-#>   -9.4210   0.0803 -11.6765   7.1376   2.6870  -8.4538  -5.3343  -1.7722
-#>   -6.3655 -12.2643   8.3479  -0.7230 -13.5265   0.0571  -4.1167  -0.2769
-#>    7.3242  -2.8557   6.1877   5.6059  -0.9670   0.8556   8.1311   3.8170
-#>    0.9440  -6.0001   5.4027   3.7200   8.2664  -4.4506  -8.2239   3.3115
-#>    1.1671   1.2033  -0.1148   4.6368   9.2238  -8.6745   9.8473   1.3444
-#>   -7.4524  -2.3227   2.5718   0.3380   4.2563  -6.7630  -0.9309 -14.5055
-#>  -10.5321  -0.5466   3.3322   8.5984 -15.1215  -3.8004  -5.8031  -4.9785
-#>    0.5278  -2.1349   0.5429   6.7788  -7.7084  -6.8593   3.6495   5.3098
-#>   -3.9173 -12.6153   6.4391  -2.6274   5.9788  -2.6989   5.6161   4.0751
-#>   -9.5523   9.0388  -3.6559   0.7355  -6.6148   2.9918  -4.4288  10.3985
-#>    0.3431  -0.1633   9.4491  -0.1662   0.1703  -8.1686   3.9250  -1.9232
-#>    1.5288  -2.3083  -5.0019  -2.2136 -13.1283  -6.2529 -12.6464   3.3033
-#>    1.8642  -1.6308   0.4104  -2.1203   6.6278  -7.2271   9.7675  -0.6935
-#>   -3.5142  -5.4236   0.4054   4.4837  -1.8545  -1.1056 -16.1740 -11.8274
-#>    7.2128   0.4841   1.1405   1.7895  -2.4622   1.0410   5.6031  -9.5526
-#>   -5.1216  -4.1475   0.7678  -7.3741   7.0402  -3.9091   5.0237   1.8940
-#>   -2.9024   6.0801  -1.3226  -1.9505  -7.1316  -1.0240  -7.6500   1.4707
-#>   -4.6808  -3.3517  -0.6546  -1.4612   6.0778   8.9817   7.7796 -10.4063
-#>   -3.9926   9.0640  -7.9229   4.0447   2.5237 -11.6426  -7.8623 -10.9089
-#>    4.1280  -9.8034   9.1217   5.1864   5.1205  -6.8890  -4.8244  -0.4584
-#>   -0.4377   6.9769   8.7374 -10.3751   9.5877  14.0208   1.6259  -4.7922
-#>    5.0793  -7.7669   5.9387 -13.6609   4.1338   1.2238  10.1254  -4.0397
-#>    4.3210  -7.1856  -3.9623   2.0145  -3.4725 -12.8630   3.5290   2.1779
-#>    0.4560  -4.6627   1.9266   2.4260  -2.8334  -6.1060   1.7071   4.3015
-#>    5.2938  -0.4437  -0.2218   4.4239   6.5981  10.4136   4.6831   3.9865
-#>    0.0827  -6.5887   1.4364  -9.5022 -10.8417  -7.3839  10.5732  -8.4682
-#>   -0.0789  -5.1351  12.8619 -10.3998  11.1001  -0.8602  -1.0153  -5.6380
-#>   -0.8525  -8.5984  -4.2362   2.2819   5.7807   3.9225   5.1029  -3.2233
-#>   -6.7520   2.5125  -7.0239   0.2492 -17.7612   7.3457  -5.1606  21.6853
-#>   -1.2298  -2.5437   6.2544  -6.8056  -5.4504  -6.6582   7.4377   4.6695
-#> 
-#> (16,.,.) = 
-#>  Columns 1 to 8  -1.3204   0.6526  10.5092  -5.6327  11.6096   5.4670   4.2140   0.0346
-#>    5.5739  -3.2615 -10.9422  15.8088  -5.3255  -9.1013  -4.6959  -0.5321
-#>   -1.4791   6.4599  -3.5330  18.5985  -7.7456  -0.7409   4.7767  -1.2939
-#>    2.1681   2.9503   8.4330  -5.2447 -17.1378   7.1880 -10.5660   0.0754
-#>   -7.0732  14.9318   3.6820  -3.9117 -10.7969  -8.7032   2.4902   0.1089
-#>   -6.4173   8.3155  -8.7507  -5.8865 -10.0633   6.2456  -5.8225  -4.2588
-#>   -0.2864   3.1815   8.6395  -0.3138   0.3553  -5.9444  -0.0749  -2.6012
-#>    1.0320  -8.3929   1.9943   1.1173   5.1423  -6.1033   0.8580   4.4847
-#>    4.8267   6.8426   0.9700  -0.1088  -1.3790   4.9978   2.2415  -0.3459
-#>    3.9409   0.6129 -12.8229  13.8631  -6.3605 -12.7444  -0.1254   3.9160
-#>    0.0052  12.2717   2.5303  -3.5619  -2.9978   1.4443   1.1008  -9.0313
-#>    2.6251  -6.9490  14.1019   9.4191 -15.9656  -6.0713  10.5775  -3.8202
-#>   -1.0193  -6.3644   1.8182  11.5701  -0.1826  -6.2748  -2.8998  16.5332
-#>   -9.2020  19.8385  -5.9341  12.3887 -10.9639   9.4341  -1.0446   8.9571
-#>  -14.0495  11.1773   9.4797   2.2405 -11.3045 -14.3312  -0.5920   8.3753
-#>    7.1392   4.5487 -10.9330  -3.0501   2.0338   0.5864  -6.0851   6.9316
-#>    2.6657   0.9035  -5.8575  -3.1847   9.1681  -1.4086   2.6142  13.2898
-#>   -2.2047  10.0648   8.4240   1.1256  -2.6297  -0.3316   4.8572  -6.3603
-#>    9.5895 -10.9603   2.9531   4.1686  -4.6578   1.8648  -3.9940  -0.8700
-#>    7.0689  -1.4863   0.9992   7.1206  -0.5830  -4.1616   6.5459   7.8172
-#>    9.3923  -5.3182  -5.8125   1.4640   1.8993   9.1349   3.1091   0.5354
-#>    5.0575   3.5568   4.8846  -3.3064   4.8651   3.0455  -4.5414   6.5744
-#>  -12.5866   3.3297   1.1292  -7.9103  -4.2261  -5.4005   4.1291   1.8921
-#>   10.1886 -21.3950 -15.0034   5.4307  -1.9230   2.3486 -17.8280  -3.1337
-#>   10.0871  13.7585  -9.1602   2.4039  -4.3966   6.6193  -0.0315  -5.2115
-#>  -10.1880   3.9860   5.6668 -11.3652   2.0414  -4.1076  -1.0125   8.7918
-#>    0.1394   5.1834   9.7742 -11.5945   1.0495  -1.1102   5.8734  -1.9149
-#>  -17.5434  -6.5157   1.6953   4.7723  -0.9014   0.5178   6.9369   2.9006
-#>   -0.3901   9.4402 -11.9338  12.7419  -8.8317  -5.6769   6.9202   0.9321
-#>   -7.3379  -7.1466  -0.9682   0.3887   4.4588   8.6968   2.9510   1.6989
-#>   -0.7674  -1.7466   3.9111   2.1979 -10.2515  -4.2773   9.5866   1.0826
-#>   -1.5783   7.1853  -6.7278  -9.1641 -15.5977 -10.6462   3.5289   5.9880
-#>   -4.7147   7.8592  -9.0302  -5.2358  -3.3409  -1.7656  -4.6556  -2.6884
-#> 
-#> Columns 9 to 16   1.0254  10.3361  11.6621   5.4113  -5.4113   1.7748  -6.8710  -9.8104
-#>    0.4625   4.9036   5.4948  -4.6719  -3.3553  -0.6379   0.6686  12.4877
-#>   -7.4537  -5.7242  -1.2048  -7.6518 -10.1702  -7.5392   1.4355  12.3038
-#>   -0.9118   1.9426 -22.7376  -2.0421  -3.2993   6.2670  -8.1239   7.8196
-#>    3.0716  -2.4960  -0.7130 -13.1407   9.6765  10.1313  14.9288  -0.7429
-#>    7.0794  -9.5038   2.5003  -5.2492  -1.7645  -1.3024  -3.8371   8.9790
-#>   -5.1030  -6.9457  -0.9513   0.6588  -1.2483  -1.0004   2.7857  -3.1608
-#>   -2.9562  -6.1328   3.7558   8.8549 -16.1291  -5.9037  -3.9408   6.5159
-#>   -1.6588   3.0108 -12.8467  -1.6978 -15.3076   9.3569   4.3810  -2.0955
-#>   -4.3906   0.5790   2.3439 -12.0252  -5.3709   9.9952  10.5764  10.3523
-#>    6.5793   1.0548  11.9372  -2.0547   8.4397  -1.5919   3.8448   2.3324
-#>   -1.5664   9.7998  22.0340   8.1030 -11.4524 -15.2741   0.8491  -3.5748
-#>  -11.9949  -1.1028  -1.6206  -2.0485  -9.2684  -1.9960   6.5735  15.1946
-#>    0.5942  -1.1728  -5.7841   3.6862  -7.8729   5.5735  -7.0033  -1.6853
-#>   -1.7875  -9.1322  -7.6958 -18.2343   4.8327   9.3955   3.9783  -3.5601
-#>  -10.6473  -8.1347 -16.5191   3.3761  -4.2651  15.5675   4.2293   0.2734
-#>    7.0049  13.8239  -7.1527   1.1260  -1.0699  -1.6748  -1.0046  -0.9413
-#>    7.5757   2.4887  13.2990  -6.5295   1.1039   4.3652  -0.2265   7.1046
-#>   -5.9953 -13.3316  -5.4457  -8.3539   4.9143  -7.1104  -1.9930   8.8147
-#>    8.4529   7.0763   6.0135   4.3569  11.1647   2.8066 -11.1874 -16.3402
-#>    2.9189  -8.3507   4.3038   3.2874  -5.8044  -2.3187 -17.9639  -7.2240
-#>    3.0913   7.9585  -5.1885   3.2803  -2.8590   9.8295  -2.5579 -16.5575
-#>    2.7880   2.8779   0.6772  -9.4118   8.2246  -8.6007  -2.6125  -6.7030
-#>  -10.5021  -5.8253   2.1096  -1.5385   2.2725  -4.3075   1.2849   1.6933
-#>   13.3302 -10.1520  -1.0219  -0.6172   9.4727   3.5504  -4.7962  -8.7593
-#>    3.4697  -6.5768  -6.3320  -1.2067   4.5735   2.2156   4.8014  -4.8593
-#>    1.3233   2.2615  -7.9351  -8.0059  -1.7954  -6.8646  -4.6570  -2.4058
-#>    0.3469   0.1460  17.7335   4.3834   9.7596  -7.1045   2.4103  -7.7036
-#>    1.1120  -6.3162  -1.7628  -1.7410  -3.0155   7.6801   0.8479  -1.0478
-#>    4.1956  10.0632   1.7495  -5.7685   3.5873  -0.3796   8.8077  -4.3659
-#>   -5.1423   1.6058   2.3845  -8.5698 -10.1795  -4.4278   7.6315   3.5999
-#>  -12.7859  -1.8685 -14.3316   3.0497   7.8189  -6.5503   9.9442   5.3623
-#>   -4.5302  -7.2908  -7.1151   4.5172  -1.1260   6.3266   7.6171  -7.9990
-#> 
-#> Columns 17 to 24   2.8025   2.8271   2.6480   3.4959   5.0904   2.8226  -0.0594  13.4359
-#>  -14.2048  -9.2178   2.9372  -0.3767  -5.0131  -6.1643   1.8080  -4.0727
-#>    1.7859  -2.5607  -1.9930   8.0479  -0.7414   3.8054   5.1039  -2.8461
-#>   10.4622  -6.9029 -11.8365  10.8772  -2.8240  -2.7649   7.2829  -5.8833
-#>  -14.1812 -10.3616  -4.2358   3.8300   1.7081   3.1369  -2.0032  -6.1442
-#>   -0.5696   2.5405 -15.5316  -5.1835  -6.0208   2.9822  -0.5974  20.8739
-#>   -5.6804  -8.5035  -1.6632  -0.6585  -2.4957   3.7135  14.2337  -4.5331
-#>    1.7501   0.6361  -5.0097  -6.8476  -5.1944  12.4870  -3.7354   0.9425
-#>    5.1651 -11.6754   1.1540   1.5580   7.1286   4.3611  -2.7560 -18.3934
-#>  -17.0134 -11.4174   0.8793  -0.6620   4.0115   1.4018  -0.8441   3.0652
-#>  -10.4883  -1.5086   8.5144   3.9381  -1.8614  13.6181  -9.9868   7.9517
-#>  -10.4059  22.3095   2.7550  -2.2265   1.2176   4.2821   5.2935  -1.3582
-#>    4.4460  -3.1476  -7.2154  -9.6695   3.5336  -1.6278  -9.2159 -10.0800
-#>    8.1050   2.0959  -3.9473  -0.7106  -2.8918   5.4461  -3.6736  -1.2730
-#>   -6.1247  -1.4076  -4.4631   1.3634   5.7326  -2.7705 -12.1562  -6.0150
-#>    6.4228  -7.8171   5.9113   3.0675   2.0606  -1.1603   4.6238  -8.2037
-#>   -1.6866  -5.6572   0.2234   4.5182   3.1158 -13.9857   6.7237   4.4937
-#>    0.1855  -2.4545  -7.3087   1.1111  -3.9793   6.8628   6.6371  11.8543
-#>   -6.6008   0.6008   4.6997  -1.5596   0.2766  -3.9507  -0.4521   3.7588
-#>   -6.1623   4.5682   6.6610   0.1241  10.9521   1.7854   2.6624  -2.3684
-#>    8.5402  15.9268   5.0533  -1.7578   0.7582   5.4772  -1.6012 -10.3979
-#>   11.4607   1.5538  -7.0335   1.5337   5.4602   3.1144  -6.8919 -15.1536
-#>   -0.9874  11.4116  -1.7802 -16.8985  -2.0688 -12.0846  -7.8504   7.7643
-#>   -4.8618  -0.8623   6.5522  -3.0675   0.4466  -6.2224  -1.0398   3.3669
-#>   -6.9103  -0.4912  13.6113   8.4300   5.0445  -1.1812  12.4340   5.0129
-#>    2.9676  -3.5193  -6.5441  -1.1419  -0.4146  -2.7309  -3.9625   0.2427
-#>   -0.5066  -4.1027   2.8202   1.5772  12.5458  -7.5701  -1.1209  -7.5376
-#>   -2.0766   4.6149  10.3450   6.7406  -4.9339   5.7498  17.3834   2.1269
-#>   -9.6193  -1.3648  16.9052   6.0183   4.4245  -0.6685   2.6400  -7.4417
-#>    4.9282   9.5929  -7.0839  -1.3978  -6.6698  -8.3408   0.2958   5.9292
-#>   -6.4539   0.5636  -5.9307  -1.6424   5.9629   3.1537  -4.6600   5.7245
-#>   -4.9213   0.7057   9.0855   3.7485   3.8237 -11.5914  -0.5496  -9.8527
-#>    2.9095   5.2565  -2.3803   9.6923  -1.2148  -3.3360  -1.4157 -13.0961
-#> 
-#> Columns 25 to 32  17.6035  -1.0098   0.4745   5.2585  -8.8529   8.4093  -2.0189 -14.7016
-#>   -4.3281 -14.1630   5.7770   3.3682  -7.6735   3.5629   0.3521  13.0974
-#>   -9.4802  -2.8482  -2.6903  -2.4335  15.1989  -3.3284 -11.1240  16.1645
-#>   -6.6805 -17.1317   0.4984   7.8714  -7.6200  -5.2047   4.7747   4.5009
-#>   -5.8401  -6.7577  -3.1531  -3.6990  10.3882  -6.1229   0.1506  14.0069
-#>   -3.9239  -1.7848  15.8552 -18.3749   4.1166   7.0067  -9.0793   7.1111
-#>    3.9034  -3.2623   6.3512   6.5142  14.1725  -5.2678  -7.8942  10.7899
-#>    4.7258   0.0566   4.4063  -9.2343  -4.1122  10.0212  -5.9066  -8.8272
-#>    7.2739  -0.5225  -9.5228  12.9427   8.1354   1.2012   1.9712  12.4760
-#>   -7.0641  -7.0924  -7.2254   6.4615  -2.3076  -5.9760   7.1168  12.0361
-#>  -15.8013   4.0385  -0.1108   4.2316  10.0294 -14.1306   3.5020   7.7976
-#>   -3.4861   0.6688   1.7526 -18.0820   5.2449 -15.6297 -13.8289   3.2798
-#>    5.3501   8.1111   3.2815   4.4101   8.1744  -0.5013  -2.3403   2.6503
-#>    1.1117   9.9882  11.2948  10.9378   7.5708 -11.2358   6.5127   2.0092
-#>   -9.1659  -7.0353   6.4879   2.5423  13.5808   1.9107   8.5529   9.1137
-#>    5.4336 -12.5380 -12.8151  16.0256 -12.6789  -4.6559  13.5395   1.8768
-#>    9.7476  -7.3086  -6.5087  12.6011   1.0711  -4.2273  -2.4932  -3.8373
-#>    7.3071  -3.0686   4.7835 -14.8320   1.5951 -10.5570 -17.9316   8.8383
-#>  -12.3674  -5.0916   6.1914   3.6960   2.2219   6.4669   6.3904  11.3875
-#>   -0.5713   3.2452  -1.6545   4.4855   6.2768  -1.4021  -2.1622  -7.2800
-#>    6.0130  -2.4392   2.8221   5.5003   2.2283  -5.5170   2.9483  -8.5641
-#>    3.1298   7.8611  -2.8450  13.6667  -4.9227   0.3805   7.6739  -5.1870
-#>    2.8829  -0.7680  12.2108   3.1766  -4.0264   7.3800   3.9469 -11.9077
-#>    8.4839  -6.2682  -5.1891  10.8249  -7.2002  -7.9541   3.9664  -1.7241
-#>    0.0130  -6.5531  -3.9633 -11.3358  -2.3152  -5.5856  -5.8848  14.1289
-#>    0.2054  -2.6318   3.5308   4.7872   6.3370  13.6606  -7.1487  -0.2621
-#>    5.9394   4.4685  -0.4925   6.0674   1.9031   6.0924   3.2171   2.2223
-#>  -12.7919   0.8670   0.7464   9.2208   5.1413  -3.4920  -0.8628  -1.1500
-#>  -10.9959   2.0829  -6.2836  -3.3308  -1.2404   0.3261   3.4622   6.7565
-#>   -1.7894   7.0095  -4.7989   5.9668  14.7276  -0.0832  -1.1292  -2.3796
-#>   -3.8813  -4.9567   4.5870 -20.2590  -3.6941  11.4605   5.8353   5.5101
-#>  -21.2222  -2.7852  -0.2706  -3.5601 -10.8011  -0.5643   7.5233   2.2526
-#>   -2.8226  -1.7783  -0.7867  -6.6398   6.8352   1.3618  -7.3804  -0.3211
-#> 
-#> Columns 33 to 40   4.6767  -5.6769  -8.2998   1.8646 -11.7082  -0.1739   0.7321   0.9858
-#>    9.6309  -4.0555  -4.5445  -6.0392  -1.8424  -4.5782  -2.2394   2.7480
-#>   19.3465   1.4504  -0.5011   0.6825   8.3566  -6.0887 -12.4131   1.4882
-#>   15.4626  -0.9147  -2.8020  10.4818   0.1867  -2.4863  -4.4813   8.9666
-#>   -2.2579  -4.4858  -2.6382  -8.7673  -2.4578   8.6333  -0.1472   0.8210
-#>   -3.8017  -4.5528   2.1585   0.3108   4.3367   4.4167  -4.1404  -7.1562
-#>    6.1018  -1.3892   2.6067  11.0037  -0.8936  -5.2789  -3.8202  -8.6666
-#>    9.7097   3.3408   0.3116   3.6396   0.1723  -5.4994  -3.0563  -3.6130
-#>    4.9959   1.7683  -1.3992  -1.3118  -4.7137  -3.1537   2.8307   8.6702
-#>    5.5646  -1.9904  -0.2973 -14.9815   3.3672   0.3944   0.3536   3.0087
-#>  -18.5556   0.8547   3.2588  -8.9740  -5.4412  -7.7150  -7.0575   5.6699
-#>  -12.5947  -4.6393  -2.0987 -10.0344   2.7829   5.2220  -0.2026  -6.7648
-#>   13.9009  10.4672   0.7112  -1.6072  -6.0489  -4.2060  -7.4302  11.4814
-#>    2.8680   2.9533   3.4821   3.1926   2.0164  -6.2120  -8.4158   2.3738
-#>    2.9694  -8.4894  -3.6779  -3.0176  -9.0022   7.1106   1.5780   7.5124
-#>    7.1181   7.5552  -9.9423   5.9438   4.1525  -1.6902  -6.9626   0.7560
-#>    3.2141  -9.8982  -6.5783  -6.5020   5.5751  -0.7571   1.3244  -4.2656
-#>   -1.6315  -6.8276  10.5276   2.9332   0.0299  -0.9580   0.6356  -1.0171
-#>    6.1094  11.5435  -2.5057   1.0803  -5.3372 -16.7403  -5.9370   0.2371
-#>  -10.7809  -6.6780 -11.6158  -7.0993   1.7148   0.5335  -0.7110  -5.7289
-#>    4.4232   8.1946  -2.5558   8.8174   9.0208  -4.4722  -3.7291 -11.3726
-#>   -5.6536   1.3000  -6.5301   3.3230  -7.4231   4.5805   7.0784  11.0849
-#>   -1.1154   0.2432  -2.7664   1.8290  12.1905   4.9875   2.9038 -13.9286
-#>   -4.1042   6.4505  -2.1113   6.7506   6.6225   9.8238  11.0429  -0.3648
-#>  -10.7701  -6.5126   3.9260   0.4233  -5.3073  -2.3215   4.7964  -4.2986
-#>   10.9507   4.4344 -11.7721   3.5976 -10.9204   3.9153  -7.7275  -1.1018
-#>   -1.1800  -1.4308  -7.2643  -2.7054  -0.8770   3.8441  -2.1754  -8.6870
-#>  -10.8437  -0.3657  -7.6537   7.2054   7.0568   0.1700 -12.9408  -9.0044
-#>   -0.5383  -5.7443  -2.7455  -9.1649  -1.6883  -5.2865  -0.0477   2.8188
-#>   -3.5584   3.7999  -4.8056   5.4437   8.3543  -2.5686 -14.0578   2.3122
-#>    3.3393  -6.5970  -3.9285  -7.5108  -5.0091   1.1976   3.0261   1.5317
-#>    6.3473  -0.3590   3.6051  -7.7109   6.6993   0.2420   0.8268   9.6148
-#>    2.3679  -4.2571  -7.5652  -2.6319  -0.2594   3.5161  -3.8648   1.3730
-#> 
-#> Columns 41 to 48  -8.9004  -2.4431  10.1769 -11.5539   0.9727   1.1449  -4.1664  -5.9406
-#>    0.7273  -1.5841  -4.8308   0.8003   4.3019  -3.4016   8.7224  -4.3127
-#>    9.4124  -5.4304  -7.3879  -1.5780  -0.8244  -9.7549   7.1619  -1.2881
-#>    4.4182  -9.6812   2.0931  -7.2892  -9.6957  -6.2069  13.2915 -14.1783
-#>   12.7210  16.2381   1.1936  -1.2641   2.7008   7.9641  -5.9156   2.2741
-#>   -0.3840  -4.3539   0.3929   4.1084  13.3946  -1.5061 -16.0275  14.1161
-#>    4.8527   4.2228  -5.5224 -10.0346   0.8754 -11.9943   3.7959  -1.2697
-#>   -3.2294  -7.6962  -0.5004   0.1692  -2.6538  -2.7955   4.5124   7.7744
-#>   -1.2764  -5.2906  -6.6582   1.0561   1.0314   8.8017  -1.1811  -5.1169
-#>   -0.8060  11.0308  -2.5343   4.9971  -3.6082   3.8307   1.2113  -1.5236
-#>    8.1704   4.0161  -3.9638  -0.7323  -7.8175   3.4363  -1.1549   2.2999
-#>   18.6318  -2.9746  -5.0073  -6.5272  -2.8055  -3.2255  -4.9634   6.8455
-#>   -2.7224  -6.3045   1.2384  10.8459  -4.7152   2.5874  11.1960   3.3570
-#>   -0.6983 -11.5129  -7.0210   0.7556  -6.1628  -4.8726   9.0647  -7.1177
-#>    6.3566   5.4712  -5.9771   5.7913  -4.8866   0.0971   5.9906  -0.9292
-#>    0.4678  -0.3558   2.8748   7.5943  -7.6338  -2.0429   9.8464  -6.7635
-#>  -14.0334   6.1741   0.1270  -3.7588  -5.0111   2.7787  -5.4135   5.7406
-#>   12.3765   2.0786   6.4566   3.8771   3.3312 -17.2266  -4.5957  -4.6832
-#>   -4.6396  -8.2818   1.9074 -12.3811 -14.7881   0.2181  13.7469   6.0143
-#>   -2.1437   8.6999  -7.6948  -5.9722   1.3065  -2.1438   1.2666  -2.7880
-#>    8.8771 -19.2104 -12.9171  -1.5808   9.4828  -5.6920   7.1086   1.3148
-#>   -3.7578  -9.0183   0.3922   3.3510  -2.0988  -1.6591   7.8891 -20.0991
-#>   -2.7687  -1.7540   1.9076  -5.1849   0.4355   5.3135  -2.5350  -6.0803
-#>    0.0246   7.8145   5.0346  -6.8477  10.7157  -0.9533  -9.1985  -8.1825
-#>   -1.9543  -8.1319  -8.0247 -20.4680  13.8195 -11.3091   1.6414   6.3563
-#>   -1.4511  11.1988   2.4813  -9.8037   3.3185   6.6449   6.7901  -5.9648
-#>   -7.7734  -3.5544  -5.1077 -13.9153   1.2176   7.7291   1.5552   1.4795
-#>    8.3889   9.3231  -4.9893   1.0248   3.7695  -7.8579  10.9412   6.6266
-#>   -5.4727  -5.4548  -6.1984  -7.9609   4.9564  -3.3660  19.5644   1.3405
-#>    0.5967  -2.9063  10.0813   2.3446  -2.4747   6.2081  -2.8591   8.8000
-#>   -7.9574  -9.9399   4.6892 -10.6437  -9.4975   6.5943   8.4337   4.7842
-#>   -0.9742   9.0712 -15.3261   2.3812  -8.8730  17.1299   4.1943  13.8919
-#>   10.7144  -5.8989   3.4063  -3.5016   9.2069   1.1498   4.2816  -0.5729
-#> 
-#> (17,.,.) = 
-#>  Columns 1 to 8  -5.1619  -3.0181   0.0799   8.0886  -8.0772  -1.7640  -2.0343  -4.7349
-#>   -9.5859  -4.2881  -1.5816  -0.3175   5.1352  -4.7297   4.2629 -17.4556
-#>    0.1543  -4.0294   1.6310  -3.1471  -1.8329  -1.6866   5.3869  -2.4516
-#>   -0.6062   7.6543 -11.9250  -3.3041   6.7607 -15.7408  10.0127  -9.7765
-#>   -8.9783   3.2641   6.1305  -6.3902 -11.9548   6.8638  -3.3556   9.9042
-#>    5.1950  -8.6193  -2.3250   1.6673  -5.1070   4.9004   1.0199   1.3473
-#>   -2.8416  -3.7654   0.7706   1.6454   7.8840   0.3698  -6.4068  -1.4886
-#>   -5.5793  -6.7922  -8.8654  23.0614  10.3158  -4.6445   0.7068  -2.0719
-#>  -10.3018  -3.6898   6.1985  -1.7263  -4.9523  -4.9590   4.9585  -3.1475
-#>  -15.3576  -6.5329   6.7014  -3.4137  -5.3419  -1.9853   5.9655  -9.1938
-#>   -9.9914  10.6697   9.6767  -1.4626   8.7081  10.7835  -8.2493   2.8484
-#>    4.6417  -4.1499   3.1414   5.5009   6.9503  11.8197  -5.4029   4.0689
-#>    5.8440  -9.3735   5.0352   5.2992  -9.7783 -13.5972   0.1664   2.9280
-#>   11.3548   6.7934  -0.6160  -0.2442  -1.3540 -15.6663   9.3547  -3.9605
-#>   -0.2968  -2.4413   7.3948   3.9985 -12.6692  -1.9594  -0.7751   3.0247
-#>    9.6975   0.2138   3.5373  -0.5736  -1.4443  -8.1797  13.9032  -4.1398
-#>    1.3615  -8.6565   4.6917   1.6749   7.3299   4.0300   1.2357   9.4734
-#>   -4.4466   5.9717   3.1726   4.6943  -6.6801   8.4973   4.4991  -1.2390
-#>   -2.3091  -0.4119  15.5392   3.3148   7.9750  -1.5371  -7.4485   3.0937
-#>    3.5304  10.8551  -6.4733 -10.7545  12.2401   2.2745  -1.5117  -1.7315
-#>   -7.3637  10.8645 -15.1257   9.1840  20.0610  -3.7527   0.8773 -10.2257
-#>    1.5459  10.0743  -5.4409   2.1041  -3.2769 -16.7276   4.5645 -10.5682
-#>   -2.6268   4.3734   0.6472   1.6766   0.9398  -2.4559  -1.0414  10.8499
-#>  -10.7332  -0.2520  -1.4968  -9.6132   3.9767  -2.8039  -5.3973  -5.8692
-#>   14.2206   3.5955   9.6403  -6.4169   9.3426   8.1698   2.8643  -2.2282
-#>   -6.4186  -4.2460  -2.9623   9.3977   3.6590 -11.1579  -3.4319   6.0393
-#>    1.2881  -1.7220  -1.5405  -2.2051  -1.8137   3.1895   2.4844   2.4990
-#>    8.5819  -4.9393   6.3451  -0.7898  10.0815   7.1112  -4.4164   7.8334
-#>    4.4946   4.7311   4.1540   1.7233   4.4152  -6.0693  11.5300 -11.6184
-#>    0.9200  -7.2788   8.5793  -9.7418  -3.6820  11.7213  -5.6273  14.2378
-#>   -2.9730  -8.6553   5.6509   4.2432  -3.6835   8.6053   6.7605   8.3489
-#>    6.7401 -10.0715  -0.1860 -10.4713  -3.0209  -3.4074  -5.3498   0.4218
-#>    8.3541   3.9618   5.6562   1.1163 -10.3233  -7.6167   2.0936  -3.4023
-#> 
-#> Columns 9 to 16   2.6554   7.9167  11.0304  -4.0841   8.3659 -15.0617   1.0746   0.0951
-#>   10.2653  -7.1887  -4.6526  -3.0189  -2.7806   3.2218   1.7490   0.8503
-#>   -2.5758 -11.4871  -2.7451   2.1423  -5.5929  -9.2981  -1.2714   5.3440
-#>   15.2209 -20.7210   7.8777  -4.8968  -3.0089  -8.5371   6.3820   5.9276
-#>   -4.4465   2.5734  -0.8134   2.3880  -4.4097   3.2617   5.6593  -8.9384
-#>  -10.4354  -1.8856  14.2443   0.8415   0.0422   6.6552  -0.5176  -6.7742
-#>  -10.1508   6.2166   2.2943 -11.7513   1.5491   8.8356   7.0511   5.0354
-#>    2.0353 -10.3984   6.9557 -10.8480  18.4747  -3.5018   3.2486  -3.2049
-#>    1.1831  -2.6879  -5.1247  10.9413 -21.6096  -4.6595  -1.9645   6.3144
-#>    4.4255  -5.3163  -0.3537  -1.4157  -8.4213   2.9359   3.8514  -9.3721
-#>   -4.2098   5.0282 -18.7914  -4.1104   8.0676  -1.0900   7.6226  -6.8346
-#>   -4.2074 -13.9924   1.3014  -3.5194   8.7787   6.4364   7.0054  -7.3548
-#>   -7.7226  -9.0022  -1.0856   5.7499  -1.0572   0.2483  -8.3625   9.5794
-#>   -1.4326 -10.7731   3.7636  13.0417 -10.8303   9.9199  -4.1907  -3.1137
-#>   -7.6242   1.8568  -9.7423  -1.0967  -1.8745   8.7429  -6.1002  -4.2548
-#>    5.0080 -12.6321  -5.0495  10.8333 -13.4834   1.3995  -9.5204   5.1213
-#>    2.6434   4.7095   0.6623   3.0407 -15.8481  -1.2436   1.1847  -6.4874
-#>    7.3450   6.5187   9.4047  -3.9367   3.4945  -6.0047  -0.9545  -0.7230
-#>   -8.0573 -17.1685  -9.0478  -9.2035  -0.3623  -1.2031   5.7464   6.7034
-#>   -6.2174   2.1358  -1.1809  -0.1612   0.1096  11.0414  -3.0298  -0.0556
-#>    8.8962 -16.2860   3.3317   1.7542   4.2848  -3.9125  -1.7668   1.5290
-#>   -0.6201   5.7843   0.1410   1.3853  -9.2887   3.0838  -6.3960   2.8845
-#>   -4.3300  -8.9639   2.3970  -1.8609   5.3062   2.0718   9.1274  -8.2183
-#>   -2.6742   9.8989   3.7277  -1.4399 -16.5996   9.0688  12.1121   6.0464
-#>    1.4948   6.4696 -10.6562  12.1046  -9.5973  10.5317   2.4813  -1.4759
-#>   -7.1796 -11.7486  -2.1333  -1.0915   6.6922  -7.2241   2.8495  -3.5509
-#>   -1.6976   4.0630  -2.1573  -6.1899  10.4746   4.8966   3.6920   1.2625
-#>   -5.1499  10.9102 -10.2700  11.9868   2.2540  18.0478 -14.5344  -3.5789
-#>    6.3958 -15.7501  -3.0503  10.2427   2.8136   3.6015  -5.5024  -8.6063
-#>   -4.2474   6.5401   6.0854   3.6492  -5.1034 -14.2622  -2.2923   1.7127
-#>    6.0082   0.6665   6.6702   1.6794   2.8281  -5.3877   1.5180   0.3557
-#>  -10.4044  -5.7640  -3.0339   5.7415  12.9303   6.6334  -7.6317  -9.7096
-#>  -11.3651 -15.4415 -11.4166  10.0479   1.4643  -3.6533  -3.1368  -0.9498
-#> 
-#> Columns 17 to 24   4.1676   7.5346 -10.3955  -4.6839   0.8027   2.8550   0.9751  -5.7473
-#>   -6.4199  -7.4061   3.9227  -1.3790   5.2387   9.0546   3.6188 -12.4515
-#>   -3.0704  -2.0439   9.9159   4.2188   5.0221   9.8404   5.9538  -6.1228
-#>   -4.2774  -4.6437  -1.9673   0.0261   4.6949  -3.4775  -3.4377  -1.4731
-#>   -0.7676   1.8158  -6.7767   1.5923  -8.7248  -6.2934  -4.0206  -8.6869
-#>    5.2254   0.5085  -4.9254  11.4029   3.0626   0.1749 -13.6412   2.6948
-#>   -4.2016   2.6745   1.2933 -11.6407   9.3893  -8.2027  -0.5136  -2.2459
-#>   -2.9029  -1.7087   6.3701   0.0140   2.1192   9.2754   6.8887   8.9853
-#>   -1.8979  -2.3461   1.6134   6.0133  -6.7247  -0.7846  -8.3433 -10.7952
-#>    5.7948  -0.8582  -1.8358  -7.5462   0.4105   4.3216  -1.8319 -11.7112
-#>    0.0190   3.6595   0.9984   2.8579 -10.6178  -6.5399 -16.9250   2.6083
-#>  -14.4299  13.9719   3.9606  -7.8635   6.7253   7.4283  -2.4251   9.9554
-#>    4.1462  -0.4103   8.6328 -13.0772  -3.6970  -0.9119   8.4376  -2.4958
-#>   10.0345   1.5690   1.6743  -1.7376 -13.0662  -8.2072   5.0286  -3.3454
-#>   -2.9310   1.0582  -0.1027  11.4803   2.2111 -19.6883  -3.5387 -17.9479
-#>    0.9670   0.4862   1.6640  -4.9562  -4.3110  -1.3840  10.0940  -9.0717
-#>   -4.9501   9.6821   5.8758  -7.8606  -7.7860  -1.8677   7.3119   3.8837
-#>    2.2149  -6.3069  -6.0500   2.8637   0.0099   2.4581   3.6942   2.2841
-#>  -11.5380  -3.9158  -1.4166  -2.4726  11.1947   6.1733   1.3971  -4.6914
-#>   -0.1422   1.8758   5.2678  -6.8755  -2.8271  -4.3313  -3.3322   0.3057
-#>    0.0091  -2.7056  11.6045  -1.7608  -1.0983  -0.1272  16.8650   4.9916
-#>   10.2011   2.1264  -2.9585  -1.9620  -9.1845  -8.9957  -7.8044  -9.0668
-#>   -6.6551  10.0978   2.3357  -5.9963   3.1873  -2.3427   5.4463  -5.4590
-#>    4.8718   7.0416 -10.5680 -16.6755  -2.4014   2.2601  -2.7843  -4.7637
-#>  -14.4118  -6.3285 -14.8899  14.0403  11.6604   6.6104   1.4341  -2.7390
-#>   -5.8211   9.2186  -4.2027  -1.8181   0.1697  -5.8698   3.5700   2.3641
-#>   -7.4844   6.4635   2.6496  -1.5543   4.3140  -8.8012  -2.4610   1.3795
-#>   -0.4812  13.2961   7.8098   4.3972  -3.9960  -9.7672  -4.6588  -7.2766
-#>   -6.0338  -7.8509   2.8048   7.2771   2.8969  -1.5515   6.3580  -4.0975
-#>    8.6986  14.3351  -9.7717  -4.0023 -13.9180  -0.9266  -3.7760   5.1942
-#>   -6.2422   2.0891  -3.1558   1.5595   2.0403   2.2207  -3.0024  -3.1902
-#>   -1.3010  -5.1145   7.8635  -2.7449  -3.6258  -4.4641  -4.6369  -3.9438
-#>    6.8597   5.6082  -5.5376   1.7434 -13.2703  -3.8859   0.5757  -2.0066
-#> 
-#> Columns 25 to 32   1.0254   7.0910  14.8846  -3.1377  -1.7441  -0.0101   8.2309  13.6490
-#>    7.2203  -9.8544   4.2957   0.8403  -5.4216   4.2901   0.5307 -11.0900
-#>   -3.2518  -5.0485  12.4350 -10.1260   3.0470   5.9382  -5.1991  -3.0149
-#>    0.9825 -17.6493  -3.7067   5.6643  -9.3720   9.9860  -2.0499 -10.5845
-#>   -5.4938  -0.9989  -7.3844   1.0720  -2.2592   6.6725 -11.4328   2.6314
-#>   -3.4160  -1.9221   6.9001  -9.0317  -1.7597   0.1183 -10.0590  -6.1533
-#>   -1.4411   2.9178   6.7159  -5.2199   3.7244   6.8215  -3.6982   7.9301
-#>   -6.6415   3.1656  13.6420 -11.6915  -3.5711   5.6936  -2.9921   3.3072
-#>   -9.9602   2.2012   0.1391   5.7160   4.5033   4.3581  -0.8962   1.0365
-#>   -2.7040 -12.5892  -0.7467   6.4007   3.1365   0.2761   5.1091  -4.8358
-#>   -6.5507  13.0072  -8.1460  -0.7292   9.5301  -1.2198   5.1274  13.7180
-#>   -1.8198   2.6547  17.5326 -10.1833  -5.6534   0.1305  -8.4429   6.1219
-#>   -3.2205   8.2305  -6.3872   8.7776   8.8897   2.1674  -2.3012   3.0600
-#>   -2.2893   4.2262   4.3550  -0.6973   3.3189   5.1070  -4.3752   0.7677
-#>   -1.8420   9.5921  -1.2874   5.4161   8.3827   4.1311   7.7046  -4.6980
-#>   -2.2840  -7.7288 -11.9842   0.1884   1.9551   0.8840   0.4875  -0.9882
-#>    5.5980  -6.7492  -0.0901  -2.6437  -0.4772  10.5760  -1.4162   2.2015
-#>    1.7957  -4.1445   0.9496 -10.8582  -6.8151   4.5452  -3.4300   6.9712
-#>  -12.0713  -3.2485  -0.1581   2.2096   9.8587  11.0674  13.6746  -0.3345
-#>   -0.0934   4.7471   6.2557  10.1419  -4.7220  -6.5351   3.8809  -4.5939
-#>   -2.1335   4.1217  11.6893   5.7794 -12.5167   3.2000   3.4059  -2.0594
-#>    4.6066  10.4228  -5.0172   5.2087   2.2536  -7.1337   3.2033  -2.7008
-#>    7.1901  -6.3060   3.5671  -9.6330  -5.9182  10.4501  -0.6683  -0.4104
-#>    3.0606   8.3018  -4.4403  10.3565  -6.3468  -4.1858   9.2453  12.9026
-#>   -1.5092 -10.2147  -2.1994  -0.7522   6.9462 -12.3251   4.1127  -4.6304
-#>   -3.3659  -2.5790   1.9965  -0.7867   7.8040  11.6107  10.2200   0.8229
-#>    3.3383   7.1378  10.7486  -3.0431   0.0634   2.2515 -10.1307  -7.2413
-#>   -2.3418  12.8547  -2.7102  -2.8552   7.1522  -3.7697   3.2059  -6.8201
-#>   -0.0820 -11.7380  13.9148  -0.6083   8.5250  -1.8080   2.8137 -12.9253
-#>   -5.3931  -4.7689  -5.3929  -7.5490   9.0840   3.3297   3.7127  10.0785
-#>   -0.8460  -2.7423   6.4142  -7.6215  -7.6490  -2.2948  -0.1514   5.8941
-#>    3.4158  -9.1364   1.2171   3.4272  -1.3323  -1.6521  -5.7197  -1.3973
-#>    1.8469   0.4327  -5.5370   1.6241  10.8228   0.9583 -11.2766   2.1041
-#> 
-#> Columns 33 to 40 -14.3575  -6.2215  -2.6464   3.5467   3.0407  -4.6165   3.2398   5.9551
-#>   -0.0480 -11.2296   9.0606  -8.4787   3.7471   9.2177  -4.2168  -8.3942
-#>   -7.6122  -3.3571   9.6595   5.2166   9.2910   1.9303   0.2418   2.0756
-#>   -5.9336  -9.8716  16.4470  -1.7783  -4.9414   9.2061  -4.3183   3.6377
-#>    4.0196  -1.7859  -6.7206  -3.6580  -2.8390  -2.7614   2.3717  -4.0672
-#>    8.6838  -8.0013  -4.4193 -15.6865   4.0136   5.3373   4.8009 -15.2233
-#>   -4.8134  -6.2068  -1.7466   5.2689   2.3783  -4.6048 -13.0424  -1.3871
-#>    2.1823  -1.4709   4.6472  -4.9556   3.6358  -1.6127  -4.5299  -0.7508
-#>   -7.7999   8.5013  -0.5993   8.4254  -4.4798 -12.0772  13.9128   7.1798
-#>   -2.4574  -7.7704  -6.0134  -1.1115  -2.3742   5.8104  -0.0299  -7.7799
-#>    4.7580   9.0302  -0.8664   2.2599  -4.7359   6.7417  -5.0842  -6.4733
-#>   14.2720   0.5971 -11.0018  -0.0784  12.7015  -7.7272  -1.3485   8.8818
-#>   10.4907   0.4184  -0.7159  -1.4922   1.9790  -9.6921   3.3352  -4.8613
-#>   -2.7084   3.6704   0.9111   5.4527  -7.8099  -1.5995   4.5562  -1.6489
-#>   -2.1861  -3.2265   4.8851   2.3370 -12.4613   3.2311  15.8262  -3.8380
-#>   -1.0990   4.2719   3.0757  15.0411  -5.6176  -3.2691  -6.7996   1.1006
-#>   -8.8203  -4.1936  -1.6645  -2.6186  10.6776   9.8482   6.9322 -12.3674
-#>    5.5215  -8.9044  -3.4960  -6.0829  -1.6044   2.5236   0.0788  -5.7065
-#>   -1.2669   0.4632  13.2928  11.7140  -7.4080  -6.8600  -8.0006  -4.2027
-#>   -4.0752  -4.7452   0.7452   6.4075  -1.4182  10.2600   3.5309  -8.4121
-#>   -4.2307   1.1806   9.6492  15.4468   5.8637   6.8539  -8.5205   1.0018
-#>   -3.8457  -0.7070   6.1170   2.9409  -7.0939  -3.4753   1.9503   6.2271
-#>   -5.9679  -1.6525   0.3704  -5.1216  -8.2476   5.7906  -7.2179   1.3314
-#>  -11.1109  -3.0333 -14.4224   1.7890  -2.4105  -0.1108   0.3998  -5.0452
-#>   -7.0083   4.6308  -0.7722  11.1411   3.5653  -0.4934   6.9186  -8.6089
-#>    2.0942  -7.0346   7.1834  -1.7625  -9.4952  -0.1650  -0.6904  -3.1321
-#>    0.0324   6.7720   4.1951  -2.1648   1.2437   6.1136   1.2496   7.0027
-#>   10.8015   5.0426   0.4105  14.0700   6.3069  12.8383 -11.9664 -13.9583
-#>    1.3122   4.0592   2.7471   6.4240   1.6898   1.8227   0.5049   4.1766
-#>   -1.7909  -5.0770  -1.5484   7.6894   5.3256 -14.3788   4.5347   0.7098
-#>   -3.2952  -1.6346   2.2955   2.4030   1.0500  -8.9837   6.7502   6.9802
-#>    6.8395  -5.0656   0.1906  -9.0577   9.8598   5.1781  -7.2335  -0.2278
-#>    4.4538   2.7797  -0.9497  -6.1691   5.6912 -10.2034   3.1724   3.2141
-#> 
-#> Columns 41 to 48  -8.6441   2.4011   5.3406   0.5169  -7.1458 -10.6428   3.4734   6.4883
-#>    3.6561  -1.3602  -4.9829  14.9695   7.3692   1.5609   3.6538  -0.9603
-#>    9.7197   3.6295  -1.4647   4.9346   9.6288   7.8971   3.1743  -8.6251
-#>    1.6472   2.3387 -15.3470  17.4664  14.3885   0.9156  -9.6962  -6.3569
-#>   11.7460   6.7559   5.7597   1.3397  -0.2342   7.9527   4.8621   5.7302
-#>    5.2752  -6.7882   7.0607   4.4035  -7.4108   2.5544 -11.0435   1.9892
-#>    0.3894   5.1207   3.2670   0.8916  -1.3623   0.8533  -0.3910  -2.0448
-#>  -14.1813   3.4252  -6.7175  -0.2159  -4.8677   0.9509  -6.7635  -3.2218
-#>   -4.9690  13.1662   4.1110   8.9466   6.0124   3.0692  10.9720  -6.5359
-#>    0.5593   3.6655   7.2326   6.2879   7.1621   6.6863   1.0141  16.3214
-#>   12.3352  -7.1740  -4.8923  -5.6806  -6.4280  10.6362  -5.3967   3.5949
-#>   -2.2432 -12.8000 -11.8296 -16.0490  -9.8189  12.8758  -2.4351  -7.8443
-#>   -4.5688   7.1990   0.5583  -1.7811  14.3203   4.1463  -2.6914  -4.7332
-#>   -1.3859   5.0359  -1.3397   1.9056   9.3781   7.6273  -7.1196  -5.7528
-#>   12.7389  11.5307  10.7647   0.5788  12.7882   1.0392  -4.6635  -2.4167
-#>   -7.0239  16.5722  -6.2209   3.7139   8.4041   1.7262  11.3945  -4.0328
-#>    2.1784  -2.5491  -0.0794   9.1677  -5.9280  -6.5202   5.9558  12.9000
-#>    0.6541  -2.0004 -10.7711   1.4766  -6.1706   7.5175   4.1388   1.1855
-#>   -5.8622  -0.4412   4.2065  22.7852  10.0497   7.5345  -7.5494  -9.1347
-#>    7.6072   8.8231  -5.0044 -11.2194  -0.6387   0.7680   1.4248   6.9167
-#>  -12.1577   3.3665 -14.2950  -4.3271   3.3356   6.4653   4.0655  -2.3738
-#>   -6.7812  10.3407   3.4721  -3.3608  10.9246  -4.7196   0.0555   2.4327
-#>   -5.3465  -8.1440  -9.7443  10.5662  -1.7553   4.5819  -9.2733   0.5123
-#>   -5.5077  -4.3240  -2.6952   1.6386 -13.8442  -3.9715  -3.8010  -1.9999
-#>   -4.1681  -4.8682   1.0707   4.2970  -5.5541  -2.3680  10.4819 -12.6713
-#>   -8.8408   7.5186   1.9883   4.9950  10.6310   3.2479   1.3208   0.8368
-#>    3.6220   9.5142  -0.6823   1.2705  -5.9588 -16.6398  -2.1621   1.5515
-#>   16.8932   7.8655   0.1815 -15.7166  -8.2259  -9.0776  12.2167  -0.3076
-#>   -6.6233   3.8283   1.9062  -0.7773   8.5527   3.2746   7.6873  -3.1137
-#>   -4.6619  -5.6619   7.5286  -3.1911  -1.0284  10.5853   6.7247   7.2033
-#>    1.2962  -2.9593   6.1153   2.5834  -6.6286  -5.0059  -6.8562  -4.3241
-#>   15.2824 -16.6599  -1.0556  -0.1420  10.4592   3.5020  -5.7064   8.5650
-#>   -5.1108   4.7232  -1.9738  -6.1196   2.2868   3.7609   9.3567 -10.4940
-#> 
-#> (18,.,.) = 
-#>  Columns 1 to 8  -2.1182  -0.7571   7.9467   2.1898   4.7717  25.2638  -0.8232  -7.9312
-#>    1.4757  -8.4381   4.6898  -6.4652  12.7088   6.1968  -6.5510  -3.3435
-#>   -1.0880   3.1956   5.9717  -2.7051   2.0740  -1.9199   2.2675  -1.2013
-#>   -1.3924   8.2906  -2.5384  11.9476  -3.7197   7.0748  -6.8147  -9.3733
-#>    5.5619  -1.2401   1.4920  -6.0486   3.3838  -1.9039  -6.3562   8.2120
-#>   -0.6116  -4.2774   9.4702   1.4873  17.8344  -8.8499 -29.3098   3.4220
-#>   -3.7875  -1.9810  -6.1796  10.2906  -1.9900  -2.1061   7.1188   6.1529
-#>   -2.5599  -9.5376  -2.6383   2.9431  -3.7980   5.2267  -1.7671  -3.9193
-#>    4.8052   1.8797   8.5406  -8.1130  -2.0360  12.9946   2.4048  -4.7191
-#>    2.9974 -10.4339  -0.2404 -13.4410   7.9839   3.1934  -4.1069   3.5502
-#>   -1.6361  -4.6846  -3.8339  -1.8360  20.3230  -7.6295  -4.0841  -7.0594
-#>   -9.9150  -3.2720   6.0693   3.1899   3.3531   1.6007   1.5613   1.2052
-#>   -8.4328  -4.1565  -8.0653  -2.6708  -9.2769   1.4444   8.3375   8.4450
-#>   -3.2125   0.9822   0.4917  12.6375   3.2021   0.8483  -0.6230   4.8014
-#>   -0.2609   3.1864  -1.5156   6.5577  12.8700   7.6108  -4.1719  -4.2897
-#>    3.2358  -6.7246   3.5930  -4.1780  -4.8662  -1.1375  -4.2261 -14.1053
-#>   10.4043   1.6688  -7.9842  -8.4965  -2.1641   5.7481  -5.0010  12.2231
-#>   -3.2635  -2.9199   6.9172  -5.2742   3.3919   0.9880  -7.8094   6.6050
-#>   -2.4746  -0.2135   7.4969  -0.3032  11.5303 -13.9905  -0.9217  -5.6705
-#>   -3.7569   2.5001  -6.3367  14.1089   8.5051  -3.3212  10.8588   5.3911
-#>   -4.0467  -4.7554   1.1179   9.3319  -3.1277  -0.4392  -0.8629   2.8097
-#>   -1.7547   2.4296   3.7441   5.2022  -5.6253   8.2018  16.8028  -5.5757
-#>    0.0685  -0.7017   3.6165   9.9757  13.0693  -0.2408  -5.1801 -11.3338
-#>   -5.8028  -5.4429  -7.2817 -11.3863  -0.9092  -0.2677   4.8687   3.4034
-#>    2.2659   4.6321   7.0772   4.8961  16.6779   0.9971  -7.9687  -5.9066
-#>   -4.8906  -2.4658 -11.6296  -1.2893   2.6321   0.9346  -0.6113 -13.0913
-#>    8.9434   9.2545   3.0081   7.0109  -0.4382  12.3121   3.2274 -14.7190
-#>   -0.4862   1.9033  -5.7876   4.4347   7.4138 -26.2398   9.5332   9.6503
-#>    2.2402   3.9792   3.3721  10.4018   6.3583  -4.3108  -1.3508  -1.7882
-#>    7.4722   2.3650  -1.8423 -17.8048  -1.3112  -4.7984   3.7357   9.3711
-#>   -4.0211  14.1959   4.6885  -7.2354   3.9748   7.9475  -8.8620  -6.2914
-#>   -3.7000  -6.9855  -3.8212   8.3691   0.8111 -11.8869  -3.7272   0.7643
-#>   -4.7139  -6.8645  -0.7060   2.7541  -4.5484   3.5338  -8.6274  -1.2171
-#> 
-#> Columns 9 to 16   7.2647  -8.6888  11.7926  -1.0136   1.5494   2.4462  -6.5419  11.3079
-#>    4.5663  12.5308   0.7116  -2.8596   5.5737  -9.2472   5.8513  -9.5661
-#>  -13.3512   1.3591  -1.0610   0.4476   5.8431   1.2887  -1.1533  -1.8306
-#>   -3.8562  -1.3592  10.7865  14.0710 -13.6829 -10.5686   3.1663  -0.1743
-#>   -6.0634   8.4946   1.7074  -4.0652   1.0906  -0.4083  -2.4810  -2.0329
-#>   22.3303   8.4309   5.9973  -7.3569 -11.3795   8.3505   2.1217  -1.1199
-#>   -7.1720   2.0795   6.0179   1.7678   7.1983   0.3938  -4.3298   5.9715
-#>    4.9156  -5.4124   0.4973  -9.1705   4.4407   4.9347  -1.4694  -2.2564
-#>   -4.8952   9.7767   2.9930  -5.5948   7.6400  -5.4677   1.7944  -2.3845
-#>   -1.0263  10.0324  -7.4797   2.0402   9.0892  -4.2136  -4.2335  -7.1426
-#>    2.9696  16.1909 -12.5469 -11.5150  -1.9482  11.0889  -3.9540  -3.3895
-#>    2.2270  -4.5721 -18.1313  -9.1965   2.6634  18.9085   7.9679  -2.6307
-#>  -20.6692   4.9193   1.0881  10.1452   3.7314  -4.5919   1.2086  -2.2627
-#>   -9.9781   7.9977  -5.2585  11.1988  -2.5901  -5.4626   3.3733   5.7326
-#>   -2.7484  12.7465  -0.2040  13.1155  -6.3980  -9.1547  -0.0957   2.8694
-#>   -0.1539  -3.9398  -0.4655   8.6836   2.9089 -17.5699  -2.7059   2.7102
-#>    4.0578  -1.0165 -17.0126  -5.9745   9.3175 -15.3041   3.0811   7.9012
-#>    3.4738   2.0146   2.5935  -9.4803  -0.1053   0.6432  -0.7223  -6.0844
-#>   -6.5118  -1.8916   9.7646   0.6670  -2.9842  -7.9055  -6.2935  10.3625
-#>   -6.9629   4.8729 -21.6642  -4.4527  15.1960  -1.6819   5.9706   0.5120
-#>    2.6332  -3.0433 -16.7360   2.7591  11.2852   1.8029   1.4326   5.5599
-#>    3.3250  -9.9069  -0.7334   9.8337   6.9544  -8.0702   4.9554  -4.5893
-#>   11.7242   9.3968   2.5485   0.8957  -2.3584  -5.4113   2.9040   6.0120
-#>   -2.5418   9.6976  -6.0312   4.0430  11.2914  -7.4692   2.5382   4.2934
-#>    2.3915  -0.9626  -0.1069 -11.9796   9.2055  -1.7904   0.1402   6.1305
-#>    8.3409   0.2522  13.4007  -0.3983   2.9108  -8.6546  -7.6775   7.6755
-#>   -1.1018   1.2193   2.2627 -10.3144   0.4891   5.3719   4.6967   9.7798
-#>    9.2502  -9.7482 -14.2606   5.1587   0.8067  -4.0890   8.0564   4.0071
-#>   -4.5599  -3.6730   0.2758  -0.8335  11.5419  -2.6657  -3.4265   7.6750
-#>    3.7316 -15.9019   6.4014   2.8236   5.3996  -3.1120 -14.9505   0.0564
-#>   -5.4596   1.8753   0.0063  -7.5509 -16.1388   4.6096   3.8762  -3.2336
-#>   -8.9335   8.1773   4.1663  12.9117  -4.6454   2.1488   2.8809  -9.6851
-#>    0.0477 -10.4034   6.2636  -0.4742   5.5335   0.5257   0.8917   2.3452
-#> 
-#> Columns 17 to 24  11.0573  13.7312   5.8543   4.8489  -2.6569  -3.6729  -2.0829   6.7676
-#>   -2.7833  -7.6586  -4.0285  -6.8365  -4.4145   3.2945  -7.1827  -4.5094
-#>    1.2952   1.4334  -2.6366  -1.1614 -13.9862  -0.6042   4.2555  -2.1690
-#>    6.4549  -7.4690  -2.0481  -4.5348  -7.2859   8.3759  -7.9426  15.4676
-#>  -18.3143  -2.0021  -5.8181  -0.4990   2.7677  -6.0340  14.8655 -11.5795
-#>    1.4829   5.7384  -3.3054 -11.3290  -0.5048 -14.0581  12.6753  -3.5082
-#>  -11.5949   1.0957 -17.6710   4.4140  -6.1736  -2.9811   9.0966 -13.4329
-#>    4.3069   2.3311  -6.4558   0.6288 -10.9957   1.1390   3.1576   1.7888
-#>   -0.2856  -6.8539  12.5494  -3.4223   0.8451   7.7194   3.4557  -3.7051
-#>   -7.3951  -7.1409  -9.8258  -3.3382  -3.3691   0.7643   7.8610  -1.5240
-#>   10.8178   8.4922   2.1662 -14.1131   4.2186  10.6684  12.8027 -13.3816
-#>   -0.2625   5.7901  -7.1812   5.2922  -3.0609  -7.8868  10.9752   4.4107
-#>    1.2552  -1.4243   1.2744   4.8681  -4.7870   8.5247  -2.4831   7.4701
-#>    0.1306  10.3847   0.1616   3.1268  -4.7955   2.5798  -0.0894  -5.6855
-#>    5.3339  25.9790  14.3555   7.6033   0.2488   0.1268  -0.5005  -2.1643
-#>   -9.2570  -6.3087  -3.6075  -7.1626   0.3360   6.7071   0.3442   1.5326
-#>    1.8577  -2.1719  -8.1049 -13.1077  -7.9606 -11.6935  -6.9597   5.5981
-#>   -1.2713   4.8206   3.2899  -1.6654 -19.0327  -9.0100   4.2865  -5.6547
-#>    4.1511   4.4826  -2.2632  -7.7519   2.3801   7.0907   6.0796  -5.8401
-#>    3.5021   1.0276  -6.6806   1.2568  -2.0366   7.1988  -6.7076  -1.0068
-#>   -2.5280   0.8024  -8.9132  10.7897  -2.8374   6.7471  -2.2640   0.6762
-#>    6.0122 -12.5526   0.2823   1.9227   3.5617  13.1899 -14.5711  -1.3615
-#>   -5.3664  21.6509   0.7484   3.8145   0.2056 -14.7323  -4.4145  -4.0317
-#>  -15.9122  -8.8177  -0.5277   4.1409  20.5291   3.4526  -2.0654  10.7065
-#>   -3.5763  -0.3758   5.0681  -7.6710   4.2845   1.7020   3.9231  -2.4412
-#>    2.4172   5.0061  -5.2222  -2.6070   0.8065  -3.1587  -3.6731   5.6538
-#>    2.0505   5.3885   6.5308   6.9079  -0.5731   2.4897   5.5510  -0.2221
-#>   -1.2687  -6.4637  -0.7992   1.5713  11.7883   4.8409   7.4817   5.2496
-#>    2.1072   2.8074  -2.4123   6.6889  -1.5585  10.1925  -1.7233  -6.7870
-#>   -0.0777  -0.0870  -2.5451  -2.8325  -0.0159 -10.7361   4.9466  -2.9215
-#>    0.8042   6.5573  -3.6933  -4.7547  -6.9004  -1.5150   6.4858  10.4649
-#>    0.4677   4.9717   2.9189   1.6511  17.4041  10.3360   7.9191   9.0941
-#>    1.1544  -7.1980  -5.6384   2.1168   6.5097   8.4443   3.2848  -2.8482
-#> 
-#> Columns 25 to 32   1.0632  -2.7192  10.7800  -1.4988   5.6075  -1.0201  24.4864  -4.5952
-#>    1.8964  -4.6640  -5.0406   5.0747 -20.3201   6.5519  -6.2392 -14.4074
-#>    1.1198  -9.0152   0.1442  -4.7465   2.0033   3.4777  -3.9870  -4.5185
-#>   -2.9254  -8.6776   1.1509   8.1511  -1.6471   1.3036  -9.9595   1.6227
-#>   -1.9584  -1.3598   0.2078   3.9571  10.6007  -5.8023   2.1676   6.2004
-#>    8.0944  -9.1235  -7.1985  -4.1770   2.2347  14.1464  -2.0111 -22.4899
-#>   -2.8796  -7.7015  -6.2215  -5.4362  11.5978  -6.0972  -7.0790  -2.9885
-#>   -3.1749   4.9953  -3.3213  -7.7443  -8.5786   4.4992   3.6853  -9.6041
-#>   -5.9079  -3.8342 -12.4933   9.6378  -6.8800  -8.5215   0.6795   3.3179
-#>   -2.3786  -2.7458   3.6221  10.2273  -4.5791   1.8903   4.0407  -1.8440
-#>    3.4746  -7.2704  -5.6237   2.9026  -1.7574  -6.0075  -5.9611   3.7171
-#>    5.2485  -5.6597  -6.8140  -4.6195  -1.5528  -3.7034  -3.9570  -0.2235
-#>   -0.6517   6.5427  -2.0068   0.1044  -2.7763   1.1070  -7.6801   3.5222
-#>    6.5342  -7.2092  -3.5259   3.8781   2.7967  -2.8619  -6.4781   1.9573
-#>   -3.2627  -8.2156   5.7127 -10.2309   8.4198   1.0317  -8.5008 -11.6586
-#>    2.6354  -4.9628   6.0390   5.1588  -0.0314   5.3271  16.1069   2.2121
-#>   -6.7892   1.7971   3.7045  10.1105 -11.8865  -2.2256  -2.0193  -3.8336
-#>    2.3820  -6.5229  -9.5870  17.5375   7.5020   4.4041  -4.9949  -2.7408
-#>   -6.9702  -5.4410  -1.7742  -8.2750 -13.7040  -1.1796  -7.8330 -20.4477
-#>    2.6058   9.6906  -4.7918   5.4476  -9.4555   6.6318   1.0639  16.5680
-#>   11.1500  -8.0351  -3.4691  -2.8308 -11.5355   2.5864   3.8273   3.3524
-#>    1.0210  -2.1057  -3.7304   2.2107  -3.7075  -5.8099  -8.6483   7.5339
-#>   -0.2247  -5.4163  -0.5882 -14.3024  -2.6323   1.0308  -3.6861 -16.4198
-#>    8.8517  -5.0068   4.0431   5.2185  -7.5437   2.1399   9.1219  -7.3401
-#>   -1.0232  -2.8021  -7.3895   2.2813  -8.9037   0.7960  10.9585   3.6894
-#>   -3.5527   6.3734  11.3317  -9.4079   4.1024   9.1470   5.1007 -10.9093
-#>    1.7934   2.0417  -7.4359  -0.9415  -7.7304 -14.8421   4.6599  15.0996
-#>   -3.7730   3.0577   2.7603 -14.7906   6.3743  12.6716 -12.3185   3.3731
-#>   -8.9482   7.4187  -3.6600   3.4782  -1.1062   7.5387   8.5087  13.0561
-#>   -3.1593  -1.4216   6.9764  -0.7125  14.8323  -3.5689   9.6071  -3.1786
-#>   -5.4040  -7.6017   4.8639  -5.5065  -6.7502   5.9897   1.7524   4.7436
-#>   -0.7224   6.5160  13.5542  -0.8484   4.8272   9.3635   0.0067   8.9889
-#>    3.8936   8.1327  -1.3547  -5.1174   7.6414   0.9399   4.8738   6.6594
-#> 
-#> Columns 33 to 40  -9.2635  -7.2035  -6.8341  -3.8920  11.1846   0.3289   8.8149   1.3694
-#>  -11.8951   3.4905   3.6577   6.1760   2.5567  -3.8852   5.0694   3.1206
-#>    5.2271   1.2251   9.4451   7.1253  -0.0516  -0.6160   2.3369  -2.2727
-#>    2.7091  -4.3054  17.7565  -1.2457  13.8621  -3.6399  11.4166  -8.5968
-#>    5.7349   1.3999   3.5504   7.1171 -14.1684  -3.7598  -9.0061   4.2168
-#>    0.1764  20.1954   2.7542  12.2646   6.3112  -0.7967   3.3466  -0.7696
-#>    8.9047  -5.0562   0.5008   5.8380  -5.7228   6.8445  -3.6734  18.9400
-#>    2.0629  -8.6991  -0.5225 -10.7714   9.7581  -8.4159   7.7689   3.5440
-#>  -17.0136   1.7361  10.8883   5.8647   6.5579   5.2743   5.7465  -1.6654
-#>   -1.0095  -2.9798   5.6213   9.2175  -6.1612  -3.8507  -2.1239  -0.5416
-#>    5.0362   1.0972  -7.7023  -1.9197  -4.8732 -11.5382  -0.7454  -2.0414
-#>    5.5874  -1.5368  -4.8733  -3.8002  -6.0718   0.0737   0.5264   8.3143
-#>    1.9344  -3.4319   5.4247  -5.5361  -2.4694   9.2598  -3.9390 -12.0095
-#>    7.2665  -5.3043  20.2393  -5.8789  -3.3490  12.6675   3.9846   1.7936
-#>   12.2226   4.7088  -0.0720   3.2021  -5.8620   5.8119  -6.5830  16.4504
-#>    4.5032   5.2409  10.1898  -5.6131   0.3540   1.0364 -11.1512  -9.0271
-#>   -6.4820 -11.0132  -6.5388   4.7742 -18.1064   6.5336  -3.4887  -0.7135
-#>   -3.6589  -0.3688  -0.8579  -2.5890  -5.6719  -7.9088   3.0846 -10.2442
-#>   -4.3303   0.2712  -4.2583  10.9800   3.5294  -0.0587  -5.5945  -1.6424
-#>    7.3768   0.5957   0.5841  -7.9975 -16.0498   1.6262   4.4650  13.6902
-#>   -5.2461  -8.1827  12.8812  -1.8316  -0.6669   0.6355  20.4224  12.9001
-#>   -1.3292   1.1440  11.3888 -18.0718   8.5401   7.8767  15.9205  -3.3331
-#>   -6.4445  -0.6811   2.3454   7.0941  -0.0060  10.7454  -2.9552   3.8102
-#>   -8.8245   6.8739   3.8587   8.6120   1.8066   5.9996  -5.2777  -3.7399
-#>   -3.8900  12.8585 -10.1478  14.4859  -4.3676  -2.4779   9.6047  13.7051
-#>   -0.3521 -13.9371 -10.2086   0.6534   5.3738 -10.6879  -4.5834   9.1824
-#>   -3.5589  12.1856   0.6645   2.0483   2.5583   8.6065   4.0014   2.7410
-#>   23.3597   6.5069  -0.3860   5.0167 -13.4162  -3.9814  -3.4062  14.9261
-#>    3.4224  -7.6012   2.4267   2.0239  -5.9643  -2.4389   8.4784  12.2254
-#>    8.1131 -10.8963 -11.4393   7.0195  -8.6953  -4.0921  -9.2943 -10.3610
-#>    9.7696   4.3104  -9.8152  -5.1547   7.7941  -1.7377 -12.3977  -1.6889
-#>   14.6476   7.0514  -2.3186   2.1118  -2.5128  16.7693 -11.8469   6.5283
-#>    7.4444  -1.3278   5.5839   2.1411   3.1325  -7.2029   6.9434  -0.3647
-#> 
-#> Columns 41 to 48   6.3361  -8.3823  14.6838  -2.5564   2.3135 -11.0018  -3.4430  -6.6691
-#>    9.5879   9.2590  -5.3786   3.0516  -6.5846  10.0990 -11.7321   3.3944
-#>   -3.6460  13.8559   1.0598  -0.7469   1.6902   6.6729  -1.9580   2.0151
-#>   -0.0767  11.7552   7.3769  -8.4430   3.5126   3.5257   5.5583  -0.5369
-#>   -3.4907  -0.4182  -8.7510  -5.0566  -3.7087  -1.6293  -1.2193  -4.2151
-#>    0.7292  12.5892  -7.2307  -1.5323   7.5259  -0.2826  -6.2068   1.0094
-#>    0.1214  -0.8100  -0.6030   6.5839  -7.0462  -0.7055  13.5199  -9.9241
-#>    7.8897  -1.4335   0.9386   3.6702   4.7301  -1.1972   6.3694  -6.8673
-#>    0.1027   7.4085   0.0746  -4.1141   1.1504   7.1094  -7.2309  -3.6300
-#>   11.9246   4.9357 -16.8775   4.4258   1.9653   3.3235  -9.4068  -1.7021
-#>   -2.9280   1.6684  -8.8813  -3.6751   1.3775   6.1987  -7.1506   7.4461
-#>   -0.2259   1.0554  -2.3421  -6.5382   4.4263   8.2247   3.2295   2.3804
-#>  -10.8960  12.0267   2.7160   8.1598  -2.4184   9.6094  -2.0357  -3.3505
-#>  -15.7383   9.7148  12.3839   4.5777   2.4778   1.9414  -2.6358  -8.7548
-#>   -5.0241   5.4350   5.5382   3.1705   5.4128  -0.5800  -2.6863   5.3659
-#>  -10.4657   2.7522  -7.5623  -1.2090   1.4477   6.0935  -0.7693  -1.6742
-#>   22.0229  -5.2352 -11.4991   9.3870  -3.7281  -6.6134  -0.3707   9.4902
-#>   -7.5351   7.1026  -4.9276  -8.8025  -3.7069   4.6289  -4.7223 -12.3302
-#>    7.2135   3.2702 -11.6549  18.1010   4.2276   4.9779   1.6759   7.6809
-#>    8.9515  -8.4539  -1.6544  10.5667 -11.8874  -8.4563  -1.8262   2.5703
-#>  -13.5672 -11.6889   9.8639   6.4763  -3.5790   4.2325  10.7076  -8.6775
-#>  -10.4942   1.1181  11.4040   1.1981  -9.4173  -2.4131  -4.2072  -2.0181
-#>   -1.2752  -1.4358   6.2455   8.8270  -0.5674   4.2308   0.0401   5.4706
-#>    1.2251  -9.2584   2.5148  13.4763  -3.8339   3.0532   3.8232   8.1582
-#>   11.2414   0.5207 -14.2519  -8.0120   0.9716  -5.9536  -3.9727   6.0368
-#>    0.1865  -8.3975  -2.1859   1.7352   6.8353  -3.5377   6.7373   3.0838
-#>   10.4572  11.4622   8.1965  -0.7065  -2.8599   2.4068  -5.4784   3.3010
-#>  -17.6197 -19.1179  -5.1196   8.2346  -3.9124  -1.4653   7.1807   3.3478
-#>   13.8698  -7.8992  -7.9530   1.0119   4.6210  -1.1669  -1.1689  -2.1323
-#>    0.2055  -7.5565  -5.8767   6.8536   6.6724  -9.7350   0.5113   5.2973
-#>   13.9984  11.7311   4.0220  -6.5534   6.5149  -0.9068 -13.3340   4.0862
-#>    6.8044  -8.1622  -1.4375   4.8625   5.6870   6.0109   0.3636   6.5547
-#>  -11.3849   1.3162   3.0693  -3.2302   6.4743  -1.3361   8.0931   2.7210
-#> 
-#> (19,.,.) = 
-#>  Columns 1 to 8   7.9069   8.2314 -15.8607 -17.8247   1.8460   3.3960   1.7360   0.4683
-#>   -8.9073   3.4705 -10.9638   4.2166   2.0623   8.7358   4.3075   7.7731
-#>   -3.2392  -4.2938  -1.1854   5.3059  -9.1970  -3.2163  -0.8529  -5.6473
-#>  -16.0983  -5.4826  -5.9025   7.2389  -1.9904  -0.2595  -5.9837  -2.8110
-#>   -0.7603  -5.9232   5.6491   1.1754  -5.2810  -9.1353  -5.6206   1.7050
-#>   -3.8353  -2.9147  -0.8285   3.0347   7.9952   5.0474   2.4767   2.0987
-#>   -4.2395  -7.5736   6.2816  -0.5787  -3.1490  -2.5334   3.8162  -0.3144
-#>   -1.0432  10.8327  -2.2433   3.8450   8.5869   8.0106   6.9709   5.5028
-#>   -3.6809   8.2100   1.3975   4.3335  -8.6595 -15.4878   2.6603  -6.5403
-#>  -13.4476   0.9709   6.4793   4.2022  -5.8665   1.9948   3.1041  11.2352
-#>   17.0529 -18.9924  -5.9974   1.5673  10.0528  -9.8677 -11.7180   4.0176
-#>    2.5034 -11.2738 -11.7232   3.2905   3.9547   0.6555  -2.0560  11.3394
-#>    2.0134   5.7511   4.8171  21.8050  -7.7353  -1.7456   1.4701  -0.3785
-#>   -3.1590  -2.1877   2.9348  11.7882 -17.0002  -8.5149   6.3678  -8.5830
-#>    7.2730  -5.6195   0.4421   3.1140 -16.6905   3.8076  -8.8719 -10.0481
-#>   -5.8159   3.5791   3.6580   2.3512 -11.1311  -0.7581   6.1737 -15.1651
-#>  -10.3767   2.9377   1.5156  11.9794  -1.1671   7.3783  12.5935  -1.0580
-#>    7.6018   3.8385  -9.4392 -16.1210  -4.3064  -3.2163  -9.3067   0.9529
-#>   -0.3496  -7.4393  -6.1523   4.5940   5.0168   5.7267  -5.7149   2.5463
-#>  -10.4903  -5.9530  -0.8680   1.7640 -11.1686   1.3398   8.3867   4.7253
-#>   -2.5409   5.8450   1.7522   5.4105   1.0961   1.1265   8.4920   2.9826
-#>   -5.9085  -3.9603   4.3299  -6.5190  -5.3264  -9.2896   3.0530   0.7985
-#>    6.2176 -12.0171  -0.4960   3.7811   2.7566  -2.6141  -2.2421  -2.5439
-#>    5.8788   3.4545   4.1160  12.4822   4.4258  11.6593   2.6726   0.7957
-#>   -9.7333  -7.4100 -12.2648 -10.8202 -10.6378  -1.1734   1.2867  -4.5883
-#>   -3.5399  -5.8196   2.7657   8.1652   3.0322   1.3477   3.9744   1.1902
-#>   -0.0897   5.3256   5.4750   1.2325   7.7582  -2.1776  11.7394   0.1351
-#>   -8.1216 -10.0971  15.2709  10.7041  -0.3764  12.1821  -8.2312  -6.5013
-#>  -19.4967   7.9488  -6.2680  -1.6368 -13.4774  -4.1064   9.2371   1.7931
-#>   -0.0632   3.0925   6.8879   8.9557   4.6627  -4.3163  -8.4271  -0.9522
-#>   10.2018  -0.9176  -4.9775   7.8032  15.2119   6.8883  -1.4054   0.6085
-#>   -9.2351  -1.6457   5.4440  11.2551  -2.2972   1.8527   4.7612   0.0586
-#>   -2.7470  -3.5868   2.2028   5.0616  -1.9473  -6.4087   3.4935  -2.2491
-#> 
-#> Columns 9 to 16  -5.4244  -2.9245  -1.2107  -9.0027  13.3219   1.9440   4.8561  -2.7183
-#>   10.7535   4.1678   1.8800   5.5829 -10.7058   8.6258  -1.2581  -6.0093
-#>    5.9274  -2.4308   4.2193   7.6270   5.6584  -2.0767  -6.9968  -5.9731
-#>    0.4131   5.1239   8.8736  -4.7305 -11.9331   9.0935  -3.7310 -17.8027
-#>   -5.1119  -4.9878 -10.6180   0.6381   8.9182  -6.2042  -9.7135  -3.2330
-#>    3.2885  -1.8013   2.8075 -17.3621  -5.3055   0.9743 -14.3689  10.6096
-#>    8.6763  -2.0296   1.8713 -12.6399   5.9778 -13.2539  -0.1304  -9.0192
-#>    6.8218   3.4581   4.1998   2.8683   5.3528   0.3076   4.8818  -2.1856
-#>   -1.0621  -3.8601 -16.4815   6.7831   0.5187  -4.3644 -10.2156  -4.1790
-#>   10.7836  -1.4334  -6.2758   8.9640   0.5866   5.1194  -1.1981 -10.0573
-#>  -11.2556  -3.9902   1.0401  15.9763  -3.9429   6.0554   8.2533  -3.9805
-#>    4.6950  -4.2295  14.3786  -0.8173  17.1867   5.5266  -0.8495  -2.7889
-#>    6.8932   5.7003  -3.2936  13.3559   3.7617   1.0504   7.2782  -8.0423
-#>   -0.6424  -2.4865   3.1472  -4.1264  -5.5375   4.5062 -11.4587  -8.3921
-#>  -10.7727  -7.5424  -0.4159  15.8691   2.7726  -8.9574 -10.0827  -4.1641
-#>   -3.5679   5.6040  -0.5242   4.5327   2.4081   1.7839  -4.5119  -8.2861
-#>   14.1972   5.9486   5.8421  -3.0795   0.4700   9.4044   6.9189  -8.0553
-#>   -2.5634  -8.9776 -10.4028  -6.9258   9.4904  10.9099   4.0087  10.6379
-#>    5.2233  -6.9021  14.9642  14.7190   4.9868  -5.5645   1.4895 -11.4390
-#>    2.5536  -1.9752   2.8385  14.3668  -6.3358  -2.8437   0.8566  -7.5364
-#>    3.3009   6.6402   2.2603  -5.8293  -1.8706   2.2362  -2.2903  -6.8235
-#>  -16.8731   8.3490  -2.8018   1.8419  -7.3747   2.7683  -6.5963  -2.6283
-#>   -2.8859  -5.0357  11.7609 -12.2117  -0.3431  -4.5218  -3.4076  -6.0098
-#>    8.7292   0.1320   3.4142  -3.4457  -2.2028  -1.9678   0.8707  -6.1005
-#>    7.0865 -11.2925  -4.8071  -3.5456  -3.9779  -6.9031  -7.8760   9.7066
-#>   -2.3910  13.3094   8.4412   7.7155   5.9992   3.4252   4.0100  -3.7850
-#>   -5.6154   2.0339   3.5498  -7.0280   4.2935 -12.5404 -11.6149 -14.0983
-#>  -10.1337  -2.7636  -8.9362 -10.0790 -15.9228   4.6759  -3.2626  -1.6180
-#>    4.8460  -2.3104   7.6842   5.6354  -3.9253  -1.7831  -8.9399  -4.0914
-#>   -2.5983  -0.2068   0.7000  -9.6218   5.8294 -10.0757   8.0538   4.3348
-#>    1.8360  -1.5328   3.6088   3.5472   9.7461  -3.1073 -18.6002 -10.2768
-#>    1.2851   0.6742   9.5674   7.6433  -6.4800  -3.8342  -3.2553  -7.6469
-#>   -1.3759  15.5534  13.5354   4.8163   4.8285  -2.8745  -5.3420  -1.2828
-#> 
-#> Columns 17 to 24  -0.5167  -3.2736   3.6042   9.2083   9.7675  -3.9386   1.3428   3.4470
-#>    2.4704   2.8545   6.8275   3.5003  -0.2367   5.6338   1.1046   4.6771
-#>   -3.0332   2.1101   8.6318  -4.8976 -14.9192  -1.6236   2.2431   2.0142
-#>    0.4408   1.3938   3.2056  -8.0020  -2.1119   6.5324  -2.4020   9.4199
-#>   -9.3392   9.3710  -3.2274  -4.2743  -0.2528  -5.7441  19.9280  -2.5542
-#>    1.9263   1.6250   2.8807   0.3688 -14.1765  -6.7254  10.7015   9.2226
-#>    1.9163  -0.4107   8.0362 -10.4422  -0.5098   3.4652   5.8927   2.2033
-#>    2.4038  -8.7245   2.6648   2.7030   1.3872  -6.1252  -9.6656   0.5002
-#>    0.1946  -2.0269   1.5145   1.2279  -0.1315   1.4630   0.6094   1.6623
-#>   -6.3521   0.4316  -4.7299   0.3104  -1.4829  -1.6261   0.6357   7.8922
-#>  -11.5962  10.6036   1.4772  -5.9238   0.6079  -0.2997  15.3069   3.6901
-#>    2.1367  -1.4953  12.1796  -5.4230  -6.6698  -5.3625   8.1637  -3.4637
-#>   -4.0125   3.2110   5.4136   0.7173 -13.9764   6.4144 -11.7281   4.7523
-#>   -0.8757   6.4271   6.8656   5.0033  -9.3574   4.0220   1.5025   8.2151
-#>  -14.3414   4.9516  -3.8332   1.1615   3.7050   3.6838   0.3889   7.1748
-#>    3.4352  -8.0321  -0.5548  -7.1979  -2.1331  -0.9266   0.7498   3.3833
-#>   -3.8955   1.0812  -7.4346   9.9988   8.9725  -0.6261 -14.2812 -16.3892
-#>    0.9259  -3.9578   9.6653  -1.8063  -2.1143  -7.3721   9.1487   4.5222
-#>   -7.4901   0.1527  14.3195   1.1760   7.2353  -5.5672   2.5676  13.1659
-#>   -0.1484  12.3372  -1.2780  -1.0691   1.3121  10.2539  -5.5109   0.9131
-#>    5.1531  -5.7138   0.8301  -4.5160   7.7156  -4.6745  -5.6056 -10.4448
-#>   10.0189   6.8799   3.0023   0.4315   0.7961  14.9472  -0.0927  -6.6039
-#>    9.3146   7.4606   6.3168   2.5325  15.7218 -14.6869   6.3143  -8.6685
-#>   -3.7615  -2.7482 -14.5030 -11.5257   2.6656   0.5920   4.0818  -3.0660
-#>    1.9609   0.6213   2.8665   0.1718   8.8866   1.9372   0.4757   7.3429
-#>   -4.1544   4.6766   0.7745   1.2428   9.3632  -0.3896  -4.7325   2.0222
-#>    1.3315  -3.0702   1.4970  -9.3075   5.4004  -2.4335   1.7001  -3.5945
-#>  -12.0965  -2.4795  -3.4041  -5.4235 -13.7871  -3.7242   2.2135   0.9609
-#>   -1.4595  -7.6355   2.5596   6.0541  -0.2605   6.7192  -2.9285  14.5957
-#>  -13.5363  -0.7604  -2.6655   3.2770   0.5550  -3.3883  -0.0947 -17.2695
-#>  -12.8532 -10.6036  -5.2803  -5.4334  -0.8143   0.9354  -2.8281  -0.3836
-#>    1.2841   8.5728 -15.6670  -2.3541  -9.3281  11.0663   9.8001   8.9577
-#>    1.2819   0.9044   5.6131   2.0612  -6.8050   1.4621  12.6808  -3.0845
-#> 
-#> Columns 25 to 32  18.7151  -3.8094 -10.6858  -7.2112  10.5903   2.8308  -5.1495  -6.9245
-#>   -8.1932 -10.3093  -4.9610   6.5884   3.5473  -1.5878   1.4735  11.9831
-#>    1.6399  -2.0138   3.1374   2.5061  -0.7592 -12.6079   5.2005   9.6991
-#>   -0.2601   2.4913  -0.1172   3.2673  -1.0057  -5.0404 -15.6786  29.6677
-#>    6.3120  -2.2729   8.2883   8.2263   4.6611 -11.1320   7.2619   0.2219
-#>   -0.0609   7.8129   3.8104  -6.3285   4.6025  -4.4485  -0.0333  20.5367
-#>   21.4471   1.3245 -10.1189   6.9064  -6.3160  -3.4137   5.9507  10.0966
-#>    2.1005   3.0113  -7.5420   1.7446 -10.8668   0.2345   2.9597   7.5663
-#>    6.8733   1.2057  -1.3649  -2.9656   0.2376   6.2730  -2.6192   3.3051
-#>    2.5055  -4.9166   1.1617  10.2103   5.4102  -3.6294   8.0282  -3.7121
-#>    6.1556 -18.8554  -6.8870  16.7942   0.8996  -4.3907  15.7691  -6.0708
-#>  -13.7109  -5.4970   2.0404  12.2095  -1.1657  -5.2565 -15.3310  13.6764
-#>   -2.0914   5.5632   5.5576   6.7898  -2.8911   6.3307  -3.9102  -2.4087
-#>    5.1732   2.5589  14.9252  -4.7789  -0.9729  -1.8489  -9.2545  10.3432
-#>    0.5507  -0.1272  14.3721  -2.8788   7.3230  -8.7260   4.2822  -8.2377
-#>    7.8071   6.3932   6.5513   3.2569   0.9689  -0.3189  -4.6282   9.9274
-#>   -7.9111   1.6218 -14.3454   7.2757   9.5468  -7.2799   4.2477  -2.9991
-#>    1.4842  -7.5389 -11.1014  -1.6944   7.2728  -9.5720 -10.4276   4.1355
-#>   -3.4725  -0.5684   0.1559   2.7386  -5.8742   4.7850   5.0468   1.2748
-#>    0.1762   6.7808   6.3146   7.9698  -2.0042  -0.3079   6.2638  -6.0213
-#>    7.8444  -1.9231   9.2642   1.0918 -10.7200   6.6738  -5.5230   9.9495
-#>   -1.0943   7.7873   4.2141  -8.8941   0.5661   8.1359  -8.9883  -2.9023
-#>   -2.6989   9.5537  13.1507  -1.4590  -5.3198  -5.8421   3.6497   2.1718
-#>    8.8567  -5.6070   5.4076  -1.1994   1.1786   6.1395  -2.2800 -11.5192
-#>    3.2517  -4.9864   5.4578 -13.7910   0.8607   0.3237   2.9753  -1.2993
-#>    3.8434   5.3917   4.0140   5.5966   4.2340  -6.8201  11.1240  -2.5896
-#>    3.2052  10.5745  -3.6056   2.5925  -1.4456  -0.6922  -3.8502  16.5866
-#>   -2.4905   0.8548   5.1917   8.9457   7.4349 -11.4280  17.5558 -10.8432
-#>   -8.6278  -1.5267  10.5918  -1.0952   0.6434   4.8316  -0.5702  -4.1505
-#>    2.0603  15.5785  -1.0938   0.4554  14.3984   0.0487   1.9924  -8.3948
-#>   -4.8434  -0.7336   7.9365  -3.0920  -0.1015  -6.8206  -2.6112   8.2260
-#>  -10.5381   6.8931   5.0467  10.2215  -5.0572 -10.9092  15.1842  -1.2059
-#>    3.0102   6.5824  11.8250   0.1481   5.1619   6.7905  -6.3298   6.1166
-#> 
-#> Columns 33 to 40 -13.0929  10.7086 -10.6413  -3.2434  -4.7143  18.9670  11.5604   0.6759
-#>    3.8253   5.3724  -3.8026   1.2284   3.1021   3.1633 -10.6655   4.8320
-#>    9.6050  15.6168  -0.1603   5.9876   5.5583   3.9613  -3.5428   2.9187
-#>    0.0542  11.3364 -16.0973   3.8528  18.8930   0.8423 -19.0941   3.0001
-#>    4.6167  -7.5432   1.4097  11.1482  -7.2509   2.3051  -1.6934  -2.9962
-#>   -1.2676   3.3272   4.4583   2.2233   0.6304  -1.1169  -3.7036 -12.3604
-#>   -7.3089   2.9711  -4.3308  18.0142  -7.9470  -0.1779  -2.5158 -10.6243
-#>   -8.7892  -0.0394  -2.9864   0.9498  -9.3213 -11.2037   1.7052   3.1814
-#>   -8.1533   6.6281   4.9492  13.8490  -3.8301   7.7220   1.4083  -1.9021
-#>    9.1308  -7.0064  -1.9722  -2.4951  -0.6262  -2.2968  -8.2641   4.3387
-#>    3.6544 -28.7199   7.4428  -0.3025   8.9910   8.9597 -18.8920  -2.7463
-#>   -4.9109   2.1997  -5.8295  -5.6553 -10.4926  -5.4169  -0.9827   5.2563
-#>    4.6452   3.1934   3.9736   3.3958   4.4744  -5.4590   7.4215   2.6005
-#>   -0.9471   5.3609   0.7467   4.1787  10.4314   0.9719  -8.7019  11.9533
-#>   12.1445   3.5598   5.1703   1.6544  14.4675   9.4715   3.7597  -2.1770
-#>   -8.2620  12.5158 -12.3281   0.5927  -1.2875   3.3224 -15.3013  -1.1989
-#>    2.0916  -0.5344 -13.9363  -4.2687   1.7251   2.1584   9.0830  -2.3336
-#>   -5.1937  -0.4773  -5.7731  -0.3001 -10.2828   9.0828   1.6291 -10.7334
-#>   -1.9560  -4.0323   3.8732   9.6128  12.4645   5.0971 -12.0427  -6.5134
-#>    7.2082 -10.4481  10.9988  -6.3425  -1.5460  -7.8643   8.3994  15.8663
-#>  -15.5418   7.9106   0.1194  -4.9896 -12.1432  -4.8835 -13.2222  11.1480
-#>   -6.0650  12.4503   1.2084  -0.6934   4.0231   5.1878   7.6225  16.0133
-#>   10.0894   3.7950   2.7662   6.9198  12.7698   9.4798  -1.6231   2.8290
-#>    3.9802   3.8951  -5.1209 -12.1517  -6.8346  11.1322 -12.7089  -8.3542
-#>  -14.0842  -6.4819  -3.2150  -0.6622  -8.4714  -3.2800 -11.8791 -14.5002
-#>   -3.6597   0.2235   1.5656   1.3861   8.3664  -1.1531   3.4840   4.8625
-#>   -0.0208   4.5306  -4.4620   5.6068  -3.5328   0.9448   7.0014  -8.7854
-#>    8.1730 -16.0610  -4.7102  -6.8084   6.8921 -13.9539  -2.5970   0.8457
-#>   -7.5805  -7.3242   0.9772  -3.8022  -2.8765 -21.1915  -8.8806   8.6112
-#>    2.7900  -1.2628 -10.5153   0.7526  11.9589  11.3937   0.9338  -0.7113
-#>    3.3273  -4.8510 -16.1544 -11.6446   4.8889   1.0366  -9.9487  -8.3986
-#>    7.2784 -17.8624   2.1096  -7.7101   3.0077 -21.8409  -7.6522  -5.3485
-#>   -9.4605  12.9928  -5.7377   8.1991  -3.6538  -4.7890  -3.8185   1.3486
-#> 
-#> Columns 41 to 48  -1.2806   4.4719   3.2787  -6.8226  -2.5020  -0.3241  16.2354   4.4872
-#>    4.2604  11.2756   1.4287  16.5132   1.0703  -2.5217  -3.6417  -3.5623
-#>   -5.9802  -4.1072   5.3458   8.4859   5.6440  -5.0054  -5.6247  -2.7213
-#>  -13.6875  12.1878   8.7390   5.3186  16.4883 -17.4650  15.0589  -7.1149
-#>    8.6370   6.1348   6.9977   5.9094  -3.5747  -0.2621 -14.0265  -8.1669
-#>    5.1393   4.4540  -1.6370 -10.1522   3.2873   4.1298   7.4150   9.1745
-#>   -3.5731 -10.3298   9.7454   5.4470   7.8061  -1.7168   3.2278   8.5204
-#>  -21.1075  -2.1749  -7.7596  -2.9712   1.7288   3.6917  16.6576   3.8277
-#>    2.7863  12.8813  21.6354   5.3553  -1.5177   2.0300  -9.4604 -10.3778
-#>  -14.4875  11.1262  -0.4382  11.3462  -7.3018   2.3005 -12.4743  -5.6966
-#>   12.2622   7.4871  -5.5479   1.3534   6.5592   7.8683   6.0947  -3.2516
-#>    5.1640 -17.4172   4.4021  -9.6653  10.3575   1.2316  -6.2373   9.5555
-#>  -16.6553  -1.8077   2.1062  -4.1136   0.4310   0.2189  -1.6960 -16.2303
-#>   -4.1715   1.8463   9.8158   2.6802   1.2722  -8.7310  10.8009  -9.0684
-#>    7.0178  12.7044  13.5289   3.1725  -2.0315  -5.1303   0.0483  -5.9967
-#>   -7.4459  -7.6461   1.2485   0.4100   4.6413  -3.3643  -1.9663  -8.7889
-#>   -1.4376   0.2320 -14.3036   7.3219 -10.1662  13.2323  -4.9297   6.0043
-#>   -2.9853   6.5275  -3.6477  -2.9087  -4.8125   1.1970   5.2914  -0.7746
-#>   -1.9150   0.9732   1.9533   8.5727  21.2805   1.1917   0.2164   2.1555
-#>    1.4902   8.4288  -2.4849  -2.1220  -5.5417  -1.3303  -0.5162   7.3754
-#>   -8.6690 -13.9623   0.6408  -1.8519   2.7357 -11.0654  12.4976  -2.1276
-#>    2.2020  12.4450  13.9324   1.6783  -4.6593  -5.5748   5.2634  -8.7318
-#>    6.9535  -3.0555  -0.3675  11.4615  -7.1063   8.8430   2.9909   8.8720
-#>  -12.3394 -23.7992 -11.7108   2.3299   7.7491   0.7197   2.0416   0.8095
-#>   15.6655   1.4036  12.8402  -3.1910   9.5950  -5.7898  -0.3393   4.2886
-#>  -11.1935  -0.6632  -4.5321  -4.1002   7.3329 -10.4798   8.2632  -4.2149
-#>    6.5294   6.4735   2.2356  -5.7527  -3.6129   8.0644   5.7421   3.7550
-#>    5.3824 -15.5725  -6.2125   6.5763   5.5347  -8.4960   2.9947   3.0953
-#>    0.0087  13.1358   1.9827   7.6309   1.6621 -10.9873  -4.9114  -9.1532
-#>   -5.0495 -20.4267   3.4558  -1.2676   6.2794  -2.6602  -7.8177   8.3995
-#>   -5.3475  -8.1220  -0.6322  -4.6946   7.1616   7.0391   7.9409  -1.3812
-#>    9.0524   1.8260 -11.0054   8.7754  -8.6430   5.8534 -23.3776   1.1368
-#>    8.2543  -9.6404   8.8720  -3.6102   9.5666  -5.0479  -3.4277  -8.2915
-#> 
-#> (20,.,.) = 
-#>  Columns 1 to 6  2.3410e+00  9.7388e+00  1.6557e+01  1.1382e+01  3.7641e+00  5.1080e+00
-#>   9.7553e-01 -8.4447e+00  1.3123e+00 -1.5725e+01 -4.9083e+00  4.1044e+00
-#>  -3.1703e+00 -2.2135e+00  4.4341e+00 -1.2445e+01 -3.2447e+00  3.6457e+00
-#>   1.2177e+01  1.5420e+00  5.5418e+00 -2.3071e+01 -1.8939e+00  1.7252e+01
-#>  -1.0726e+01 -4.2155e+00  6.5425e+00 -2.0108e+00 -4.7632e+00 -1.3778e+00
-#>   1.2218e+01 -3.6186e+00 -4.9115e+00  5.6926e+00  3.1619e+00 -1.5542e+01
-#>   1.1675e+00  1.8411e+00  1.7625e+01 -1.9243e+00 -1.8360e+00  5.9005e+00
-#>  -2.1542e+00  3.3940e-01 -4.9044e-01  7.3563e+00  9.6633e+00 -3.6952e+00
-#>   8.6159e-02 -9.9189e+00 -3.8149e+00 -8.9140e+00  2.6082e+00 -6.3848e+00
-#>  -1.0125e+01  1.6594e-01 -2.5587e+00 -6.3884e+00 -1.0598e+01 -5.6895e+00
-#>  -1.2456e+01  1.3919e+01  8.3685e-01  1.1546e+00 -3.0186e+00 -3.8498e+00
-#>  -2.6311e+00 -2.4535e+00  6.6286e+00 -1.7185e+00 -5.9649e+00 -2.6087e+00
-#>  -1.0975e+01 -2.2859e+00 -4.9682e+00  1.6251e+00 -8.6998e-01  9.8512e-01
-#>   2.8754e+00  2.6769e+00  2.8849e+00  9.1190e-02  4.8219e+00  5.3024e+00
-#>  -1.0107e+01  8.5539e+00 -2.2039e+00  1.1878e+01  3.7640e+00  1.7298e+00
-#>   5.3624e+00 -4.1503e+00  1.0721e+00 -9.7069e+00 -2.6360e+00  6.1107e+00
-#>  -4.1990e+00  5.3183e+00  9.7740e+00 -1.4839e-02 -2.9959e+00 -9.8873e+00
-#>   7.1682e-01 -6.7365e+00  1.7742e+01 -2.0493e+00 -4.0481e+00 -2.4705e+00
-#>   5.6811e+00  2.1429e+00 -1.9895e+00 -6.0990e+00  7.1933e+00  1.0245e+01
-#>  -1.0327e+01  1.1902e+01  8.1764e+00  2.9050e+00  3.5568e+00  4.8856e+00
-#>   9.8634e+00 -7.3801e+00 -1.8072e+00 -1.8462e+01 -9.1422e+00  8.2301e+00
-#>   7.4528e+00 -3.2333e+00 -2.7164e+00 -2.6745e+00  4.9729e+00  8.2682e+00
-#>   9.8821e+00  6.4739e+00 -2.9487e-01 -9.3617e-01  1.5004e+01  1.2282e+00
-#>  -4.8038e-01  7.8481e+00  3.1612e+00 -1.7786e+01 -7.0473e+00 -4.7113e+00
-#>   1.3829e+01 -1.1918e+00  4.7191e+00 -9.9064e+00  3.7553e+00 -5.6204e-01
-#>  -2.0430e+00  1.0027e+01  7.2484e+00  7.6063e-01  6.9578e+00  4.1638e+00
-#>  -1.2486e+00 -3.1506e+00  7.0924e+00  6.9274e+00  1.1315e+01 -6.7493e-01
-#>   4.5251e+00 -1.1089e+00 -4.6790e+00 -4.9297e+00 -2.0166e+01 -3.0425e+00
-#>   2.0341e+00 -5.0193e+00 -1.5881e+00  1.4271e+00 -5.1175e-01 -1.9088e-01
-#>   4.3895e+00 -3.2604e-01  2.8351e+00  6.5263e+00 -6.4667e+00 -3.6202e+00
-#>  -4.6403e-01  1.6272e+00  3.1020e+00  6.5796e+00  1.4924e+01  4.7444e+00
-#>  -8.6713e+00  7.4556e+00 -1.9416e+01 -6.8462e+00 -1.1393e+01 -6.3260e+00
-#>   8.3200e+00 -1.1023e+01  3.5368e+00 -1.8314e+00 -7.4983e+00 -1.0943e-02
-#> 
-#> Columns 7 to 12 -2.4804e+00 -1.8602e-01  7.1954e-01  2.9089e+00  1.7618e+00 -1.3436e+01
-#>   1.7067e+00  8.4794e+00 -2.6234e+00  7.7493e+00 -6.4802e+00  2.0458e+00
-#>   5.1771e+00  3.1818e-01 -1.7405e+00 -6.4634e-01 -7.8719e+00 -2.0841e+00
-#>  -8.7515e+00  8.5336e-01 -1.5769e+01 -5.3437e+00 -3.2970e+00 -2.0343e+00
-#>   2.2304e+00 -5.4520e+00  1.9013e+00  2.4374e-01 -2.1490e-01 -1.4346e+01
-#>  -2.6801e+00 -1.8525e+01  2.5054e-01  1.1545e+00  1.2923e+00 -2.2221e+00
-#>   5.4859e+00  3.2670e+00  5.4010e+00  1.0159e+00  2.6045e+00 -3.3450e+00
-#>  -5.5536e-01  1.9618e+00  3.5680e+00  8.0874e+00  9.1611e+00 -2.3273e+00
-#>  -8.0280e+00  1.5038e+00 -2.8943e-01  6.2901e+00 -5.8491e+00 -1.6066e+00
-#>   7.5733e-01 -8.2033e+00 -9.2451e-01  7.4625e+00 -6.7360e+00 -8.9020e+00
-#>   1.2189e+01 -1.8027e+00  1.6689e+01  3.4474e+00  2.9138e+00  1.7912e+00
-#>   1.3682e+01 -1.6062e+00  9.7473e+00  4.5117e+00  5.6799e+00 -5.4646e+00
-#>   6.8369e+00  1.3800e+01 -6.7010e+00 -2.2776e+00  6.9862e+00  7.4298e+00
-#>   5.5939e+00  7.5994e+00 -9.1281e+00 -1.0513e+01  3.1338e+00  2.2780e+00
-#>   4.0438e+00  9.6674e+00 -8.9905e+00 -6.4321e+00 -2.2459e+00 -2.7442e+00
-#>   1.7242e+00  3.3175e+00 -1.9010e+01 -5.4166e+00  2.1368e+00  1.3751e+00
-#>   9.9083e-01 -4.8365e+00  4.8378e-01  1.6902e+00  2.2087e+00 -3.4764e-01
-#>   5.4890e-02 -7.2534e+00 -2.5185e+00  1.2493e+01 -2.4334e+00 -6.3627e+00
-#>   9.5054e+00 -4.4612e+00  1.8223e+01 -3.7394e-01  3.6688e+00  7.5635e+00
-#>   2.2095e+00  4.6699e+00  9.9971e+00 -6.1401e+00 -1.0550e+01 -1.5797e+00
-#>  -9.8198e+00  9.8286e+00  5.4587e+00  1.0334e+00 -3.0479e+00  1.7580e+00
-#>  -1.2700e+00  2.6403e+01 -4.4089e+00 -8.6787e+00 -8.4800e+00  4.4394e+00
-#>   2.8741e+00 -1.9863e+00 -3.2514e+00 -8.6687e+00  9.8180e-01 -1.7195e+00
-#>  -5.3990e+00  2.8465e+00  1.2402e+01 -8.9504e+00  2.4825e+00 -1.1775e+00
-#>  -4.4550e+00 -8.1371e+00  6.7530e+00  3.7530e+00 -1.0986e+01 -2.4960e+00
-#>  -2.8496e+00  2.0082e+00 -4.6388e-01 -9.5946e-01  1.0177e+01 -3.2097e+00
-#>  -9.3333e-01  1.1898e+00 -4.7260e+00 -5.8988e+00  1.5007e+00  3.4544e+00
-#>   3.4987e+00  3.1301e+00  1.5726e-01 -1.7080e+00  4.4588e+00  4.7642e+00
-#>  -2.8878e+00 -6.6251e-01 -3.1073e+00 -3.9279e+00 -6.6434e+00 -1.0017e+00
-#>   7.0556e+00 -1.6597e+01  2.5636e+00 -1.2606e+01  7.0497e+00 -1.9386e+00
-#>  -1.7508e+00 -1.2447e+01 -1.0717e+01  5.4835e+00  5.0638e+00 -3.2764e+00
-#>   4.4112e+00 -5.0044e+00 -1.2282e+01 -1.1170e+01 -6.7855e+00 -6.1712e-01
-#>   4.2148e+00  1.2460e+01 -8.7559e+00 -2.6166e+00 -1.9169e-02  1.4165e-02
-#> 
-#> Columns 13 to 18  6.2091e+00 -8.8143e+00  7.2104e-01  7.2597e+00 -1.5449e+00 -3.9369e+00
-#>  -9.3598e+00  5.4552e+00 -2.3879e+00 -1.3020e-01 -1.2810e+00 -6.2295e+00
-#>   1.6856e+00  5.1225e+00 -6.9954e+00  1.0107e+00  1.1691e+00 -1.7933e+00
-#>  -3.2094e+00  4.2271e+00 -1.9460e-01  6.9871e-01 -1.8737e+00 -6.9162e+00
-#>  -5.5509e-01  1.1511e+01  9.7265e-01 -4.1372e+00  6.0545e+00  4.0498e+00
-#>  -6.5004e+00  3.4623e+00  3.2891e+00 -1.6722e+01 -3.2627e+00  1.3829e+01
-#>  -2.0242e+00  1.3205e+01 -8.8209e+00  9.6122e-01 -4.1757e+00 -2.7641e+00
-#>   1.6212e+00 -1.6687e+00 -4.6750e+00  2.5037e+00  2.8769e+00 -8.8224e-01
-#>   6.9457e-02 -9.3542e+00  1.1674e+00  6.2896e-01 -1.2544e+01 -5.5968e+00
-#>  -6.5634e+00  9.7293e+00  6.9997e-01 -4.0996e+00  7.8806e+00  1.4738e-01
-#>   8.3325e+00 -7.4368e+00 -1.0828e+00  4.0310e+00  1.0186e+01  1.6531e+01
-#>  -9.2470e+00  1.6434e+00 -1.0851e+01 -2.7858e+00 -4.7217e+00  1.2983e+00
-#>   3.9226e+00 -3.9145e+00  2.3867e+00  1.5888e+01  6.1320e+00  5.3633e+00
-#>   9.3941e-01 -3.2628e+00 -1.4717e+00 -9.6640e-01  9.1844e-01  5.7049e+00
-#>   1.4595e+00  1.0113e+00  6.7419e+00 -4.7996e+00  4.4700e+00  1.5168e+01
-#>  -4.1207e-01  7.1577e+00  7.2850e+00  3.0012e+00  6.6942e+00  3.8092e+00
-#>  -5.2720e+00  1.0192e+01  2.8500e-01 -8.5123e+00  1.5029e+00 -1.8070e+00
-#>  -8.1098e+00  1.7250e+00 -1.1236e+01 -7.7844e+00 -5.8833e+00  3.2155e+00
-#>   1.0499e+01  1.0083e+01  6.7967e+00  4.1068e+00  1.0419e+01 -5.3592e+00
-#>   2.2096e+00  2.1852e+00 -1.7720e+00  6.6781e+00  9.3576e+00  2.2509e+00
-#>   5.5971e+00  3.2116e+00 -1.7596e+01  6.4026e+00 -3.3209e+00 -8.8648e+00
-#>  -1.5210e+00 -1.3915e+01 -2.6886e+00  1.4786e+01 -4.6967e+00 -7.7192e+00
-#>  -7.6820e-01  8.3450e+00 -1.9640e+00 -6.6878e+00 -2.4184e+00 -7.9350e-01
-#>   7.4614e+00  5.3085e-01  5.5515e+00  6.6437e+00  5.0550e+00 -1.3595e+01
-#>  -7.7221e+00 -4.6417e+00  3.9403e-01 -1.3851e+01 -1.0311e+01 -1.3360e+00
-#>   6.6917e+00  1.2243e+01  1.4173e+01  2.8141e+00  1.1076e+01  5.8287e+00
-#>   1.0787e+00  2.0636e+00 -1.0247e+00  3.1193e+00 -9.4799e+00  8.2093e+00
-#>  -1.5041e+00  7.0780e+00 -7.8316e+00 -1.5371e+01  7.1263e+00  5.9010e+00
-#>  -8.2715e+00  9.9196e-01  2.1497e+00 -2.1559e+00 -1.2710e+00 -1.9854e+00
-#>   1.1778e+01  1.7334e+00  3.4908e+00 -2.5709e+00  1.1166e+01 -7.3452e+00
-#>  -5.5082e-01 -4.1049e+00  2.4903e+00  8.0472e+00 -3.3471e+00  3.5109e+00
-#>  -5.2418e+00  6.6022e-01  3.3127e+00 -5.4575e+00  1.7177e+01  6.8944e+00
-#>   1.4118e+00  3.3244e+00  4.7817e+00  1.2690e+01  1.1262e+01  5.2541e+00
-#> 
-#> Columns 19 to 24  5.7987e+00  9.9046e-01  1.3476e+01  2.2373e+00  4.8450e+00 -3.3785e-01
-#>  -6.4077e+00 -5.5996e+00  7.7242e+00 -4.9139e+00 -1.0795e+01 -2.7210e+00
-#>  -7.8080e+00 -9.8749e+00 -8.0328e-01 -5.6685e+00 -6.5051e+00 -7.3282e+00
-#>   4.4262e+00 -4.0880e+00 -3.1434e+00 -1.5922e+00 -3.5972e+00 -4.8698e+00
-#>   8.9376e+00 -1.6125e+00 -3.0707e+00  2.6647e+00 -8.6386e+00  1.0014e+01
-#>   1.0788e+01 -8.3395e+00  9.1440e+00 -2.9792e-01 -5.8211e+00  5.8759e+00
-#>  -6.4088e+00 -1.2216e+01 -6.1322e+00 -6.9714e+00 -1.8267e+00  3.8377e-01
-#>   6.6123e+00 -1.4050e+01  4.3107e+00  5.4709e+00  2.8144e+00 -4.5840e+00
-#>   1.3259e+00 -9.2734e+00 -6.1174e+00 -1.4116e+00  8.0506e+00 -1.7683e+00
-#>  -1.3442e+01 -1.9701e+00  7.1781e+00  4.8789e+00 -1.0749e+01  4.3147e+00
-#>  -1.0874e+01 -2.7071e+00  5.0782e+00  3.2553e+00 -3.3053e+00  5.0959e+00
-#>   4.7742e+00 -9.7450e-01  4.0312e+00  4.8259e+00  2.0546e+00  1.6256e+01
-#>  -6.2039e+00 -7.7300e+00  2.7282e+00  9.7221e-01  1.7280e+00 -1.1206e+01
-#>  -3.4114e+00 -5.4780e+00  1.9617e+00  1.9247e+00 -1.0711e+00 -6.1604e-01
-#>  -4.0607e+00  6.2097e+00 -6.0131e-01  1.2949e+01 -9.1161e-01 -5.7228e+00
-#>  -4.4931e+00 -6.3089e+00 -1.1048e+00 -3.4241e+00 -5.9472e+00 -1.1120e+01
-#>   1.4240e+00 -6.8421e+00  5.2643e+00  1.2383e+00  3.9429e+00 -1.4067e+00
-#>  -5.4825e-01 -1.4219e+01 -7.1825e-01 -3.0608e+00 -4.8196e+00  1.2558e+01
-#>  -9.2407e+00 -1.0295e+01 -4.5680e+00 -6.3542e-01 -5.6528e+00 -1.1106e+01
-#>   2.5570e+00  1.3100e+01  3.2021e+00  3.3889e+00  7.0477e+00  6.1366e+00
-#>  -3.4840e+00  6.5269e-01 -1.1508e+01  3.1331e-01 -2.6091e+00  8.6547e+00
-#>  -3.7476e+00 -1.5001e+00  4.7892e+00 -7.2544e+00  6.8576e+00 -2.3800e+00
-#>   1.2855e+01  4.0347e+00 -4.9090e+00  1.6522e+00 -3.1490e+00  7.2887e-01
-#>  -1.1071e+00 -1.1343e+00  7.0469e+00 -4.3051e+00 -6.4920e+00 -2.1248e+00
-#>  -6.6440e+00 -2.3582e+00 -5.1569e+00  1.7230e-03  4.1819e+00  2.6481e+00
-#>   8.3807e+00 -1.0780e+01 -4.4174e+00  6.4726e+00 -1.4760e+00 -1.2222e+01
-#>   1.2455e+01  1.3503e+01  6.7132e+00  3.2466e+00  9.0439e+00 -1.0962e+00
-#>  -7.0192e+00  1.2580e+01  7.1967e+00 -9.6692e-01 -2.7230e+00  7.7308e+00
-#>  -7.6719e+00  7.7843e+00  5.2237e+00  4.2453e+00  6.7228e-01 -9.5117e-01
-#>   3.6038e+00 -6.0937e+00 -1.4692e+00 -8.3313e+00  1.5697e+00  4.7399e+00
-#>  -1.2687e+00  1.0278e+01  8.8936e+00  2.8074e+00  2.1382e+00  3.5530e+00
-#>  -1.9756e-01  9.7066e+00  3.1387e+00  7.1569e+00  2.4309e+00 -8.4276e+00
-#>   7.7145e+00 -3.3162e+00  9.2129e+00  1.5138e+00 -6.6110e+00 -5.4324e+00
-#> 
-#> Columns 25 to 30  3.3448e+00  9.3022e+00  9.5999e+00  6.9354e+00 -5.0616e+00 -6.9058e+00
-#>   5.9573e+00  3.5726e-01 -9.9542e+00 -3.3779e+00  6.1937e-01  6.9114e+00
-#>   2.3182e+00  3.1634e+00 -5.6326e+00 -9.8146e+00  1.1031e+00  4.6291e+00
-#>   3.7577e+00  2.9056e+00 -1.2893e+01 -7.0990e+00 -5.8789e+00  5.4299e+00
-#>  -2.0569e+00 -1.2289e+01 -1.2072e+01 -8.9648e+00  6.1291e+00 -4.8387e+00
-#>   2.4939e+00  3.9368e+00 -3.3553e+00 -3.7930e+00  2.2403e+00  1.5174e+00
-#>   4.7722e+00 -2.3783e+00 -3.4526e+00 -7.4857e-01  4.7284e+00 -5.9305e-01
-#>  -4.4239e+00  2.2117e+00  1.6915e+00  1.6951e+00  6.1919e-01  7.1786e+00
-#>  -8.2375e+00 -1.1487e+01 -3.2686e+00  1.9129e+00  3.4600e+00  6.1822e+00
-#>   9.6635e+00 -9.8398e+00 -1.4344e+01  1.1570e+00  3.5877e+00 -3.4732e+00
-#>   6.8537e+00 -1.6598e+00  1.4938e+00 -5.8247e+00  1.0250e+01 -5.8323e+00
-#>   7.7056e+00  4.1740e+00 -1.0906e+01 -3.1704e+00  2.1178e+00  7.9223e+00
-#>  -5.4461e+00 -4.5399e-01 -2.1419e+00  3.8774e+00 -1.9193e+00  5.1422e+00
-#>  -3.3196e+00 -2.4690e-01  2.8738e+00 -2.3391e+00  8.6590e+00 -3.8078e+00
-#>  -1.1259e+01 -8.6266e+00 -2.9897e+00 -6.1473e+00  4.7037e+00  4.2867e-03
-#>   1.7899e-01  2.0520e+00  3.4490e+00 -6.9371e+00  2.1701e+00 -6.4096e+00
-#>   1.5743e+01  1.5727e+00 -8.0126e+00 -1.6293e+00 -6.9487e-01  9.7779e-01
-#>   3.3199e+00  4.9184e+00  9.6237e-01 -1.3034e+00 -6.9815e-01  1.5008e+00
-#>   1.7309e+00  1.7355e+00  3.3040e+00 -1.1595e+01  6.4257e+00 -2.0536e+00
-#>   9.0737e-01 -3.9525e+00  4.7663e-01  6.2257e+00 -3.6348e+00  7.5429e-01
-#>  -1.6843e+01 -2.9076e+00  5.7143e+00  5.1020e+00  5.1055e+00  6.6208e+00
-#>  -1.3249e+01 -3.3027e+00  1.0028e+00  1.5995e+01  2.1968e+00  4.1618e-01
-#>   1.8869e-01  4.9186e+00  6.6267e+00 -1.1144e+01  1.2518e+01 -9.4801e+00
-#>   7.2734e+00 -4.7865e+00  7.6977e+00 -2.5835e+00  6.9857e+00 -1.1586e+01
-#>   1.9629e+00 -7.9237e+00  8.7230e-01  3.6833e+00  1.0750e+01  7.8505e-01
-#>   2.2178e+00  2.6605e+00 -3.7676e+00 -8.0763e+00  2.7502e+00 -1.2980e+00
-#>  -3.3262e+00 -2.9508e+00 -6.5470e+00  1.2192e+00 -5.2483e+00  1.0321e+00
-#>   1.7810e+01 -1.1750e-01  4.5557e-01 -8.7464e+00  1.0301e+01  1.7402e+00
-#>  -1.9575e+00 -7.2531e+00 -5.6767e+00  4.7833e+00  1.0424e+00 -8.0447e-01
-#>   9.4049e+00  4.8694e+00 -3.4308e+00 -4.0935e+00  7.7406e-01 -8.3420e+00
-#>  -1.0877e+00  5.9013e-01 -8.3452e+00 -6.0349e+00 -2.7546e+00 -1.5708e+00
-#>  -4.6523e+00 -1.1413e+01 -7.7647e+00 -1.9390e-01  2.4253e+00  9.0860e+00
-#>  -5.4974e+00  2.3994e+00 -4.8689e+00  2.3564e+00  1.8606e+00 -4.4984e+00
-#> 
-#> Columns 31 to 36 -5.3996e+00 -4.0268e+00  1.0155e+01 -9.1129e+00 -1.8529e+00 -5.9859e+00
-#>  -1.1870e+00  9.0568e+00 -5.8302e+00  2.2678e+00  1.4472e+01 -7.0498e-01
-#>  -3.1906e+00 -3.1934e+00 -5.5015e+00  1.1824e+00  1.1202e+01  5.9822e-01
-#>  -4.0763e+00  5.6502e+00 -1.1138e+01 -5.5295e+00 -6.0794e+00 -9.0080e+00
-#>   1.4124e+01 -8.0207e+00 -1.2152e+00 -2.9232e+00 -1.2306e+01 -6.1385e-01
-#>  -1.5200e-01  1.1691e+00  5.9120e+00  9.2071e+00 -1.2299e+01  1.2687e+01
-#>  -4.5410e+00 -7.0710e+00 -6.6179e+00 -9.8775e-01  9.3494e-02  9.7834e-01
-#>  -8.4071e+00 -4.0261e-01  3.7053e+00  3.0223e+00  1.0222e+01  5.3209e+00
-#>   3.2476e+00 -7.2387e-01 -8.1169e-02  5.4877e+00 -4.6037e+00 -3.2524e+00
-#>   7.5073e+00  7.5331e+00  3.5078e-01 -1.7766e+01  6.9871e-01  2.0369e-01
-#>   2.2511e-01 -2.8367e+00  2.4152e+00 -2.4106e+00 -9.4229e+00  5.5880e+00
-#>   4.5797e+00 -3.7804e+00  4.6730e+00  1.4458e+01 -1.3785e-01  5.0291e+00
-#>  -4.1167e+00  7.9227e+00 -4.6733e+00 -7.1373e+00  1.6804e+01  4.3820e+00
-#>   8.5466e-01 -1.0724e+00 -9.3670e+00  1.3986e+00 -2.3463e+00  6.8835e+00
-#>   1.4297e+00 -4.2350e+00  9.0299e-01 -2.1366e+00 -5.3894e+00 -2.7163e+00
-#>  -5.1330e+00 -1.9521e+00 -1.0124e+01 -9.9508e+00 -5.3707e-01 -1.4966e+00
-#>   4.0181e+00  1.2901e+01  1.0945e+01 -1.3669e+01 -8.6388e+00 -4.9692e+00
-#>   2.7508e+00  4.1519e+00 -2.2471e-01  6.2458e+00 -1.2785e+01 -8.8307e+00
-#>  -7.6881e+00 -5.7679e+00 -7.7874e+00 -5.1019e+00  1.2219e+01  1.0732e+01
-#>   1.0853e-01 -5.1944e+00 -5.5256e+00 -4.7699e+00  6.8243e+00  6.7839e+00
-#>   6.1342e+00 -7.3736e+00 -4.8876e+00  6.9786e+00  1.0532e+01  1.9532e+01
-#>  -7.2484e+00  4.8691e+00 -6.1163e+00 -2.4217e+00  9.1861e-01 -7.0110e+00
-#>   9.6960e+00 -1.0111e+01  4.1335e+00  8.7717e+00 -4.5262e+00 -1.9480e-01
-#>   1.0519e+01 -2.2790e+00 -5.3841e+00 -1.5754e+01 -4.0010e-01  5.5570e+00
-#>  -1.7870e+00 -4.3120e+00 -8.3646e+00  5.1878e+00 -3.8105e+00  5.4791e+00
-#>  -6.1213e+00  2.6515e+00  3.8603e+00 -7.3960e+00 -2.4629e+00 -3.2340e-01
-#>   2.4277e+00 -6.0967e+00 -6.1727e-01  9.6043e+00  8.1445e-02 -1.4710e-01
-#>  -1.2415e+00  1.0929e+01 -8.9700e+00 -8.4613e-01  1.9625e+01  3.9038e+00
-#>  -2.6456e+00  4.6997e+00 -1.1893e+01 -3.1487e-01  1.3500e+01 -5.4387e+00
-#>   3.8680e+00  1.5947e-01  1.4209e+01 -1.6989e+01 -9.0062e+00  3.9879e+00
-#>   9.3004e-02 -1.7697e+00 -4.5250e+00  7.9326e+00 -4.7444e+00 -9.6158e+00
-#>   4.5809e+00 -5.6403e+00 -5.0796e-02 -5.9721e+00  7.1729e+00  1.8613e+00
-#>  -5.4809e+00  1.7845e+00 -8.3083e+00 -5.4888e+00 -2.0179e+00  4.4420e+00
-#> 
-#> Columns 37 to 42 -4.7855e+00 -8.5175e+00 -2.1857e-01  8.4637e-01  4.3660e-01 -4.9466e+00
-#>   6.6450e-01  4.6346e+00  2.6782e+00  1.2447e+00  3.8391e+00 -8.0498e-01
-#>  -1.1130e+01  4.2234e+00  7.1485e-01 -1.3026e+00 -3.9174e+00  4.1765e-01
-#>   8.3580e-01  1.3614e+01  3.9566e+00 -1.0713e+01  3.2567e+00 -8.4020e+00
-#>   7.6823e+00  3.8936e+00  3.1150e+00  1.7484e-01  4.1188e+00  1.7145e+01
-#>   2.6063e+00 -2.1592e+00  3.8965e+00  2.8466e+00 -1.9440e+00 -3.9970e+00
-#>  -4.6261e+00  1.2012e+01  1.5563e+00 -3.5953e-01 -1.2297e+01  2.0953e+00
-#>  -5.7136e+00 -1.6435e+00 -3.1510e+00  3.9118e+00 -6.4690e+00 -2.4583e+00
-#>   1.0818e+01 -1.7416e+00 -9.2704e-03 -7.6917e+00  7.7324e+00 -2.0817e+00
-#>   7.8892e-01  1.1853e+01  1.3597e+00  1.0491e-01 -9.5310e-01  5.4994e+00
-#>   2.7781e+00  8.3081e-01  1.2500e+00 -2.1802e+00 -1.1053e+01  1.8365e+01
-#>  -5.6311e+00 -9.8486e+00  2.1665e+00 -2.4590e+00 -2.2154e+00  6.3714e+00
-#>  -4.4185e+00  9.0754e+00 -4.9723e+00  4.0641e+00 -2.4477e-01  1.1559e+00
-#>   3.8127e+00  4.3389e+00  1.8134e-01 -1.1438e+01  1.4193e+00  9.3104e+00
-#>  -2.5857e+00  1.2633e+00  5.8720e+00 -2.4309e+00 -1.2344e-01  7.6392e+00
-#>   1.4477e+00  1.0082e+01  2.5753e+00 -3.5404e+00  1.1207e+01 -2.6013e+00
-#>   2.7662e+00  1.3309e+01 -1.3517e+00  8.3357e+00 -5.1607e+00  1.3805e+00
-#>  -5.8706e+00 -1.7318e+00  2.8528e+00  1.9490e+00  2.3670e+00 -3.3641e+00
-#>   2.8802e+00  1.3370e+01  1.8935e+00  1.2284e+01 -1.6036e+01  5.7118e+00
-#>   4.4686e+00 -3.7408e+00 -6.7829e+00 -8.1280e-01 -6.6447e-01  9.5033e+00
-#>   7.0788e+00 -7.5171e+00  1.4984e+00 -8.3525e+00  1.0497e+01 -4.3993e+00
-#>  -2.2460e+00 -9.2195e-01 -4.3723e+00 -1.5712e+01  1.6383e+01 -6.0502e+00
-#>   1.1910e+01  7.7961e+00  9.7424e-01  1.0935e+01 -9.6052e+00  1.5065e+01
-#>  -2.9370e+00 -1.0523e+01 -5.9286e+00  5.4506e+00 -9.7247e+00 -1.1499e+01
-#>   1.0424e+01 -1.3085e+01  6.8869e+00 -6.7083e-01  6.7912e+00 -3.1413e+00
-#>   9.4696e+00  1.3121e+01  8.3503e+00  5.9973e+00  1.9038e-01  8.1521e+00
-#>   1.2455e+01 -3.8683e+00 -1.1434e+01 -6.7846e+00  5.1093e-01  1.0225e+01
-#>  -6.8801e+00  1.1190e+01  5.6877e+00 -1.1347e+01 -8.6186e+00 -8.9230e-01
-#>   6.7188e+00 -1.9456e+00  2.7723e+00 -2.1238e-01  5.3955e+00  1.3889e+01
-#>   2.5153e+00  1.3421e+01  1.0657e+01  1.0813e+01 -4.0389e+00  4.3639e+00
-#>   2.3169e+00 -6.5107e+00  1.1071e+00  5.0577e+00 -1.9199e+00  2.6549e+00
-#>   3.7111e-01  4.1470e+00 -1.4876e+00  6.1349e+00 -3.8993e+00  1.3496e+01
-#>   3.9903e+00 -2.5372e+00  2.3113e+00 -1.3299e+01  1.5658e+01 -2.1184e+00
-#> 
-#> Columns 43 to 48 -5.3974e+00  1.1470e+01  8.0168e+00 -3.1737e+00  1.8088e+01  6.7016e+00
-#>   8.4993e+00  7.5901e+00 -3.8522e+00 -1.5282e+01  2.7711e+00 -3.9009e+00
-#>   5.4798e+00  8.2084e+00 -4.3640e+00 -5.6298e+00  2.8553e+00  6.0013e-01
-#>   1.5117e+01 -3.8010e+00 -6.0661e-01 -8.6124e+00  7.9563e+00 -8.5322e+00
-#>   1.0926e+00 -5.7196e+00 -7.8125e+00  2.9962e+00 -2.4641e+00 -3.9728e+00
-#>  -6.9750e+00  3.9074e+00 -9.7547e+00  1.8155e+00 -9.4942e-01  1.4033e+00
-#>   7.8986e+00 -2.0966e+00 -4.5563e+00 -9.9288e-01 -3.9173e+00  9.6752e-01
-#>  -2.1302e+00 -3.0563e+00  5.9163e+00 -7.8072e+00 -5.2031e+00  4.8729e+00
-#>  -3.2703e-01 -5.2523e+00 -1.7050e+00  6.8670e-01  6.2048e+00  2.3407e-01
-#>   3.8850e-02  9.0948e+00 -3.6616e+00 -6.1142e+00  7.1880e+00 -1.5549e-01
-#>  -6.8444e+00  4.9850e+00  2.1235e+00  4.4228e+00 -3.7574e+00 -8.3262e+00
-#>  -3.4195e+00  7.0761e-01 -6.9093e+00  2.0876e+00 -1.3823e+00  1.0538e+01
-#>   7.4552e+00 -2.3935e+00  1.1926e+00 -3.8090e+00 -8.4963e+00  6.3465e-01
-#>  -2.4364e+00 -1.1607e+00 -6.0250e+00  5.9558e+00 -3.0977e+00  1.4288e+00
-#>   1.1530e+01 -5.9603e+00 -4.1689e+00  1.2215e+01 -5.4808e+00  1.5768e+00
-#>   3.7910e+00 -1.1363e+00  2.6201e+00 -3.1267e+00 -7.8219e+00 -8.4765e-01
-#>   2.4809e+00 -2.6889e+00  9.0631e+00  1.3237e+00  6.6355e+00  4.8937e+00
-#>   2.8647e+00  1.6486e-01 -3.0638e+00  3.1515e+00  1.1612e+01  2.6685e+00
-#>   2.0890e+00  8.1913e+00 -5.3012e+00 -7.3151e+00 -5.1472e+00 -5.9437e+00
-#>   3.8042e-01  9.8934e-01 -1.1415e-01  4.0065e+00 -2.8091e-02  4.7139e+00
-#>  -1.7802e+00  4.7301e+00  1.3032e-02 -3.6905e+00 -6.6128e+00  5.0625e+00
-#>   3.5504e+00 -8.2332e+00  3.7096e-01  9.8524e-02  3.4324e+00  2.8376e+00
-#>  -9.2314e+00 -6.9470e+00  7.7655e-01 -5.2664e-01 -8.1657e+00  3.3885e+00
-#>  -1.4360e+01  1.4785e+01 -3.7140e+00 -6.3802e+00  6.6014e+00 -1.1859e+01
-#>  -7.1300e+00  1.4353e+01 -9.8361e+00  9.5068e+00  4.5462e+00 -4.0918e+00
-#>   2.4090e+00  2.6027e+00  8.3217e-01 -2.4805e+00 -4.2917e+00 -5.1048e+00
-#>  -3.8890e+00 -9.2622e+00 -2.4179e+00  9.1678e+00 -1.0741e+00  1.4275e+00
-#>   1.0725e+01  3.6622e+00 -1.0390e+01  3.2040e+00 -8.1849e+00 -1.4154e-04
-#>   2.5473e-01  1.4962e+01 -6.1097e+00  7.3571e+00  3.6925e+00 -1.6461e+00
-#>  -4.1509e+00  6.1366e+00  3.2962e+00 -5.4747e-01  3.5650e+00  2.8045e+00
-#>  -4.0917e+00 -2.1213e+00 -6.7569e+00  2.4749e+00  4.6886e+00  1.8253e+00
-#>   4.5376e+00 -1.0110e+01  1.1122e+01 -1.1604e+00 -8.3908e+00 -3.9016e+00
-#>  -4.6760e-01  1.4584e+00  5.2119e+00 -3.8599e+00 -8.4865e+00  2.6378e+00
-#> [ CPUFloatType{20,33,48} ]
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-Conv2d — torch_conv2d • torch
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    Conv2d
    
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    Conv2d
    
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    input
    NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)
    weight
    NA filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)
    bias
    NA optional bias tensor of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or a      tuple (sH, sW). Default: 1
    padding
    NA implicit paddings on both sides of the input. Can be a      single number or a tuple (padH, padW). Default: 0
    dilation
    NA the spacing between kernel elements. Can be a single number or      a tuple (dH, dW). Default: 1
    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
    
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    conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor 
    
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    Applies a 2D convolution over an input image composed of several input
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    .. include:: cudnn_deterministic.rst
    
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    Examples
    
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    # \dontrun{
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-# With square kernels and equal stride
-filters = torch_randn(c(8,4,3,3))
-inputs = torch_randn(c(1,4,5,5))
-nnf_conv2d(inputs, filters, padding=1)
    #> torch_tensor 
-#> (1,1,.,.) = 
-#>  -1.2050  3.5601 -7.8325 -0.6184 -5.9300
-#>   6.9992  0.3900  1.3901 -2.5574  6.5135
-#>  -2.3490  3.9786  4.1985 -9.6240  8.3693
-#>   0.1293  0.5186 -2.7322  9.4996 -1.1448
-#>  -5.2164 -12.5999  8.9623 -8.9888  7.3018
-#> 
-#> (1,2,.,.) = 
-#>  -0.8450 -8.2603 -2.9019 -5.5598 -4.9509
-#>  -5.6410  1.2666 -5.8790  2.1525 -1.3928
-#>  -0.2024  2.1010  6.3255 -0.5656 -2.5797
-#>  -0.3285  6.7826  5.8021 -1.7078  7.7191
-#>  -0.0922 -8.5321 -5.4983  0.7221 -3.5291
-#> 
-#> (1,3,.,.) = 
-#>   2.5094  9.2626  5.0731 -3.6366  3.3318
-#>  -0.2142  2.5216 -0.6932  8.3285 -0.4724
-#>  -2.7548 -2.3855  1.6016 -9.5280 -0.9662
-#>   1.6404 -7.5112  0.5470 -3.7868  0.9206
-#>   0.0776 -1.8698  3.2148 -2.8353 -1.9503
-#> 
-#> (1,4,.,.) = 
-#>   -1.0442  10.8465   5.7719   2.8028   0.3426
-#>   -1.9633   3.7087  -3.1929  -2.0919  -8.2913
-#>   -0.9261  -3.8192   9.3410  -9.4944   8.6766
-#>   -5.9383  -2.2824  -4.0878 -10.5887  -2.2831
-#>    6.7283   2.9823   0.6066   0.9479  -1.2099
-#> 
-#> (1,5,.,.) = 
-#>  -3.4877 -1.5824 -6.4475 -1.1290 -2.3183
-#>  -2.2229 -5.1762  9.2085 -3.5268 -3.8315
-#>  -6.3696  2.3858 -12.1739 -2.6510 -3.0961
-#>   4.4813  3.8243 -0.4043  2.4245 -0.3913
-#>   0.6648 -2.4358  0.4671 -3.7840 -3.5538
-#> 
-#> (1,6,.,.) = 
-#>   0.8825  0.3176 -6.6245 -6.0051  2.4430
-#>  -2.0265 -0.9768  6.7438 -7.4899  2.0777
-#>  -6.8935 -2.2333 -3.3011  2.0151  2.5786
-#>   0.2469 -4.8563  2.3537  0.5968  3.6600
-#>  -1.4781  0.2248 -3.1730 -6.7766 -3.0347
-#> 
-#> (1,7,.,.) = 
-#>   1.5889 -4.5167  0.0906  7.6198  1.8365
-#>   5.0933 -2.3551 -2.6166 -7.5808 -2.0514
-#>   4.0381  6.9844 -2.3409  2.5503  6.2012
-#>   6.1860  8.5588  0.8248  1.6048 -4.7339
-#>  -2.7566  3.9998  8.0454 -1.6114 -0.9366
-#> 
-#> (1,8,.,.) = 
-#>   4.3153 -3.2445  0.4090  1.5033  6.4462
-#>   6.4586  2.8058 -3.3607  5.9048 -2.4407
-#>  -3.9344  4.6081  5.3624 -0.2189 -0.3718
-#>  -4.3796  5.2653 -0.4628  8.5329 -6.1232
-#>   0.7405  5.8740  5.0339 -0.6710  0.8794
-#> [ CPUFloatType{1,8,5,5} ]
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    NA optional bias tensor of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or a      tuple (sT, sH, sW). Default: 1
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    NA implicit paddings on both sides of the input. Can be a      single number or a tuple (padT, padH, padW). Default: 0
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    NA the spacing between kernel elements. Can be a single number or      a tuple (dT, dH, dW). Default: 1
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    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
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-inputs = torch_randn(c(20, 16, 50))
-weights = torch_randn(c(16, 33, 5))
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    #> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 8   1.6673  -7.0390   6.6254 -13.9097  -9.2569  16.1860   3.0554 -10.1673
-#>    0.5097   0.4955   5.0436  -2.2605   3.2619   1.0813   8.2840   0.2337
-#>   -2.5402  -1.4119  -6.6792   0.1580 -12.0189  -2.8290   5.7440  10.1772
-#>   -6.6198   3.2866  -5.4540   2.0456   3.6355   7.4837 -10.9005  -5.6650
-#>   -4.6747   5.7736  -5.4007   1.2693  11.4762  -4.7003 -11.3653  10.1400
-#>   -7.4153   3.9516  -1.1868  -8.5232   6.7928  -4.0613 -12.1954  -0.3727
-#>    3.3955   1.0648  -4.0864 -17.0257  -3.4968  -4.9347 -25.7271  21.9273
-#>    4.0335  -6.6207  -3.6258   2.1134   2.8998  -6.8774  10.1487 -13.0325
-#>   -0.7558  -1.2329   0.9330  12.2378  -7.8136  -0.6589   7.9927   1.7098
-#>   -7.7476  11.0338 -19.6308 -11.0443  -1.9108  -9.0969 -28.1443 -17.9797
-#>    2.7401   3.4970  -6.8374  -8.1578   1.5898   9.3208   4.2085   2.1895
-#>    3.1280  -2.2744   3.1126   3.8573  -5.6051   2.0268   2.0747  -4.4871
-#>    3.2771   4.8169   5.4240  -9.3082 -13.5079  -0.9002  -4.1523   5.5984
-#>   -3.5472   5.3228  -0.3093   4.4044   0.9189  -6.4752  -6.5480   7.4419
-#>    4.2013  -5.9650  -2.9532  -1.8045   4.5317   0.8231   5.2075  -0.9755
-#>    1.6888   6.3345   8.0313  -4.3153  -1.1394   6.0972 -11.7238  -4.2819
-#>   -0.4871   7.4014  -7.3978  -2.9028  -5.0449  -7.1657  -3.8541  12.1393
-#>    0.8300  -2.1989  18.5210  -6.7047   1.8052 -12.0026  -3.6008  12.7004
-#>   -5.7435  -4.3290  -5.1741  -4.9028  10.4417  -1.7813  22.8982   4.6736
-#>   -1.8184   1.6866   2.4972 -10.6010  -1.6615   4.2066   1.2256   1.8704
-#>   -0.1898   7.4767  21.4571  -1.9761   8.4871  -4.1088  -4.3248   3.2695
-#>    2.8483 -11.0110   7.3260   0.7999   3.4976   5.7566  12.8856   4.1038
-#>    6.9826   9.8364  -0.9694  -7.2072   2.1751  -9.7158 -20.3140  -4.7768
-#>    0.2967  -5.7820   1.4100  -1.7626  -0.5567  -2.8442  11.3496   0.9707
-#>    2.5259   7.6391   2.8814   8.4676  -8.6886 -16.8321   4.9696 -23.1633
-#>   -3.6094  -7.7429  -3.9817 -10.3633 -14.8211 -17.5294  -7.3543   1.4741
-#>   -3.6305   1.5408  -2.2006   8.1134   1.1769  -3.0465   5.4047  -7.1063
-#>   -0.2056  -3.0883   2.6383   1.4320  -0.4309  12.6957  20.8133  -1.0188
-#>    0.7445  -2.1118   1.9698  -2.5180   0.3591 -17.7520  -5.9807  -7.7320
-#>    3.2831  -7.6201  -3.1870  -8.1050  -8.9224  -8.1959  -4.7006 -10.0807
-#>   -1.7069  -4.7109   5.5420   0.4770   3.4143  15.3951  -8.6150 -10.6463
-#>    4.9333   8.1781   2.6415   5.0800 -18.8387 -13.3438   0.6360  16.3387
-#>    2.4939   8.4147  -4.0838  -7.4578  -5.3779  -6.8781   3.7359   9.3849
-#> 
-#> Columns 9 to 16   4.5019   6.9288 -21.3335   0.1317  -3.8836   8.3590  -0.8443 -14.3927
-#>  -16.2857 -11.4820  -1.4024  -4.5153   1.9758   0.6438  -2.7761  10.1361
-#>    6.2420  -0.6045  -9.7481   9.3241  -3.6135 -18.6855   6.7881 -13.9915
-#>   -5.0487 -20.3735  28.4884   3.9854  20.9909  -0.9224   7.0288   0.5621
-#>   -3.5373 -15.3569 -16.2579  16.5925  -7.9983  -9.0862  -6.5943   0.2070
-#>   13.7029   5.4600  -7.5984  -6.4349   7.3174  15.5161   2.0219  -1.0355
-#>    3.6252 -20.4715  -3.8647 -20.7537   7.9750   0.7964 -23.7706   9.1045
-#>   -8.7950   4.0828  -4.5432  -6.1630  -5.2080 -14.8888   9.1260  -5.9252
-#>    2.9966   4.8565   6.0109  -8.5393  19.8787   3.5916   4.0040 -11.9161
-#>  -11.2591  -1.4102   8.5272  -8.9441  10.3345 -13.4799  -0.9897  15.2467
-#>   -0.6632   8.7770 -15.7039 -13.8364   1.2821  -0.6675   0.4520  -0.1015
-#>   17.6055  15.4246  -8.5307  13.7545   6.1392   2.1994   5.0420  -3.2646
-#>    5.2685  -6.2487   3.4035  -6.7103   4.0975   7.0198  -4.6465  -7.1480
-#>    7.5268  -3.9966   9.9433  -3.9344  -6.8677   6.9691 -16.2891  22.7894
-#>    5.2153  16.0680  16.4236  18.3746  -5.5733  -1.2358  -6.8528   1.5499
-#>    4.9569  -3.0487  -1.9484   0.2809   6.0391   0.1817   2.8151 -11.2639
-#>   29.2747   7.6625  -8.7079   2.9776  -6.0190  -6.8860  -3.6460   2.3650
-#>    8.1998  -6.1037  -6.8587  -6.0042   4.2655  10.6292  -6.2967   0.6930
-#>  -14.4762  16.4459  -5.9406   2.0809   1.8835  -3.1317  -8.9321   7.7105
-#>  -10.7999  18.7483  17.0834 -10.3893  -8.3545 -21.9094   4.0324   2.8529
-#>   -6.2272  -3.0918   1.9956  -4.4372  -6.3339   5.5328  -6.0873 -10.6416
-#>   -5.9817  -6.9611  12.4265  -5.7492   3.2616  -6.5960   6.5149   9.2240
-#>    8.0986  13.9525  21.4184  -4.3470   9.5946 -18.5461  -4.5604 -15.4378
-#>    3.7024  -0.7089   2.5686  -0.7054   6.0643  12.1628   1.9096   6.4987
-#>   10.7530   8.5200  -5.6235   2.7819   6.0993 -11.6488  18.3623  -5.3475
-#>   -0.5536  -8.5774   0.8194   7.4602 -11.3240   5.9768  -0.3840  19.7081
-#>    0.0850   0.9243   2.3494  12.6000  -6.7075   8.3957  -7.9544  15.4305
-#>    1.4948   1.6475 -10.0262  -0.6992 -20.6828   5.6993   3.6267  -8.4266
-#>   -3.9348   2.5818   4.7236   2.2962  -8.2360   3.9196   1.1975  14.9994
-#>    7.2291 -14.4824  -7.3407  26.6378   4.6584  -1.7192  -4.7126  -6.6773
-#>    3.6147  -1.7117   3.2719  -0.1214   3.2613 -12.0086   0.1217 -11.8695
-#>    2.6794  -8.8757  20.7463  13.6375 -16.4280  14.3362 -13.4708   9.1810
-#>   11.7337   6.5971   4.8227  -7.0015 -12.9038  22.7038 -19.3795  -5.8170
-#> 
-#> Columns 17 to 24   1.2900 -12.9553  -5.3724  15.7169 -10.8972 -11.2200  15.5498  -5.0368
-#>    2.0881   8.0263  -3.9599   9.3983 -10.2026   2.6811  -5.9319  -7.8959
-#>   14.9066   7.4782  -0.0191  -0.6530  -7.1498   9.4691 -11.9981  15.5602
-#>   14.5616   5.2064   2.5051   8.9144  17.6842 -13.5671  -3.9489   7.7915
-#>   -9.7128   6.7477  -3.1993   1.6146   4.5125   5.5407  -8.2644  13.3508
-#>   -1.6102  11.9611   3.4677  -4.9038 -10.2262 -14.4330  -1.2227 -12.5192
-#>   12.0007 -14.9848   0.9463   3.5031  17.4539   9.3853  -1.6176  -9.6670
-#>  -18.4721   3.3622   8.1907  -2.8999  -0.2637  -4.8769   1.5459  16.6135
-#>    2.1102   5.4149  -1.7087  -8.4103  12.7386  -9.9078  -9.3740  12.7742
-#>    5.3657   5.1944  12.6113   8.4549  15.0561 -10.8250  11.2875  -8.3614
-#>    4.6304   9.5156 -12.6676  -0.9019 -10.6211   9.9370   3.3044   3.2810
-#>   -4.6163  17.0280   1.7505   6.2276 -11.3447  -6.5945   1.7422   0.4499
-#>   -3.0405  -5.3669  -1.8315  -2.1907  -6.5904  -0.9806  -0.6572  17.8599
-#>    4.4193  18.8522  10.2451   2.9497  21.5558   0.1101  -5.1855  -9.5943
-#>   10.4257  13.2620   2.2022 -19.5122   2.7581  -3.0927   1.9384   7.3883
-#>   10.6498  -4.7587   2.6286  -6.5227  -4.8643 -16.4109  11.9423 -26.0725
-#>    3.3000   9.5719  11.0230   9.4557  -4.9966  -4.4449   4.4763 -18.4214
-#>  -15.8333  -7.3848   4.0740   6.5697  -5.7416  -4.4711   8.0335  -3.5673
-#>   11.2547  11.7943 -27.4378  -5.9246   8.8010  20.3112  -3.0324  16.8786
-#>   -2.6789   4.0453   0.9162  12.4067   5.2146  -2.5160  -9.9002  16.7632
-#>  -19.3769  -4.4897   5.8856   5.8640 -14.5450  11.0825   1.5232  -3.6449
-#>    6.0268  12.5299  13.8870 -16.7846  -9.3446   0.2853   0.7077 -11.7750
-#>  -12.7468  10.7160  31.0325  34.9867  -5.8856 -11.4765 -10.4610  -2.5913
-#>    9.3896  -6.9599  -7.6279  -5.4562   0.0194  -6.9496  -2.8263  -5.2451
-#>    4.0190  -7.9305  -7.1466  13.0450  -5.5047  -4.4661   0.9274  -4.7775
-#>  -13.4600  -0.2809  -0.7337   9.6314   1.7702   3.1068   9.6836  -3.6410
-#>   -0.2111   0.3837 -10.4137  -3.0894   8.8677  -2.2090   7.1865  -3.8667
-#>   13.4651   7.1765  -1.6467  -4.9560  -9.6018   8.1471  -7.0450  17.0260
-#>  -17.4430  -1.8719  -8.0025 -15.9723  11.6767  -7.6636  -7.0144   3.6021
-#>   18.1120  -0.0250 -18.4019   9.8127  -2.1751  -2.7134   8.3464  -1.5143
-#>   -3.8167  10.2692   6.5966   7.9059   1.1906  10.1638   1.1750  -2.4618
-#>    0.1265 -15.9027   2.7108  -6.4089   4.9851   1.2107 -11.7264  -1.2454
-#>    0.4524   0.2076   8.5678  -6.2080   0.6733  -7.2406   0.8338   3.5937
-#> 
-#> Columns 25 to 32  -0.0293  14.3552   3.6278   7.7933  -5.4766  12.6476   1.8703  -2.8825
-#>   -3.2107  -9.0870   7.0099   5.4586   2.7359   2.2581  -3.8164   8.9624
-#>   13.3699  -4.2088  -4.7406  -8.7636  -5.9517   2.2329   1.8833  21.2654
-#>   12.8826  -7.6350  -5.2774   2.4938   9.3194 -13.5257   5.9729  20.7365
-#>   10.1223   4.4283 -13.0912  -9.2791  -1.8947   0.4621   3.4288   8.0413
-#>   -9.1683   7.7603   3.0516  -5.1836   2.5889   8.7409   5.4927   4.1831
-#>   -1.1417  10.9090  -7.1233  -3.5366  -1.2165  -3.2714   3.8553   1.1704
-#>    6.5912   4.1697   7.7293   7.9883  -9.9514  -5.4976  12.7131  -0.1949
-#>   -4.4844  -5.7362  -3.9648  10.1807  -6.2640   5.3054  -4.7080   4.0852
-#>   12.6666  12.5517  -4.1955   7.5249  -0.7672   3.2016   6.5978  20.8497
-#>   -7.9074   7.2881  -4.8798  -1.4548  -8.7517   3.9573   9.5329  -4.8086
-#>    0.3169  -3.8601   1.0305   0.5803   1.2628   4.3491 -12.1400  -2.7427
-#>   -0.3467  13.3886  -6.3979   2.0452   2.3282  -2.0779  -2.4584 -16.2387
-#>   -5.7151   9.2725  -0.0182   1.8764   6.9025  -7.1871 -10.9513  -6.7978
-#>    2.4439  -1.6886  -7.7059   0.1886  17.1840  -6.1218  -5.3057   5.4783
-#>  -16.9525   6.0691  16.1657 -11.4185   3.4004  10.6037   4.7744   2.5440
-#>    9.5940   2.7641   7.8744  19.0521  -3.7261  14.7517   3.5746  -0.4117
-#>    0.4406 -12.6979  -6.8351   3.1805 -11.3419  -1.0762   9.5960  -4.1767
-#>  -11.9938  -6.4985   9.3770   6.9766  13.3734  12.1043  14.3930  -7.0519
-#>   -2.6697  -2.4679   1.1577   3.0986   6.3140 -21.5625  -0.3885   0.4263
-#>    0.3668  -3.9383  20.8603   6.6954  -2.7366  -0.2231  -0.5172 -13.0651
-#>   19.9507   1.6934   7.1942 -15.3310   3.2378   8.5819 -14.4676   0.2683
-#>   -2.5942   1.2458   9.2131   9.8679   8.3943 -12.4564   4.6223   3.0705
-#>    2.0545  -1.3666  -0.5177  -9.2698  -7.6781  -1.9081  -3.4399  -3.9338
-#>    8.1274  -0.1854   5.2153 -11.7028   8.3669   4.8400 -13.4963  11.7893
-#>   -0.1239   6.0862  -7.2274   0.1266   2.1916  -9.3351 -11.9587  -7.4673
-#>  -20.4607   4.0279   7.3662  -0.7345   5.7650   3.7344  -0.5221  -2.1129
-#>   11.4275 -12.4868  13.0824  10.5353   4.0451   1.8503  18.2519  -0.7406
-#>   13.0047 -15.2421   9.2275   1.4917 -11.4757  -5.3868   3.8877   4.4101
-#>  -10.2052  18.6921 -13.4181 -20.0699  12.8999 -12.0904   0.3806  -5.5696
-#>   -2.4651 -10.7724   8.9007   1.9734  -6.8980 -11.9957   6.5855  -4.4643
-#>   -1.0077  -4.1095  -2.1013  -0.5406   5.8052   9.8388 -20.2044  -2.6470
-#>  -13.4139  -2.1838  -1.5024  -6.3311  -6.9503   3.3761   7.3990   7.9767
-#> 
-#> Columns 33 to 40   1.8592   0.9761  -2.6963  -3.8153  -1.8090  29.3572 -11.8402  11.8519
-#>   13.3483   5.1859   4.9052  -0.4473  12.8364   2.1392  11.2240   1.1636
-#>    2.9127  -0.4139  -9.8866   2.1386  -0.3520  -3.4992  15.7872  -1.9768
-#>   -8.7932  10.8440  -0.3113   9.1708   9.1623 -17.6051  14.4912  -2.6571
-#>   -5.4569  14.1891   1.8065  17.6668   2.9545  -3.7614  26.8721   6.9820
-#>    5.6159 -18.5910  -0.6680  -9.2854   2.9860   3.0446 -10.3332  -9.6543
-#>    6.3406   1.3053  -2.6143   0.2845   7.4428   8.5844   1.7969  -1.5125
-#>   10.9239   9.0027  22.6637  18.8975  -4.2188  12.3108  26.5707   8.6210
-#>    1.2633  -9.0421   0.9745   2.6553 -11.1772 -12.4188   1.3170  -5.5598
-#>  -12.8508   6.2380  -4.0621  14.0566  16.2944  -9.8447   8.1251  11.8212
-#>   10.8548  17.6606  -0.6996  -0.6495   1.4041   2.4018   7.0875   3.6098
-#>   -3.0109  -0.2962   6.1093   6.9493  -6.2817   4.9856   9.9639   0.5589
-#>    0.5565  -0.0766   1.2112   1.3211 -15.6654   0.7365  -1.7787 -11.4870
-#>    1.2893 -10.7174  -7.8088  -3.3270  -0.2953 -10.0667  10.5948  -3.7164
-#>   -7.5186  -7.9848 -14.5698   2.6338   6.9504  -1.8349  14.9450   9.0353
-#>   -3.8914   2.3522  -3.9384 -14.0598  11.8851   3.7743  -4.7451 -18.7160
-#>   -7.9114   4.4290   1.2984  -6.1245  10.8040   9.1649   9.3862   3.8943
-#>   13.5192  -0.3540  11.7615 -10.4322 -10.5759   5.1959 -10.5739  -2.5550
-#>    4.1348 -11.0223  -7.5457  -1.8305  10.4051   9.8118   5.1208   5.6186
-#>   -4.8263  -6.7968   5.5902  12.7691   7.9257   1.9996   6.5328   8.3577
-#>    0.7681   7.4137  12.9171  -3.9795  -2.3992   5.0543  -2.1804   6.8998
-#>    7.7960  -0.6246  -0.4729   2.6995   5.4509  -0.4801   2.1876   4.9933
-#>   -6.3639   0.3813  12.9830  -2.4914  -6.7726   1.7444   4.6179  18.1880
-#>    6.7140   2.4883   8.6126  18.9305 -10.5894   3.3141 -21.9243   6.1176
-#>    0.2413  -8.9027   3.3162 -13.0962 -12.3184  -3.8753 -23.0307   9.0538
-#>    0.0690 -15.0865   1.8602  -8.6315   0.5089  -0.7437  -5.9029   3.0767
-#>    7.1451  -8.2692 -11.5116  -8.8415  -2.0170  -7.0324  -3.6529 -10.3245
-#>    6.1436  -0.5680   4.2794  -0.3316  -7.4252 -10.7011   7.0821   7.7291
-#>   -1.3997   6.5191  -0.3735  -6.5781 -10.0386  -6.9226  -2.5003  -9.5659
-#>   -0.0958  -0.5062   2.8923  -3.4108  13.0374  -2.9370 -10.4329  -3.1501
-#>   -8.9609   2.8826   0.2373   5.7849  -1.4304  -5.0798  12.6846  12.8611
-#>   -0.7236  -1.0731  -3.0510  -9.1343 -18.2925   1.9743  -0.5286  -7.9524
-#>   10.3919  13.0850   0.9242  -0.9788  -6.7773   0.8554  -4.7509  -5.3867
-#> 
-#> Columns 41 to 48   2.9985 -15.5869  14.0373  10.4037 -11.1567   6.1454   3.8270   7.7380
-#>   -1.6961 -13.4523   2.7170  -5.3499  -5.4221  10.5192   9.8349 -12.1479
-#>    6.2730  22.6802  -7.0960 -14.5570 -13.0734  24.1451   3.9818   4.1208
-#>   -1.6407   8.4934  -6.2687  -4.5555  -9.5029  15.1876   8.3357  -3.0061
-#>  -12.4818  22.4958  -6.0299  -8.6670 -10.3577   2.4604  10.0247 -10.8954
-#>   -6.8458  -6.1891   1.1851   2.8739   2.8533 -10.4645   1.1883  -5.4889
-#>  -18.7008  15.1108   8.5953  -5.3394  -2.5032   6.4586   3.9125   3.6250
-#>    2.3395   4.7180 -12.1364  -2.4026   6.8779   8.2134 -22.8381  -3.5587
-#>   15.7945  -2.3467   5.5252   4.7041  -6.9610  -8.8453  -2.2314  11.2754
-#>   -4.7374 -10.4254  10.9580 -13.0100  -6.7119   0.3756  16.1945   8.9202
-#>   -6.7409   4.5078   4.3692  -4.6630  -5.9395  -3.9312  -5.5752   1.4460
-#>    1.1583   8.8820   2.3125  -8.9832   1.3572   5.7707   4.3816 -12.6141
-#>    1.1008   6.2556  -5.6111  -7.0873   0.3703   3.5847   0.6188  -5.6186
-#>   -5.2835  -8.3637  -0.4618   5.6316  -1.7833  -8.5794  27.2491 -10.4892
-#>   20.8741   8.0639 -15.5463  -7.4028   3.6106  -1.1026   0.9431  -4.4649
-#>  -11.5054  -5.7812  19.4337   8.4593   7.6994 -14.5653   3.4009  -3.5659
-#>  -13.8089 -11.7804  10.2397  -9.4642  -1.2912 -14.0060  -6.6083 -11.9624
-#>   -8.1229  -1.9699  11.3045   4.4870  -1.1037   9.2278  -1.8346  -6.5917
-#>    8.1041 -13.1606 -10.0848   2.4079  -2.9753 -14.2536  -8.2830  25.1459
-#>   -0.6315  11.0338 -11.0389   4.5759  -1.7932  18.5247  -1.9834  -1.4612
-#>  -11.3549  -0.3709  18.6718  14.9252  -3.7786  -3.9237  -4.9956  18.7164
-#>    9.1790   5.0087  -4.8780 -10.6788  -4.4748  -7.9521   2.3956  11.5146
-#>  -14.9233  15.5074  13.5288   6.9501  -3.6375   6.9654  -4.0685  -8.8993
-#>   -1.0245   4.7855 -17.6042   4.1208   1.0548  -8.5788  -1.7149   1.0637
-#>   -5.1705  13.9681   1.5986  15.8308  -3.9914   8.3166   1.1126 -11.7575
-#>    2.7832 -21.6206   0.3791  -5.4350  18.9783   0.2022   6.9384 -23.6424
-#>   -1.1030  -4.5934  -1.3159   2.3002   2.8800  -6.3804  19.1025 -11.9186
-#>    1.8217   2.0252  -7.3752   7.9834  -7.1141  -2.5236  -6.1048  14.2583
-#>   -7.0049   7.8784 -14.4774  -6.2137  10.1269 -15.6804  -6.8904  -4.6614
-#>   10.0098   8.4671   2.8254   9.2529 -16.7819  -3.6309  18.5456 -10.9203
-#>   -0.6490  -6.3074   0.1846   4.3354   9.1514 -12.6226  -3.6332   2.4962
-#>   11.7775  -1.8143   1.0238 -24.0774  13.3624   0.3635   1.7397  -2.5824
-#>  -13.1913   2.5277 -12.8994  11.0974  -3.0293   2.3099  -0.8103  -0.4676
-#> 
-#> Columns 49 to 54  -8.7345 -14.2799  19.6350  13.5499   1.2315  -0.6506
-#>    1.8484  -3.2535  -1.7324  -4.3937   4.1733  -0.5664
-#>    4.5108  13.2596  -4.4172   1.1377   3.8892  10.2091
-#>    7.5502   1.0322  -9.1656 -15.8731   5.8408  -5.2419
-#>   -2.2194   8.1269  11.4847  -5.2739  -5.0256   0.6433
-#>   -4.1628  -0.1577   1.1322   9.3402   4.0405  -2.5579
-#>   -2.4551  21.4342  -0.1518   0.2222  -0.9053  -2.7780
-#>   10.6593  -5.1491   0.3012   6.6177  -8.4856 -11.1581
-#>  -11.0929   5.7385  -7.6207  -0.4959  -1.9499   2.3844
-#>   -4.4337  -0.3689  -3.9270  -5.0881   3.1659  -2.1273
-#>    1.6437   0.2100  -3.8414  -0.0949  -6.4092  -2.9916
-#>  -10.3230  11.3656 -11.9414  11.8128  -5.7713   2.6200
-#>   -4.4394  -3.3487   2.7528   3.8788  -1.4185  -3.0869
-#>   15.5630  -6.5025  -3.7371  -6.6396  15.1147  -0.3786
-#>   -3.5993  -1.2830 -12.9225  -9.8014   6.9267   7.7926
-#>    1.7097   5.5124  -1.5683  -0.6643  -3.9920   5.6530
-#>   -6.5705   5.0899  10.4892  -1.9026   2.3790  -1.7570
-#>   -9.6865  -3.1503   5.3201  -1.7256   1.1293 -11.1168
-#>   -4.3125   4.4370 -12.0474  -2.7226  -2.3004  -5.8733
-#>    6.1387  -0.5534  -3.6291   9.1612   7.0297   1.6557
-#>   -7.3084   9.0659   4.9779  -3.0977  -8.6063   0.9893
-#>   20.1320  -8.8056 -12.3684   6.0840  -1.6641   4.9350
-#>   -3.0457   9.5258 -15.0322   7.7813   1.9794  -3.8203
-#>    0.5432  11.5130 -26.2873   3.9583   8.1876   1.2593
-#>   -5.0762  12.8825  -2.9607   6.7716   2.5277  -4.8843
-#>   -3.7113   7.3289  -1.8610   6.7798   4.0303  -2.3700
-#>    1.6202  -2.6948  -2.9366  -5.8387   7.3190   3.1959
-#>    1.5668  -3.8051  15.0303  -7.3860  -2.8871  -3.9352
-#>   -2.9899 -11.8343  -5.7724  -2.9391  -0.1798  -5.7964
-#>   -2.9931   4.5864   7.4006 -21.0496 -10.1332   5.1122
-#>   20.1991  -6.5378 -11.0948   0.1378   7.5001  -2.8968
-#>   -2.7824 -17.3546  -6.3185  -4.0368  12.6433   7.2955
-#>    0.4172  -4.4497   9.9093  -2.1454   8.5557  -5.9379
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 8   4.7596  -2.3708   1.3418  10.7392 -17.9585   1.6765   9.6306   3.8409
-#>    2.5987 -11.9234   5.6093 -15.4016   8.0875 -11.7553 -11.3902   8.7209
-#>  -11.8239   2.1317  -9.5167   8.5529  -5.5999  -8.7316  -8.2675  12.9377
-#>  -10.1575   1.3394  -2.0061   6.9250   0.4979  -6.0003   2.4058   8.1599
-#>  -12.7172  -1.2893 -14.0448  -3.4130   1.7811 -20.7635  -1.9964  -6.3883
-#>   -1.9728  17.3032   8.5093   4.1580  -6.2205  18.0032  -1.7450   2.6589
-#>    3.5242 -16.6260   7.9907  -1.1886 -14.1955  -7.1470   7.9479   7.5243
-#>    0.5199  -7.8098  -5.8438  -1.0146  -5.0564  12.4495  -1.4228 -13.6972
-#>    0.1183   2.4802  12.7326  -6.0799  -1.6831  -6.8250   9.4823  10.3730
-#>  -12.6137  -9.4658  -8.8782  -6.6302  -1.4513   2.4207  23.0161  -8.5375
-#>   -1.3446  -0.7345  -8.8468   5.0346   0.8501  -2.3404  -1.5003  -9.4550
-#>    0.1520  -2.6062   4.4482  -8.2256  -5.3967  -2.2162  -3.8969 -12.9057
-#>    7.7093  -0.7853   7.2272   3.5096   1.8355  16.3071  -9.8703  -3.0199
-#>    1.7039  -7.8727   4.3188  -6.3996   9.3464 -11.0304 -13.0502   8.8442
-#>   -5.0000  -0.4485  -5.8625   0.0639   0.5489  -8.9442   3.0572 -10.4764
-#>    2.1024   1.1575  -7.3141  -3.1504  16.4440   1.9490   4.5007   1.5405
-#>    2.2730   3.5882  10.6477  -8.7302   5.1281  -5.5718   1.3978  -8.2277
-#>   13.8522  -0.6762  10.2911  -0.2770  -7.3265  -7.6784   1.6779   2.8196
-#>   -3.8064  -8.9778  -0.1520   1.8026  -7.8690  -4.4951  12.5004   1.7195
-#>  -14.5756   4.0345  -6.2921   5.8808 -14.3969   9.0118  -9.3838  -3.6941
-#>    4.7236  -4.2033  -8.3519  -4.8034  -9.7167   9.4050  -4.0289 -11.0073
-#>    5.3240  -7.0412   1.8191 -11.3021  12.7159   9.0398   8.1060   2.1124
-#>   -6.4629  -2.5036  -7.5835   3.1017 -11.9905  -8.9950  11.2799  -2.9150
-#>    4.2767   4.3477   1.0800  -2.5998   2.8277  10.7435   9.3785  -9.6822
-#>   -4.4303  20.7324 -13.0990   6.1930  -6.2529   0.7377  -7.4001   1.3349
-#>    4.4720   2.8303  13.6389  -7.9152   8.4899  -3.1532   8.3466   0.4572
-#>   -8.3623  15.7370 -10.7719   4.4032   9.9055   3.7759  -2.8595  -1.7905
-#>   -4.8775   1.8915  -0.8530  10.2657 -15.9343  10.3426 -17.7020   2.0729
-#>    0.5613   7.1272  19.9651  10.9519  14.0965   2.4315  13.4485  -1.4175
-#>    4.8714   8.1319 -10.5989   0.3411  -9.3492   7.6087   9.2715   9.6984
-#>   -1.3565  -7.4533  -4.8182 -17.3904  -0.5276  10.7067  -8.2810  -3.0651
-#>    5.9905   1.0488   8.9671  16.8183  11.3654  -6.4070   3.9435  19.8416
-#>    3.5665  -2.9801  -1.3262   8.9776   9.5841   2.2091  -2.9954  -3.0973
-#> 
-#> Columns 9 to 16  -8.8073  -4.6426 -13.9884  -8.2320  -8.5217  -0.4775  -7.4151  -0.6725
-#>   10.0546   4.3717  -1.5863   1.3989   7.1575  32.8837   3.4143  -0.7769
-#>   -8.8521  -9.9139 -19.5135 -24.9521   0.7116 -11.7181  -5.9171  -5.1497
-#>   -3.9503 -13.6314  -5.8326  17.8691   6.6317  -7.6286   4.8017  10.8725
-#>  -12.6786 -19.4017  -9.4109   2.1331   6.1469  11.5832   4.8107   5.5907
-#>   23.6291  -7.2367   7.5458   1.4301   9.4437  -3.4435   9.0182   6.6427
-#>    2.9611   0.3858  -5.4467   4.0315 -21.9230 -10.6043   3.1202   1.5852
-#>    3.9447   3.1710 -12.9762   8.3977   9.4892  -0.5182   2.5873  17.2142
-#>   -1.3194 -13.1223   6.6894   7.0331   6.4437 -12.6453 -14.5226  -3.8072
-#>  -10.4038  -7.1690  14.0111  -8.5985  -3.2720   0.6783   8.7759   8.8802
-#>    2.2255   7.0874  -0.6579  -2.0451  -3.7498   0.6559   5.8038   3.7002
-#>   14.7065 -16.0033  -5.9809   5.9349   7.9587  -9.9855 -17.9542  -3.7742
-#>   -1.4432   1.0548 -14.3342  13.1105   2.1546  -9.1868  11.2042  -0.9932
-#>   12.1984 -12.8600  -2.5919  -0.8897  11.8771  10.0617  -8.9914 -11.5302
-#>    7.5468 -12.1222 -26.0768  -6.2028  -0.7591  -1.2010  -6.0798 -11.1108
-#>    8.7007  20.9135  -2.9024   1.6970 -11.4094   8.3639  10.0946   6.3963
-#>   12.4040   4.4726  24.0844  -5.9889   9.6856   1.4792  -1.8792   2.4935
-#>    2.5080  18.3730  -7.6850   9.7258 -11.3493  13.4590  -1.7244   9.6702
-#>   -5.4163 -14.2738  -4.3608  10.3636   3.8761 -12.1021  -6.0958   1.4944
-#>   -5.7651 -10.1263 -17.2028   5.7702  -3.2691  -9.1889  -2.5732  -3.3792
-#>  -10.0545   4.1091  14.0733 -10.4982  -5.7342   2.3171  -6.4773   3.3353
-#>    2.6782  -2.8125   2.7590  -1.9897  -9.1181  11.7984  -9.0359   1.9233
-#>   -5.2344  10.2287  -2.8782   1.6119  -0.4775  -8.1459   1.1576  -9.2421
-#>   21.9949  -8.0431   1.7145   4.7288 -22.4366   4.0880 -12.0808   1.6562
-#>    7.5187   9.7269   0.6820  -2.6167  -1.1286  -1.0346 -18.5821  -5.9096
-#>    9.3526  -5.6035   5.2310   9.7429  -3.3496 -10.7880  -7.9118 -15.5445
-#>   13.6427  -0.6605  11.3279 -13.0369   9.5204  -4.8265   1.9385 -12.8543
-#>   -1.0619 -13.6995  -2.8276   4.1690  18.6644  -5.6350  -5.1124   1.0305
-#>   17.6611  11.3794   4.5687  20.1350   3.8588  -1.0378   1.2913   5.7805
-#>   -9.2243   5.0020   3.6845   5.8329 -21.8466  -0.6807   0.5413  -0.8968
-#>   -0.6459  -3.9581  -4.2478  -0.6667  -8.4845  -8.5001   2.8692   8.0881
-#>   -4.8527   8.2332  -5.3847 -15.9781  14.2163 -13.9345  -3.4176  -7.2562
-#>   -1.8429   9.3069  -3.8057  -1.8225 -20.4709   2.7353   2.2712  -5.1033
-#> 
-#> Columns 17 to 24  11.0662 -14.5526   0.0697 -19.0220   4.6790   3.9423  -2.6753  -2.9578
-#>   22.7239   6.9675   0.6424   3.7429  16.1882  -1.5087 -16.0539   0.1969
-#>   -2.5550  -3.6210  -3.0403   3.2895  -9.0184  -1.8333  -1.3289   3.3007
-#>   -0.8008 -13.9414  -3.3939 -18.7868  -4.2458   9.7082   1.1865 -18.0323
-#>   -5.0873  11.8188   7.8642  11.5776 -16.0727   9.4908   8.4264 -15.9399
-#>    9.5338   0.5156   0.8218  24.7352  21.4326  -1.3723  -3.8085   4.0040
-#>   -3.6941  13.9830   3.1614  -2.4057  -5.2300  12.6373   0.7486 -10.2209
-#>    9.6279   5.2914  -2.1667  -2.6882  -8.5979 -17.3494  14.1774   4.5505
-#>   -4.9814 -17.5009 -15.7751   2.3943 -14.3330  15.0807 -10.8200  -6.3016
-#>    1.0375   7.5819   1.8242  -2.1025   3.4078  -3.6889  -3.6870  13.7473
-#>  -10.5505   8.0478   7.4250 -13.4552 -14.5107   3.5241  -1.5158   4.6435
-#>  -17.6329  -8.9843  -0.5239  11.9668 -16.0605  -6.9913  -9.6883   2.4846
-#>    3.9703  10.5117   2.4437  -9.5863  -8.5419   4.4804  15.5980  -8.0862
-#>   -3.8186   2.0125   2.0543  10.0792  -7.5268  -4.8376   5.2885   7.0757
-#>  -12.5689  -8.2614 -13.2759  12.4033 -19.5609   3.0369 -12.8527  -5.7830
-#>   -4.6646   0.1257   6.0242   4.2538  14.7014  -8.8032  -5.5753 -10.4496
-#>  -10.2244  -0.1577  13.0210  10.7659  -2.0343  -4.4945  -2.3216  -4.1657
-#>    0.5649  -8.4057  26.7812  -6.8665   1.8525   5.4294  -5.2585   4.8750
-#>   -3.3987   2.3718 -11.2156  -6.4853 -24.5315   2.5088  -9.4710   4.6227
-#>   -7.9132   1.8410  11.1107 -11.2720  -4.2379  -2.7606  -5.2530   5.4750
-#>   -1.3218  10.2854   1.9285 -11.3502 -11.5231 -10.5135  -5.7751   1.5308
-#>   -4.0835  -6.0362   1.4618  -5.1609   6.1913 -23.7162   6.2317  14.7307
-#>  -17.7677 -17.0161 -13.5697   2.4813   8.5010  -6.2259 -17.0806 -15.3741
-#>   11.8556  -4.8654 -11.5342  -4.0967  11.5691   1.4684   7.8547   9.3808
-#>    1.8603 -12.9068   8.3558  -8.8895  23.3471 -17.4276   1.8093   7.6683
-#>    9.5079   3.8055   7.7854  20.9576  -6.5707  15.5529   2.2398  21.4755
-#>   -5.8233 -11.2661 -21.2273   1.8201   1.9201   0.9845 -15.9731   7.6355
-#>    4.1302  -6.0695  -8.4113 -10.1005 -17.6905 -16.4190   6.3698 -12.3462
-#>  -17.9427  15.6452  -5.7567 -10.7185  12.8775  12.0741  21.4769   6.2621
-#>   17.3807   3.2854   2.0233   0.4028   7.7633   4.6181   7.1242 -18.0502
-#>   -2.6197 -14.2332   0.0485   9.7610   3.9811 -18.7429  12.5354  -6.3579
-#>   -9.0786   0.7897   3.0636 -13.3762   1.8459   8.2314  11.0418   3.6651
-#>   -2.2425   2.0437  -5.5021  -3.8781   9.9642   2.8891  -2.2660 -11.2317
-#> 
-#> Columns 25 to 32 -32.6698  -8.6093  -5.0129 -11.3094  24.4550  -0.5704   4.8933  -5.5201
-#>    1.2721   0.6487 -10.9427  11.6122  12.3350   0.6388   1.1434   3.0152
-#>    8.1651 -14.7024 -10.9904   2.9376   5.4284  11.7591  15.3603   5.0108
-#>   21.2289 -14.6108   3.7578  23.7265   0.1149   3.1634  -7.0561 -25.7672
-#>   -6.4865   1.1524  13.9062  17.8211   4.4666   6.4054   4.1235   7.4927
-#>  -24.0737 -10.2488  -0.7837 -19.7711  -3.5405 -14.7599 -11.0616  -5.3042
-#>    0.7789 -17.5263 -15.1668   8.9913  -2.4021   9.1531   9.4817   7.4751
-#>   -6.0258  -2.9461  13.5621  15.0060  -5.9288  -7.2522  13.6002  -4.5906
-#>   -5.3995 -12.3453  -2.9668 -10.6944   0.0278   3.2880  -3.7492   5.9309
-#>    7.2386  11.6521   6.7375   4.7726  10.0783  -2.9344 -19.2203  -3.8975
-#>   -3.8018   5.9055  -9.5356 -17.2161   1.6056  -3.9030  -2.1836   6.9117
-#>  -22.5193   1.6938  -3.8648   6.0373   6.0638 -18.1423   2.2888   1.0071
-#>   -0.0912   4.5126  12.5903   5.7911  -4.7895 -15.4148  -1.4881  -7.6777
-#>    8.1668  -1.7108 -10.8280  16.8029   8.0118   0.1464   0.8490 -14.1483
-#>    9.1446   1.4189   0.1367  -3.3847   9.2224  -2.9130   2.0731 -13.4869
-#>   -1.2765  21.6822   5.4889  -5.0004  -3.4493  -2.9209  -9.9478   7.0307
-#>   -4.0638   6.2533  -9.5217  -2.7831  -0.7303  -1.4290   7.1134   8.5808
-#>   -5.4981 -17.6882  -6.5339   9.6844   6.3867   3.6524   4.6800   2.2742
-#>   -1.4602   1.0576   5.5146 -13.2516 -14.4008  13.8107 -18.6747   5.9339
-#>    4.9861  -4.7771  20.6300  15.5541  -6.0074   5.1128  -0.4189  -2.9462
-#>   -1.4486  16.5589  19.4602   1.9526   3.5924  -7.0477  -4.7282   1.1304
-#>   -0.3443   9.8070 -16.0824  -8.1581   3.0776 -12.2659  11.0605 -22.4701
-#>    8.4359  13.0354  13.1142  -1.8719 -10.8537   3.0869  11.9608   4.5571
-#>  -11.3341  -7.3854  -0.7040  -8.9241  13.4767  -0.2498   1.4465   7.7940
-#>   -9.5916   2.3081   9.4823  -5.6463  -4.7764 -17.7450  14.1080  17.3467
-#>   -0.5413   2.8060  10.5147   1.7546  -3.5436  -6.1599 -14.4421  12.4286
-#>    1.0812  -9.0801  -1.7089 -18.4518   2.6798  -9.2463  -9.5171   6.6413
-#>    4.7788  -0.4133   2.3009   0.4398  -5.0574  -4.7202  -5.6557 -13.3648
-#>   12.6261  -3.5589  -2.4662   1.1209 -23.9096  -2.2812   6.0163  -7.3213
-#>    1.1171  20.2254  -3.8290 -15.7041   5.4705  -1.6709  -2.7804  10.5089
-#>    9.1564   5.4900  -7.7571  14.2231   5.9711  -1.7668  -1.0377 -10.1896
-#>    4.7588  -0.2267  -7.0410  -8.5436 -12.5272 -17.1023  18.5268  -5.4560
-#>    8.0987  -4.0980  -0.5273 -11.0374  -7.9019  10.1087   0.3397  -2.1266
-#> 
-#> Columns 33 to 40  -0.6891   5.1512  -5.9616   8.3684  -6.5541   6.7590  -1.5969  -3.3233
-#>   -7.1381  -5.8954   2.3722   1.1176  -1.5182  -0.4858   4.1055   0.5757
-#>  -12.1200   1.3603   4.2367   6.1071   7.8496   0.7433   7.9517   7.7278
-#>   -6.7656  -8.5868   2.2282  -0.5914  -4.4096   6.2110  -1.2708  26.3763
-#>    1.6977  -3.0817   0.9547  11.4690   8.4347  -0.5228   9.5944  -1.6138
-#>    4.3577  -4.5182  -3.2134  -9.1625  -9.4456  -3.9361  -7.6599  -1.6247
-#>    2.2651   6.7368   9.1694   7.3736  11.9476 -25.9909   7.7422   7.7991
-#>    6.8059  -2.5797 -14.0586  -0.4316   4.3446   1.4599  14.3628 -19.3045
-#>   -2.8454  -5.5364  -2.1157  -6.6131  -0.8127 -10.4145  -5.6694  10.0690
-#>   12.9586   1.9260  -3.7913   6.1513   1.6149   0.6863 -12.8163  14.7614
-#>  -17.7495   0.5206   3.7497   1.8556   7.8428  -7.2770   0.9337  -3.4212
-#>  -13.5710   6.9932  -2.0936  -2.0107  -1.9852  -1.7724  17.9422   7.7792
-#>   -3.7335  -7.4195  -2.4935  -3.5388   2.8353  -8.7390   6.6221  -8.8004
-#>   -9.6807 -15.3747   4.5706  -4.4656  -1.0659  -1.2077  -4.3275   0.8064
-#>   -1.5919   8.4855   4.3687   2.5481   3.1221   1.6635   7.3577  -9.0880
-#>    0.5298   7.4672   4.1995   9.2957   0.1546   5.4831 -13.1099  -8.7616
-#>   -9.7556 -26.8600  -9.2641 -11.0434 -10.5407 -10.8140  -0.6515  -5.8294
-#>   -0.2582   5.4815   3.4851  -1.8528   9.1895   5.7904   2.5789  15.6803
-#>    6.5863   3.9697   9.1019  -7.4410  -7.5782 -16.5352  -7.4639  -8.7216
-#>    3.1715   6.5688  -5.2957  -4.3494  -4.3843  17.6800   6.5693 -15.0530
-#>    8.9078  -1.9871   4.1219   7.5827  17.6163  14.7036  -0.9792  -9.6755
-#>  -16.9230  -0.4241  -8.5176  -0.6277 -15.3566 -11.9403 -10.3961   3.3311
-#>    1.5606  -8.6993  -6.6938  -7.1477  12.3101 -12.5791  -3.6695 -11.3222
-#>   -7.9792   4.5259   9.5120   1.5035   1.6864   1.8410  -7.3137  -4.2054
-#>   -5.8180  10.1696   4.6879   5.7464  -1.5702  28.5283 -13.1240   6.4315
-#>    2.2603   3.7824  10.2709  -9.1696   6.4035   7.2668   7.3593  -2.1308
-#>   -0.7404  -3.1743   9.5376 -12.0170  -5.0920   3.1090   5.7006  -8.4534
-#>   -6.4615 -25.2439 -10.9172  -2.2400   4.7356  -2.1891   2.6827 -10.5127
-#>    9.5131   7.4656  -6.5773   4.6907   4.1973  -3.3962   7.0490  -7.4300
-#>   16.4267   3.5051  10.1579   7.0891   9.1914   6.3337  -2.7389  14.1222
-#>    1.3694   0.3559   1.9932  -3.0715   0.0079  -0.8437  -4.3533   3.1633
-#>    2.0113  -3.4719  11.3453  -3.5211  -6.7155 -10.9955  -4.1942   2.4041
-#>   11.0641 -13.8099   4.6128   2.8432  11.7525   3.2607   1.2708  -2.1138
-#> 
-#> Columns 41 to 48  -0.1047   7.1639 -12.7312 -14.9411  -1.7243 -12.6823  -5.2909  -1.5054
-#>   -0.6198  -5.0780   3.3587  -3.7096   9.7318 -11.5688  -6.0776  -5.0235
-#>    3.5723  -9.2005   2.5989 -21.5485 -12.2536  17.0624   1.4070  -7.9798
-#>  -12.1266   2.2245  -1.4824  -2.2374   6.7505  22.1302 -10.3626  16.0516
-#>   12.9833 -10.8692   8.1235   6.8576   1.9169   5.8651  10.7781   1.8127
-#>   13.4862  14.4447   7.6561   0.8456  -6.2709 -10.2808 -14.2315  -5.1059
-#>  -11.6531   3.8384 -13.5760 -15.2587  -0.4386 -11.6019  -3.1254   1.9812
-#>  -14.4318  -8.3302  -5.7316   1.3431   3.1469  -0.5234   2.0978   5.0587
-#>   11.3827  12.2602  13.5625   8.1668 -13.3577  -5.9555 -10.2404  -0.4668
-#>  -20.0554   6.6514  -5.7848  -3.7281   2.9242  -1.2270  -3.3500   2.0820
-#>   13.5036   4.9700  14.1329  -3.3614  -2.4387   4.3636   5.2511 -26.7314
-#>   12.3755  -6.0302  13.0706   7.0045  -4.3136 -20.3503   6.1174  -7.6721
-#>  -11.2236   2.2306   0.2531   6.8231  -1.9899   3.6850   2.7379  -7.5945
-#>    7.6483  -7.3374   9.9872   4.2223  12.5574   4.1922  11.5467  -1.8445
-#>    6.6880   1.3918   6.2407 -23.0986   3.5472  15.1577   6.0838  -9.5015
-#>  -25.3432  21.5193   0.5058  -5.4625 -14.2084  -2.0676  14.9385   2.1116
-#>    5.4331   0.7524  -4.1156  10.5738 -15.1925 -10.3904  11.5019   8.5438
-#>   -0.5497 -12.5371   5.6004  -4.6952  15.8923  -8.7153  -3.9162  -8.2549
-#>   -0.2119  -0.2621   3.6054 -26.5559  -0.8848 -10.6993   3.7716 -17.5107
-#>  -19.2452  -4.8849   2.6568 -10.7631  -3.6147  18.3777  10.0724  -3.3694
-#>   -8.3158 -15.7886  12.6284   4.6058  -7.6070  -6.1975  23.2880  18.2722
-#>   -0.4041  10.9108  -5.3681   8.0119  -4.9525  14.1613  12.6936  -3.8161
-#>  -14.9089   6.0440  14.4983  -9.9542 -14.4793  -4.1608   2.9876  28.2071
-#>   -7.5434  14.3462 -13.1011  -0.0543  -0.1406   1.4744  -9.4161  -4.9771
-#>  -14.9983  13.5030  10.9352 -10.9594  -6.3634   6.1297 -18.0191   1.8998
-#>    3.8310 -10.9236  -3.8793   6.5608   5.8434  -7.0148 -15.9521 -13.2939
-#>   17.6064  -5.7444  -0.3266  -0.0692 -11.4574   5.3437 -12.4108  -8.0583
-#>   10.7164  -5.7833   1.8178  15.0389   3.0004  -2.2704  -3.4458   4.3220
-#>    4.8007   3.8398  -4.0033  10.0028   4.0875   9.7484 -17.8828 -12.7776
-#>   10.3227  -1.1501  -2.4754 -20.4586  13.4596  -9.2950   1.5691   2.9662
-#>   -6.1917  -0.8440  -3.0441  14.1943  -4.7117   2.9086   9.5871   7.8146
-#>  -20.5611  12.6771  -7.1091   2.4571  -3.8733  -0.2544   7.2356  -6.9050
-#>    3.2720   2.4587   0.8837 -15.8807   4.8638  -8.7061  -8.4504  -8.2424
-#> 
-#> Columns 49 to 54  21.8557   4.4202  10.8168   7.5174  -3.7758   2.1122
-#>   -2.8456 -15.9854   2.0339   7.9619   0.4806  -0.4653
-#>   -9.5906   5.3457   2.7396   8.0575  -3.6745   1.8922
-#>    9.1292   8.5034 -25.1697  -2.3926   3.7174  -3.3314
-#>    5.9401  -1.1539   3.8906   7.1575   0.5660   2.4047
-#>   -5.3541  -2.6976   4.3906   2.5245   0.3295   2.7928
-#>  -10.4245   7.0469  -6.8186  -4.2369   2.9327   2.8947
-#>    3.0145   0.9534   5.5193   7.3609   0.7635   1.8795
-#>    1.0380   8.5256 -16.1735  -1.1572  -3.3035  -0.8527
-#>   -5.8692 -11.7066 -13.6596   4.0496  10.6453   1.8254
-#>   -5.8070  -8.3313  21.3003   0.7767  -2.3350   3.4440
-#>  -12.4272 -14.2899   1.9738   2.6648  -7.6391   2.7511
-#>  -16.9396  -9.4806   4.9570   0.1588   3.8355   3.2884
-#>    5.2529  -7.1969  -4.2218  -6.2748   2.3049  -1.3600
-#>  -13.7700  15.0260 -11.5880  -3.3049  -0.6822  -3.0384
-#>  -10.5514  -9.3660   5.5534   1.1381  -2.6856  -1.1547
-#>   -2.5532  -0.4677   5.0003   5.1773   0.7936  -0.4262
-#>    6.8253  -5.2287  -6.6104   1.5442  -2.8864  -0.4845
-#>   11.6100  14.6819  -9.3433  -6.4061 -13.6912  -0.5850
-#>    0.3866  -5.1808   4.5291   7.7504   1.9457  -1.1015
-#>    0.5982  -8.9206   4.5865   0.8641   1.1949   1.2615
-#>   -5.3683  -4.6591   0.5351  -8.2208  -2.7109   3.5416
-#>  -13.6418 -10.2297  -9.1510   5.2640   2.1182  -4.0404
-#>    4.0355  -8.2871  -9.8668  -5.2217  -8.3299   0.6824
-#>   -3.4248  -5.1316   9.4518   0.5314   0.5506  -2.2042
-#>    1.9089  -0.8906  -2.7357   3.9258   4.9725  -0.2202
-#>    7.4031  -6.5030  -0.5144   0.6044   1.5107  -1.8649
-#>   -2.6418   0.4153   3.0424   8.7083   1.7534  -0.0252
-#>    4.0239   3.5966  -7.6770   0.8856   5.5774  -2.7345
-#>    2.3516 -11.4263   0.2037  -6.9024   2.5088   3.9246
-#>   11.4006  -1.0101  -2.1060  -2.4315   0.8993   1.6014
-#>  -14.6229  -1.0623  11.6151  -9.1313   6.1282   1.5521
-#>   -2.1762 -12.8544  -2.2436   5.0173   2.8554  -0.1953
-#> 
-#> (3,.,.) = 
-#>  Columns 1 to 8   5.0065  -7.5648   4.3697   5.4119   1.1572  17.5422  -0.4933   3.3253
-#>    4.5796   7.3903  -1.7693  -2.0851   2.1409   3.3564   1.4481  -4.8963
-#>    1.6970  -2.0240   0.1673 -13.3189  10.1225   4.5772  12.1823 -12.9104
-#>    4.4423   0.3706   1.9800 -14.1760   1.7023  -7.2400   0.9277 -10.3556
-#>   -4.9464   5.5538   3.4204  -2.9786  -1.0806  -6.1542  -0.2451  -9.0744
-#>   11.9943  -1.9920   5.2217  -4.9694  -3.6795   7.3552  11.9307   8.9454
-#>   -6.8636   5.1646   3.3317  -2.5187  -3.2920   2.8625   8.4681  -1.5043
-#>   -3.4725  -0.3777   0.8911  -0.7854  -1.9812  -1.6493 -27.8762   2.8427
-#>    4.7066  -3.2704  -0.3828  -6.5696   7.0008  -5.8864  13.5979 -16.6016
-#>    5.4407   3.1746  19.6670   0.1120  -2.7427 -11.9844   1.0637   5.8977
-#>   -7.6526  -4.7646   7.5172   2.7917 -15.0073   1.5688  -6.8070   5.4306
-#>    2.9191   1.0430   0.1736  -4.6040  -3.2349  18.2567  -0.5415 -12.4009
-#>   -2.2711   4.5532   4.4744  -0.5926   2.2176  14.9981 -11.5919  14.1614
-#>   -5.8899   2.6850   0.1409  -2.7625  -0.4703  14.8921  -3.4326  -3.3055
-#>    0.4423  -8.7097 -10.6624   8.3148   6.7439  -2.4797  12.4699 -20.2065
-#>    8.0021   0.3554   0.3091  -3.3935  12.4163  -1.2953   4.3594  -4.7604
-#>   -2.1517  -1.1949   6.7236  -9.2434 -11.6224   6.0957  10.3943  -1.4627
-#>    0.7060  -1.5135 -20.7254  -6.8302  -3.2998  15.6868  -5.5653  -9.2229
-#>   -5.0176  -0.9663  -1.7610  15.8706  -1.0764 -10.6421  -5.5809  -3.3236
-#>    1.9067 -10.7770  -3.6526  10.3817   9.1903   6.5684 -17.1229  -7.5234
-#>    1.2750  -3.1628  -1.8613   8.5306   1.5362   0.4427  -4.1968  -0.8201
-#>   -2.1752  -1.8308   6.5429   7.1769  10.1203 -15.6256  -3.5803   4.7920
-#>   -3.9520   1.5366  -1.0068  -8.6163  -5.8413   5.2089  -2.8411  -5.1499
-#>    4.3731   3.5496  -1.5140   6.9102  -1.4003   8.9362  -6.4642  -4.5631
-#>    4.2081  -9.6222  -3.5010   0.5712  -2.3747   6.0067  -4.5879   8.2174
-#>    6.5613   5.2677  -1.1577  15.0782  11.2545  -8.9979   2.4309   3.5133
-#>    4.0774  -0.2183  -7.1818  -3.6122   4.8120   5.3588   7.6044   0.1746
-#>   -8.7328 -10.5805   1.2447   3.5645   6.8815  -6.6055   1.7821  -3.7090
-#>   -6.2692  -0.6318  -4.2597  -8.9461  -2.7332  -6.6875  -7.8548   4.0098
-#>   -0.3510   3.9007 -14.3480  -0.3358  -9.3153  -3.4372  -6.5250   6.8921
-#>   -1.2713   2.7064   5.8096  -5.4592   1.9853   5.8544  -6.8093   2.7735
-#>   -2.0104  -1.4024  -2.1828  -2.0720  10.3598   8.6430  -0.3503   3.7573
-#>   -4.4944   2.3952  -2.2312  -6.9977  -4.7600   2.7363  -0.5609   6.7697
-#> 
-#> Columns 9 to 16  -6.9998  10.6761   8.6290   1.0619   4.6502  -8.7584  -8.5783   5.7564
-#>    1.2974  -3.5524   6.9653 -19.2955  -4.2965   3.1079  13.4470  -4.0555
-#>   -5.2354   4.2810  -0.9473  18.6950  -8.9680   9.0909 -22.2445 -16.3682
-#>   -1.2449   6.2986 -12.2026  19.6986  -8.0719   4.8052 -13.2800 -24.9230
-#>    3.1749   5.6027  -5.8446   4.7225  -1.5331   9.1026   6.1800   6.6361
-#>   10.8301  14.3328  -7.5849  17.7657   6.8040   2.6532  -6.7402   2.3008
-#>   -2.4575  -5.3909   3.3260  12.3631  10.6478   1.0650  -6.7919   6.5352
-#>   -6.5352  -1.1951  -2.9045  -4.2426   1.1777  -9.2049   1.3871  22.7300
-#>   -6.4794   8.2347 -10.4154   4.7242  -8.1403   7.2454 -13.2459   0.8178
-#>   -1.7903  -5.8577   0.6970   9.9278   5.4227   9.8878 -14.7169  -0.7110
-#>    6.4007  -7.5623 -10.7833   4.0560   5.7243   6.1790   6.8927  -6.2734
-#>    2.0545  -3.5389   2.0349 -10.0992  -3.0352  -4.9398   2.8822   0.6213
-#>    9.8403   9.5956 -10.4498  -1.1934   5.3889  -9.9235  -3.0167 -12.1756
-#>   -1.9861  -9.2124   6.4746  -6.2048  12.4562  -2.5963   7.2102  -7.0540
-#>    6.5246   4.7411 -12.6090   2.8226 -13.1843   7.1695  -9.3513  -5.5817
-#>   12.1244   6.2580  -2.8042  -7.4370   5.9493  12.8463   5.8486  -8.9301
-#>    2.3902  -0.9217   6.3379   3.5433   7.7250  -5.8269   6.4756   0.2072
-#>    4.1890   0.4687  13.0917  -8.7310  -4.4070 -12.8233  -3.4196   5.6194
-#>    7.1521  -6.5397   0.6541  -6.3224  10.2388   4.7956 -12.6320   4.4017
-#>   -1.5785  10.3988 -10.7020   4.6607  -7.1149  -2.0043  -1.2336  -6.8194
-#>   -2.8968   2.4503  -5.4942 -14.4237   6.6551  11.6778   8.5156  11.1045
-#>   -1.1568  -2.7098   1.0471  -3.7183   4.8246  -1.9663  -4.6640  -1.1049
-#>   -8.5518   7.5451 -15.9437  -1.2995   0.7863   6.2623   1.3795   1.0162
-#>   -1.5262   8.2184   1.0894   1.9374  -4.0241  -0.2278 -15.7278 -12.5403
-#>   -3.6396  -0.5381  -9.3408   4.3941 -12.5852  11.0130   7.4217  -5.1584
-#>    3.5167  -0.9388   8.3914  -6.8105  -2.8391 -21.0392   0.0891   6.5242
-#>    6.7332  -3.6219 -10.7829   7.0740   7.6049   5.0294  -0.9974  -3.7430
-#>    8.4995  11.3926 -10.2824  -2.8517  -9.0784  11.1800   2.0026  -2.0633
-#>    4.7858 -14.6920   7.6764  14.7249   7.3598  -4.3916  -7.6974  -6.0448
-#>    4.9977  -2.4565   6.1780  -5.7729  -1.9491   7.0341   9.1896   2.2460
-#>   -3.9617   0.7266  -8.6600   0.5977   9.7916   8.5833  11.3199  -4.5682
-#>    3.0461   7.4758   2.2648   3.1652  15.9415  -9.5217  -7.9929 -14.9009
-#>    0.0593   8.0995   5.7684  11.2536  -1.8930   0.8747  -0.8771 -17.4723
-#> 
-#> Columns 17 to 24   2.9991  -7.0031  -4.3098   3.9696  -3.8698   3.7981  -3.4638  -5.8145
-#>    9.8929  -0.6985   1.5678  -7.6750  -1.0575   3.5687  10.1861  -2.8345
-#>   -3.5003  11.2459  -9.4071   7.3753  -0.4676  -7.4027  -6.5143   4.4750
-#>    2.7744   3.1267  -5.5507  -3.7926   4.7195  -4.2368 -16.2356  11.4313
-#>   14.7289   3.2852  -5.1273 -26.9714  -5.7715 -11.4138  -6.7957   3.3423
-#>   -0.2818   5.4439  -0.5775  -0.4554  -2.5376  18.6859  12.4762 -13.8969
-#>    4.9449   1.3183  -0.1317  -5.1529   3.8452   9.2586   1.8964  13.9726
-#>   -1.6536   0.3676 -13.3478  -4.2053   0.0647  -6.0252   9.3051  -6.1649
-#>   -5.5615  14.3067  -1.6848  12.2309  14.4537  -8.1157  -0.8579   1.5930
-#>   -0.9635  -1.4980   4.2655 -21.3527  -7.6679  -7.1719  -0.3917   1.2707
-#>    3.1651   2.3176   4.5623  -2.1620   2.1102   1.5631   6.1667 -12.4041
-#>   -3.5800  15.3647 -13.4311  -7.2799   0.5813  -0.9263  17.0942 -18.8560
-#>   -1.9807   1.5200 -10.6795  -0.0452  -4.0646  16.5639  -0.9831 -21.0682
-#>   -0.0563   2.6941 -12.5726 -15.9221  -5.0472  11.8357   9.6567   2.6775
-#>   -0.7082  15.5344 -10.7737 -14.3932 -12.7231 -17.7728  -9.7606   3.9705
-#>  -11.7585  -0.7036   6.9729   2.0480  -1.0557  13.8488  -3.0825   1.0844
-#>   -5.2574   2.7561  -4.0692  -7.9613  -7.0095  11.1284  14.2156  -2.0798
-#>   12.7254 -12.6184  -4.4636  10.5271   8.7149  -3.2069   5.7323   4.0469
-#>  -14.2750   1.7038  10.0903  -2.5975  15.6578 -16.2574  15.1585 -11.0779
-#>   12.1564  -2.2589 -12.5739 -10.4091  -6.1980  -5.2244   4.4709  -3.7656
-#>  -18.4174 -15.6443  -1.5797  -5.4252   6.9559   2.8517  16.7162 -15.9417
-#>   -9.5823  11.4253   6.4563  -7.3173  11.0476   4.3020   4.4361  -3.1137
-#>    2.2054   7.7500   4.4457 -11.8929 -18.6036  10.0097  11.4196   5.3231
-#>   -1.3877 -14.1901  10.9835   5.7080   2.7517  -2.3153   2.5598   3.1702
-#>   11.1839  -7.5466   6.8821  12.2485   3.6899  10.8344  -6.7247   2.7138
-#>    6.8281 -12.7237 -12.4240   0.8836 -10.6440   2.3363   0.7084   4.0937
-#>   -3.7046   3.6566   3.7686  17.9101   2.9497   3.7957 -10.0212   7.2813
-#>  -15.1208   3.2913  10.3041  11.0956   2.9664   5.2545   3.8653   5.1548
-#>    0.9173 -13.3204   5.8748   4.8021   4.3568  -2.2284  -1.8059   4.8992
-#>   -1.8480  -7.9347   0.0099  11.5403  -5.5167 -14.6196 -13.5348  -3.0772
-#>    6.2677   3.2872  -3.0857 -13.0179 -17.1925  -3.1941   4.9096 -10.0129
-#>   -4.5396   7.1775  -5.0795  -2.1652  -4.6862   9.7565   4.7690  -8.4607
-#>   -6.4992 -17.9611  -0.7486   8.4730   0.2359  14.2265  -4.9010  13.5942
-#> 
-#> Columns 25 to 32  -0.4502  -2.3367   5.3966  -1.9736  14.0377   3.0119  -8.4109 -13.9355
-#>    3.9575  -4.0850  -0.7513  -8.3361  -0.8708   9.1271   0.5462 -14.4554
-#>   -6.7282   1.3936   7.7278  15.0878   3.4429  -3.3617  -7.3842  -6.6403
-#>   -6.4748 -13.2080   3.8655  10.9370  -5.1134 -14.1147   8.0535 -11.6929
-#>  -12.5487  -1.3291   3.7207  -1.6684 -11.1489  -1.3516   9.4046  -2.0490
-#>    5.3655   9.5782 -17.2964  -9.7356   0.9319  11.7845  -0.2503  -2.2241
-#>   -4.9538 -22.1325   8.2270  17.2945  -4.1733  -6.3520  -0.8176  -5.2402
-#>   -4.1229  10.7951   3.8800 -14.9516 -13.4749 -13.0130  -2.2828  -2.7846
-#>    4.6756  -9.9426  -6.9022   4.8072  15.7269  12.2702  -3.2373  -5.9807
-#>    4.7910  -8.1889   3.8486  -5.5455 -18.6664   8.1700   7.6792  -5.5944
-#>    5.4961  -5.3140 -14.3234   2.9961   0.9553   2.8977  -3.2754  10.3027
-#>   -0.4717   7.9712 -12.2355  -3.7001  11.2613   4.8899 -19.9430   3.0561
-#>   10.5788  11.4136  -4.8488  -8.5414   1.8690   1.0350  -0.8902  -6.1415
-#>   10.3371 -10.8624 -12.7971   1.8257  -3.5686  -1.8141  -6.7775  -5.4592
-#>    4.4009  -3.1121   4.6466  18.3852  11.2915  -3.8269  -7.2640   2.5717
-#>   13.3067  -0.2645  -2.1777  -3.1643   4.2687  12.0935  10.4656  19.6666
-#>   -8.0351  -4.7948  -8.3152 -14.3852  -4.9098   0.2674 -12.8918  -5.0915
-#>   -6.9013  -3.8669   6.1630  -0.5587   1.0668  -7.0706   6.9699   8.8962
-#>    4.4448  -4.7124 -12.8896  14.1769   3.3869  -5.7435  -8.1179  -5.2362
-#>    4.2559  16.4625   2.6017  -3.6140  -1.8479 -22.0223  -7.7596  -0.5762
-#>   -7.4018  16.2872 -12.9994  -7.2869  -7.2918  13.4027  18.9048  17.6126
-#>    1.9429   6.2656   1.1654  -6.0094   7.2305  -3.4192  -6.5580 -11.7218
-#>   -1.4432  -3.2813  -9.4318 -10.5674  -1.7319  17.8822  14.0319  -6.5661
-#>   -4.2170   9.2337   2.8840   1.7105  -0.0033   4.7875   7.6331   0.7086
-#>    1.5496  -4.3581 -11.1832  -1.6342  15.4827   0.6272   0.9326  19.2356
-#>   -2.5731  -0.6965  15.7973   2.5878 -13.0473  -8.7761  -7.2038  10.3990
-#>    0.1765  -2.1546  -2.9154  17.0378   1.3992   1.8228 -11.6180  11.1169
-#>  -19.8042   5.9646 -15.4726 -11.0236  -6.0788   4.6658  -4.0302  -6.4444
-#>  -16.1552  12.7106   8.4312   1.0798  -3.3709 -10.7590  18.7330   1.0471
-#>    2.8258 -19.5791   2.6700  24.3105   0.5782  -0.9183  -3.7057  -0.9869
-#>    2.3721   4.8013  -6.0742  -5.4514  -2.3220   0.5604  13.2491   5.0343
-#>    8.4863   3.8528   6.6870   0.3344  12.9178  28.9992   1.1368  -3.5024
-#>    7.6834  -2.3466  -9.0723  -5.1307  -6.5264  14.4130   9.0037  -5.7521
-#> 
-#> Columns 33 to 40   6.1166   3.4755   3.0107  10.4820   2.2313  21.4302  -0.3769   7.2492
-#>    7.5885   2.9510  -5.9707  -3.1287  -6.9030   0.4736  -9.9255 -11.9223
-#>   -7.3547  -2.6745  16.3513  -9.9079  -7.4035  15.0191   6.9734  -0.3176
-#>   -3.9057   7.4514  -3.5686  -0.0520   5.5190  -8.5440  16.0547  -6.0800
-#>   -3.9551   9.4788   2.8746 -14.6228  -8.6366 -17.5136  -2.6790  -0.4776
-#>   -9.5146  -5.2213  11.0260 -14.8032   6.0196   1.7087   6.6660  14.5329
-#>   -5.6591   6.8087 -15.9567  -2.8290  -4.5220  -7.0795   4.1778  -3.3880
-#>    2.3287   9.4812   1.4361   2.4778  -4.0854 -14.8061 -15.7904 -13.1929
-#>   12.3652 -12.8175   4.8446   9.9093   2.9067  24.6350  18.0183   2.1561
-#>  -12.6300  -1.2267  -5.1545  12.1703  -2.8878  -1.3358   8.3011  -2.3132
-#>   -0.8691  -1.6677  17.0963   2.4648  -7.9536 -11.2632  -4.9028  -4.6315
-#>   -2.7268   0.6535  15.4282   8.6797   3.8988   5.2600   4.8597   1.6352
-#>   -8.4937  -0.0901   5.7381   5.0044  11.3519  -8.4415  13.9622  -1.7646
-#>  -16.1659   4.4432 -11.6796  -0.2280   7.2947   0.4916  23.7616   4.4628
-#>   -7.2801   7.3802   3.8974  -3.3115   3.7231   2.3252   9.6789   3.7708
-#>   -3.8670 -11.5865   1.2037  -4.6438   7.7230  -3.4565  -6.9513   5.7064
-#>  -12.7542 -12.4351   2.0368  -1.3465   7.0032   5.8571   1.5565   0.1240
-#>    2.4653  -3.8384  -6.6116  -0.0328  -2.6110   9.6295  -1.0983  -7.9041
-#>   18.4993  -2.1162  -7.7738   0.2191   8.9795  11.0376   7.3325   9.2761
-#>    3.1431 -11.9472   0.6696   7.9047   1.0182   4.9934  -7.1324  -9.2449
-#>   12.1020   1.9057  -1.7627   9.8111   6.0275  -2.3225  -1.9654  -5.9898
-#>   -5.4913  -1.6481  21.1171  15.0078  -6.5409  -2.4634  -9.6712  -3.9974
-#>  -27.1950  -3.4378  -0.7142  -6.0477   9.2275 -22.3768 -14.0585   7.8989
-#>    0.9481  11.1605   5.6543  -4.2198   5.2720   9.0667  -8.2835   0.0860
-#>   -1.8083 -11.5910   3.3375 -21.1419   0.6927   7.6406 -25.4208  12.1868
-#>    2.0968  -8.3505   5.4435   8.9627  10.5742  14.8554  12.7098  -5.0643
-#>   -0.4590  -4.4083  -0.0300  -4.9880   8.3520  -3.7739  19.8174  -0.7580
-#>   10.4876   2.0805   1.9403   2.2905  18.1642  -4.9934  -3.4718  -1.4246
-#>  -16.6475  11.8381  -9.6935 -20.3861   8.1342  -1.1187   8.6158  10.3533
-#>    5.5304  -1.2605 -15.8330 -13.0794  11.5950 -10.1415  -4.5288  -9.6068
-#>    1.0786  -3.2658  16.3202   0.3046  -4.1398  -7.0007  -3.2427  -0.0090
-#>    2.3637  -7.6107   4.8096 -14.9503  16.2826  -1.3325   3.9388  11.2878
-#>   -5.3557  16.9209   0.8013  -1.4738   3.4657  -9.9022  -1.0349  -4.0073
-#> 
-#> Columns 41 to 48   1.3345 -11.9278   4.8337 -20.2754   3.6607  -7.6054 -10.6370   0.7393
-#>   18.4758   4.1840   5.1066   0.5228   4.7843  25.5798   1.4430  -4.8073
-#>    2.2567 -12.8952   0.7386  -6.6147 -16.7057  21.4841  -9.8661  -0.3491
-#>    0.1938   8.7895 -10.5219  -5.8892  -1.2568  25.8267  -9.5847 -17.0484
-#>    0.5941  13.5316  12.5077  11.4497 -15.2622   7.9676   3.8970  -5.2587
-#>    3.5475  -0.9832   4.7096  10.4700   7.5481  -6.0933  -1.1715   0.8234
-#>  -15.7558  10.3998  -3.9478  -2.2627  -3.1319 -13.5418  22.4130   5.0423
-#>  -15.0093   0.8777   8.9129  -0.8943   6.5934  -8.7079  -9.1901   1.2953
-#>    0.7930 -18.1611  -9.1900  -1.9323  -4.9864   0.0681  -2.9314   1.2715
-#>    8.6339   5.4117  14.6414   2.9027  18.3501   9.9014  -8.9372  -0.7209
-#>    4.2320   3.9845   7.9440   3.8098  -7.0927   6.7669  -6.9893  17.8406
-#>   -7.9320 -18.7648  -7.3420   2.7295   5.3951   3.3670  -8.2192   4.4860
-#>   -2.9330  20.8977   5.6355  -2.1919 -12.0907  -3.9535   6.5782   8.8423
-#>   -9.3347 -10.6555 -17.5191  -6.0478  -6.2106   1.9033  17.4623 -13.6784
-#>    1.0635 -20.0535  -9.3549 -19.6059   0.9665   5.6258   2.6148 -15.1398
-#>    7.1171  13.7309  10.8487  -5.9154   2.0694  -2.6388   2.8409   7.6188
-#>   -9.5625  -5.8672  -2.7635   9.1451  -1.4415 -10.1499   8.3045  -3.1570
-#>   -0.8245  -1.6012   8.4346   7.0643   0.2006  -6.0378  -5.0282   7.1434
-#>  -20.1260 -14.5046  -4.1219 -16.9426   2.5031   7.4219  -3.9130  -0.1316
-#>   -9.7300  -6.6407 -14.1674  -9.5041   4.6654  13.3797 -17.6050   5.9562
-#>   -5.4223 -14.4687   7.3019   5.0101 -12.4661 -10.5039  -1.3772   9.8727
-#>   -1.9127 -18.7884   8.1959   1.4009  -0.5677  -3.3554   7.6146  -4.5400
-#>   -8.3890  -4.7103 -10.4115 -10.5468 -11.2971   6.0386  -0.3137  -4.2605
-#>   -0.4814  11.7382   8.6822   6.9266   7.6994  -3.8786  13.3594   5.3836
-#>    6.7302  -2.0560   2.8992  -1.6217  -7.0485  -3.6673  -8.4619  20.2970
-#>    1.4750   1.6396   1.5047  14.3273  12.3591  -3.2715   6.9656  -9.5265
-#>   11.8076   1.5431 -13.8370  -2.2326  -7.8101  -4.1678   6.3293 -11.9680
-#>   -4.2533  -2.1013 -11.6261  -1.8696  -1.4513  -3.3382   0.7499  -3.3817
-#>    1.1298  16.2342  -3.1134   0.6689   2.9858  -5.9440   6.0355  -6.6737
-#>   -2.8073  11.9571  12.6337   8.9343   4.8625   2.5022   5.9198   4.3786
-#>   -1.4268   0.1202  -1.7580   5.3506   5.1749   1.9549  -8.0605  -6.0836
-#>   -2.4020   8.9237   7.2497 -13.5399  -2.3874  12.1784   4.0796  16.0535
-#>    0.6426  23.0241   2.1876  -4.6716  -2.3914   4.1827   5.9588  12.7554
-#> 
-#> Columns 49 to 54 -14.4496  -8.8349   1.4433   2.1690  -3.7491   0.9587
-#>   -7.7497  -6.5779  -8.7464   9.5712  -5.7366  -5.5686
-#>    4.6983   5.0069  -3.8794  11.1549  -4.5174   4.5328
-#>    1.5492  -0.2539  -1.7878  -1.7838   2.4953  -0.5657
-#>    3.1202   0.9545   3.0343  -1.6115  -8.4593   5.7329
-#>   -4.5750   7.9842   3.8035 -13.5802  -5.4309  -4.1773
-#>   -5.8305   6.6706   5.9399  -4.3707   6.1034  -0.6240
-#>    3.9163  -7.3941  -6.3986  -1.3349  -4.0819  -4.4972
-#>    4.2833   1.5292  -2.7047   0.2604  -0.3884   3.3314
-#>    7.3679  18.8472  -7.1299  -6.6655   1.7560  -7.6865
-#>   -8.4181  -8.1825   2.7599   5.0031  -9.8373  -0.7030
-#>   -1.7533   7.0408   1.4097   4.1171  -2.0185   0.9093
-#>  -15.0229   2.6766  -0.4257  -7.9381  -2.5720   4.4902
-#>    0.2040   1.1599  13.5789  -7.6558  10.2325  -6.6165
-#>    4.6438  -3.2434  10.0951   3.6194  -4.4035  -0.0836
-#>   -9.2463   2.0903  -0.0023  10.8511  -6.4154  -5.2644
-#>    2.7451 -13.9360  -9.0759  -7.8088   2.8181  -5.4235
-#>    3.7136  -6.3668  -1.2021   2.3239   7.6207  -3.6235
-#>   -0.2203   3.0907 -11.3325  -1.1252 -11.7583  -3.8903
-#>    7.2152   0.2197  -1.1622  12.1529  -2.1452   3.1754
-#>   -9.8983   2.5051  -0.5053   1.2530   1.9963  -3.8447
-#>   -6.6937   8.3050   0.6103   1.0726  -4.1585  -3.8931
-#>   -0.9202   5.0524   0.0446   8.4999   8.9261  -1.5784
-#>    4.6479  -1.3398  11.7661  -1.6349   0.1920  -0.1233
-#>    8.7547  -3.9087   3.6918   5.1973   3.0588   2.7503
-#>   16.5450   9.8928   0.2408   0.5691   6.6154   1.0948
-#>    0.1952 -11.5720  13.2281  -6.8009   3.6701   0.9840
-#>    2.0523 -17.0915  -1.5819  -4.7299  -3.5012  -0.7101
-#>    7.6151  -2.8723   3.9770  -5.7623   2.6486  -0.8233
-#>   -3.1527   4.0325   8.9116  -5.7383  -0.0428   7.4691
-#>    7.4466   9.5679  -3.7968   6.6464   2.2457  -3.8793
-#>  -13.5084   7.2839  -5.4541  -4.2447   0.3259   3.5467
-#>    7.9819 -10.1110   6.5147  -0.2204   2.2264  -2.8546
-#> 
-#> (4,.,.) = 
-#>  Columns 1 to 8  -2.1050  12.3998  -0.4774   1.2383   5.8022  -6.9281  -5.4728   6.0727
-#>   -1.8157   0.5752   1.5176   9.4034  10.4397  -3.4623  -8.0909   8.9566
-#>    2.8523  -1.4397  -2.0096   0.4399  -2.9058  13.6328   1.0217  13.0719
-#>   -1.5294   2.2925  -6.5809  -2.0880 -19.8867  14.5897 -13.2773 -11.1911
-#>    6.7109  -7.0110   0.6445  -0.2109   1.6469   9.4859  -5.7427 -10.0133
-#>   -4.8364   5.8289   4.6019   3.3430  -1.8501 -10.7135  21.6716 -15.6815
-#>    7.1862   0.7821  12.1798   8.3415  -3.7003   6.7931  -1.0732  -6.0702
-#>    6.0033  -0.7861  -5.6532   8.0808   9.2785  12.5299  14.3445   3.3772
-#>   -2.2405   4.2441   2.1795   2.5741 -12.6495   2.4456   6.2146  -5.1256
-#>    3.2793  13.7299   3.0425  -3.4000   3.7587  10.1255   7.9639 -18.8988
-#>    0.8084  -8.2614   1.0017  10.9275  10.0781   5.7191  -3.9948   0.1607
-#>   -1.2355  -1.4259  -3.7475   1.8783   0.8166   0.9565  -4.1186   5.8185
-#>    1.4094 -12.0985 -11.9980   7.8775   9.0769  15.9720  -4.7439   2.9385
-#>    1.5571   0.0629  -0.1035  -1.5539  -3.8787 -14.4867  -3.6049 -22.4549
-#>    4.0708  -4.1490   0.1908  -5.8443  -4.5887  -0.4553  -0.2272  -8.7493
-#>   -4.0910  10.3983   0.2122   2.4811 -10.1094   2.2425 -10.9766   5.7353
-#>   -1.1070  -3.0927  -0.9344   0.5191   3.5854   0.6241  -0.4108  -3.0465
-#>   -6.5693   4.7755  -9.9835   3.8776  -1.0840   8.8799 -12.0977  14.3494
-#>    1.4439   5.9479  14.3636  -2.7812  -2.9505  -1.6697  -2.3293 -10.8652
-#>    5.3586   0.2086  -5.2899  -5.4280   9.1076  -3.8572   5.6281   6.1843
-#>    2.3577   3.8243  -3.7754   0.7548  -6.3366  -7.6611   0.5793   1.8813
-#>   -1.0511   7.6017 -20.3287   7.5251  -7.2557   5.9654   8.1025  14.8469
-#>    7.8334   0.2638  -5.3560   5.1233  -0.2849   1.2744  -9.1953  -2.2904
-#>   -3.4615   2.4915  -1.4092  -8.7137   1.1823   4.8117   8.3513  16.4244
-#>   -3.6477   8.8516  -8.0689   0.4341   8.3788   2.0504  -8.3104  11.5212
-#>   -5.6302   0.9044   3.8023  -4.9904  18.6028 -11.3102   7.7670   3.6899
-#>   -0.8651   1.9924   8.2783  -7.4464   4.8625 -15.3295   0.5469  -9.0181
-#>    0.7463  -6.4614   1.3538   3.3951   2.1724  12.7520   7.3761 -11.6228
-#>    2.6962 -12.6195  -3.5614   6.3831  -0.7390   4.9695   7.9886   9.1374
-#>    0.5365   6.8245  -1.8546  -7.4207 -12.1718  12.5915  -8.2897   5.3908
-#>    0.0312   4.4716  -0.3820   3.3666 -15.6750  -0.7308  -5.7290 -10.0158
-#>    0.0913  -9.0728 -10.2081   0.3705   0.5208  -4.9135 -13.3187  -3.3307
-#>   -2.4412  -8.5937   2.6922   0.0637   7.2697  10.2367  -0.7692   7.1194
-#> 
-#> Columns 9 to 16   1.4035   1.4040   4.8057 -16.6953 -10.7570  15.9873  -3.9211   2.1780
-#>   -1.6966  -5.1725  19.3307  -6.3238   6.2951   4.6960  -2.6081  20.1427
-#>   -1.5471  -7.7275   3.1516  -0.9020 -10.0211  -7.9036   1.2798   0.7613
-#>   -0.2585  -4.4660  -6.3808  -6.9989   7.8038   0.3097   3.0570  -5.2005
-#>    1.4197   1.7325 -17.5488  -3.1597  13.1648   2.9228  -3.4837 -14.5672
-#>   15.8725  23.6275  -5.4269 -23.7333 -21.2530   0.0843   1.8968  14.8612
-#>   13.6775  10.2984 -13.2271 -13.6772   5.7547   2.0044  -3.7326 -19.6737
-#>  -16.4962   6.2314   5.0742  -0.6590   0.0642   0.3928 -11.1059  -7.1018
-#>   11.2382   0.8385  -1.0045  -4.1813 -10.6938   4.4621  14.5863 -18.2117
-#>    1.6609  -5.2850   5.2872   0.7011  -0.5309 -15.8560   4.4899 -23.4390
-#>  -14.5825   4.0461  11.6341 -17.4078  -7.0555  -5.2670   0.8999  -5.6901
-#>    2.8568   6.2582  10.6664 -14.0320   4.2543   4.6557  -4.4138   2.9445
-#>   -5.7549  11.1731  -8.0999 -14.7524   7.7781  -2.1008 -18.4997   2.9257
-#>    2.8536   8.2467  13.3471   0.4846   5.7097   1.9348  -6.6551  -8.1418
-#>   -3.3299  -4.0816  13.1217  16.2571  -1.7483   7.3860  14.2170 -11.9363
-#>   11.0684   5.0762   5.3195   0.1432  12.0400  -3.7210   2.7910  -1.7928
-#>   19.4745  -2.3785   8.6529  -4.5426  -2.4921   7.6448   0.1893  -0.8252
-#>   -1.9241  -9.2395   5.6588  -7.6330  -0.7623  -0.6414   9.5515   8.5972
-#>    9.1962  -6.1070   6.8629 -12.9698  -1.6276  24.5720  16.0840 -17.1156
-#>  -19.7998   6.6669  12.4325   8.5895   4.8028  -7.0879 -12.0306   1.7905
-#>   -9.7381 -10.4174  12.7223  17.6191  16.4640   1.4797  -2.2388   4.2665
-#>   -3.5749   1.9684   4.4351   0.8422   3.2918 -15.7919 -17.1336  -9.5118
-#>   -5.4865  18.5721  13.7783   1.7104   9.1968   8.7916   5.1292  -8.2372
-#>  -10.1325  19.9818   2.2621  -2.6759   1.3810  10.1955   1.6041   0.8605
-#>   -8.0232   8.5082  -8.4552  -5.3418   2.3370  13.6287   2.0805   8.9783
-#>   11.1102  -8.4891   2.2244   2.1921  -7.0178 -18.9312 -15.8038   1.8319
-#>    6.3255  -1.7980 -12.3073   0.5514   2.2092   2.9887  -5.8563   2.6762
-#>  -15.6557  -3.5616   2.8305  -8.2303 -19.6977  11.7540   1.1495   1.8021
-#>   -2.1927  17.4035  -0.7762   0.3718  -5.3043  -6.2854  -6.9760 -15.9494
-#>   15.0398 -13.4662 -24.0948   1.7141  14.0322  16.5744   0.9768  -0.3527
-#>  -14.3824   5.7630  15.4500  -0.7237   2.4711   6.4872   2.1575  -3.1848
-#>   10.7607  -2.2199   4.4435   4.3157   6.8573   2.1049 -20.8255  23.3302
-#>   -2.3852  14.4152   1.5751  -5.2666  -9.6740  -7.5906   0.5588  12.2082
-#> 
-#> Columns 17 to 24  -0.3194 -14.9899 -11.1432   7.6206  -2.6515  20.3641   6.9513   1.0261
-#>    8.1171  -7.2204 -20.3917  -8.9583  -0.4736   9.6779  12.4881  -6.0153
-#>    7.8626  -5.3077  -1.0013  -4.2854  -3.4871  12.5138  -4.1042  -4.1187
-#>   -0.2872 -14.5971 -15.9643   6.7193  11.3281  16.3837  -6.2956  -8.5059
-#>    7.8821  -2.9166  11.2147 -13.4587  -1.7634   8.1924  -8.6742  10.9291
-#>   -5.3366   3.4671   7.0700  10.0996  -3.0505   5.1970  -6.2224  -0.0521
-#>    3.1443  13.5610   8.5994 -11.0686   4.1030   1.0456 -10.7196  -4.6311
-#>   -5.1903   1.7303 -10.9142  -3.1094   1.8742 -16.7151  -0.4450  14.1182
-#>   -5.8055  -7.5304   3.0536  -5.1155  -7.8851  18.8317  -2.9984  -7.9829
-#>   -9.4636   0.1752   1.8338  -3.9810 -18.1357   7.3099   3.1274 -19.7982
-#>   -1.5617  16.0187  -1.5374 -20.1662  -5.5443   7.1038  -4.8856   3.5684
-#>    4.4075 -20.2555  15.7781   2.6051  16.9903  -1.7933  -4.4984  -6.9582
-#>   -0.3479  13.9096 -15.6244 -11.1793  -3.5875 -15.4238  -7.4633   8.6940
-#>  -12.2850  -4.8526   8.2597  -7.7774  10.2423  10.6756  15.3577   3.9057
-#>   -9.5595 -12.0798   5.7455  13.8555   2.1398  11.5047   2.4477  -7.0594
-#>    6.9720  -3.8493  11.8396  11.0641  -2.8332  -0.1463  -7.4761  -6.3588
-#>    5.5957   1.3788  12.8145 -16.4845  -2.3937  -4.1697   2.6650   9.6846
-#>   16.3475  -1.5722  -3.2263  -1.4248   2.0256  -1.4739   4.0604 -10.2989
-#>   -0.6586   3.4046   4.6824   0.4668  -9.5215  -0.6462  -1.9205   0.3976
-#>  -16.8921   9.5151   0.8390  14.2056  -3.7218   9.2721  -5.7017  -8.5205
-#>  -14.3077   9.3742   3.4875   9.6195  -6.0802  -3.7222  10.8635   5.9279
-#>   -2.1217  -0.1907   4.4814   6.3114   4.5167  -3.5535   2.8435   9.9919
-#>  -13.9939  15.1288   4.2053  -9.5740   1.5628   3.6481  -1.3641  -3.7322
-#>    8.7557   8.7261  25.1453   0.8178   4.6255  -6.7057  -8.6511  -0.1091
-#>    7.4447  -1.8325   3.1293   7.0337  -2.7863   1.6286 -16.0444  -3.8087
-#>   -2.8873   2.8116   0.0587   3.4188  -9.5266 -14.8540   4.2804  -8.2651
-#>   -9.8895  -6.4253 -10.2173   5.8122  14.4629  -1.7608 -11.0493   3.2803
-#>   -1.0160 -13.9493  -9.9600 -15.3795  -3.2849   5.6923  -7.4746  12.0561
-#>    2.1805   4.5089  -2.3862  -4.1503  10.5217 -16.1267  -6.3030   6.9384
-#>   -1.3073   7.8117   7.1677   2.6476  -8.0955   8.7233   2.8246   4.7810
-#>  -18.4368  -2.3778   0.1608  13.3684   6.7485   6.4731   6.1167  -1.2516
-#>   -0.6043  24.9120  -1.4423  -6.7582   8.9642  -8.6571 -14.0482  16.8610
-#>   17.7286  20.2208  18.4647  -7.7855   1.3575  -9.3874  -0.5325 -11.0693
-#> 
-#> Columns 25 to 32   5.6326   5.8350 -13.5859   2.6041  -4.8413  -0.6250  -6.9518  -5.0680
-#>  -12.4608  -1.0832  -3.9246  -6.0014 -14.6060   9.3799  -5.2067   9.0054
-#>   -9.7539   1.2320  -1.4374  -8.5207 -10.2753  -2.1604  -1.0834  -9.2973
-#>   -2.7700  -3.0238  -6.5836  22.4792  -6.0836  -3.0262   9.0822 -13.0279
-#>   -3.6857  -8.2444  -9.6928  -5.6210  -3.9370   3.0351   7.6838 -11.3569
-#>   -0.2301   6.7172  -1.5944  -1.4519   0.7078 -22.6055  -0.0719  -9.2618
-#>   22.1931 -19.8122  -7.2434  -1.8270  -7.0690 -20.1464 -11.8704   2.4956
-#>    8.5558 -13.4251   9.5341  -4.0036  -0.1540   8.6128   7.1111   7.8143
-#>    7.0072   4.0444 -14.5680 -15.6934  16.3534  -8.4037   2.4889 -12.2590
-#>    4.0391  13.6764 -10.0684   6.1171  -5.3206 -15.5514   5.7784  -2.1322
-#>   -0.0031  -7.7351  -9.6109   6.7777  10.3609  -5.9732  -5.8301   0.0472
-#>   -7.5225  -8.9016  -3.5149 -12.1527 -13.5420 -10.9205   1.6544 -16.7605
-#>    0.7409  -9.4100   4.2403   6.1743  13.0698   4.1074  -2.2450  11.6677
-#>    7.2575   5.4975   1.5036   8.9499  -6.6605   1.1732  10.0563  -2.5328
-#>  -13.7980   2.2803 -11.4877  -8.7844 -11.2398 -18.5173  21.1523 -24.5849
-#>  -13.6781   4.3191  -1.2661   3.1900  -9.3190  -2.6247   2.3197  -7.3712
-#>   14.6047  -0.4196   6.9357  15.3953   5.5046  -5.5453   0.5608  -0.1292
-#>    7.9832   2.5611  -7.4931   8.8954 -12.2209   9.0451 -13.8076  15.4440
-#>   16.5112   0.9398   2.9589  -0.9302  10.1620  -6.0823  -2.3119  11.8207
-#>   -2.4773  11.4658   4.3734 -14.3540  -4.6328   6.7320  10.7169  -2.2529
-#>    5.1290   7.3429  -6.4964  -1.1527   9.2697  11.9739  -7.9363   7.1066
-#>   -6.5489  -4.9425  -4.4167  11.7435  -4.6573   3.6331  -4.8614  -0.2404
-#>    4.7502 -13.9168   8.9244   1.8197  -3.4975   2.8928   5.0875 -16.2945
-#>    8.9716 -22.2495  -7.6811 -13.3519  -6.7208  -3.6837  -6.2659 -10.6813
-#>   10.0090  -2.3997  -3.9190  11.7128 -11.4862   2.7779  10.6134 -17.2960
-#>    5.2759  10.7843   6.3188  -9.5183   3.7632 -13.0112   7.6870  -9.9708
-#>  -17.1446   0.4763   1.7853   8.8129   0.8247 -12.7194  13.7973 -10.3702
-#>   -2.9073   7.9717  -1.6721   4.9187  17.0543  27.8806  -3.1481   3.5462
-#>    0.6223  -5.8077  21.9198  14.0404   1.6793  11.8098  -2.5856   5.5326
-#>   -1.5546   8.4663 -19.7711  -3.5745  -9.0930   2.5057  -8.8058   1.5028
-#>    2.9323   2.4301   0.4083   1.3978   2.1401  -3.1432  -9.2657  10.6113
-#>   -8.3944   2.1382   3.2571   5.1617   4.1159  11.8319  -2.4654   3.5631
-#>   -9.5940  -3.8198  -0.0708   0.2324  -0.3559   5.0562  -9.3026  -0.0156
-#> 
-#> Columns 33 to 40   5.4751  -4.9685  -1.1140  12.0255  -2.7069   0.1429  -3.9142  -1.8313
-#>  -21.6627   7.3637  -1.2541 -10.8029  -6.5101   4.8416  -2.5360   5.8329
-#>    0.8076  18.1017  -4.4381  -0.4216  -1.6496   0.0826   8.9062  -9.5129
-#>   11.0231  -3.6290  -7.2440   1.0281   5.9202  -2.9915  -0.1684  -6.0654
-#>   -1.8620   4.0979   3.5261  -2.2876  -3.8825  -4.7818  30.0266  -5.1267
-#>    9.2627  -9.3801  -6.3897   4.1681   3.5414  -1.2313   2.2550  -7.4358
-#>    3.8479   0.4866  21.6011  11.6342  -7.4530   9.2773   4.7980   4.0954
-#>  -12.4142   0.9372  -1.0704  -8.8271 -10.6478   7.2930   3.4420  -7.5093
-#>    0.1698  -2.8374  -9.1786   3.2934   9.4190   9.5889 -12.3461  -6.6323
-#>    6.0238 -16.7210  -2.1618   6.7680   9.0894 -11.5117   9.2601   4.0035
-#>    7.0340  -5.0686  -6.7381   6.7828   3.2271  -1.7524   6.9165  -1.2493
-#>   -2.5774  -9.4541   5.8497 -10.5635   7.0962   4.1460  -3.2895  -4.5333
-#>   -0.0374  -5.8546   0.1963  -1.3632 -10.0908   9.7387  -1.9190  -8.9648
-#>    2.5153  -6.6226   7.6842  -8.7523  -0.5876  -4.2538   0.6357   7.1436
-#>   15.0600   8.3653  -5.3016  -8.9727   1.9099  -4.7034 -14.8080   6.8321
-#>    5.2180   1.6998   9.6705  -1.0297  -0.2300 -18.0583  -5.9676   2.8278
-#>   -6.6438  -3.8793  10.7047   1.1690 -10.4911  -4.8902  -8.9453   2.6225
-#>    2.6865  -7.0715  17.3035  -1.0292   8.6419   1.5127  11.3743   4.6567
-#>   -0.8823   1.9922  -1.6805   2.6441   7.0532  -3.5863   1.5426  -9.0900
-#>    3.4192  22.3566 -13.5973  -0.9803  -0.6945 -13.5652  -6.6271  -8.6951
-#>   -4.7106  -1.8860   4.7969  -3.3994  -0.0136  -1.0474   7.1799 -12.5076
-#>   -3.5109  -6.8928 -10.2952 -27.2561  -4.6149   1.7142 -12.5523  -1.0221
-#>   -6.7093  10.3142   8.2573   2.4934  -1.7516 -11.4688  -2.4966 -15.7521
-#>    7.1038  -8.7811   1.0369  -5.1111  10.7257  -0.7682  -2.9512  -2.0453
-#>  -11.9724  15.1011  -3.2683   5.3593  10.3720  -2.2955   6.3947   4.5287
-#>   -6.6439   6.2459  -5.5014  -5.6457   0.1835  12.0601   2.1755  11.0305
-#>    1.2823  -1.0151   0.4840   9.5057   6.6848  -7.5415   3.5770   1.3109
-#>    7.9927  -9.9826   1.0853 -13.3779  -7.0886   3.5114  -5.8115  -1.8867
-#>   -1.5721  -1.4694   5.1739  -5.5387  13.7887   3.5508  14.0987   8.0543
-#>   11.2043   6.1202   1.7812  17.8911   9.0120   2.1963  15.4762  12.7197
-#>    8.6884 -13.1554  -4.0781   6.2083  10.8189  -9.2288  -7.2779   3.9982
-#>   -8.0184  16.5070   5.8675  -2.3072 -16.1820  18.5364  -5.8738 -16.4123
-#>    3.2305  -0.1945   8.0007  15.5950   6.0886   3.3087  -5.3623   0.7048
-#> 
-#> Columns 41 to 48  -8.6006  -4.8646  27.5523  -1.5430 -15.4093   7.7829   7.3981  -0.3793
-#>  -15.8642  -0.2972  -3.1149  -0.9991  -3.9445 -12.1184 -10.9670   6.3001
-#>   -0.7089 -11.8555   2.6656   0.5943  16.2644  -5.9812 -12.9371  -1.6014
-#>  -12.7581  -0.4289   6.1141  -8.4463  -2.0868  -6.8926  -2.6126  -9.0016
-#>   -8.2506  21.2704  -4.0491  -2.2546  12.1547   0.1667  -8.4576  10.7930
-#>   16.0594   4.1562  10.0196  -7.5173  -0.3256  16.0685  -0.1030 -16.9110
-#>   -0.6021  12.6787   2.3527  -8.8386  -7.3754  13.4047  -8.9777  -5.7262
-#>   -2.1249 -14.3050   5.0873   6.6320  -6.5322 -16.8134  -7.4323  -3.4885
-#>   12.5125 -10.3117  18.7218   4.0142   2.5446   8.3031   0.4404   3.8229
-#>   -0.8576   1.2573   0.1647  -5.6551  25.1561   2.0695 -17.7567 -22.7935
-#>   17.9452 -11.9911   9.8603   7.5845  -2.0050   6.4060  -4.7783   0.6968
-#>    3.0729  -2.5976  17.7593  -0.2200 -14.6376  11.7562  12.4318 -22.8368
-#>   -3.3752  -8.7275  22.4086  10.6214  -6.4601  17.0018   1.2447  -9.9543
-#>  -17.1848   7.0295 -20.1981  15.9027 -13.7001  14.6122 -12.5801   8.7190
-#>    7.3749  -8.2294 -10.3922   5.2209  10.2326  -1.1034  -2.3768   5.0779
-#>    6.5220   6.8331  -5.5146 -26.3487  10.0968   7.4076   4.2510  -0.1241
-#>  -16.3551  23.9117 -12.9491  -6.2878  -8.0376  -8.9809 -11.6969 -16.2643
-#>   -3.3320  -7.2563   7.0096  -5.1591 -10.8903  -8.3051  11.5121  -4.8205
-#>   30.1463 -27.2953 -10.3255   1.2407  -3.8466 -11.4208  10.3090 -21.3064
-#>   16.6340 -28.7060   7.5365  -0.2053  -9.2341  -6.5734 -10.7766  -2.7444
-#>   11.6982  -2.6341 -11.3039  -7.5007   5.1457   7.6688  26.6237 -15.1442
-#>   -9.7540  -8.5738 -14.3518  -5.7013  13.9404  -3.8985  -0.1138   8.8463
-#>  -10.4308  11.0512  -5.5217 -22.5346   1.6473  -5.2179  -9.4511  -7.9082
-#>   -2.4912   0.3161 -11.0292   6.5892   0.1307  -1.6834  -1.0987   1.6923
-#>   -3.9673  -7.9401  22.3718  -2.3691  11.7177 -11.3505   6.1523   3.1161
-#>    2.6399  13.7468  -0.3763  18.8045   8.5708   4.0262  -8.7736   4.2290
-#>   -2.8576   7.1759  -8.0101  10.8422   6.3865   5.0936   1.2168   9.2354
-#>  -10.8633  -1.0824  -0.8987  11.4758 -11.9743 -10.4646  13.7240  -1.7358
-#>  -14.1178  21.2416   3.7955  24.8035   6.0983   0.3335   3.4862   4.4295
-#>   11.0182   5.3442  -3.7926 -20.3325  23.6837  -3.2902  -8.2127  -9.5710
-#>    1.1583   2.6508 -16.1883   2.1579 -12.3988  16.1296   1.6853   2.6362
-#>   -4.6801  -1.2721   3.6902   8.4217  20.9060 -13.4412  18.0424  -1.5549
-#>   -8.8554  17.1304  -6.1151   8.3124 -10.3070  -2.1585 -10.7280   1.6565
-#> 
-#> Columns 49 to 54  -6.7632   9.0205   3.9832   1.1877   3.4006   5.9775
-#>   -8.4520  -5.9237   2.4574   0.5103  -1.2747  -0.2187
-#>   -6.3469   0.8673  -5.0039  -2.8374  -1.5800   0.5130
-#>  -15.8223 -10.4915  -2.9986  -0.9827  -1.1368  -2.5114
-#>  -10.8001   7.0442   9.2466   8.6179   2.7842   0.3649
-#>   20.2535  11.2160   3.6390  13.7110  13.4839   8.3034
-#>    4.1736  -9.9043  -5.7932  -7.1145   1.6513  -1.3660
-#>   -2.9477  -6.7830   2.2032   4.6185   4.4635   2.1922
-#>  -19.0045   9.1668  -3.2519   5.0947  -1.5906   1.6095
-#>   -5.0198  -7.6808 -18.1146   5.1944   4.6247   2.0399
-#>    8.0772  10.3144  -3.3351   4.8508  -1.3147   1.5389
-#>    6.1753  -1.1323   7.2598   9.2451  -4.0144   4.3835
-#>    2.9787   0.5584   7.9027  -0.5738  -0.4446   3.5570
-#>   -2.4050 -19.9975  -0.9384  -1.0172   1.1957  -3.5614
-#>   16.0283  11.9335   1.4864  13.4882   0.2626   2.1828
-#>    6.7011   1.7985  -3.5908   3.4675   0.3625   1.4105
-#>   14.4403   6.5076   8.2722  12.7859   4.8887   5.4573
-#>   -4.6987  -9.9025  -0.4740  -6.8387  -0.7232  -0.5840
-#>   -0.4707   3.2551  -7.0753   1.6114  -1.6163  -0.1045
-#>   -3.9348   7.0254  -7.9377  -5.2363   0.3976  -1.0753
-#>   -5.7204   6.0980   1.9556  -4.8694   0.6084  -1.2481
-#>   -8.0397   4.9181   1.6892  -0.0915   0.5730   0.9259
-#>    5.1109  -1.9765   3.3636   4.5646   6.2376   3.4933
-#>    6.6943 -21.3563  -4.1890  -4.2812  -1.6969  -2.0259
-#>   15.2933 -11.0076  -7.0053  -1.5960  -3.4357  -2.7896
-#>   -1.0028 -14.8088  -0.0199  -5.7920  -1.0041  -0.3028
-#>   11.7314  -0.1637  -1.2221  -3.7700  -1.0029  -3.3862
-#>   -2.0893   6.2374  18.0681  -0.3367  -1.2742  -0.1907
-#>   -0.0101  -6.8336  -1.6090  -7.7200  -3.4319  -1.2397
-#>    5.7481  -7.0355   4.3746   0.9063  -8.7526  -4.9835
-#>    7.4789  -5.0154   6.5255   6.2043   1.7799   4.7899
-#>   -6.0105   7.1342  -0.0061  -2.5677  -8.1663   1.6100
-#>   -0.1814 -12.1102  -1.1473  -2.8114   7.7898  -4.2462
-#> 
-#> (5,.,.) = 
-#>  Columns 1 to 6 -6.4262e+00 -1.0302e+01 -8.0426e+00 -3.3373e-01  1.1466e+01 -1.5697e+01
-#>  -1.1519e+00  2.0001e+00 -4.2894e+00 -7.6826e-01 -1.1227e+01  2.5261e+00
-#>   1.6970e+00 -3.3100e+00 -4.4895e+00  1.3259e+00  1.8962e+01  8.1508e+00
-#>   2.2075e+00  2.1765e+00  1.4384e+00  1.5917e+01 -1.1309e+01  7.0364e+00
-#>   3.8093e+00 -3.8368e+00 -1.6455e+01  1.2298e+00 -1.0367e+01 -1.5784e+01
-#>  -9.8932e-01 -5.0788e+00 -1.5136e+01 -9.8048e+00 -6.8520e+00  1.4605e+01
-#>  -4.7910e+00 -5.1260e+00  1.6453e+00 -5.3460e+00  8.2288e+00  2.0704e+00
-#>   6.3422e+00  9.8608e+00  5.8286e+00 -5.4466e+00  5.3061e+00 -8.3891e+00
-#>  -4.0661e+00 -2.3066e+00  2.0202e+00 -1.3354e+00  8.2298e+00 -7.6327e-01
-#>  -1.5477e+00  7.6396e-01  4.5311e+00  3.5211e+00 -1.4649e+01  7.8986e+00
-#>   2.5916e+00  2.3080e+00  6.7754e+00 -1.1042e+01 -1.3366e+01 -3.8321e+00
-#>   2.5285e+00  4.0808e+00  8.8407e+00 -6.2941e+00  1.3328e-02  3.6616e+00
-#>   4.6916e+00 -5.0228e+00  7.1309e+00 -4.2475e+00  1.1867e+01 -8.2675e+00
-#>   8.0642e+00  7.6726e+00  5.1226e-01  4.8608e+00  2.9767e+00  2.1985e+00
-#>  -4.2219e+00  6.4403e+00  1.7694e+00  4.1442e+00  7.6349e-01 -1.0233e+01
-#>  -9.3862e+00 -3.3176e+00 -1.1481e+01  2.5465e+00 -3.9686e+00  1.6527e+00
-#>   8.8190e-01 -3.4220e+00  1.1836e+00 -2.9330e+00  1.0608e+01  1.6665e+01
-#>  -1.4457e+01 -2.0386e+00 -7.8769e+00  9.1628e+00 -1.2645e+01  8.8112e+00
-#>   3.3646e+00  2.8787e-01 -2.8648e+00  9.7906e+00 -4.8234e-01  2.3953e+01
-#>   5.5200e+00  8.0760e+00  8.1474e+00  1.6808e+01  3.8783e+00 -2.1914e+00
-#>  -2.4432e+00 -6.1003e+00 -3.4941e+00  9.1001e-01 -1.7641e+01 -9.1526e+00
-#>   7.2679e+00  2.8391e+01 -5.7809e-01  6.8612e+00  1.4567e+01  3.5080e+00
-#>   6.7239e+00  6.1337e+00  1.0413e+01  1.2382e+00  4.5004e+00  2.7951e+00
-#>  -2.6557e+00 -6.5517e+00  2.6339e+00 -3.3769e+00  4.9165e+00  1.9107e+00
-#>   4.2102e+00 -3.3543e+00 -1.2687e+01  2.5395e+00  2.5638e+00  1.9196e+01
-#>  -3.8726e+00  2.0134e-01  4.1404e+00 -6.0846e+00  3.6685e+00 -6.0531e+00
-#>   2.7909e-01  1.0917e+00 -7.4160e-01 -2.7103e-01  8.1912e-05 -3.0494e+00
-#>   7.3136e+00  6.4701e+00  1.0309e+01  7.2169e+00  2.8521e+00 -2.0348e+00
-#>  -2.8647e+00 -3.3199e+00 -1.1787e+01 -2.0081e+00 -4.9858e+00 -7.1792e+00
-#>  -9.1410e+00 -1.2388e+01  3.4625e+00  1.7626e+01 -1.7789e+01  2.6632e+00
-#>   5.0764e+00  6.8630e+00  8.2448e+00 -1.5367e+01 -6.3745e+00 -8.8637e+00
-#>   3.8776e+00 -6.5466e+00 -1.8389e+01 -6.6696e+00  5.8418e+00 -6.7904e+00
-#>  -3.2302e+00 -8.0956e+00 -5.7334e+00  1.4126e+00 -2.7921e+00 -3.9389e+00
-#> 
-#> Columns 7 to 12 -4.0465e+00  8.8923e-01 -1.4303e+01 -9.4046e+00  6.4015e+00  1.5997e+01
-#>  -2.9243e+00  1.7137e+00  4.3121e+00 -7.3565e+00 -1.0214e+01 -1.2201e+01
-#>   7.1019e+00  5.0987e+00  1.9010e+00 -2.4699e+00 -3.0563e+00  2.9805e+00
-#>  -8.1475e-01  8.9229e-01  1.4172e+00 -3.4651e+00  5.2722e+00  1.2982e+00
-#>  -5.4192e+00 -9.6536e+00  6.7028e+00 -2.9846e+00  3.8598e+00 -8.2039e+00
-#>   1.7103e+01  4.1835e+00 -1.9952e+00  5.5272e+00 -1.5506e+00 -9.4011e+00
-#>   2.2505e+00 -1.9357e+01 -2.2002e+00  2.4192e+00  1.0321e+01  1.7182e+01
-#>  -1.0851e+01  9.3675e+00 -1.4618e+00 -1.9284e+00 -3.1685e+00  9.2756e+00
-#>   1.1996e+01 -8.0681e+00  3.1487e+00  4.6738e+00  7.7214e-01  9.2540e+00
-#>  -2.0899e+01 -2.4664e+00 -1.0292e+01 -2.3162e+00  4.3699e-01  3.0183e+00
-#>  -7.5234e+00  4.0622e+00  2.8611e+00 -5.0612e-02 -2.7615e+00  1.0727e+01
-#>  -6.7269e+00  1.7070e+01  1.3479e+00  2.8591e+00  1.3784e+00  1.5057e+00
-#>  -2.5754e+00  1.4918e+00 -1.6116e+00  9.1770e+00 -2.8859e+00  6.1093e+00
-#>   4.5459e+00 -6.6762e+00  1.0926e+01 -8.5546e+00  5.9326e-01 -4.5555e+00
-#>   2.1496e+00 -6.6026e+00  2.0679e+00 -2.2635e-01  1.2354e+01  8.0611e+00
-#>  -1.0398e+00  2.4987e+00 -6.6752e+00  1.6018e-02 -5.0908e+00 -7.8365e-01
-#>   2.1959e+00  6.0708e+00  4.3137e+00  2.1468e+00 -6.7604e+00 -2.5864e+00
-#>  -7.0599e+00  5.5481e+00 -4.1306e+00 -1.1196e+01  7.3052e-02  2.0858e+00
-#>  -1.9106e+01 -4.3791e+00 -1.5648e+01 -3.9139e+00  4.2754e+00  1.4006e+01
-#>  -1.2236e-01  1.2929e+01  1.3368e+01 -7.0120e-01  8.7745e+00 -6.1214e+00
-#>  -1.8933e+01 -2.1145e-01 -5.3975e+00 -3.9796e+00 -9.0409e+00 -8.7610e+00
-#>   7.7373e+00 -2.1278e+00  3.6165e+00  7.6281e+00  4.9305e+00  7.7253e+00
-#>   8.2651e+00  1.7974e+01  1.0775e+01 -1.3353e+00  8.9461e+00 -3.0251e+00
-#>   4.0353e+00 -5.0770e+00  7.8504e-01  8.8365e+00 -3.8695e+00  1.3443e+01
-#>  -7.6197e+00  1.1457e+01 -8.3000e+00 -1.5143e+01 -4.2876e+00 -8.1806e-01
-#>   3.5782e+00 -2.8537e+00  9.9314e+00  2.0893e+00 -3.4795e+00 -1.2507e+01
-#>   6.0520e+00 -1.2071e+01  9.9488e-01  2.2217e+00  5.7808e+00 -1.6132e+01
-#>   1.4790e+01  1.4483e+00 -1.9808e+00 -3.8774e-01 -8.9233e+00  4.7249e+00
-#>   1.2036e+01  2.3744e+00  3.1593e+00  1.5919e+00  5.9775e+00  2.4469e+00
-#>  -1.0775e+01 -9.3839e+00 -1.6414e+01  5.2102e+00  1.5185e+01  5.9463e+00
-#>  -3.5521e+00 -7.9817e+00 -2.9207e+00  5.7468e+00 -1.1332e+01  8.4551e+00
-#>  -1.0195e+01  1.2005e+01 -3.6723e+00  1.1780e+01 -1.6271e+00  7.6696e+00
-#>   2.1713e+01  5.2783e+00  9.3111e+00  8.0497e+00 -1.6087e+00  4.5775e+00
-#> 
-#> Columns 13 to 18 -1.8652e+00 -1.6296e+01  1.8492e+01 -8.9194e+00 -2.5883e+01  1.1869e+00
-#>  -6.7993e+00  5.6378e+00  1.4581e+01 -4.0838e+00  6.1944e+00 -2.9574e+00
-#>   2.2431e+01 -2.6491e+00 -1.2315e+01 -4.1996e+00 -2.0293e+00  9.9904e+00
-#>  -1.8455e+00  3.2875e-01 -6.2301e+00  1.0648e+01 -3.1354e+00  4.8623e+00
-#>  -2.4562e+00 -4.0854e+00 -1.4479e+01 -4.5397e+00 -1.3717e+01 -3.2325e-01
-#>   1.6040e+01  9.0584e+00 -8.3440e+00  3.6967e+00  9.8770e+00  2.2980e+00
-#>  -1.2911e+01  1.2589e+01  6.9104e+00  3.5571e+00 -5.1544e+00 -7.4146e+00
-#>   6.1413e+00 -1.8190e-01 -3.3456e-01 -2.6882e+01 -1.4690e+01 -1.1079e+01
-#>   1.1708e+01 -9.3690e+00 -1.1440e+01  9.3169e+00  7.7772e+00 -1.8925e+01
-#>   1.5197e+01  1.3778e+01 -5.0570e+00 -9.0739e-01 -6.1582e+00 -2.7271e+00
-#>  -2.1772e+00 -1.6658e+01 -6.2780e-01  5.7757e+00 -1.0697e+01  2.5060e+00
-#>  -1.1280e+01  1.2305e+01  5.9221e+00 -3.2115e+00 -7.5718e+00  8.3008e+00
-#>  -1.5762e+01 -2.2615e+00  1.2355e+01 -1.1957e+01 -9.3039e+00  6.8012e+00
-#>  -8.7712e+00  8.6509e+00  7.4090e-01  9.2350e+00  1.9766e+00 -1.6338e+01
-#>   1.4290e+01 -3.2918e+00 -1.5809e+01  1.0007e+00  6.8876e+00 -3.0997e+00
-#>   1.4291e+01  1.4412e+01 -8.0414e+00  9.9127e-02 -4.8554e+00  3.1393e+00
-#>  -9.8472e+00  9.9958e-01  2.5397e-01 -4.2138e+00 -1.1304e+00 -1.6261e-01
-#>  -1.1017e+01 -4.3670e+00  1.7037e+01  8.7364e+00  1.3894e-01  7.6550e+00
-#>   3.4132e+00 -3.2857e+01  2.2227e+00  5.0719e+00  2.8524e+00 -1.2048e+01
-#>   1.4289e+01  4.6767e+00 -1.4386e+01 -8.2073e+00 -1.0569e+01 -1.3552e+01
-#>   3.5160e+00  1.1464e+01  7.5946e+00 -5.3213e+00 -1.9107e+01 -1.2373e+01
-#>   3.4990e+00 -7.3915e+00 -1.1307e+01 -6.4806e+00 -5.4687e+00 -2.5775e+00
-#>  -2.8884e+00  2.8935e+01 -6.5814e+00  1.8260e+00 -8.2682e+00 -1.2801e+01
-#>   1.1532e+00  3.3882e+00 -1.7850e+01  2.8981e+00  1.5333e+01 -9.5075e-01
-#>   5.5637e+00  1.7700e+01  7.4352e+00  8.8628e+00  7.4742e+00  2.0415e+01
-#>   2.9779e+00  2.2275e+01 -1.2943e+00  6.2881e+00  2.5515e+01 -1.5408e+01
-#>  -1.9378e+00  5.2831e+00  2.9522e+00  1.0344e+01  4.0745e+00 -1.2546e+01
-#>   1.8626e+00 -2.3987e+01  4.6421e-01 -5.2962e+00 -1.8111e+00  4.6379e+00
-#>  -8.5860e+00  9.5225e+00  9.0563e+00 -7.0315e+00  4.4661e+00  8.3436e+00
-#>   9.7812e+00  5.3378e+00  1.3720e+01  1.2092e+01  1.2569e+01  6.4263e+00
-#>   6.8753e+00 -2.3651e-01 -7.5481e+00  5.3557e-01  2.3002e+00 -2.2790e+01
-#>   2.4410e-01 -1.2898e+01  1.6192e+01 -1.8877e+00  3.8370e+00  6.8946e+00
-#>  -5.2318e-01 -5.6432e+00 -6.6090e+00  4.1998e-01  5.3408e+00  1.3252e+01
-#> 
-#> Columns 19 to 24 -1.4445e+01 -7.1449e+00 -1.5830e+01 -8.5493e+00 -1.3246e+01 -1.0818e+01
-#>  -5.8257e+00  2.5380e+00  4.0747e+00  3.3717e+00  1.2595e+01 -3.3998e+00
-#>  -7.3894e+00 -4.2604e+00 -1.2218e+01 -4.3442e+00  9.4085e+00 -2.5070e+00
-#>  -4.1200e+00 -3.6416e+00  9.7555e+00  2.0301e+01  4.9697e+00 -1.1485e+01
-#>  -3.8639e+00  3.4220e+00 -1.6872e+01  3.9867e+00  2.3141e+01  1.2290e+01
-#>   1.7509e+01  1.1767e+01 -8.2266e-01 -3.9060e+00 -1.0922e+01 -1.0966e+01
-#>   7.7470e+00 -1.4266e+01  3.9405e+00 -6.0676e+00  1.2489e+00  8.5741e+00
-#>  -1.4520e+00 -7.8036e+00 -8.0052e+00  4.7120e+00  1.9998e+00  9.7785e+00
-#>   7.1853e+00 -5.4099e+00 -1.1661e+00 -9.6124e-01 -3.5060e+00  2.8660e+00
-#>  -4.3456e+00  1.4829e+01  1.3369e+01 -6.0624e+00 -6.6249e+00 -4.8913e+00
-#>   6.3133e+00 -5.3194e-01 -1.5952e+00 -2.1070e+00  2.6445e+00  5.3696e+00
-#>  -1.2118e+01  5.5716e+00  4.6971e-01 -4.8059e+00 -5.0967e+00 -9.4180e+00
-#>   1.5625e+01 -2.5577e+00 -1.1879e+01 -4.4374e-01 -8.2021e+00  2.3238e+00
-#>  -4.9951e+00  1.4574e+01  1.0015e+01  6.7822e+00  1.8982e+00 -9.4548e+00
-#>  -1.3176e+01 -7.8126e+00 -1.8460e+00 -1.2364e+00  8.4664e-01 -1.9703e+01
-#>   1.4479e+00  1.4478e+01  5.8062e+00  9.4571e-01  8.8584e+00 -5.1504e+00
-#>   1.1455e+00 -6.0675e+00  9.1297e+00 -1.1173e+01 -4.6109e+00  5.5358e-01
-#>  -3.5382e+00  9.8266e+00  1.4570e+01  7.3645e+00 -3.1711e+00  8.0725e+00
-#>  -1.3910e+01 -1.2044e+01 -1.6904e+01 -6.0675e+00 -4.3612e+00 -8.3484e+00
-#>  -4.3631e+00  5.4232e+00  3.4341e+00  2.0606e+00 -5.7591e+00 -3.5410e+00
-#>  -8.8304e+00  2.7338e+00  5.3586e+00 -4.3458e+00  8.0168e+00  4.1562e+00
-#>   3.4144e+00  1.1629e+01 -4.1753e+00 -2.2655e+00  2.4787e-01 -1.4080e+01
-#>   1.0627e+01  7.8785e+00  1.1896e+01 -7.2031e+00 -4.6135e+00 -2.0067e+00
-#>   1.1385e+01  1.1738e+01 -1.7749e+01  1.7336e+01  8.0784e+00  7.6059e+00
-#>   1.2071e+01  9.1543e+00 -9.2724e+00 -3.8312e+00  7.5362e-03 -8.5426e+00
-#>   1.0760e+01  6.5716e+00  5.0156e+00 -6.8049e+00 -1.5221e+00 -1.8318e+00
-#>   5.8628e+00 -5.8969e+00 -2.5412e+00 -7.0412e+00 -7.1329e+00 -4.1548e+00
-#>  -5.9491e+00 -4.2658e+00 -1.1386e+01  4.8017e+00 -4.1747e+00 -1.5459e+00
-#>   6.2477e+00  3.3169e+00  1.0762e+01  8.2661e+00 -4.6610e+00  1.3204e+01
-#>   1.6609e+00  8.2084e-01 -2.4961e+00 -1.3010e+00  5.8499e+00  1.2042e+00
-#>  -1.8975e+01  9.0164e+00  6.3102e+00  1.0104e+01 -4.1855e+00 -2.9264e+00
-#>   1.7456e+01  2.2383e+00 -7.5770e+00  1.1101e+01 -7.0678e+00 -7.7324e+00
-#>   1.2254e+01  1.9148e+01 -9.5767e+00  1.3521e+01  5.4999e+00  1.3138e+01
-#> 
-#> Columns 25 to 30  4.7672e+00  3.9393e+00  5.8296e+00 -3.1557e+00  9.4979e+00 -2.1713e+01
-#>  -6.2923e+00  1.0280e+01 -9.7095e+00  2.7831e+00  3.1785e-01  1.5338e+00
-#>  -1.8244e+00  2.1574e+00  1.1629e+01  8.0770e+00  4.8884e-01  1.8520e+01
-#>   1.5629e-02 -1.8364e+00  1.8026e+01  1.5302e+01 -2.8889e+00  1.3968e-01
-#>   1.1179e+00 -9.3442e+00  2.7496e+00 -6.8790e+00 -4.7582e+00 -1.4035e+01
-#>  -6.6229e-01  6.1338e-01 -6.1479e+00 -9.7994e+00 -1.1166e+01 -3.6618e+01
-#>  -1.2571e+01  2.6986e+00  3.4589e+00  1.0057e+01  6.3649e+00  1.6529e+00
-#>   1.3031e+01  4.0839e+00  2.0510e+00 -1.5870e+00 -9.3332e+00  9.5409e+00
-#>   1.5391e+00 -2.6677e+00  9.4479e+00  1.2192e+01 -1.5544e+01  4.3714e-01
-#>  -5.6149e+00  1.4890e+01  2.8167e+01  2.1890e+01  3.7060e+00 -2.3475e+00
-#>   3.1602e+00 -6.4318e+00 -5.0051e-01 -7.0032e+00  2.8286e+00 -5.1985e+00
-#>   2.1729e+01  4.4250e+00  5.0968e+00  6.9575e+00  1.7531e+00 -2.5253e+00
-#>  -1.0086e+00 -2.7643e-01  4.9174e+00  3.8020e+00  1.1939e+01 -2.0344e+00
-#>  -6.7084e+00 -3.3351e+00  7.7175e-01  2.1211e+01  2.6425e+01  6.7001e+00
-#>   8.5745e+00 -6.3598e+00  4.2359e+00 -6.8517e-02 -8.5386e-01  5.5816e+00
-#>  -8.9783e+00  4.1437e-01 -8.1550e+00 -8.5186e+00 -3.6995e-01 -4.0838e+00
-#>  -9.3662e+00 -9.4380e+00 -1.6104e+01 -1.6740e+01 -1.4065e+01 -1.4101e+01
-#>   4.0266e+00 -6.0926e+00 -2.1354e+00 -1.2275e+01 -3.8985e+00 -4.7642e+00
-#>   7.2370e-01 -8.8531e+00  3.0463e+00  1.1427e+01 -4.3989e-01  3.9344e+00
-#>   1.6464e+01  1.1835e+01  9.4309e+00  1.2652e+01  4.9765e+00  2.0312e+01
-#>  -5.0763e+00 -5.5975e+00 -5.2981e-01 -9.1223e+00  7.8726e+00 -1.1486e+01
-#>  -5.5547e+00  4.3114e+00  1.8073e+00  1.1570e+01  9.9499e+00  6.8600e+00
-#>  -6.6898e-01 -3.5121e+00  1.2643e+00  3.2227e+00 -3.4610e+00  1.8418e+01
-#>   4.0002e+00  1.0894e+00  8.4338e+00 -5.1922e+00  3.8902e+00 -6.6107e+00
-#>   3.9277e+00 -6.1210e+00 -1.3713e+01 -1.8275e+00 -3.6425e+00  1.0173e+01
-#>  -3.0410e+00  1.9244e+01  1.1407e+01  2.2155e+01  3.5196e+00  1.0021e+01
-#>  -1.0214e+01 -3.6439e+00  5.5281e+00 -1.3027e+01  1.2913e+01 -8.8776e-01
-#>  -1.2979e+00 -2.1701e+01 -3.9833e+00 -1.3435e+01  1.7802e+01  5.0667e+00
-#>  -8.7162e+00 -4.8520e+00  4.9603e+00 -6.5037e+00 -7.7042e+00 -2.6138e+00
-#>   2.7359e+00 -1.6787e+00  1.8766e+01 -6.5021e+00 -6.3636e+00 -1.6554e+01
-#>  -2.1235e+00  3.6546e+00  2.7977e+00  2.9845e+00  4.1591e-01  1.8891e+00
-#>  -1.6986e+01  5.1112e-01 -5.8523e+00 -8.3724e+00  1.6497e+01  1.3320e+00
-#>   3.8910e+00  1.3113e+00 -4.9165e+00 -1.6934e+01  1.6983e+00 -9.6708e+00
-#> 
-#> Columns 31 to 36 -2.1679e+01 -2.4109e+01  8.4593e+00  7.2496e+00 -7.6315e+00 -2.8294e+00
-#>   9.3554e+00 -1.6916e+01 -9.3243e+00 -4.0114e+00 -7.3527e+00  4.0655e-01
-#>   1.4200e+01  1.5864e+01 -5.9292e+00  1.3862e+01  2.3485e+00  2.4782e+00
-#>  -1.8995e+00  2.3539e+01 -2.6220e-02  1.8460e+01 -1.2601e+00  1.1129e+01
-#>  -1.4494e+01 -9.6573e-01 -5.0733e+00 -1.0772e+01 -2.2918e+01 -3.4051e+00
-#>  -2.4427e+01  5.6512e-02 -2.4875e+00 -1.4292e+01 -5.5592e+00 -1.3762e+01
-#>  -1.4701e+00  8.7661e+00  3.7526e+00  3.6377e+00 -1.2703e+01 -9.5928e+00
-#>  -1.0701e+01 -1.4778e+01 -3.5459e+00 -1.4791e+01 -1.0391e+01  1.0136e+00
-#>  -9.1675e+00 -1.6266e-01  1.1090e+01  1.5069e+01  1.9667e+01 -7.0169e+00
-#>   2.6015e+01 -2.2708e+00 -1.2905e+01  1.6435e+00 -2.3520e+00 -6.1251e+00
-#>  -1.7792e+00 -2.3453e+01 -1.9723e+00 -4.5929e+00 -1.6097e+01 -4.4947e+00
-#>  -5.4744e+00 -6.4192e-01  1.6510e+01 -2.6668e+00  2.4133e+00 -2.6066e+00
-#>  -1.9293e+00  1.5536e+01  1.1604e+01 -1.0907e+01 -1.0784e+01  1.3530e+01
-#>   1.2010e+01 -9.5282e+00 -7.4836e+00  8.5354e+00  1.5636e+01  6.4028e-01
-#>  -8.9697e-01  1.3402e+01 -8.8782e+00 -7.5711e+00  1.0922e+01 -1.6372e+00
-#>  -8.5683e+00  7.1512e+00 -3.3600e-01 -8.1503e+00 -1.5620e+01  2.9184e-01
-#>  -7.3744e+00 -9.9613e+00  3.9222e+00 -3.9316e+00 -4.0211e+00 -1.2877e+01
-#>   3.4622e+00 -2.0458e+00  1.0160e+01  1.9177e+01 -1.0452e+01 -7.6392e+00
-#>  -1.7070e+00 -1.3017e+01 -3.2206e+00  4.0881e+00 -4.6195e+00  1.0797e+00
-#>   1.7468e+01  9.2982e+00 -1.2351e+01  2.2376e+00 -3.9956e+00  1.3261e+01
-#>  -4.4173e+00 -1.4719e+01 -9.7518e+00 -1.7765e+01  1.4858e+00 -3.2349e+00
-#>   3.7165e+00  5.9601e-01  1.4029e+01 -1.0961e+01  8.7351e+00  7.7419e+00
-#>  -6.1524e+00  1.3197e+01  3.9808e+00  4.3193e+00 -1.2020e+00 -1.2825e+01
-#>  -5.0571e-01 -1.0672e+01  1.8364e+01  8.0442e+00  1.3081e+01  8.4952e+00
-#>   4.4564e+00  5.5969e+00  5.6686e+00 -1.1134e+00 -7.3456e+00  7.7669e+00
-#>   1.3500e+01  4.4614e+00  4.9841e+00  8.9211e+00  2.0098e+01  3.8844e+00
-#>   3.6128e+00  1.1122e+01 -4.4044e-01 -3.6093e+00  1.3210e+01  8.5443e+00
-#>  -1.8090e-01 -1.0985e+01 -4.1344e+00 -5.0467e+00 -1.5144e+01 -1.9789e+00
-#>  -1.3420e+01 -7.0009e+00  8.1848e-01 -5.0013e+00  1.8268e+01  8.2374e+00
-#>   6.3996e+00  1.8506e+01 -1.1341e+01  7.0303e+00 -1.0490e+01 -4.4531e+00
-#>   5.5464e+00 -1.7529e+01 -7.0866e+00 -8.8303e-01 -5.3377e+00 -7.3746e+00
-#>  -8.8998e+00  1.7904e+00  4.3107e+00 -1.4987e+01 -1.3366e+00  5.2522e+00
-#>   8.7654e+00 -7.8483e-01 -7.9464e+00  1.0005e+01 -1.1502e+01 -2.4911e+00
-#> 
-#> Columns 37 to 42 -1.2982e+01 -4.2126e+00 -3.8256e+00  3.6204e+00  3.1385e+00 -3.8542e+00
-#>  -7.1096e-01 -1.1357e+01  1.9772e+01  3.5039e+00  4.9852e+00  1.4891e+01
-#>   1.5609e+00 -2.5245e+00 -8.8441e+00  4.7378e+00  3.7088e+00  8.7780e+00
-#>   7.1751e+00 -5.4980e+00 -1.6342e+01  8.2999e+00  2.4324e+00 -7.6220e+00
-#>   1.0249e+01  1.2807e+01 -6.5801e+00 -5.8649e+00 -4.9927e+00  2.6560e+00
-#>  -2.1048e+01  6.4206e+00  1.0280e+01  5.0778e+00  1.0445e+01  4.2637e+00
-#>   1.2026e+01 -6.7137e+00 -8.5624e+00  2.7173e+00  3.2165e+00 -3.5499e+00
-#>  -2.1791e+00 -2.1789e+00  4.3917e+00 -1.3265e+01  5.4625e+00  1.8335e+01
-#>   7.3245e+00 -5.1060e+00  7.6107e-01  6.8573e+00  1.4891e+01 -4.1370e+00
-#>   1.1617e+00  9.7119e+00 -6.5697e+00  3.8368e-01 -1.1239e+00 -7.7994e+00
-#>   4.0031e+00 -3.4288e+00 -1.2127e+00 -7.2412e+00  2.3004e-01  8.5065e-01
-#>   5.2983e+00  5.3690e+00  5.7870e+00 -2.9230e+00  4.3193e+00  4.7594e+00
-#>   1.4966e+01 -4.6703e+00 -2.1251e-01 -1.6869e+01 -6.9216e+00  9.3986e+00
-#>   1.6732e+00 -2.7369e+00  5.2217e+00 -2.9938e+00  5.3384e-01 -3.6932e+00
-#>   6.5449e+00  4.4988e-02 -8.0596e+00 -1.0725e+01  9.6437e+00 -1.2317e+01
-#>   1.0285e+00  8.7999e+00  1.4183e+01  1.0116e+01  9.0127e-01 -5.8294e+00
-#>   4.4251e-01  1.2525e-01 -8.3928e-01  8.1308e+00  1.8535e+00 -1.1609e+01
-#>  -1.7475e+01 -2.8897e-01  6.4601e+00  9.6699e+00  2.5221e-01 -1.0648e+01
-#>   3.5728e+00 -6.6077e+00 -1.7886e+01  4.0742e+00  1.7618e+00 -3.7558e+00
-#>  -2.0264e+00 -2.7089e-01  4.3377e+00 -5.0581e+00  2.4265e-01  8.6139e+00
-#>  -9.2931e-01  6.4229e+00  8.4352e+00  6.6326e-01  1.1871e+00  1.4929e+00
-#>   6.7805e+00 -1.0345e+00  1.5609e+01 -1.3094e+01 -1.1205e+01  5.9305e+00
-#>   3.6864e+00  1.0229e+01  1.1679e+01  4.0239e+00 -4.9793e+00 -2.2810e+01
-#>   1.8718e+00 -2.4655e+00 -4.8008e+00  9.0004e+00 -3.6858e+00  6.8290e+00
-#>  -5.7660e+00  2.6562e+01 -6.6253e+00  5.0441e+00 -3.5265e+00 -3.7034e+00
-#>  -1.9097e-01 -7.5070e+00 -2.1687e+00 -1.3804e+01 -1.7359e+00  1.9275e+00
-#>  -7.2095e+00 -9.9872e+00  3.7455e+00  4.0918e+00 -9.8750e+00  3.0196e-01
-#>   5.1022e+00  1.0027e+01 -1.4910e+00  1.2692e+01 -6.9118e+00  7.6790e+00
-#>   1.3758e+00 -7.4730e+00 -1.0146e+01  1.7413e+00 -2.0952e+00 -3.0648e+00
-#>   3.3219e+00  4.3010e-02 -1.1769e+01 -5.4523e+00 -8.0371e+00 -1.5348e+01
-#>  -4.3930e-01 -1.0443e+01  8.5679e+00  1.2639e+00 -1.0131e+01 -7.0613e+00
-#>   1.1237e+01 -1.5008e+01 -1.1160e+01 -1.6202e+00 -1.6306e+01 -3.6980e+00
-#>  -4.0589e+00 -8.3484e-06  2.1629e+00  1.3816e+01 -3.1079e+00  4.6911e+00
-#> 
-#> Columns 43 to 48  9.5938e+00  9.8600e+00  9.7126e+00  1.8094e+01 -1.2962e+01  4.5320e+00
-#>   8.8597e+00 -3.8651e+00  3.1434e+00 -1.8626e-01 -5.8093e+00  1.1049e+01
-#>  -2.7338e+00 -1.1493e+01  7.7413e+00 -7.4347e+00 -2.8650e+00 -4.0432e+00
-#>  -5.0291e+00 -3.8119e+00  7.6550e+00  4.3292e+00 -3.0579e-01 -3.6069e+00
-#>  -5.2778e-01 -7.3304e+00  5.8848e+00 -1.2196e+01  7.4105e+00 -1.9333e+00
-#>   2.8526e+00  1.9088e+01  4.3373e+00  1.6240e+01 -1.1476e+01 -6.8856e+00
-#>   1.4744e+00  1.0112e+01  8.1093e+00 -3.6238e+00  1.1439e+00 -7.5707e+00
-#>  -5.3667e+00 -7.7414e+00 -1.0272e+01  6.6667e-01  1.0756e+01  7.8223e+00
-#>  -1.2314e+00  1.2076e+01 -2.8305e+00  7.6454e+00 -1.7534e+01  7.1417e-01
-#>   7.0782e-01  2.2438e+00  1.0972e+01 -2.3305e+00  3.3615e+00  8.5989e+00
-#>  -2.4608e+00  1.9173e+00  4.5344e+00 -9.2493e+00  9.1561e+00  3.1954e+00
-#>   5.0468e+00  7.4412e+00  9.2711e+00 -8.5012e+00 -9.2720e+00  4.4542e-01
-#>  -6.5111e+00  6.7097e+00 -4.1257e+00  2.3944e+00  2.2047e+01  7.6683e+00
-#>   6.7561e+00  7.3840e+00  7.0542e+00 -1.7891e+00 -1.0972e-01 -1.3271e+01
-#>  -7.1963e-01  1.1568e+01 -4.8449e+00  2.3278e+00 -1.7470e+01 -4.0480e+00
-#>   4.8309e-01 -5.2807e+00  1.1232e+01  6.8060e+00 -2.8304e+00 -1.1613e+00
-#>   3.3958e+00  5.9262e+00  1.4133e+01  1.1143e+01  3.2042e+00 -6.2356e+00
-#>   7.5310e+00 -2.0502e+00 -2.2026e+00 -8.8068e+00 -4.4770e+00  2.5733e+00
-#>   6.1012e+00  4.0045e+00  3.6754e+00  3.0534e+00 -8.3425e+00  1.6813e-01
-#>  -1.0076e+01  1.3927e+00 -2.6783e+00 -9.6086e+00  3.5074e+00  1.0511e+01
-#>   1.0308e+00 -4.1848e+00 -5.3140e-01  4.9027e-01 -1.4362e+00  1.7054e+00
-#>   9.5634e+00  2.1233e+00 -2.3149e+00 -2.8003e+00 -1.4120e+01  1.0925e+01
-#>  -8.9510e+00 -2.0107e+00  1.3626e+01  7.5999e+00 -1.0009e+01 -8.6540e+00
-#>   5.8128e+00  4.3800e+00  5.1025e+00 -3.1124e-01 -9.5418e+00 -8.0206e-01
-#>   4.5954e-01 -1.9820e+01 -1.0856e+01 -2.4205e+00  3.2851e+00  3.5446e+00
-#>   7.4073e+00  1.0443e+01 -9.5225e+00  9.4974e+00 -3.1614e+00  8.7247e+00
-#>   6.9447e+00 -3.9645e-01 -7.9139e+00  6.0300e+00  8.5722e+00 -1.7283e+00
-#>  -1.0806e+01 -1.5662e+01  2.5612e+00  4.0088e+00  5.8340e+00  7.7349e+00
-#>  -4.5984e+00 -2.4696e+00 -5.8806e+00  3.0326e+00  1.4204e+01 -2.3575e+00
-#>   2.8538e+00 -1.9296e+00  3.6106e+00 -2.7019e+01 -9.1201e+00  1.2406e+01
-#>  -1.4059e+01  6.0087e+00  7.9251e+00 -2.9546e+00 -1.3986e+00  1.0299e+00
-#>  -6.0447e+00 -2.5707e+00  3.1745e+00  9.1589e+00  4.0167e+00  9.2205e-01
-#>  -3.8542e+00 -1.4597e+01  1.0325e+01 -2.1651e+00  9.4333e+00 -1.0715e+01
-#> 
-#> Columns 49 to 54 -2.8126e+00  6.2290e-01  1.2094e+01  5.7506e+00  6.0703e+00  1.8163e-01
-#>  -6.2051e+00  1.0590e+01 -1.1221e+01  4.7619e+00 -6.3597e-02  5.8108e+00
-#>   7.0618e+00 -3.9845e+00  1.2555e+00  5.8426e+00  9.2383e+00  8.9532e-01
-#>  -1.9856e+01 -6.1150e+00 -5.9260e+00  2.8530e+00  9.8016e-01 -2.5336e+00
-#>   1.4864e+01 -6.8976e-01  1.2750e+00  2.0616e+00  3.1718e+00 -6.2053e-01
-#>   4.0523e+00  1.1325e+01  1.2280e+01  3.6007e+00  3.4541e+00 -3.6653e+00
-#>   8.3939e+00  1.1663e+01  1.1109e+01  8.8074e-01  5.1258e+00 -6.6330e+00
-#>  -2.4700e-01 -5.0268e+00 -2.8844e+00 -7.5633e+00 -1.9285e+00 -1.2111e+00
-#>  -3.1770e+00 -1.8981e+00  3.9011e-01  7.7184e-01  5.0163e+00 -5.7405e+00
-#>   6.6273e+00 -1.8221e-01  1.1584e+00 -2.7757e+00 -2.0168e+00  2.6052e+00
-#>  -5.7066e+00 -5.4887e+00  2.3780e+00 -6.5638e-01  7.6786e-01 -7.7979e-01
-#>  -5.6415e+00  6.8836e+00  2.1560e-01  7.0334e+00  1.0236e+00 -6.6481e+00
-#>  -1.0836e+01  4.5798e-01  9.5420e+00  7.7963e+00 -3.3061e-01 -1.6403e+00
-#>   4.2271e+00 -1.2098e+01 -1.3656e+01  2.4545e+00  1.2756e+00  2.7649e+00
-#>  -2.0405e-01  4.1072e+00 -4.2119e+00  9.9203e+00  2.4864e+00 -4.2951e+00
-#>   7.1166e-02  1.0848e+01  3.1254e+00  7.9914e+00 -3.9279e+00  3.5528e+00
-#>  -5.9704e+00  1.2113e+00 -1.4374e+01 -3.6522e+00 -7.1488e-01 -3.2987e+00
-#>  -9.3306e+00 -2.4419e+00 -2.1639e+00 -3.6770e+00 -6.2138e-01  5.5423e-01
-#>   4.0496e+00 -1.0450e+01  3.8506e+00 -3.9680e+00 -7.0082e+00 -1.1381e+01
-#>   5.7722e-01 -1.5393e+01  1.1042e+01 -1.5000e+00 -3.2943e+00 -7.7883e+00
-#>  -1.3542e+01  7.2040e-01 -1.4342e+01 -1.2992e-02 -1.2799e+00  5.8970e+00
-#>  -1.1502e+01  1.9461e+01 -8.2887e+00  5.0860e+00 -2.7199e+00  5.8708e+00
-#>  -5.4900e+00 -1.0352e+01 -9.3241e+00  5.3902e+00 -1.1108e+01 -4.9735e+00
-#>   1.1056e-01  5.0094e+00 -1.0799e+00  4.5442e+00 -6.3421e-01 -1.9181e+00
-#>  -5.7869e+00 -1.0112e+01  8.2043e-01  4.2532e+00  4.8572e-01 -3.0658e+00
-#>   1.2075e+01  5.5917e+00  6.5248e+00 -3.1185e+00 -3.3772e-01  3.6188e+00
-#>   9.3177e+00 -2.4290e+00  6.1698e+00  3.9114e+00 -8.6027e-01  6.6250e+00
-#>  -1.5134e+01 -1.6484e+01 -1.0285e+01  9.0815e-01  6.7053e+00 -6.5309e-01
-#>  -8.6335e+00  3.7887e+00 -6.0815e-01  7.2834e+00 -4.3596e+00  1.6165e+00
-#>   7.0482e+00  1.2581e+00 -2.9573e+00 -1.2959e+00 -5.8671e+00  1.1073e+00
-#>  -1.2857e+01  1.1212e+01 -6.9868e+00  1.1672e-01 -4.6736e+00  7.4065e-01
-#>   1.4155e+00 -7.0236e+00 -6.6755e+00  1.3262e+01 -4.7395e+00  4.9263e-01
-#>   1.0220e+01 -1.6622e+01  5.0489e+00 -5.7797e+00 -5.4130e+00  2.3046e+00
-#> 
-#> (6,.,.) = 
-#>  Columns 1 to 8  -1.2906  13.5946  10.1609   1.2739   2.2736   2.0222  -0.3510   3.4442
-#>   -5.7275  -1.5648  -8.0162   0.4344   3.3434  -8.8662   5.8548  -2.1173
-#>   -2.5602  -3.4971   7.1341  12.4275  -2.3825  -1.8784  -1.5559  -8.6028
-#>   -5.2301  -2.8007   0.5347  11.6950 -20.4631  -3.5238  -4.4829   6.4217
-#>    6.0463 -12.9998   3.9110   5.6952   9.0395   5.3502  -2.5195   1.1746
-#>   -6.3904   2.9205   7.7085   5.1703   9.8267   7.5979   9.2738   5.0525
-#>    5.5847  -6.8299  11.9292  -0.2180   1.3347  11.5043  -1.4268  -6.0069
-#>    0.2869  10.9916  -0.4642   1.6550  10.1435   9.0110  -5.8498  -6.5407
-#>   -5.0244  -0.8937   5.7387  -3.5939 -20.8283  -1.1995   2.6885   0.3491
-#>    1.9330   0.2644   4.1328  18.1709 -11.5749   5.7500  23.1029   3.4215
-#>    6.1167  -2.6856  -1.9285  -4.0633  15.5307   0.2766  -2.4825  -7.5930
-#>   -1.4727  -6.1985   1.0367  -9.4084   5.9970  -5.9196   8.1893   2.3675
-#>   -0.8975  -0.4740   2.3347   2.8178  11.5984  -0.6683  -8.1911   2.5873
-#>    6.2782  -5.5500   4.2726  -1.2471   3.3512  -4.0309  -6.6749   0.4875
-#>    6.7282   6.7253   4.8038   6.0504 -10.3696   4.3334  -2.6760  -1.5284
-#>   -5.7392  -2.9991  -1.2506   5.1530  -5.3925   8.0087  -2.9595   2.1494
-#>    3.1805  -3.1789   3.7345  -1.2337  14.6059  11.3571  10.6334  -5.8997
-#>   -7.5094  -1.0523  -0.3500  -2.5702   0.7187  -4.1282   4.0303  -7.1820
-#>    8.0910  11.2153  16.4467  -2.5688  -4.1481  -5.9578  -9.3378   5.4305
-#>   -2.5413   9.1377  -0.1849   0.8289  -4.1381  -8.1889  -7.9123   7.7997
-#>    6.4039   9.6086   6.6445   6.9689   9.6904  -8.8979   9.8943  -1.0717
-#>   -2.0200  10.0184 -16.7365  -9.6430  -1.2828  -0.5102  -8.9521  -8.7141
-#>    1.0829  -0.9323  -1.9788 -11.2347   1.2045   3.7669  10.7768   6.0730
-#>   -6.5144  -1.7845  -3.0716 -19.3201  -3.5435   1.3338  -0.8777  -3.1286
-#>   -6.1790   8.0965  -6.8826   2.2282   4.6184  12.5645   3.0924  -3.1263
-#>   -4.5024  -0.9420   1.5130  -8.1597  12.1098   2.0007   5.8567  -2.0337
-#>   -0.1062  -1.1714  -0.1613 -14.2301   2.1413  -3.3113   0.8350   2.4379
-#>    8.3067   7.4910  11.0588   2.2626   5.9629  -6.2164  -7.1244  -0.4086
-#>    3.5105   1.5456  -4.8592   7.5274   3.0848   3.7589  -1.5261   6.4598
-#>    7.7199   1.7536   2.7952   3.6789  -5.1802   6.6892  14.5219  10.6791
-#>    0.7732  -0.6465  -3.0562   6.5930  -9.0988   1.7741   1.0375  -0.2780
-#>   -3.2239  -0.4057   0.9958  -8.9499   0.6236   0.7190 -18.1055   4.8374
-#>    0.3086  -7.5484   5.2433  -0.9087   9.3846   2.9502   5.1732  -3.6332
-#> 
-#> Columns 9 to 16  14.4922  -9.0520   1.7655   4.2005  -0.9748   1.0361  -3.8319  -3.0438
-#>    0.3332   6.1866  -1.8103   2.5202  -4.6763   7.3212  11.9548  13.8410
-#>    2.7913  -6.1743   9.6053 -13.5915   9.1463  15.6517   1.2033  -8.0341
-#>    6.7896  -3.6931  -1.0360 -15.0298   2.2860   7.6720   0.8738  -1.3508
-#>    0.9471   3.8266  20.7929  -9.0604  18.7446   7.6388   0.2071  -9.4922
-#>    6.1561   8.5326  -1.2114   5.5687  -5.6578   1.3177  13.0493  -9.0726
-#>    1.6027   9.1719   5.6655  11.2977 -10.5055 -12.8792   2.7278   1.8168
-#>  -15.2426   1.5844  -3.5033   7.4899   3.4438  18.5491   8.6613  -1.9612
-#>    7.7457  -3.5828 -14.3712   6.5652 -15.7928  10.0078   8.9950 -14.0851
-#>    6.0489   2.3172  -9.3602   2.7904  -3.2131  13.4063  19.7302   6.5122
-#>   -7.6034   1.8109   8.9422   7.0092  16.0588  -1.2942   2.1396   7.1782
-#>   -0.0329 -13.2314   5.2734  -9.0207  -6.3200  15.1783   5.7580  -5.6126
-#>  -13.0665  -0.2777 -12.8806 -10.6230   9.8472   0.1550  -2.8208  -4.6425
-#>    8.4266   7.6153  -4.0710  -7.4449 -12.7797 -12.6976  11.2124  -1.8019
-#>    2.2260  -0.8911  -8.0302  -6.1254  -6.7894   3.7435  -2.2531 -15.0217
-#>    4.3711   7.2751   3.4861  -9.9310  -9.6459   5.1806  -3.0555  -1.2542
-#>    7.7591  -1.4780  10.0699   6.3439 -10.2799  -1.5439 -16.2412  -3.7900
-#>   -6.9606   2.6252  -1.3341   3.2982   0.2523   4.2664   3.4766  -0.9335
-#>   17.7835   2.1302  -7.5523   3.5047 -18.0717  -6.1698 -13.9270  -5.4293
-#>  -11.9347  -2.7808 -11.9411 -15.3471  18.3825  10.5389  -3.8798   0.3755
-#>    3.5219   5.9095   0.5010  -5.5723 -11.3272 -13.9500   0.7060   7.0516
-#>    2.6564 -11.4641  -5.9555  -2.0962 -14.1869  13.4808   7.7111  -4.0473
-#>  -11.7709  -7.2278   3.2929  -4.9161  -5.4113   6.6190   9.6433   6.6927
-#>   -1.6251  -0.1537  -0.8085  -4.9274  -2.5047  -7.8605  14.4556  12.5811
-#>   10.3315  -6.0728   8.8520   6.2369  16.2882   6.1643 -18.3501  -9.8453
-#>    1.9906   3.0279 -15.3214   9.9443  -2.8701   1.5651   1.0282   9.6610
-#>    3.4091   0.9268   2.7886  -4.3620   4.8404 -10.7781  -2.5085   7.5129
-#>    0.1259 -11.2227  14.8560  -6.3446  18.5227  -0.2072  -1.8997 -10.7735
-#>   -2.5384   0.8208   1.6124   2.8615  11.5942  -3.2394 -12.9641  12.4291
-#>   -2.6944   2.3566  -2.2074  -2.1300   4.6018  -9.9700   1.1242  18.0932
-#>   -2.4919   4.8262   3.4302  -6.9013  -7.1076  -2.1004   8.5142   1.9163
-#>    5.3395  -0.6464 -13.5380  -5.0712   0.9941  10.4185 -17.7384  -5.8762
-#>  -12.3580  -1.6421   9.2891   3.8499  21.0648  -9.4984  11.6074   4.4902
-#> 
-#> Columns 17 to 24   5.7558  -5.1015   3.7358   3.3919  10.1574   4.8178 -21.1594 -12.2321
-#>   -3.0562   0.7140  10.4205  -6.7750  -5.5420   2.6335  11.1883 -11.4260
-#>    3.0956  -0.0853   5.8817   8.6862  -4.5400   7.8507   6.7873  -6.8118
-#>   13.3198  -0.1248  10.6121  -0.0760 -18.0245  11.9826   0.3320  14.2314
-#>   -4.0194   2.2549   2.3223  -6.7249   4.2722 -14.4571   9.4587  11.3563
-#>  -17.4306   4.3258 -21.1685   8.0124   7.3428  -5.9854 -11.9992 -12.7875
-#>   15.3551  11.2782  11.0832  -2.1655  -4.6535  -4.5613  -4.1144   8.5404
-#>    7.6964  16.3118   2.3128  -8.6316   0.7952   4.3366  -0.4963 -19.6819
-#>    8.0289  -7.0709 -13.0450   5.5670  -1.3630  -7.3526 -19.8146  -2.7441
-#>    8.7356   7.6161  20.4846   4.1788   1.2975  10.3680  22.0978   6.2228
-#>    5.9167  -9.3964  -5.7256  -9.7319  -4.4493 -12.1559   1.1821  -4.4289
-#>    9.2950   0.7511  -0.2363 -12.1582  11.4554 -10.0148   5.8216 -10.9486
-#>    9.9000  16.4000  -0.7640   1.1786   4.3074  10.8577   0.4916  -6.8029
-#>   10.3147   5.5437  -7.1659  12.2463   2.2565   0.3769  20.3979   8.4227
-#>   -0.3663   2.1757   5.1778  -5.3807  -1.6984   2.3133   2.5216  -2.2583
-#>   -0.9128 -14.7464  15.0432  -9.6708   0.6328  -7.2687   6.0182  12.2746
-#>   -3.4113   5.8405  -1.8712   9.3081   5.1289  -6.4269 -10.4528   8.3959
-#>    8.7077 -19.5687   9.4994  -7.7430  -1.2161  -5.4623  -9.8941   1.4983
-#>   13.8759   4.1344   9.4766  11.2207   2.2961  -8.7867   5.9747  -7.1402
-#>   12.1295  12.9967   0.6639  -1.5193   0.4410  10.6945  13.6656  -3.1914
-#>    5.4100   3.8496   1.6437  -3.5398   7.0846  -9.9937  15.4963   5.8231
-#>    7.5987   3.5565   4.1458  -7.8678   0.6812  -4.4799  24.1990  -5.0146
-#>  -10.0771  -3.8329   4.9017   7.5790  -6.6872  -0.7476   9.1992   7.2369
-#>   -5.0835   5.0043   3.1980  -5.0508   3.8299  -4.9766  -9.6292  -1.7594
-#>  -15.1777  -5.5674  -3.6914  11.2230  -6.3116  13.1206  -4.2019   3.9232
-#>    4.7549  14.9044  -9.8537  17.3155   9.6950   7.1425   4.4975  -5.3993
-#>  -16.9693   2.4203 -20.4703   8.3267  -2.2390  -6.7381  -2.9412  12.7916
-#>  -11.6139  10.8966  -3.9312  -0.5212  -6.7344   3.3800 -12.3245  10.8742
-#>   -5.0495 -12.9478  -1.5114 -13.5049   9.3209  -2.9167  -2.9070  -9.1915
-#>   -0.3623  -6.9541   8.1159  12.0325  -9.8860  -8.7181  -2.5255   5.7182
-#>    1.4827   2.1857  -3.2521   2.9576  -7.1097   4.2946   8.4858  -0.9879
-#>   -0.2729   2.7021 -13.6980  -0.0209   6.9267  -1.2844   7.1979  -2.7620
-#>   -6.2110 -11.8811   5.1298   3.0679  -5.8170  -4.0543  -4.4873 -10.4857
-#> 
-#> Columns 25 to 32   2.5510  -0.2038  14.5818  -4.6291  -0.7677  -1.7932  12.4390  -4.2010
-#>   15.1813   0.3858   5.0331  -2.6030 -12.5818   8.9777   6.5527 -18.1512
-#>    6.1780   5.2303   5.6359  -4.6120  15.2766  -8.3173  -5.6248  -1.8824
-#>    8.1504  -1.0806  12.6728  -0.5206 -17.4869  -0.0462   8.9059   6.3680
-#>  -12.1255   4.2941  12.3835  -4.6695   2.1640   9.4901  -1.9697 -12.8155
-#>   -2.8595 -11.3560 -23.4308 -12.8827  -3.2424 -22.6272   4.2050   7.6287
-#>  -11.3116   4.9729   6.0999   3.8416  -6.1895  -9.4986   3.3053  -7.4039
-#>    3.3873  -0.7899   4.5732  21.9898   6.3325   6.0113   2.0164  15.7458
-#>   10.9122   2.3831  -5.2849  -9.6523   7.8680  -2.9722  -0.3612  14.7341
-#>   -0.2008  11.2113  -2.5775  -2.3837  -0.5849   0.4306  -6.9612   1.6097
-#>  -14.1493  -8.8999  -1.6389  11.8062   4.1949  -5.4295 -10.4731  11.8199
-#>    8.0969  -8.5132 -24.8994   4.6152  -3.4242  -2.6175  -4.3879  -4.9619
-#>   -9.4342 -15.1767  -5.6920  21.9019   5.1764 -10.5333  -5.2888  16.3868
-#>   15.5493  11.3486  -0.2930   4.1731  -7.7393  -3.6599  -7.6600  -5.2174
-#>   -3.5141  -4.6415   0.3840  -5.3498  -2.1020  -3.9604   0.0056   3.5127
-#>   -4.7510 -12.2849  -4.9326  -5.1865 -12.1735  -7.7928   4.1647  -2.0988
-#>   -1.1027  -9.5895   4.5745  -5.6741   4.7831   2.0540  10.7514 -14.1080
-#>   -2.9644   5.5836  -8.3494   5.9076  -1.2470  -1.0219  11.5192 -13.4809
-#>   13.2887   6.7956  -0.6082  23.5026 -13.5884  -6.3495  15.0499  18.1156
-#>    3.5274   6.5587   2.6680   5.6992   0.6544   7.6869 -19.7288   5.1232
-#>   -0.5296  11.7520  -6.7091  -1.0494  13.8793   6.6161  -9.0327  -8.9474
-#>   -3.4582   1.6826 -11.8708   2.1495  11.1554   2.5380   1.2823  -2.7771
-#>   -0.4756   8.0583   0.6827 -11.9828  -2.3517  11.3830  -0.6355  -2.7412
-#>  -11.4423  -5.4743 -15.1870  12.4140  -9.7539 -17.8683  -0.7848   7.7927
-#>   -0.5499  -6.9762 -12.1503  10.2159  -0.7098  -0.9954  -0.4118 -11.7689
-#>   -4.6858  -6.1957   0.0519  -7.1924  -2.5395   2.1236   1.3794 -15.5540
-#>   -9.3657  -0.6970   5.4592 -16.0231   8.2241  -4.5828   1.4532   4.9496
-#>   12.8412  -3.9305   7.5273  -3.0112  15.4585  -6.6945  15.4474   1.7287
-#>  -22.4385  -0.7342 -11.1161   4.6486  -2.7083  -9.0396  14.7538  -1.0047
-#>    4.5947 -21.1098 -16.8718  -6.1116  -9.9848   6.5708  -4.7937  -2.0188
-#>   24.0556  -8.9986 -12.3429  17.4882  -4.6548 -13.4062   6.2575  10.7854
-#>  -31.1434   1.9465   0.7855 -15.7536  10.1426  -1.6422  -8.7910  -0.0079
-#>  -10.3111   0.0067  -2.4724  -6.9653  -3.8297 -21.0870  -3.7484  -4.1639
-#> 
-#> Columns 33 to 40 -13.3018  11.4347   4.3881 -15.3451 -14.7796 -10.7482  -9.8222  -7.6255
-#>    5.7348  -2.8622  10.3386  -4.3851  -3.2253   2.3243  10.5710  -9.1831
-#>   10.8954  -7.3811   7.6372  11.4209  -8.9989  -3.6543 -15.2089  -7.5075
-#>   -4.4141 -10.8794  11.8271  -1.5246  -4.1511  -3.5885   0.3060   1.0262
-#>    1.4067 -11.2596  -3.3783 -10.3016  -5.7747  -2.5181 -14.6493  -8.2378
-#>  -10.3898  -6.8135  -0.2176  13.7231  -7.8071  -5.1213  -9.7749 -10.0052
-#>  -14.7091   9.5471   3.8610  -1.7426  -9.3497  -2.6168  -0.9749   1.7457
-#>  -22.4270  -0.7327   3.1410 -10.7137  -3.9862  -6.6951 -16.3995 -10.5144
-#>   -3.1132   3.0083  12.4543  13.9422  -2.3628   2.5201  11.7110  -3.1082
-#>  -16.7121  -8.7479   7.5455  19.0781   1.5504  -4.3607 -11.3715 -11.4387
-#>    1.3740   2.8587   1.4961  -0.6484  -5.6926  -0.5601  -9.7146  -4.9405
-#>    3.9314   3.1979   5.8303   0.5067   5.4296  12.0537  -3.3515 -11.3496
-#>  -24.2184   7.8634  14.0017   4.3131  -0.1126  -7.2120  -9.5349   0.8077
-#>   -4.7040 -15.6956   7.7416   8.2861   8.7321  22.4792   2.6020  -9.6495
-#>    9.5377 -11.9823  -6.4117   8.4295   0.3117  -2.6650   7.8302  -3.7887
-#>    5.5278  -2.7329  -2.2512  -7.1119   1.3326   8.0848   2.5164   0.9978
-#>   -1.0899  14.8330 -14.3933  -7.3525  -7.7192  -7.0869  -3.4828 -12.8309
-#>    8.4395   4.9174  -4.2364  -3.0753  21.9154   3.6303   6.2544   0.7341
-#>    2.3007  -6.2655   5.5432  -5.3002  -9.3214  -8.0130   7.6070   1.5934
-#>    4.3254 -11.5373  -4.2664   2.0801  -0.6847   3.1335  -5.3338   1.9036
-#>   12.3775  -7.0790 -28.5314  -0.3746  18.1311   2.2420   0.0830 -12.6389
-#>   -3.0356 -13.9939  14.8970   4.8902   7.3808  -2.6823  -6.3736 -12.9497
-#>  -16.7200  -2.1116  -2.9996   6.8710   2.5699   5.4890   8.0162  -2.9067
-#>  -13.6207  -4.2545  11.4654  14.8817  -2.2713   0.8434   7.1582   1.3200
-#>    5.0770  -2.2040  12.7372  11.4328  -1.7731   5.3843  -4.7258   5.3155
-#>    9.3367   7.5425  -4.8037   6.9640  18.1435   8.1916   6.8829  -1.8885
-#>   13.0478   2.9280  -0.1942   1.3672  -6.5255   2.8133   1.1775   7.4638
-#>   11.6807  -0.5185  -1.4964  -4.7613  -6.2701 -10.8524   3.5170  -3.0822
-#>    3.3242  -5.4642   4.7088   5.5320   8.1024  -5.8204  -3.0596  10.6272
-#>   -1.0657   3.4552   2.3027  -7.0692  10.1231   0.8105  -8.5921  13.5097
-#>  -15.2484 -10.5065  -3.6962  -1.5426   0.5034   8.4190   1.9529 -18.8593
-#>  -11.2276  11.5599   1.5902   2.1219  -1.6046   3.9939  -3.8218   6.1057
-#>   -2.9041  -0.2319  20.5079  10.3067 -19.0063  -7.7289   0.2409   9.4719
-#> 
-#> Columns 41 to 48 -14.8274  11.1019  -2.0624 -16.6093  14.9514  -5.6329  -3.2294 -11.8248
-#>  -25.6942 -11.7620  15.1268 -16.4583  -2.4520  -1.4868  -7.3511 -11.5673
-#>   10.0451   9.0156  -5.6282   1.9462 -13.0216  -7.2568   5.5590  13.4736
-#>    4.9235  -5.8640   5.4821  17.9450  -0.4013  -9.8630   3.4781   6.9655
-#>   15.0701 -16.0830 -11.3884   5.5517  -4.9922  -9.6732  -1.6430   6.1311
-#>    3.8547  13.6872   1.2339   1.6836   8.5749  -8.7089 -14.2435  11.3817
-#>   12.4370   6.2683  -8.8350   5.0442   2.9360  -2.7908 -10.8230  12.0263
-#>   -3.1593  -1.5564   3.6364  21.1648  -2.1498  -4.9529   5.2285   9.4477
-#>   15.8922   8.4917   1.9509   1.4087   7.6780   7.7388  -8.4368 -12.4227
-#>  -16.1673  13.1916 -15.0930   1.8549   9.6663 -17.2185  -1.4829   7.0265
-#>   16.0285  15.2274   3.2461  -3.6231  14.0717  -8.7764  -1.0086   3.2117
-#>    9.4831  -2.8242   3.4838   5.2793  -4.4744  -3.2964  -3.9987  -9.1133
-#>    7.7553 -12.7563  10.4662   5.3940   4.2116  -5.8236  -9.4847  -3.2442
-#>    1.3194 -12.7428   4.0818  -1.5940   1.5243   5.1842  -7.2948  -5.7164
-#>    4.8879  -2.9317  -6.2709  -9.3459 -11.3165   5.0118  -0.1904  22.1940
-#>  -21.0022   6.7985   4.4091 -10.0046  -6.7588   3.2733   2.6421  -4.7261
-#>    3.9414   9.6563  -5.8217  10.3224   2.6729 -11.2405  -0.9543   5.5213
-#>   -1.2199  -1.8814   4.7612   1.8873   8.5311  -6.8704  -0.2994 -12.2560
-#>   17.0057   1.9599 -10.4429  -0.1507  -9.0125  11.3883  15.8209  -4.7667
-#>   -3.3215  -1.4828  -8.9326 -14.8883  10.0406  -0.3560   2.7622  10.9950
-#>  -19.2001  -0.0726  -1.9566   5.3143   7.0787  -2.4969  -4.9595 -22.0236
-#>    1.4301   6.5826   6.1883   3.8234  -0.1367   5.4273   4.0576 -11.0166
-#>   -7.8817   0.6544   6.0732  11.9236   7.7939   0.3150 -18.9965   1.4273
-#>    2.6034 -10.2298  -4.7003  -5.3183  -3.9666   9.8251  -2.7840   2.1820
-#>    8.2280  11.6911  -6.6334   7.4555   8.5682  -8.2273  -3.4682   0.9171
-#>  -14.0598 -11.5994   1.5577  -7.6353  -0.4864   5.9775  -6.4676   8.4718
-#>    9.0035   1.8517  12.9204   1.1901  -7.3776   9.1212  -8.3669  13.1364
-#>   17.7747  10.1759   8.1650  16.2932  -3.2379  10.2901  -3.4087   0.9077
-#>   26.6333  -1.2646   5.5650  27.8534  -8.3948   2.2099  15.4857   8.4266
-#>    5.2051  -7.7246  10.6129  -5.9848   1.8629   1.8079  -2.9143   2.8773
-#>   -4.5634  -6.9991  -0.1841  -1.9090  -3.3952 -11.1046   1.2958  -5.0358
-#>   -1.5619  -4.0569   0.3958  -2.8646  -3.9297   5.8160   6.0127 -14.5869
-#>   -2.1185   6.3288  14.7029   5.6887  -7.8463   9.2926  -5.1830  11.8611
-#> 
-#> Columns 49 to 54   7.1016 -15.8208  -2.0877   8.7288  -3.1307  -0.8803
-#>   -0.4871   0.7266   4.5678   4.0105   1.2923   5.1693
-#>   20.2181  11.8863  -7.4998  -6.5552  -5.5006   2.7607
-#>    8.0890  -2.5741   1.3882   7.7029  -3.1784   0.5210
-#>    2.2496  -4.2309   5.2862  -3.9217   4.9795  -0.7292
-#>   -8.6583   6.5319   0.0370   2.8436  10.4838  -0.7236
-#>   -1.1649  -4.4350  -6.3319   5.5223  -1.6165   1.1725
-#>    4.8659   3.2011  -8.5719  -1.7483  -1.6685  -1.5847
-#>   -2.2247  13.8979   4.1977  18.8982  10.7389   0.0871
-#>    3.9782  -7.1217   0.0354  -1.4489  -5.5193   1.8505
-#>   18.8135  -5.2025   0.9429  -3.0978   1.4687  -2.5012
-#>   11.6686  16.0039  19.1609   9.8649  14.1246   6.5454
-#>   13.3683  -8.2871  -6.7784  -8.3795  -0.2653  -3.5511
-#>  -21.7449   8.3611  -4.8974   7.8489  11.9476   5.2951
-#>   11.3051   4.7276  -3.7736   9.6208   4.5921   6.3180
-#>   15.5787 -15.6322  -1.9335   2.3981   0.3116  -0.4815
-#>  -12.1117   9.1798  -4.6968   0.7476  -1.0237   3.7553
-#>   -7.8043  -0.7208   8.2035   3.4730  -4.0109   1.5793
-#>    3.9935 -16.5139 -31.5667   0.2528  -0.0504  -9.1094
-#>   11.5752  -5.3425   3.3273  -0.7484   2.1920   2.7529
-#>    0.3867   1.0408   2.0689  -6.1926   0.9572   1.3587
-#>   14.7566   4.6171   8.8172  -5.3254   3.3079  -2.3980
-#>    8.7173   8.6772  12.3852  11.0251  11.5906   0.0299
-#>    4.6733  -7.0466  14.9059  -4.1834  21.6208  -1.9280
-#>    3.1884   6.7935  18.1165  -7.7078  -2.9576  -5.1778
-#>  -18.3818   6.2766   7.1564   2.9596   0.0514   5.4900
-#>  -11.5391   3.1277  -0.8124  -2.9626  -9.9937  -3.3890
-#>    2.6400   7.5140 -11.0859  -9.9491  -5.3755  -7.1824
-#>   -5.2797  -3.5233   2.8756  -5.8039  -2.0460  -5.4051
-#>    4.4817   5.8428  14.4519   2.6131  -9.7038  -6.9624
-#>    1.5933  -4.5518   0.6980  -4.8872  14.2196  -2.3431
-#>   13.2308  -9.6762  -9.5056  -3.2635  -4.0156  -6.7775
-#>   12.9216   5.4440   0.0763  -1.1853  -3.6075  -3.9249
-#> 
-#> (7,.,.) = 
-#>  Columns 1 to 8   2.9533   1.0860  -8.1119  -0.0494  -5.6527  -3.9036   1.4153  14.9349
-#>   -0.0426  -5.0686   0.9671   9.3674  -1.9175  -4.4150 -20.3450 -11.1734
-#>   -5.7712   0.4861  -5.7229   2.3722  -0.5183   4.7114  -5.0389   9.6950
-#>   -4.9070  -3.9710  -4.8358  -5.3924  -1.4841 -23.6475   3.8715  10.7274
-#>   -2.5420  -0.1729   3.0972   2.3531  -9.6480  -8.7437  19.5345  -5.5293
-#>    4.4810   5.3877   7.0822   7.6412  12.4621 -10.4872   4.7130   3.0768
-#>    0.2558   8.5553  -1.4754   6.0410   3.9560  -4.5169  11.5759  20.3742
-#>    8.0860  -2.6291  -2.9121   7.6992   3.2772  -6.0553   4.4048   6.1588
-#>   -4.3041  -3.4693 -10.2610  -6.9206   8.7375   2.1002   2.5182   1.9419
-#>   -4.9646  -0.1113  -1.3192  -9.4292   4.6407 -18.0276  -4.2440   6.6633
-#>   -5.8289   5.9275   2.1018  -1.3827  -2.2998   1.0696   4.6979  -9.5232
-#>   -2.5592 -10.3871  -4.0104  -1.7658 -10.1056 -11.3087  15.4066  -7.7634
-#>    6.3138  -1.6091  -5.9848 -13.2303 -23.1200  -7.9112  10.0865  -1.6915
-#>   -0.5376  -1.2372  -2.6146   1.1744   3.0030   2.3626   0.6140  -4.1495
-#>   -1.0276   4.0819   1.0181 -17.9625  -1.2514  -7.6360  -2.6066   4.1145
-#>  -11.0159   6.8261  14.0863   4.3507  -5.7617  -3.7358   2.9299  -8.3246
-#>    2.3352  -3.0110  12.2271  18.0495  -4.5932  -7.1297   5.2973  -8.6118
-#>   -0.5394  -6.9015   5.5024  -7.8668  12.8111   1.7763   9.8438  -1.7404
-#>    3.3877  -4.2540  -2.2542  -6.0180  18.3600  -5.4755   3.0592 -11.9726
-#>  -10.2080   7.4090   4.1472  -2.6033  -4.1521   3.2111   8.6643  11.6759
-#>    1.0144  -5.1425   0.0963  -7.1641  -0.3397  19.8292  -0.3321 -16.5396
-#>    4.8501   8.3368   3.3213   1.6215  -0.6207   0.4590 -15.0183 -14.6612
-#>   -2.5804   3.4855  16.8857   9.2382   3.0253  -6.7524   0.1960  18.0831
-#>    1.7473   0.6880 -11.5274  -8.9468   3.7359 -17.2174   1.2827   8.5942
-#>   -2.4625  13.4675  13.3387  -0.6154   4.2879 -11.0969  -5.7258  21.5706
-#>   -4.4280 -14.9012  -8.8041 -14.0777   4.9153  13.6668   5.2954   3.0269
-#>   -1.2733  -3.2601  -1.8351  -5.1108  -3.5288   1.6957  -8.3590  -5.3381
-#>    4.1251   0.9375  -3.7061  23.1854  -1.0771 -12.4512  -2.0129  -4.0775
-#>    4.0202   0.9914  -3.9503  -4.0601   7.2828  -3.2260   9.1126   8.2552
-#>    4.5288  13.4074   1.2214  -2.5435   6.7230   2.8578   9.0410  -2.8760
-#>   -3.6678  -2.4159  -3.7118   9.2260   8.0656  -4.5218   7.3985  -8.2251
-#>    5.0364  -9.3228  12.9259  -8.1170 -17.5855   3.5425  -3.0200 -10.1435
-#>   -6.1906  -4.4283  -0.9003  -0.7803   9.6682   3.5322  -0.8297   8.7635
-#> 
-#> Columns 9 to 16   1.5017  -8.6068  -6.9181   0.8914  -5.0705  15.9220   5.7279   0.2399
-#>    7.3119   1.3518  -5.9874  -0.2507   7.6702  -9.8376  -3.9085   7.4234
-#>    8.6301   2.0793 -11.9055  -3.7911   2.2005  -7.8006  -5.2940  21.5218
-#>    3.0038  -1.6677   1.7950  -4.5508   2.2488   5.3606  -9.1701  11.3947
-#>   -5.7708   0.4336  14.7154  -3.9453   7.6350   4.7351  -8.6861  15.0580
-#>   -1.3754 -17.9018  -1.1444  12.8072  -1.4358  -3.2275  -3.6352   7.4320
-#>   -5.0755  -5.4128   1.8288  -5.4710  -3.5631  14.3366  15.0004  -6.0529
-#>  -10.4102  -6.2087   0.9492   6.0118   7.5276  -6.7697  10.3562 -11.1823
-#>  -11.1926 -13.0750   9.3369  -2.0888 -19.6530 -11.2297   0.6006 -12.6120
-#>    3.0238 -16.1934  -2.7229   1.8922  13.2706   7.3099 -13.3667   1.7925
-#>   12.4466  -0.6007   5.8977  -3.2004  -0.3359  -1.2151   0.8104  -3.3276
-#>   -1.6860 -14.1189  11.2619  -6.9821   4.1982 -13.8986  -2.2289  16.0245
-#>    7.7072   3.2524   2.7273  -0.8512   1.0577   2.9917   7.9595  -3.3827
-#>    7.0726  -7.8132   0.4509  -2.2803   6.6153  -4.2307   3.3257  12.5965
-#>   -7.9716 -23.9474  -3.5218   1.5840   9.3275   1.9662  13.4866  -2.4736
-#>    2.7921  -3.6859  -3.9322  -4.6172   9.0890   2.9150  -0.3922   4.2977
-#>   -5.9871   9.6314   9.5661  -4.8246  -8.7754  -3.9940 -18.3416   4.9380
-#>   -0.0987  -0.1037  10.6268  -0.9204 -13.3450  -1.6316  10.4843   2.5481
-#>  -14.0458 -11.3099   3.7968  -0.4273  -0.2426   5.3267   2.5750 -17.5287
-#>   -2.2571  -2.0932 -14.2280  11.7172  17.4334   3.4308   0.5894   6.1905
-#>    0.2811  15.8760   2.4301  -2.2227   7.1458  11.6980  -1.3617   1.6834
-#>   -1.4061  -1.7128  -5.3377 -13.2902  -0.3802 -10.0704  11.9042  -0.1962
-#>   -7.3468  -1.7626  11.2446   4.4840  10.7810 -18.4213 -13.0671  14.1763
-#>    2.9852 -13.0677  -2.5682  -2.7484  -0.3983   0.4290   6.8680 -12.5134
-#>   13.3903  -0.9157  -2.4934   3.4122  -3.4299   2.6442 -15.6976  18.5193
-#>   -6.8927  -4.6039  -1.2142  -5.0980  -6.1670  -5.9587   2.6590   0.4891
-#>    9.6014  11.4081 -11.5250  -7.0517  -0.4210   3.0345  -4.5130   0.2101
-#>   -0.8494  15.2047   9.5280   1.9386 -13.3232  -3.7443 -10.3539 -14.5014
-#>   12.9063   7.2197  -0.1236  -8.5376  -2.7291  -1.1630 -10.6046 -17.5495
-#>   -0.0908  10.3624  11.3390   0.9138  -0.4849  13.4013  11.7459  -8.2455
-#>    1.9956  -6.9150   2.7161   1.0772   2.4628   0.0884   9.3908   5.6051
-#>    5.1412  20.5724   7.5165   1.3354   9.4225   1.5700   0.4186  19.6713
-#>   13.9985   7.4152   2.8320  13.5761   6.1628 -12.5014   2.0404  -2.9885
-#> 
-#> Columns 17 to 24   9.7553 -13.1200   7.1868   4.8436   2.1946  -6.9015   1.4137   9.7216
-#>   10.7145 -15.9968  -7.6058   3.1467   9.2966  14.3546  -0.1738   9.8027
-#>   -9.2212  -0.5251   3.5527   5.3623 -11.8983   4.0554   4.5675  -6.3613
-#>   -2.6589   8.1325   7.2018   2.1848  -4.5962   2.5252   1.0252 -14.3119
-#>   -7.3349   2.4963   6.5116 -10.1706  -3.1291  -7.2340  12.6099  -0.4696
-#>   -0.1394  -7.5460 -22.2137  -2.3903   2.1341 -22.2161  17.2772  28.6837
-#>   -2.1571   5.4578  -1.2019 -22.6302  -2.6689   9.1606  -3.8338   2.8104
-#>    4.4399  -4.1536  -0.9956   9.4338  -4.5337  -8.7534  -5.4436   5.9705
-#>  -11.4283  -1.5159  -6.5968  10.1358 -15.0663  -4.5839  -3.3741   7.3909
-#>    5.8492  11.3488  -2.2672  12.7561  -7.0420  -2.7220  15.0504   8.2648
-#>  -18.0644   3.9038   1.3762 -20.8657 -11.8200   4.8428   3.3412 -21.4536
-#>   -5.5596 -23.4892  -6.5741  11.1924 -15.3357  -2.2479   6.0471  -0.6416
-#>   -5.0136   0.5910  -2.1618   6.7253  -1.5704   6.2009   1.3919   9.3597
-#>   10.9548 -13.9191  12.5985  -1.2022  10.8440  -5.5861   4.4983   3.3493
-#>   -1.2791   3.0089   9.8931 -13.5114  -4.6666  -1.9426   4.4158 -10.9277
-#>  -12.9990   0.6456   8.3221  -4.9207  -0.7035  17.5786   7.1658   5.9181
-#>   17.2244 -19.4162   2.3888  -0.8057 -11.6695   4.9724  13.7872   1.6801
-#>    5.4351  -6.6086  -7.4150  -2.8655   4.8108   5.5622   6.3642 -10.0064
-#>  -11.7144  11.1556   1.1453  21.3122 -11.9525  -6.5990   9.2495  -5.2196
-#>  -15.3457   6.0296   4.1158  -0.5219   1.6137 -11.4727   8.4630  -2.4792
-#>    2.8535   0.0787   1.0286  15.6627  13.0209  -6.1135  14.4283   9.9701
-#>    2.5635  -3.7947  -1.3162   6.7904   0.7413 -17.1797 -21.7695   8.1626
-#>   -3.6684  -4.9924  -2.0509  -4.8486  -2.1283  -4.5470  -1.8739   9.6315
-#>  -14.5102   9.8342  -1.4152 -10.0871   9.7394  12.9369 -12.2200  -8.0341
-#>  -10.4517  -4.9812   2.9651   7.1184  -1.4927   6.9153  16.0767  -8.4478
-#>    4.6687   3.0401  -1.0172   2.2060   1.4107   2.0808 -11.0041  -0.6075
-#>   -4.7864  -0.0843   2.4379 -12.3772   9.2019  -3.9495  -1.5036 -11.3102
-#>    0.9289  -7.3573  -4.8422   5.4141   8.8234 -12.1827   6.7583  -5.4115
-#>    4.5193   7.5225  -6.1402 -12.0619   3.5380   2.5877 -13.4143 -13.8412
-#>    4.8879  11.3728   8.5100 -22.6038 -11.1467   9.6744  10.0143 -21.5391
-#>   10.9835  -5.5687   1.3013   2.8671  -5.4365  -4.9046 -20.4971   2.6865
-#>  -18.5385   4.6653  -6.3887  -0.4565   3.7783   1.8561  -6.8878   2.6661
-#>  -20.1066   1.6469   2.6033 -19.8456   8.6572   3.5506   8.2641 -13.4063
-#> 
-#> Columns 25 to 32  -1.2997   2.5660  11.4786  21.9807  10.5563  12.5488 -16.1947   8.9865
-#>   30.3387   4.3806  -2.5467   6.4973   8.2565  11.6147   1.4779   1.1958
-#>   -8.6803  -5.0393  -4.5055   2.4810  -7.9344   7.4099  -4.2127  -3.3463
-#>   -4.6563  23.6231 -14.3282  -0.1461   2.4661  14.0052  -8.7004  -1.3035
-#>    5.4801  16.1340   1.9629  14.6541   3.6288  10.3801  -2.2284   3.0719
-#>    9.1457   4.7753  26.7420   1.9238  16.1791  -2.2101  -5.3664  11.4996
-#>   -2.6553  23.0783  -3.7477  -4.2019  11.1723  -4.5220   4.4127  -4.7281
-#>   -2.5818   4.4406  10.7327   2.0380   2.0969   9.6335   3.9120   8.2685
-#>  -33.8974 -12.8143  -6.0901  -9.0709  -7.8182   8.9614  -2.5334 -12.7631
-#>    3.2786  -1.6036   7.5980  -3.9095   1.2602   2.3739   3.2521  -4.8686
-#>   -7.3327  10.4040   5.5009   4.0083  16.3784  -8.6116   5.7804  10.3379
-#>  -14.2673  -6.2080  -4.0674   0.1512   8.3497  -9.4116  -4.0423 -14.0406
-#>  -20.3385   3.9015   2.0622   6.2539   6.8554  -7.8619  -3.0550  -4.7275
-#>    2.5882 -12.9390 -22.0963  -3.0532   1.9954  -0.9987   3.1953  -8.4127
-#>   -5.0772  16.6097  -9.3671  -0.4289  -0.8395  -4.9621  -8.1858  -7.3859
-#>    7.6867  20.9398  -0.7564 -10.0955   4.2113   3.5465  -1.3963   8.6430
-#>    1.0266   7.9831  -4.8256   2.7832  20.5387  -2.9075   1.2404   7.3119
-#>    4.3127  -3.5009   1.9082   1.2073  -3.6349   0.1758   7.0910   2.4704
-#>    5.2905 -13.3534  18.8257 -26.3553   2.9182 -13.3157   3.7616  -7.0972
-#>   -9.0565  -9.4113  -7.3716  -6.4189  -1.4251  -0.6970 -11.1698   6.4259
-#>    9.8357 -20.6985   6.5946  -6.4993   1.4075 -10.1752  -2.8046  -0.5371
-#>   -7.0588 -15.2924  -4.0809  17.3121  -5.1022  14.4247  -6.8520  13.0136
-#>   -5.4876 -12.7528 -31.4428  -4.2435   5.3583   0.4308  -8.7641  -7.9566
-#>    2.7864   3.8519  -3.5083  -1.5176  -2.9368   9.7348  -5.4117  -6.1777
-#>   -1.2136 -10.0024  -8.1429  -4.3171  -6.6987  -1.9511  -9.4266  -5.6240
-#>   -7.5783  -7.4562   6.2961  -6.0867   1.4181 -16.5222  -2.1903  -8.6514
-#>    9.0910  -3.3295   1.5234  -4.1993   1.2210  -0.2420   2.8981  -2.0521
-#>   -5.5707 -18.4647  -0.6560  -2.7191   1.2130  17.2127  -7.8321   4.2030
-#>    4.6750  -2.4481 -14.0304   6.0465  -5.7216   2.8683   3.5524  -2.6627
-#>   -4.3774   4.2675  11.3429  -9.4706  -2.9024  -6.9048  -0.8443  -2.9756
-#>   -6.5006   5.7395  -0.4179  -7.7384  -0.1501   9.6269  -4.5812  12.0688
-#>   13.9179 -15.6514  -3.5161   5.1031  19.9115  -3.0878   4.0127 -15.4881
-#>    6.5677   1.2792  -3.8230   2.6097 -13.5379  10.4018   0.3715   7.4503
-#> 
-#> Columns 33 to 40   5.3052  -7.0661   4.7852   8.9610 -15.2011  -3.8709  17.9290   1.6281
-#>   -5.1879   5.2382  -1.0851   8.2228   4.6034  -5.9043  -1.7003 -11.1631
-#>   -6.9663   6.2021   3.7609 -16.0715   0.8179   4.0172   2.3748   2.4703
-#>  -18.8565  15.3300  -2.0820  -7.5073  -6.7966  15.5286 -13.4748   6.2408
-#>    0.8957   3.5834  12.3402 -17.6054   6.3492   7.4437   3.7392  -8.5734
-#>   -2.9738  -7.2329  -2.5599   1.7708   2.4251  -5.8812  -5.8491  -5.3014
-#>    6.4677   0.8822   1.9027  -8.3738  -8.0058  12.0594  -8.5700  -5.1170
-#>   18.9312  17.7423  -3.8196  10.4943  -0.9489  -7.5846  -2.0368   1.1635
-#>   -2.4501   4.5884  -6.6799  -4.8542   5.8958   1.9316   7.5522   4.8995
-#>   -9.1462 -12.1461   8.4455  14.3685 -16.3043  -9.3552  -8.0756  -6.7934
-#>   10.4133 -12.1937  15.6228  -9.6192   2.0900  -1.8004  -6.6100  -7.5872
-#>    8.8462   0.4353  11.5399  -4.4965  -6.6862  -1.5440   7.6812  -2.9164
-#>    5.2504  -0.7551   9.2843  -1.7086  -3.6233   0.1112  12.3992  -0.2854
-#>   -9.8543  -2.7453   2.0754   1.1477   6.3524  -7.0851   0.0442  10.8561
-#>   -9.9474   9.1364  -0.2938   2.6699   1.5531  15.9298  -7.0656  -4.3737
-#>   -2.8141 -23.9811   2.7360  10.6291  -3.0334  10.8106   0.6688  -9.3946
-#>    4.9860   0.2085  -0.2466   1.6133  -3.4966  14.4860  -2.2270 -23.5474
-#>   -2.5455  -2.8775 -15.3958  -7.0275   3.9818  12.7210 -13.5770  16.3814
-#>   11.6903  -1.1662  -3.7867  17.0957  10.7837  -6.2491   7.7903  -1.4236
-#>    6.9868   3.5043  10.6076  -3.3494   9.3721  -1.1851 -16.0425   7.7467
-#>    6.9866  -8.2193  -5.6146  15.1631   4.8171  10.9608   0.2572  11.4145
-#>   -4.2670   7.7636  13.9072  14.0630  -3.8046  -5.2689  -9.9894   1.9107
-#>    9.0186  -1.3330   2.4330  20.1800 -10.8788  -3.1260  -5.8801  -9.1647
-#>    5.5179  -1.6877 -19.2072 -14.4144  -5.0496  -7.9839   6.6813  12.4073
-#>  -12.5223   2.0891 -12.9087  -7.6491  -1.4437  -8.5859   1.9822  11.2169
-#>    9.9112 -18.6615  -7.3252  -5.5876  -1.6898  -3.7585   0.5299 -15.4517
-#>    5.1917  -6.7378   0.6070  -1.0705   0.7240  -6.5471  -4.8843  -0.6816
-#>   -7.0180  16.8131  -3.6724   3.8947  11.0605  -0.7513  -1.2543  -1.7039
-#>    8.3646  10.8441 -21.4233 -16.3048   6.7304  -8.0358 -16.3618  14.3174
-#>    3.0343  -7.0327  -5.2200   8.3443 -11.4185 -15.7410   5.5705  -2.6389
-#>   -1.9251  -4.4846  10.0314   4.7699   2.6357   2.8893  -2.4603  -1.3923
-#>    1.6812  -1.4077  -6.8064   0.4776  -2.6904  10.8166   4.8353 -27.0714
-#>   -7.4296  -3.7325  -5.3549  -8.5688  -9.1646  -6.8415  -2.5583   6.1067
-#> 
-#> Columns 41 to 48   3.1174  16.3425   7.7411   4.7303   6.4072  -0.6967 -15.2469   4.0689
-#>  -19.8591  20.3703 -22.3870  -0.0186  -3.8217  -4.0515 -10.3993   8.4956
-#>    6.2955  -6.7682   1.2646   1.5836  -2.6305   5.3780  -4.7371  -6.3778
-#>   -3.3552   3.5098  -1.0505   8.3239  -8.7252   8.8065   6.4772   8.2973
-#>   -3.9051  -7.4153  -2.2978   4.8423  -6.7482   2.8870  -4.6284  12.2837
-#>   -3.4785 -24.9972   3.0788   1.1094   2.2645 -26.4156   3.6031  -6.0978
-#>   -5.8234   3.4496  -2.9199  -8.7993 -10.5120  -4.7851 -11.1427 -23.1279
-#>  -11.7796  -0.4631 -13.1887 -11.4661   2.6153   7.1247 -10.4822 -15.9656
-#>   13.4413 -11.6371  -0.9134  13.4668  -4.4373  -6.4646  -1.3270   2.8753
-#>   -2.1881  -6.9783 -10.5101 -10.4630  -3.0733  -2.1742   8.1180  -6.3409
-#>   -1.8502  -9.7822   2.1889  -0.1678  -5.9537  15.3674  -0.7551  -4.7234
-#>   -8.0885   8.0203  -4.0589  -8.8241  -8.3500  -2.8535   9.3532  10.9975
-#>  -18.7868   0.3839  21.5336   9.5365  -2.7460   8.3454   6.3590  -6.5452
-#>    1.0877  11.8411  -7.7273  -3.8096  -1.4145 -16.3855  13.4120  -4.7363
-#>   15.1865   5.0968  -3.6312   1.6900   9.4821   2.4396   3.0815   5.8806
-#>  -10.7069   8.0684  -4.1255  13.1902  -7.9577  -1.6689  -4.5992   7.6382
-#>    2.7812  -9.5274   8.5902  -4.6735  -2.9463   1.7383  -6.5527 -14.1294
-#>  -17.0843  14.5295   7.6360  -1.0162   0.3424  23.1627 -11.0931   6.9220
-#>   -9.3723   6.4530  -1.4624  -0.4699  -5.1367   4.7219  -9.0282   4.5032
-#>   12.0693   1.0159 -23.7530  -1.8362  -2.2409  -5.2844  -4.3661  -0.5389
-#>   -0.3056   3.2738   3.7991  -2.3939   8.0024  -1.8377   5.1208  10.9426
-#>   -9.6241  14.9390 -16.1278  -9.0459 -16.5176  -9.3462   0.4452   4.7589
-#>  -10.3488  -1.6794  -5.0653  -8.7524  -7.6349 -16.4446 -11.3896  -7.0746
-#>    1.4225 -17.2559   4.2896   8.8625  10.0452  -8.2771  -0.2759   7.0084
-#>   -8.0495  -7.6525   2.5955  -0.5583  15.6281 -17.6703   0.5897  11.1602
-#>   27.0729   5.4696  -1.3197  -9.3038  13.9750  -2.9348   4.7927  -2.7989
-#>   29.5099 -20.9963  24.6360  -6.2216  15.2439 -11.4710  16.2514   6.2927
-#>   -3.5272 -19.5338   3.2136   7.2999   0.0861  -2.9485  -9.8887   0.4744
-#>   -1.2962  -1.0744  14.8624  -2.6720  19.5613  23.0580  -6.7557   7.1725
-#>  -17.8225  -0.3951  -7.6426  10.5787  -2.1145  -5.5434  20.5069   3.5584
-#>   -2.0788   2.3581  -3.1871   7.5324 -25.1909   4.3661   7.6013  -0.0651
-#>   12.2884   8.4565  20.5663   9.6368  18.6797  -2.5369  13.3668   1.8755
-#>  -10.1990 -10.4719  -0.4565  10.4234   4.8150   1.9160   2.6964  -5.6770
-#> 
-#> Columns 49 to 54   4.7604 -17.0443   4.2362  -0.1506   5.7847   4.8275
-#>   12.6656   2.2082 -15.0857  -9.7875  -1.2137   7.7689
-#>   -7.9383   3.0470  -2.7657 -15.1841   7.5981   3.2548
-#>   13.3431  -7.0009  -9.5811   3.9706   8.5158   0.8813
-#>   -2.0129  13.4008  -1.1993  -6.5928   6.5611  -4.5572
-#>  -13.2717  12.4965  -5.4569  -0.3310  10.9039   5.8270
-#>    8.9575  -5.0186  -2.2719   0.3510  -4.7358  -3.2742
-#>    6.5051   0.5433  -8.9381  -0.5561  -6.3295  -1.4019
-#>    3.8464  -4.6707 -13.7753  -0.9268   5.7068  -1.6860
-#>    3.8926  -6.4982  -3.0241   1.7773  -3.8354   2.0144
-#>   -6.7829   7.4228  -0.6287  -1.7426  -6.6208  -2.8569
-#>  -12.3822  14.1381 -11.0661   9.7871   2.0780  -1.8625
-#>    8.6162   4.5793  -2.0453  10.8411  -8.9352  -2.9064
-#>    8.2304   9.6105 -18.9576  -9.7135   8.1245   6.5796
-#>    4.2747  15.7482   0.5116   0.3624  10.7449   3.9772
-#>   -4.2663  -3.5648  -1.8642   7.4056   0.9312   5.9999
-#>   -2.1748  -9.0366  11.5350  -4.8309  -3.1486   9.7313
-#>   -3.5441  -3.7128   3.9071   1.9930  -5.4614  -4.3329
-#>   -3.4657  11.1438 -16.1882   8.2909   5.3822 -11.1595
-#>   -6.5959  -4.9608   1.6281   5.4256   1.1819  -7.4167
-#>  -12.6978   0.9185   9.9471  -9.0970  -8.7337  -0.1304
-#>  -14.7662   1.3690   5.9851  -0.7951   3.1230   6.4365
-#>    7.6856   0.4292 -14.4094  -0.8849   0.1469   3.4671
-#>    5.4269  22.9641 -11.5888  -0.5557  12.3558  -2.9540
-#>  -17.0178   1.4444  -3.5835   1.6870   7.0000  -9.2378
-#>   -3.3226  -4.0039   4.7945   6.7741 -10.1068   4.7550
-#>   -3.1680 -10.0941   1.2355  -4.2952   3.2572   4.4980
-#>   -1.9808 -11.6065  -7.0829  -7.6412   7.5375  -9.0860
-#>   -5.1760  -1.4036  -5.5432  -5.4713   3.0101  -2.3162
-#>   10.2289   1.8357  -9.1445  22.0267  -3.5414 -11.8593
-#>    4.8033   6.7724  -6.2342   3.3569   0.4787   4.3952
-#>   10.7536   3.2190  -5.0490  10.7763   0.7685   2.7364
-#>    3.8563   5.1250  -8.5096  -8.6343   6.7572  -4.6396
-#> 
-#> (8,.,.) = 
-#>  Columns 1 to 8   7.1508   6.7372   5.9819  -0.0601   2.6715  -5.1312  -4.7280  -6.3784
-#>    7.6682  -5.1930  -3.7598   6.8804  11.7423  -9.1464  10.0633   6.0598
-#>   -5.6035  -1.0263   1.6200   0.2141   4.6984  -2.8773 -12.6556   2.3026
-#>   -2.9972   0.1561   7.8324   2.6244  11.8952   4.3623   3.7364 -25.3673
-#>   -9.6151   1.6634  -0.2185  -7.4899   4.8237  -4.0181   4.5082  -0.4462
-#>    4.0094  -2.8231   0.4799   3.3922 -11.7035 -13.0875 -10.3550   5.3582
-#>    3.9708   2.3987   2.3578  -3.6164  -9.5522   6.8756 -12.5137 -12.8285
-#>    0.9439   0.7691   1.1446   3.5504  -5.8556   2.2269   1.1489   3.9464
-#>    1.8752  -4.9550   3.1504  -6.5187  -8.1246  12.7283 -14.8513  -1.5619
-#>    0.9727  -0.8928  15.7404   4.6312   8.3961  12.8783   7.6629   6.6705
-#>   -6.8592  -0.9109  -4.1087  -2.7627 -13.6369  -0.1231   2.1088  -2.5499
-#>   -3.5121  -2.3167  -5.9088   3.2831  -0.5246  -4.8987   2.8872  16.5708
-#>    1.6531   6.3598  -0.8847  22.2141  -2.2368  -8.2070   1.2624  -2.6903
-#>   -1.7068  -4.0863  -5.5869   3.6179   9.6339  -3.1257  29.5030  -1.4866
-#>   -2.4773  -5.0205   4.1964  -6.4092  -4.1407  -5.0958  -4.7861  14.8317
-#>    6.5156   4.4029   6.6035   1.2587   1.1257 -16.5518  -3.6082  16.4726
-#>   -1.0780   3.4886   6.4448  -3.6593   0.8148  -5.3412  -0.4133   4.7233
-#>    9.4846   7.6809   6.4711   2.0481  -1.3859  -1.0715   4.2044 -20.6968
-#>   -4.3761   1.6670   2.4490  -8.4354  -2.7067   9.7746   2.3280   3.9501
-#>   -3.8817  -6.5747  -2.0872   2.3250 -12.6315  -5.6352   1.2802  15.7961
-#>    4.2989   1.5583   4.6880   5.4209   1.1403  -0.6907  -0.5091  16.0139
-#>    1.2061 -14.1669  -8.0375 -17.3101   5.6985  -0.8953  12.6394  -9.9687
-#>   -5.8559  -8.4070 -11.6112  -4.7989 -24.5551 -16.0300 -10.0286   9.7208
-#>    8.2044   6.9436  -3.6526  23.1866   8.5349  11.6573  -0.4488   2.0429
-#>   -3.9382  -3.7899  -5.7767   5.4737   2.7066  -1.5215  -7.6963  -3.5784
-#>    9.5496   8.5969  10.5426  15.4591   5.8838  14.7248   8.8217  16.1680
-#>   -1.1191  -2.1120  -6.3763   1.0351  11.4906  -7.1137 -11.8626  -0.3931
-#>  -10.9741  -4.9215 -13.0459 -15.8233  -3.4078 -12.5638   2.9955  -5.9202
-#>   -0.1297   8.3024  12.6717  10.2556  13.5064  13.4313   0.2281  -1.7045
-#>    1.6663   3.6732  -3.0221   3.8763  16.6424  -5.3196   4.7441 -17.1505
-#>   -6.5405  -3.9878  -7.3507   1.7192  -9.0308  -9.1152  14.1827  -6.6667
-#>    0.8203   4.1849   3.5764   1.3127   3.9395 -12.8159 -17.8305   2.2912
-#>    3.0475   8.3816  -2.4021   6.5701   4.2364  -9.9423   0.9770  -9.7009
-#> 
-#> Columns 9 to 16 -10.0151  -9.5474  16.3357   8.2495  -1.5964   0.5695  -4.5643   9.8078
-#>   30.0856   0.3242  -7.8498   8.6488   2.6957  -5.3110  -8.6937  -1.7189
-#>    1.6592  -7.2534  -2.4388  12.4133   5.6499  15.6555   9.9671   1.4648
-#>   11.3157  -6.9102  -4.3567  -1.6289  -1.9218   3.2060   9.5204 -16.0418
-#>    7.6599  13.5402   0.1472   3.2803  -7.8039   5.7619  12.2552  -0.9779
-#>  -15.4246  20.6626  18.6428  11.2379   3.3039   9.3777  -0.9136   1.9426
-#>  -14.6024   3.6175   9.5540  -3.6661  10.4562  15.3631  18.0100  -3.4185
-#>    1.6321  -2.9346  -6.9568  -8.9791  -3.5516   2.2035  -0.5501  -7.4288
-#>   -4.0139  -5.5305 -11.6019   8.2809 -23.6624  -2.0877   3.3591  -1.3592
-#>    3.6416   8.6007 -10.4185   7.1283   4.3517  13.6260   5.8551  -3.1176
-#>  -11.1471 -15.0331  -2.3048  -1.3674  -2.6871   3.3351  -2.7968  -7.0620
-#>   -6.1378  14.2888  -1.8011  -2.4272  -3.7110  -0.9645  -4.7425   9.3068
-#>   16.9800  25.1799   9.1233  -7.2462   4.7891  -5.0251   0.7113  -9.1539
-#>    9.0895   3.0683 -24.9544   6.3863   5.4586  -9.7773  -0.5664  -7.1917
-#>  -10.7116  17.3930 -13.5514  11.1032  -0.2145  12.2160  -5.1363   2.3689
-#>    0.3445  -2.2634   0.8781   3.2789   4.5291  12.1612   6.0505   2.4993
-#>    5.3868   3.0195  -0.2482  -5.4812   8.4448   9.9567  -0.4724  -6.5618
-#>   10.9629 -12.1021  13.6602  -3.9014 -15.5234  -8.2013  -9.3046  12.3432
-#>  -14.5482  -5.0352 -10.1014  -4.7969  -0.9369  10.4475 -10.6851  -0.4472
-#>   12.1259  -6.1691  -3.0353   4.5765  10.1662  -0.7835   2.3517 -10.8543
-#>   -8.3979  -4.6937 -15.2274 -22.2141  -8.5639  -6.7124  -0.9374   7.7074
-#>  -17.2386  -7.9246  -6.5068   3.9143  11.5533   3.2035 -11.3446  -2.0618
-#>    9.9309 -13.9416 -10.4132 -16.5851   3.7950  10.6597   2.1974  -4.4408
-#>   -0.4629  -0.9287  22.9732   1.8167   9.5406  -3.2515   1.6038   1.8185
-#>   11.0190 -21.9261  22.7242 -17.9158  -7.3786  -7.2195  -0.3001  18.1530
-#>    5.9330  19.3633  -2.7365   9.7048  -4.1241  10.0479  -3.0356   8.2367
-#>  -10.5649   2.1628 -13.9123   9.5574  -5.5361   3.2461   1.5565   3.8338
-#>   -3.5414 -14.0745  -3.8302 -14.1880  -6.9356  -9.2233  -1.5608 -11.7840
-#>   -5.1279   1.8206  -1.8614   5.3786   8.2715   2.3127  -1.2312   3.2170
-#>   -1.4440   1.4858   9.7767  -1.3624  -2.6655   5.8860   5.7552   9.4817
-#>    1.4178 -16.0870 -17.0147  -6.7973   3.1098  -0.3210   7.8234   1.2850
-#>  -10.4717   1.5837  -1.9258  -2.0323  13.3036   7.7405   7.7084  -5.0524
-#>    9.6043  -7.5064   0.8189   0.4072   9.6478   1.5139  20.2461  -1.0598
-#> 
-#> Columns 17 to 24   8.1001  -1.0351  -6.7461 -11.0419  -8.1504 -15.6931 -19.7889  -2.3716
-#>  -10.0340  -5.0651  14.4485 -11.9413   6.1078 -14.9792   2.4590 -15.2920
-#>   -3.9651   6.6120  10.5014   7.5160  -9.6985   4.0468  -7.4204 -11.1921
-#>  -13.8032   4.4341  -0.3191  14.4921 -10.4447  -1.8446  12.3376  -0.8948
-#>   -8.0753  -1.2589  -8.4688   3.2476 -26.8242  -4.4277 -13.5342   2.0415
-#>   -4.3877  -0.1280 -20.6544 -12.8968  10.8765 -10.9568 -11.3636  17.6642
-#>    9.3794  -1.1094  -9.6291   3.5754  -2.1581  -8.7307  -2.9342   4.1637
-#>   -6.2747 -11.1756  -8.4495 -13.4040 -13.1809 -13.8689  -7.2018 -13.4809
-#>   12.8036  16.1512   5.4492  15.9207  -0.3596   0.6861  13.7002  -4.8839
-#>  -10.9333   2.2218 -14.8068  13.8960  -5.6335  -0.0825   5.0305  -6.7653
-#>   14.1881   0.2138  -1.2540   2.3505  -2.9398   3.0173 -16.3710   2.6998
-#>    3.5745  14.2715  15.6316   2.2813  -7.2114  -1.7036  -5.6726   6.8289
-#>   -1.3762 -16.8109   1.8873   5.7485  11.4382  -7.3215 -15.7174   4.9650
-#>    0.9686   5.8856  -8.1113   7.1000   5.8756  -9.7769  10.2416   7.2427
-#>  -10.5910  22.1120  -8.2317  -3.1513  -9.2054  -6.5088   1.6777  -1.6041
-#>   -9.1672   1.9800 -16.2627  -9.7974   3.4530  13.2416  -4.8867  -3.5655
-#>  -12.8684 -11.0718 -13.4428 -13.2373 -10.2448  -2.0117   7.0435   3.1549
-#>   -1.6896   6.6603  15.4529   7.5885   4.8641   7.3906  -2.2209  -6.2453
-#>   -2.5293  -9.9172 -13.6721  -0.2927  -6.7671   8.2823  19.9966   7.8450
-#>    2.8503   4.1798   1.5580 -14.8536  -6.3334   2.6650  -4.5798  -8.2165
-#>   -4.0300 -14.2928  -2.4017 -20.5895   4.7263  14.9711  -2.4875  14.4660
-#>    5.1115 -13.9482  -3.6005   4.3206   9.7899  -5.5324   8.6255  -7.1614
-#>    5.7668   2.7724   7.4530 -12.2066   3.6582  -2.3568  -3.9457   3.7212
-#>   13.5755   5.8316   0.1492   9.8553  16.3314  15.4315  -7.3261   3.8208
-#>    5.3305  12.3318   1.8238   2.7613  11.6012   8.4931  -3.4332   5.4188
-#>    8.1775  12.1450  15.1162   2.3753   2.2250  -3.6509  12.8902  -2.8555
-#>    7.8905  10.0615  -0.4912   6.3662  -0.6782   9.5253 -15.2438   2.5168
-#>  -11.2149 -10.0954   3.3422  -2.7070  -7.0759   2.4042  -3.7015   1.2223
-#>    2.3879   6.8082   0.2533  11.3374  -1.4567  14.0965   2.2258  -1.8460
-#>    3.4320   9.3158  -0.6690  17.7832   9.3512  13.3587   9.3576   5.0262
-#>    9.3727   1.7643   2.7273  -7.8212  -7.6490  -2.7739   7.7751  -6.1483
-#>    0.2295   0.1979   2.4044   6.5403   8.0871   1.0703  -1.9011   7.0253
-#>    4.3509   5.3380  -7.0635  10.6731  10.6836   0.4986  -7.3226   1.9557
-#> 
-#> Columns 25 to 32   6.7455   1.7868  -8.8835  -8.2019   4.5433  -1.0239  -3.1092   1.6769
-#>    8.4804   5.0411  11.5685  -0.9770   2.6991  12.6585  -1.4397 -14.9938
-#>  -10.9128  -5.3340  -9.6483  -6.0048   9.7009   3.1092   5.1960   1.1162
-#>   11.1955  -2.2595   9.9399  -9.6134  -1.3063   9.5693   3.4519   1.7172
-#>    3.1671   1.4267  -2.8816  14.6492   7.2395   1.8898   5.8684  13.9336
-#>    4.4651   8.7700  -5.7401   6.6528  -5.2405  17.1295   8.9922   8.9185
-#>   -3.7940 -18.7592 -11.9993 -18.3577  10.6142 -13.1228   3.9631  11.6180
-#>   -1.4272  -1.2968  -1.0754   3.7228  16.9619  -9.0493  -7.8127   6.2468
-#>   -7.6175   2.0480 -11.3912 -27.6616  -2.1672   1.8430   2.4369   0.5673
-#>   -3.9454   5.5963   8.3102   2.1835 -15.3158  24.8253  -5.0833  10.2927
-#>   -8.1090   8.9425  -9.0912   7.1353   5.2831  -4.3799  -4.0059  25.5929
-#>   -4.5726  -0.3739 -18.7746   9.9125 -14.6715  15.2602  -3.1135  14.5251
-#>    6.0025   3.2679  10.2790   5.0409   1.6172  -0.0848  -5.3120   4.0880
-#>   10.8606  -4.5095  20.0533  -6.8515 -18.1393   7.5030  -8.9183  -7.8453
-#>   -3.4917  -7.7637  -9.8252  16.4568  -2.8053   8.6339  13.7757  -0.1181
-#>   14.0185   9.1554   5.6943   0.7750 -10.0743   2.1277  -2.2721  13.1413
-#>   10.1655  -4.2152   1.7572   4.1830   2.1623  -8.7048  -3.9587   1.0580
-#>   -6.0097  10.9930   5.1062   8.3181   0.8457  -0.1577 -18.2184  -2.3131
-#>   -5.5882  -2.2534 -22.0129   4.8418 -15.4079   5.5410  -4.4979  -0.6346
-#>  -14.6428   7.7315   6.3648  -5.0603   5.9393  -1.0989  11.1776   6.7368
-#>    9.9365  24.6246  -1.8262  14.6768 -21.6443 -14.6404 -12.6476  -2.4409
-#>    1.4070 -16.3869 -19.9686  -6.7619   4.0466   3.5202   4.3108   2.6778
-#>    9.7351   1.3257  -9.6109 -21.4991  -2.8457  -9.2122  -3.7684  12.5494
-#>   -1.6200  -0.4281  -3.1035  -1.5048   1.5300  16.4868  -5.0592  -1.0714
-#>    8.1314  15.9266  -7.8683  -6.8566  -8.5155  -0.3033 -16.4273  -7.1421
-#>  -13.8999  -5.4036   8.2061   1.4796  -2.7902   4.8665  -6.9505   0.0025
-#>   -7.4138  -4.6629  -4.7055  -3.7802  -9.6058   6.1842  -2.8387  -6.5854
-#>   10.8963  14.8133   0.6713  -1.0068   0.1435 -19.0033   6.1731 -15.9684
-#>    3.5799  -7.0449  -4.7986   4.5287  -7.1192  -8.4011   5.7986   1.1786
-#>  -13.1699 -20.9364  -9.5993   5.8298  -7.7431  23.6490   4.8607 -11.3314
-#>    6.8217  -2.7287  11.7833 -12.5530  -7.4013   7.5500  -6.6971  10.5138
-#>   10.9274  13.0032  -9.6783  15.5270  -2.9641   2.4258  -6.9870   2.7114
-#>   12.2095  10.0677  12.3627  -0.1106   3.4960   3.6681   2.2930  -6.1216
-#> 
-#> Columns 33 to 40  -3.3622 -10.3849  10.2555  11.8728   5.8306 -10.4878   0.4853   8.9719
-#>   18.4105  -7.9307  11.4194  -0.1296  -5.5568   3.6137   9.7678  -2.1159
-#>   -3.3697   0.7608 -12.6738 -10.1236   4.4349  10.4471  -4.0326  -0.8935
-#>   -0.3232  -0.7280  -0.2048   5.9241   0.5660 -11.9498  -1.1860  21.1517
-#>   -2.7066   3.8992  -7.7092   2.9674  -2.1234  -1.3887   0.8307  -2.0074
-#>   -0.5790  16.3288  -1.7148 -13.5067  -1.4230  -3.2069  -0.7859  12.1890
-#>   -4.6540  -1.9929  -0.4003   0.7257 -10.6383 -10.1322   6.7866   7.2317
-#>    3.8970  -3.4188 -11.7834  -4.6428   4.0508   2.1324   4.1699  -4.1890
-#>    7.3724  16.4938  10.6386  22.3735  -2.0468   7.5446 -19.1999   2.6527
-#>  -13.2517  -5.3879 -10.5804   5.9759  -2.5601  -3.3287  12.2843   6.9087
-#>   -0.9873   0.0392  -3.8649   9.3947   2.5094  -8.7465 -14.7925   5.2627
-#>   12.9071  17.7511   6.9966  -1.4220 -10.6707   4.9446  -6.7811  -4.1808
-#>    3.2866   2.8973  -1.3446  14.3974   7.3142   4.2670   1.8701   1.2805
-#>    9.1203  -7.5222  -4.2375   3.9771   1.2064   2.9481  -2.7665 -11.2163
-#>  -11.7766  -6.0367 -11.9295  -6.2336  21.8730   1.4385 -13.9398   0.4111
-#>   -4.0622   4.9269  -9.6263  -9.0863  15.4455  10.2114  -8.1764   5.1174
-#>    7.1776  -7.6390   4.4247 -12.3476 -15.5966   7.9522   2.4543  -2.4679
-#>    2.6628  -2.8408  15.8058   2.4088  -8.0436  -3.6906   9.7034  -3.1776
-#>  -18.7927 -17.2439 -23.2489  19.8174  22.7149 -10.6282  -5.0924  -4.8641
-#>   -8.5925   1.8419  -9.6410  -8.1455   9.6088  -5.6521   3.3799  -1.7245
-#>   -3.4918 -10.8292  -2.7675 -10.0738   3.9026   9.0146   3.3701 -11.1880
-#>   17.8822   4.8984   0.0398   2.5109   6.7534   4.2931  -5.9973  10.8163
-#>    1.7897   5.7650   2.3038 -28.0216 -12.0954  -4.8501  -7.7992   4.6672
-#>   -0.3011   3.7991  -4.8132  16.1519  13.6011   4.7395  -7.8466  -8.0761
-#>   -6.7641  -1.3968  -2.4330 -26.8625 -14.1981  -3.8306   5.0209   4.4976
-#>    6.8127   7.9355   5.9901  -5.3127  -5.0978   8.3256   0.7121  -8.4116
-#>   -1.6115  12.8354  10.4227   0.5847  -5.0641   2.4588  -4.8166  -3.2331
-#>    4.9475  -2.3680 -10.0948  10.5419   2.5680  -7.0339  -1.7302  -0.8638
-#>  -13.0108  -5.0026   6.1649   5.7417  -4.4212 -12.3816  11.8004   4.8924
-#>  -11.2446 -16.1600 -13.2574  -0.8420  -4.7720  -2.6097  13.6350  -3.3475
-#>   17.9535   1.4541  -8.9213  -3.8055   1.3003   2.3363 -12.3381  -5.6345
-#>   -9.3921  -4.6415  -0.2370 -11.6773  13.2384  -5.2224  -6.8675   5.8824
-#>    5.9682   6.0117 -12.0649  -5.3713   2.1772 -21.1429   3.9779  -3.2444
-#> 
-#> Columns 41 to 48  -7.4203  -4.3417  -8.7223   9.8122   3.8623  -7.4629  13.9484 -23.2190
-#>    3.4445  -1.3355   7.6531  10.1566  -4.0611  -8.9193  12.1846  13.4072
-#>   -9.1451  -2.1885   2.3641 -12.4758 -16.5542   0.9120  -3.5647  12.2927
-#>    4.6303  -5.2585   1.0651  -4.5631  -7.7577  -0.9423  -7.0766  11.9225
-#>   -3.1262  -4.0675   2.1898  -8.6319  -2.6677   4.4339  -0.6671   5.0801
-#>    6.2554   2.5598  -0.1234  13.0930  -1.8810  28.3096  -6.6054  -0.4741
-#>  -11.8179 -12.9049   4.6068   2.6987   2.6213 -11.2886   3.4658  -5.2631
-#>   -2.5020   5.5396  -3.7473   0.5592 -12.7861 -18.4386  20.1443  -9.0295
-#>   -6.0943  -6.2892 -15.0037  -2.9393  -1.0958  15.7839 -17.5685  -4.1025
-#>  -14.5351 -11.2419 -10.0126   8.4245  -3.8231  13.4642  -2.1626  19.3573
-#>   -2.6913   1.2570   0.0191  -5.7630  -1.7038 -12.0102  -2.8262  -1.9102
-#>   -9.3014  -2.7707  -5.6902   3.2790 -12.9711   6.5359   6.9999 -10.7766
-#>    2.9156   8.3690  -0.5395  -6.0872  11.8689  -4.0404   2.1184  -9.6522
-#>   12.3743  -1.0288  12.3481   1.4885  -0.7266   4.8815   0.7529  -0.9136
-#>  -19.4519  -7.9898   7.5399   2.6351  -5.8580   6.3750   0.1136  -2.1771
-#>    1.6941  -6.4103   0.5496  11.4846   7.6194   3.6779   1.9675   5.1756
-#>    3.4512   6.0714   4.5734  27.0274   3.3834   8.3279  -5.7394  -1.7655
-#>   -2.3163   7.9076  -5.1612  -5.3446   9.2760 -25.0731  13.2667   8.3713
-#>    6.8684  -9.4366 -14.1991   8.6951  10.7399  -2.8100  -2.2233 -10.4838
-#>  -14.9936  -2.4823  18.8716 -10.5186 -11.2061   2.7645   9.7941 -20.5271
-#>    2.9695  10.8395 -10.4739   5.1254  -4.9681  -5.0584 -18.1055   2.5028
-#>    2.7110   0.8587  -4.6111   0.3774 -11.9666   3.8982  -5.3369   6.5355
-#>  -12.3133 -15.5227  -2.7682   4.9590   1.8040   0.7331  -1.1232  -6.3707
-#>   -0.3749   5.4597 -11.5046   2.4737   4.4048  14.9951   5.7543 -16.8669
-#>   -2.6534  -2.9687   0.4743  -3.9876 -11.8613   8.7067  11.5766 -13.5679
-#>    0.9820 -10.8301   6.7273  -1.5765  -8.2368  14.5064   6.3943   7.6894
-#>    3.5332  -6.1911   6.6732   4.3271  -9.1047  14.4457 -19.7375  12.9010
-#>   15.6519  13.4092  -7.4260  -5.9673 -16.3715 -12.2971 -15.9744  -9.7981
-#>    8.2944   9.8776 -10.1648 -10.2498  -0.8161  -5.1461   8.3299   0.3224
-#>   -5.7386  -5.1824   0.2582  -9.6135  15.1354  -0.5289 -12.7296 -13.0442
-#>   13.5382  -0.9888 -13.6341  -5.3329  14.3248   0.2444  -4.2781   4.5478
-#>    9.2803  -0.4922   5.8904   9.9608  -0.0805  11.7786  -7.9851   1.9345
-#>    2.3301  -4.4072   2.7353   0.9836   3.9349  -6.1501   1.5588 -10.2998
-#> 
-#> Columns 49 to 54 -20.8314  16.5843 -13.9129  -2.0479   6.8089  -3.7914
-#>    6.2111   6.6074  -5.1333   2.0526   0.3237  -2.0470
-#>  -14.5700   7.4540  -0.3076   7.1954  -6.8094  -4.0095
-#>  -15.0809   0.6740  -0.7762   0.2321 -12.1556   2.6609
-#>   14.3694  -1.3963   6.5437  10.2236  -8.9210   2.5166
-#>  -12.3399 -26.4408   2.1555  -2.6613   3.1474  -2.0783
-#>   -1.7603  -6.9358   7.6874   1.6465 -10.0625  -5.6285
-#>   -2.7790   3.9554  -1.3829  -9.3238  -5.7191  -2.2334
-#>  -11.1100  -4.3702  14.1653   4.4415   3.3352  -1.5379
-#>  -14.6190  -1.4964  -1.2528  -9.3638  -8.0048  -4.8460
-#>    8.4043  -8.5918  -5.1574   0.0703   1.7428  -1.1935
-#>   -0.7531   1.1208  -4.4809   4.9666  14.7488  -3.2977
-#>    4.3365   3.2914   4.2250  -2.9052 -11.3092  -4.1889
-#>    3.3596 -16.7166   3.0995   1.4962   1.6846  -3.7894
-#>   -9.7414   8.1937  13.6883  -2.5184  10.6000   3.3366
-#>    2.6495   2.5624  -7.4683   2.6998   0.0631   2.7538
-#>   -8.5029  -4.9118 -18.4429  -3.2778   8.0432  -8.2741
-#>   -1.9020   6.6837  -5.4427   6.1116   7.2660   0.4438
-#>   -7.0378  -4.7777  15.0084  -2.7419  -2.9724   3.1454
-#>    4.6097   4.8506   3.8411  -4.8637 -13.3390   3.0000
-#>    9.8125  -6.7690   6.7033   5.7351   3.2553  -1.0613
-#>   -0.3601 -13.9705  -1.0009   4.9286  -7.7348  -2.9671
-#>   -2.5662  -6.6644  -7.4420 -13.8237   5.5117   3.2655
-#>   14.5174  -5.0537  21.5743  10.0272  -5.0012   3.7732
-#>  -15.3295  16.5149   0.0156  -2.6484   1.1358   2.8868
-#>   13.3596 -12.8918  -0.3230 -11.0566   6.0814  -0.1005
-#>   -2.1543 -12.3791   9.6814  -9.1736   1.9768   0.4381
-#>   -2.3409  -1.2850 -13.5981   0.2571  -8.0662  -3.2677
-#>    7.2799  -3.7789  -4.2514   2.2253  -5.6518   1.1974
-#>    2.7535  11.8568   1.0693  -0.8272   2.2264  -0.9253
-#>   14.5961 -14.8933  -9.8182  11.7285   0.6507   0.6431
-#>   -2.7609   6.4170  -0.6261   1.7213  -7.1515  -4.1353
-#>   11.0345  -4.5719  -3.2490   0.7722 -13.8753   4.9701
-#> 
-#> (9,.,.) = 
-#>  Columns 1 to 6 -3.0844e+00  3.9575e+00 -8.8483e+00  7.1485e+00  1.4732e+01  3.7237e+00
-#>   1.3299e+00 -3.2750e+00 -3.2767e+00 -6.6849e+00  3.0363e+00 -1.1108e+01
-#>  -5.3153e+00 -1.0945e+00 -2.6950e+00  6.9211e+00  4.3921e+00  4.0269e+00
-#>  -6.4988e-01  2.1014e+00  9.9262e+00 -3.4379e+00  1.2616e+01  2.0861e+00
-#>   1.9079e+00  1.3251e+00 -2.1251e+00 -5.3450e+00  3.2996e+00 -4.3239e+00
-#>  -6.0243e+00 -9.6375e+00 -1.1247e+01  6.9145e+00 -4.1306e+00  2.5840e+00
-#>   6.0369e+00  6.4653e-01 -2.5032e+00 -1.9317e+00  1.9454e+01  5.1159e+00
-#>   6.6771e+00 -9.7476e-01  4.0785e-01 -4.2783e+00  2.9150e+00  1.3538e+01
-#>  -9.8473e+00  1.0038e+00  7.7229e+00  3.0641e+00  5.7014e+00 -1.2515e+01
-#>  -4.6366e+00  1.6778e+00 -6.7110e+00 -2.1135e+01 -1.1663e+01  9.1680e+00
-#>   2.4572e+00  1.0856e+01 -8.8700e+00 -6.6691e+00 -9.6360e-01  1.3725e+01
-#>   1.5261e+00  2.6883e-01 -3.6022e-01  6.8276e+00 -1.1354e+00 -5.7788e+00
-#>   6.0398e+00 -3.6515e+00  1.9147e+00 -9.9658e+00  1.8608e+00  1.6815e+01
-#>   1.0912e+01 -1.1609e+00  4.9686e+00 -8.8477e+00 -5.8313e+00 -2.2854e+00
-#>   4.3442e-01  6.8882e+00  7.6787e+00 -8.1718e+00 -5.9922e+00 -2.9733e+00
-#>  -2.9825e+00  2.4150e+00 -1.2465e+00 -7.3483e-01 -1.5889e+01 -1.0715e+01
-#>   2.2427e+00  3.7664e+00 -2.2868e+00  4.5995e+00  1.0914e+01 -1.2551e+00
-#>  -9.6294e-01  5.3982e-01  1.4011e+01  6.6027e+00  3.2266e+00 -1.1214e+01
-#>  -1.2467e+00  5.1257e+00  2.9094e+00 -4.5639e+00  3.2488e+00  4.4284e+00
-#>   2.6206e+00  8.2820e+00  3.8331e+00 -4.8418e+00 -1.3598e+01  9.1072e+00
-#>   6.8628e-01  1.1245e+01  1.8393e-02 -2.6590e+00 -2.7642e+01 -3.6005e+00
-#>   2.3190e+00 -2.0259e+00 -1.0040e+01 -6.6561e-01 -1.7592e+01  3.9268e+00
-#>   7.6194e+00  5.9051e+00  1.9740e+00 -8.0803e+00 -1.1348e+01  5.4960e-01
-#>  -8.1681e+00  2.1004e+00 -3.6650e+00  1.0278e+01  5.2578e+00  2.8381e+00
-#>  -4.0629e+00  3.3020e+00 -6.8705e+00  6.6090e+00 -5.2204e+00  8.8572e+00
-#>  -2.8817e+00 -7.1743e+00 -5.8274e+00 -1.4024e+00 -1.6453e+00 -1.2242e+01
-#>  -1.8511e+00  5.2588e+00 -3.8408e+00  1.7996e+01 -2.0355e+00  4.0652e+00
-#>   2.0675e+00  8.8085e+00  1.3759e+00  7.3567e-01 -9.5449e+00  3.0625e+00
-#>   1.1249e+00 -2.9889e+00  1.9286e+00 -3.4670e+00  1.6181e+01  1.6632e+01
-#>  -9.4529e+00  2.5716e+00  7.1519e+00  3.4283e+00 -7.8391e+00  4.5298e+00
-#>   6.1931e-01  1.0553e+00  6.9422e+00 -1.0306e+01 -1.4759e+01 -7.0191e+00
-#>   9.1682e+00 -4.0056e-01 -9.7737e+00  9.0546e+00 -5.7929e+00 -5.6782e+00
-#>   2.3222e+00 -4.0269e+00  3.3736e+00  3.6830e+00  1.0166e+01  6.4749e+00
-#> 
-#> Columns 7 to 12 -5.3888e+00 -2.9333e+00  1.5363e+00  5.4292e+00 -9.1061e-01  1.2076e+01
-#>   1.2150e+00  1.0525e+01  6.0796e+00  2.6328e+00 -1.1000e+01  2.9484e+00
-#>   3.1673e+00 -8.9752e+00 -7.2490e-01  1.5361e+01  2.7796e+00 -7.5944e+00
-#>  -1.4430e+01  9.7151e+00 -9.1587e-01  3.2778e+00  5.2619e+00 -3.7392e-01
-#>   3.7892e+00 -5.1312e+00 -9.0128e+00  5.1808e-01 -3.1414e+00  2.7353e+00
-#>  -6.7068e+00 -9.0294e+00  1.1638e+01  1.0029e+00  1.1856e+01 -2.6498e+00
-#>  -1.1853e+01 -9.1865e+00 -9.7623e+00  7.9592e+00  1.4931e+01 -5.3246e+00
-#>   1.1537e+01 -4.9769e+00  5.7990e+00 -1.7946e+01 -2.5516e+00 -6.2747e+00
-#>  -1.0631e-01 -1.8550e+00 -6.2494e+00  1.0040e+01  1.2557e+01  1.6425e+01
-#>   1.5632e+00  8.7546e-01 -1.0170e+01 -1.5403e+00  8.9363e-01 -3.8374e+00
-#>   1.6753e+01 -1.7494e+01 -1.5118e+01  6.6152e+00 -3.2020e+00  9.7728e+00
-#>   1.8719e+01  7.2675e+00  1.6485e+01  2.5752e+00 -2.4923e+00  1.7008e+01
-#>   6.3191e+00 -6.5026e+00 -2.9797e+00 -1.0865e+01 -5.6393e+00  2.7777e+00
-#>   2.7979e+00  8.5942e+00 -2.8034e+00 -1.0326e+01  1.2389e+01  1.6556e+01
-#>   1.3892e+01  6.8219e+00  7.3136e+00  1.1366e+01  8.1391e-01  4.3374e+00
-#>   4.8084e-01  4.3842e+00 -4.2161e+00  1.8518e+00 -3.7542e+00  9.6281e+00
-#>  -2.2650e+00  7.0146e+00  1.1047e+01  6.1887e+00  1.8057e+01  1.4751e+00
-#>  -2.4461e+01  9.5519e+00 -8.2912e-01 -1.0050e+01 -8.2641e+00  4.6827e+00
-#>   7.4679e+00 -3.5030e+00 -1.4055e+00  1.9115e+01  2.4359e+01  8.1015e+00
-#>   2.4131e+01  2.4365e+00 -6.8482e+00  2.9907e+00 -4.4200e+00  2.4605e+00
-#>  -2.3553e+00  6.7272e+00 -1.4875e+01 -1.3162e+01  9.2889e+00  6.4767e+00
-#>   2.3963e+01  1.2350e+01  8.0211e+00  1.3432e+01 -1.4131e-01  6.6445e+00
-#>   1.3471e+01  7.7444e+00  7.6033e+00 -2.4399e+00  2.5683e+01 -6.2176e+00
-#>   8.8612e+00 -4.4346e+00  1.4746e+01 -2.9410e+00 -6.8655e+00  6.2316e+00
-#>  -3.5537e+00 -7.2698e+00 -4.7409e+00 -7.2148e+00 -5.8450e+00 -5.2691e+00
-#>   1.3115e+00  1.6191e+00  4.7505e-01 -1.5181e+01 -1.1890e+00 -1.3413e+01
-#>   2.0220e+00  4.9279e+00  2.4231e+00 -5.2500e+00  4.8225e+00 -1.3004e+01
-#>   1.6325e+00 -8.4463e-01 -5.2737e+00 -4.4634e+00  9.0230e-01  1.5081e+01
-#>  -9.6546e+00 -1.6568e+01 -2.3547e+00 -9.3497e+00 -9.6521e+00 -4.5624e+00
-#>  -1.1359e+01  4.4158e+00  7.5889e+00  5.7622e-01 -7.3438e+00 -1.0305e+01
-#>   3.7111e+00 -3.7338e-01  1.4702e+01 -6.3014e+00 -6.5463e+00  2.1742e+01
-#>   9.0815e+00  2.7954e+00 -1.5308e+01  1.0759e+01  3.7131e+00 -1.3179e+01
-#>   3.3553e+00 -8.0263e+00 -4.2763e+00  8.2564e-01 -3.5557e+00 -6.8022e-01
-#> 
-#> Columns 13 to 18 -7.4942e+00 -1.4010e+01 -5.3054e+00 -8.4081e+00 -3.8957e-01  7.1862e+00
-#>  -7.1702e+00 -7.3959e+00  2.7816e-01  9.9267e+00 -2.1226e+01  2.2359e+01
-#>   3.7178e+00  8.9062e+00 -1.9239e+00  3.9103e+00 -3.0656e+00 -2.7693e+00
-#>  -6.8793e+00  6.7350e+00  7.0029e+00 -1.1243e+00  9.9269e+00 -6.0519e+00
-#>  -1.9346e+01  9.3772e+00 -1.6757e+01  8.7048e+00  2.2695e+00 -9.9442e+00
-#>  -2.1270e+01 -1.9596e+01 -1.2583e+01 -2.6858e+01 -2.5704e-01 -2.3715e+01
-#>  -1.5152e+01 -1.1151e+01 -1.4880e+00 -1.2560e+01  1.4956e+01  1.3154e+00
-#>   1.4843e+01  1.0410e+01  6.9810e+00 -2.2508e+01  5.1875e+00  9.2162e+00
-#>   8.8139e+00  8.1885e+00  1.5398e+01 -6.9044e+00  1.1073e+01 -4.3035e+00
-#>  -5.6897e+00  1.0101e+01 -2.1102e-01 -8.2544e+00 -7.0694e+00  1.1567e-01
-#>  -5.6823e+00  4.4739e+00 -1.0758e+01 -1.8899e+00  3.8160e+00 -8.9889e+00
-#>  -6.3853e+00  3.2104e+00  5.0729e+00 -2.8138e+00  2.5488e+00  3.5416e+00
-#>  -1.3139e+01 -1.6489e+01  1.7487e+01  5.5499e+00  9.9309e+00 -5.3077e-01
-#>   3.8411e+00  6.1458e+00  6.2721e-02  8.6752e-01  1.6274e+00  1.2679e+01
-#>   3.6154e+00 -6.3606e+00 -7.1458e+00 -9.3122e+00  5.1054e+00  3.1354e+00
-#>  -8.9520e+00 -7.4094e+00 -1.3353e+01  1.1036e+01  3.9082e+00 -9.9292e+00
-#>  -8.0426e+00 -2.7335e+00 -1.6823e+01 -1.5870e+01 -2.4214e+00 -1.9141e+00
-#>   3.9491e+00  5.3443e+00  1.4690e+01 -2.9426e+00  4.3608e+00  7.3118e+00
-#>   4.9603e+00 -4.8410e+00 -8.5335e+00 -2.0889e+00  2.1733e+00 -6.1265e-01
-#>   7.4755e-01  1.5469e+01  6.2350e+00 -9.7556e+00  1.9131e+01  4.6571e+00
-#>   1.3613e+01 -8.7656e-01  5.4168e-01  9.9604e+00  1.6535e-01  9.2111e+00
-#>   4.1108e+00  1.3246e+00 -3.6717e+00 -3.7175e+00 -1.5989e+01 -3.8839e+00
-#>   1.0627e+01  1.4176e+01 -5.0590e+00 -2.0990e+01  1.3081e+01 -3.2022e+00
-#>   2.4884e+00 -8.7539e+00 -1.5358e+00  9.2289e+00  6.3881e+00 -6.5302e-01
-#>   8.6044e+00  5.5272e+00 -3.5769e+00 -4.3029e+00  8.1571e+00 -1.5234e+01
-#>   1.0456e+00  1.2951e+01  1.2300e+01 -7.5131e+00 -8.2078e-01 -5.4459e+00
-#>   1.1622e+00 -3.2786e+00 -2.4809e+00  9.2949e+00  4.5887e-01 -5.6015e+00
-#>   1.0684e+01 -8.1112e-01 -7.1593e+00  6.1027e+00 -6.0294e+00 -4.8785e+00
-#>  -3.0858e+00  1.3670e+01 -2.5882e+00  7.5500e+00  7.6051e+00 -9.8345e+00
-#>  -1.4309e+01 -2.4152e+01 -4.3382e+00  2.0233e+01 -6.3286e+00 -1.8082e+01
-#>   1.5897e-02  3.6208e+00  2.3562e+00 -1.3223e+01 -9.8380e+00 -4.8735e+00
-#>   2.0443e+00 -1.5113e+01 -3.1743e+00 -4.9740e-01 -1.1086e+01  3.2641e+00
-#>   1.5150e+01 -5.8453e+00 -1.1136e+01  6.0637e+00 -6.3859e+00 -2.8524e+00
-#> 
-#> Columns 19 to 24 -5.6893e+00 -1.9453e+01 -3.5957e+00  1.5872e+01 -1.3028e+01  5.1139e+00
-#>   6.9340e+00  1.2190e+00  1.2480e+01  1.2942e+00 -1.6315e+00  1.4260e+01
-#>   1.2432e+01 -4.2904e+00  2.6480e+00 -1.8259e+00 -1.5810e+00  3.2235e+00
-#>   1.4535e+01 -7.8076e+00  1.7186e+00 -7.7738e+00 -7.1399e+00 -8.9696e-01
-#>   8.6471e-01  5.4114e+00  1.1150e+01  5.3790e+00  7.0586e+00  1.9771e+00
-#>  -9.0428e+00 -6.1166e+00 -1.1130e+01  1.3702e+01 -7.3280e+00  1.9078e+00
-#>  -1.2262e+01  4.3546e+00  6.0670e+00 -3.4008e+00  8.6816e+00  7.8010e-02
-#>  -2.0900e+01  4.2239e+00 -9.2654e-01  7.3594e+00  8.6544e+00  2.1153e+00
-#>  -1.1320e+01  1.2950e+01 -8.8325e+00  5.0883e+00  1.2992e+01 -1.0941e+01
-#>  -5.0212e+00  2.9902e+00 -3.3185e+00  5.6894e+00 -1.4010e+01 -1.0241e+01
-#>  -1.1013e+01 -3.3661e-01  2.2183e+00  1.8726e+00 -1.7061e+00  5.6472e+00
-#>  -8.8008e-01  4.3267e+00 -4.8622e+00  7.6512e+00 -5.9359e+00  2.2392e+01
-#>  -3.8189e+00 -8.8454e+00 -2.0480e+00 -1.0577e+01 -4.6067e+00 -1.5634e-01
-#>   1.4468e+00 -4.0155e+00 -7.7699e+00 -6.3497e+00 -1.2672e+01  6.8436e+00
-#>   2.3073e+00  1.2243e+01 -9.7741e+00  1.7353e+01 -8.2820e-01  1.3717e+00
-#>   1.4566e+01 -1.2031e+01 -2.8944e+00  1.1853e+01  5.4134e+00 -1.2361e+00
-#>  -8.3577e+00  2.4359e+00 -1.3276e+01 -4.0730e+00 -1.0948e+01  1.3474e+01
-#>   3.8182e-01 -6.4262e+00  3.8115e+00 -2.2974e+00 -5.7632e-01  9.6762e+00
-#>  -5.3428e+00  8.6593e+00 -6.0399e+00  1.8404e+01  2.2284e+00 -9.9725e+00
-#>   4.7909e+00 -2.2855e+00 -4.3346e+00 -5.4306e+00  2.7422e-01 -2.0013e+00
-#>  -8.8541e+00 -6.9699e+00 -1.3400e+01  8.0493e+00  6.8156e+00 -9.7870e-01
-#>  -7.3828e+00  2.8325e+00  7.9561e+00  8.8198e+00  1.1244e+00 -1.6940e+00
-#>   4.4528e+00 -1.8023e+00 -6.6527e+00 -3.9291e+00 -5.5194e+00  1.8781e+00
-#>   6.1969e+00 -1.2150e+01  1.4234e+01  1.0549e+01  1.6503e+01  5.4961e+00
-#>   1.9726e+01 -1.9853e+01  1.1585e+01 -2.1674e+00 -1.4682e+01  5.4222e+00
-#>  -7.7661e+00 -2.6476e+00 -8.8573e+00 -3.4332e+00 -9.9211e+00  8.6490e-02
-#>   1.4378e+01  4.0144e+00 -9.0094e+00  2.5586e+00 -7.2253e+00 -7.1700e+00
-#>  -1.1073e+00 -2.0247e+01  1.6292e+00 -1.5077e+01 -1.4288e+00 -1.5738e+00
-#>  -1.2809e+01 -8.0958e-01 -3.1857e+00 -1.3108e+01  4.3087e+00 -1.0828e+01
-#>   1.0087e+01 -8.5014e+00  8.2723e+00  5.4861e+00 -1.5241e+01  5.1375e+00
-#>   4.1856e+00 -6.3674e+00 -6.0491e-01 -3.4339e+00  3.0145e+00  7.3578e+00
-#>  -2.0565e+00  7.9100e+00 -1.1618e+01 -1.0194e+01 -1.0763e+01 -1.8640e+01
-#>   6.7430e+00 -1.1374e+01  2.1704e+00 -7.5976e+00 -8.7040e+00 -7.4410e-03
-#> 
-#> Columns 25 to 30  1.3558e+01  3.3122e+00  2.0959e+00  1.5647e+01  1.0997e+01 -1.9436e+01
-#>   2.6344e-02  7.2531e+00  1.2503e+01 -9.3091e+00  3.1167e+00 -1.1548e+01
-#>  -2.7886e-01  7.5301e+00  4.8612e+00  2.8948e+00 -2.8620e+00  6.4170e+00
-#>  -6.6708e+00  1.7084e+00 -1.2577e+01 -4.7185e+00 -4.7590e+00  1.1023e+01
-#>  -5.3235e+00  1.5145e+00  8.6505e-01  8.3207e+00 -2.0379e+01  4.0297e+00
-#>   1.5575e+01 -2.6839e+00  7.3206e+00  1.3451e+01 -5.3136e+00 -4.7260e+00
-#>   6.1753e+00  1.2691e+00 -7.8798e-02  1.5560e+01 -1.3044e+00 -2.4853e+00
-#>  -5.1093e+00  2.5675e+00  4.8341e-01  2.7719e+00  5.3591e+00 -2.0743e-01
-#>   8.9852e+00  9.6871e+00 -6.3932e+00  3.0404e+00  7.4364e+00 -1.0637e+01
-#>   4.8914e+00 -6.7086e+00  3.4064e+00  8.1951e+00  6.9123e+00 -8.8660e+00
-#>  -7.4082e-01 -3.7787e+00  7.6736e-02  1.8565e+00 -6.1790e+00  1.2534e+01
-#>   9.1540e+00 -4.7321e+00  5.9293e+00  1.2966e+00 -1.2415e+01  1.3284e+01
-#>  -1.3991e+01  9.3310e-01 -2.2098e+00  6.6424e+00  4.0365e+00  1.3118e+01
-#>  -1.1929e+01 -4.9660e+00  8.5513e+00 -3.3803e+00 -1.0103e+01 -2.6659e+00
-#>   3.5112e+00 -6.5770e+00  5.2533e+00  6.0158e+00 -4.1765e-01  9.3664e+00
-#>   3.5893e+00 -5.5666e+00 -8.5346e-01  1.0235e+01 -2.8308e-01 -5.9867e+00
-#>   1.2652e+01 -2.2574e+00  5.1448e+00  1.3336e+00 -1.3777e+01 -2.0938e+01
-#>  -9.7569e-01  7.9474e+00 -7.8825e+00 -3.6059e+00  4.1351e+00  1.9781e+00
-#>   4.8112e+00 -4.8876e+00 -1.4709e+00  4.2883e+00  2.9530e+00  1.8854e+00
-#>  -1.3174e-01 -6.2074e+00  4.1328e+00  6.5042e+00 -9.7857e+00  6.5055e-01
-#>  -3.8156e+00 -1.3507e+01  1.6801e+00  2.0514e+00  5.6115e+00  3.8388e+00
-#>  -6.6274e+00 -7.6941e+00  1.3906e+00 -8.0568e+00  2.6097e+00 -1.2881e+00
-#>   6.0680e-01 -3.7622e+00  1.9647e+00  1.7416e+01 -1.8234e+01 -1.8430e+00
-#>  -4.6856e+00 -4.4744e+00  3.9488e+00  9.2486e+00  5.6437e+00  1.2768e+01
-#>   5.5231e+00 -5.7111e+00  9.3682e-01  5.9836e+00 -9.2493e+00  3.0670e+00
-#>   9.8058e+00 -2.6402e+00  1.1336e+01  6.2685e+00 -2.6886e+00  7.6441e+00
-#>   5.9363e+00 -6.5192e+00  7.8321e-01 -4.0427e-01 -1.3133e+00  3.0544e+00
-#>  -6.3091e+00  1.0040e+01 -1.2751e+01 -1.1534e+01 -5.8298e+00  2.7647e+00
-#>  -7.4592e+00  9.4103e+00  1.4476e-01  5.4149e+00  6.6634e+00  1.8745e+01
-#>   2.3166e+00 -2.2831e+00  2.4495e+00 -1.4437e+01 -4.8693e+00  4.2169e+00
-#>  -2.0170e+01 -3.3310e+00 -8.3002e+00 -5.6621e+00  2.9494e+00  1.0479e+01
-#>  -1.6103e+01  2.7127e+00  1.2102e+01  1.2768e+01  1.7274e+01 -4.4694e+00
-#>  -8.0425e+00  1.6162e+01 -6.0518e-02  1.0420e+01  4.4894e+00  6.6914e+00
-#> 
-#> Columns 31 to 36  3.6761e+00  1.0637e+01  1.4562e+01  5.9846e+00  4.0722e+00  9.0006e+00
-#>  -5.1966e+00  1.5071e+01 -3.9094e+00 -8.8287e+00  1.9831e+01  1.1739e+01
-#>  -1.7767e+00  5.8233e+00 -7.6486e+00  4.5091e+00  7.6294e+00 -5.1123e+00
-#>  -6.5812e-01  5.4449e+00  9.8120e+00  3.2020e+00 -5.4120e+00  8.7914e+00
-#>  -1.8320e+00  2.6157e+00  2.6970e+00 -2.7039e+00 -6.0996e+00 -3.1244e+00
-#>  -3.5625e+00 -4.4053e+00 -2.8956e+00  1.3660e+01 -5.8036e-01  1.0343e+00
-#>  -8.0378e+00 -1.0343e+01  1.2668e+01  3.7481e+00 -5.9861e+00  1.0574e+01
-#>  -5.5252e+00  6.8272e+00 -8.0569e-01  4.2449e+00  7.8909e+00  1.2782e+01
-#>  -5.3252e+00  7.3852e+00 -6.1989e+00  8.6960e+00  2.6933e+00 -1.5599e+01
-#>   1.6819e+01 -2.1453e-01  1.8835e+01  4.1777e+00  5.2485e+00  1.8756e+01
-#>   1.0849e+01 -3.0162e+00 -1.2584e+01 -1.1527e+00  1.0408e+01 -3.3177e+00
-#>   6.6933e-01  6.6165e+00 -7.3807e-01  7.7241e-01  7.5175e+00  8.1878e+00
-#>  -2.0470e+00  1.7072e+01  1.0649e+01 -1.3827e+01 -7.3002e+00  7.4366e+00
-#>  -1.2941e+01 -1.1538e+01  7.9025e+00 -1.5590e+00  4.0351e+00  4.5150e+00
-#>   1.2127e+01  9.3004e-01 -7.7179e+00  8.3346e+00 -8.8323e-01  3.2883e+00
-#>   1.3323e+01  4.5492e+00  4.5777e+00  3.5815e+00 -6.6529e-01 -8.3083e-01
-#>   6.3809e+00 -1.5644e+01  1.8017e+00 -5.0205e+00  4.5602e+00  1.4026e+01
-#>  -1.9529e+00  7.3174e+00 -2.8223e+00  8.9153e-01 -5.3674e+00  3.2901e+00
-#>  -1.2805e+01  2.5653e+00 -2.9729e-01  8.0468e+00 -4.1089e+00  1.0789e+01
-#>  -8.7715e-01  1.3078e+00 -1.4629e+01  8.8644e+00  1.8758e+01 -1.1461e+01
-#>   8.1886e+00  7.9778e+00  1.5112e+01 -6.0972e+00 -7.2719e+00  5.6152e+00
-#>  -1.0019e+01 -2.1547e+00  6.7985e+00 -1.1222e+00  3.7693e+00 -3.7409e+00
-#>  -1.3928e+01  1.2986e+00 -1.6339e+00  2.8692e+00 -1.0651e+01  1.3526e+01
-#>  -6.4898e+00  6.9418e+00  1.5826e+01  1.5640e+01 -1.0684e+01 -6.8203e+00
-#>   1.0347e+01 -1.3085e+01 -1.5079e+01  2.2178e+01 -1.2524e+01 -2.0429e+01
-#>   1.3768e+01  6.9692e+00 -7.8130e+00  3.4595e+00  6.7786e+00  1.5439e-01
-#>   1.0279e+01 -7.5763e+00 -6.7798e+00 -5.3758e-01  3.0871e+00 -5.8363e+00
-#>  -6.6974e+00  9.5524e+00 -5.5794e+00  4.1938e-01  1.8463e+00  4.4021e+00
-#>   3.0242e+00 -1.3227e+01  6.3768e+00 -3.2215e+00  2.1403e+00  1.8556e+00
-#>   1.7823e+01  5.1552e+00  3.1660e+00  1.7945e+00 -2.3683e+01  9.8308e-01
-#>  -1.4320e+01 -3.1440e-01 -9.9361e-01 -8.1371e+00  8.5137e+00 -2.5419e+00
-#>   1.5456e+01 -1.1247e+01  9.6495e+00 -1.7449e+01  1.5162e+00  3.9664e+00
-#>  -4.0748e+00  1.2323e+01  1.0367e+01  1.8966e+00 -6.5433e-01  9.7306e-01
-#> 
-#> Columns 37 to 42  6.3193e+00  4.1580e-01 -5.5026e+00 -5.2077e+00  3.8273e+00 -1.7859e+01
-#>   6.0152e+00  4.8147e+00 -3.1357e+00 -1.1225e+01  4.4666e-02 -2.4823e+00
-#>   1.6241e+01 -1.2788e+01 -7.8586e+00  4.2744e+00  8.6902e-01  1.9180e+00
-#>   4.2309e+00 -1.2683e+00  1.1844e+00  5.9364e-01  1.2986e+00  7.0099e+00
-#>   1.8031e+01 -1.3735e+01  3.0254e+00 -6.7109e+00 -8.0463e+00  4.5866e+00
-#>   1.2389e+01 -1.3599e+00 -1.3221e+01 -1.2302e+01 -1.9856e+01  4.3722e+00
-#>  -2.8307e+00  1.1498e+01  3.3417e+00 -2.7400e+00 -1.3726e+01  8.2211e+00
-#>   1.5877e-01  1.0815e+01  2.8379e+00  4.1172e+00  1.7293e+00 -2.6715e+01
-#>   5.9303e+00  1.5277e+01 -1.0294e+01  1.1066e+01  2.9799e+00  4.0886e+00
-#>   9.0479e+00  9.2719e+00  1.0262e+01 -6.9908e+00  7.9677e+00 -3.2507e+00
-#>   5.7520e+00  8.5322e+00  8.9157e+00  4.7481e+00 -7.1603e+00 -1.1803e+01
-#>  -2.4535e+00  2.2372e+00  4.7389e-01 -2.4764e-01  6.1815e+00 -6.0736e-01
-#>   2.7749e+00  9.9736e+00  8.4133e-01 -1.1915e+00  1.6670e+01  5.8009e+00
-#>  -1.0435e+01  1.1925e+01  6.6147e+00 -1.0507e+01 -2.8868e+00  5.8958e+00
-#>   4.8700e+00 -1.6851e+01  1.1281e+01  3.3564e+00  9.3250e+00 -8.3811e+00
-#>   4.7449e+00 -6.5033e+00 -9.0380e+00 -7.3736e+00 -1.5892e+00  5.9191e-02
-#>  -1.1771e+00  7.8254e-01 -4.0813e-01 -2.0650e+00 -2.8252e+01  2.5435e+00
-#>  -8.5939e+00  1.0028e+00  5.0527e+00 -2.2370e+00  7.3478e+00 -1.6387e-01
-#>  -3.6050e+00  1.1006e+01  2.0796e+01 -8.6269e+00  9.8430e+00  1.6179e+00
-#>  -2.2736e+00 -2.3937e+00 -1.7850e+00  1.6858e+00  3.9957e+00 -1.4926e+01
-#>  -6.3791e-01  8.6191e-01  1.3340e+01 -8.5667e+00 -2.6413e+00 -1.2077e+00
-#>  -1.3345e+00  9.3251e+00 -1.2439e+01  2.0178e+00  3.7639e+00 -8.5700e+00
-#>   4.7723e+00 -2.5637e-02 -4.7521e+00 -6.5413e+00 -1.6969e+01  7.9883e+00
-#>  -1.3204e+01  4.1165e+00 -1.6554e+01  8.3988e+00 -7.9562e+00 -3.6346e+00
-#>   4.0281e+00 -1.1415e+01 -1.5121e+01  1.3674e+01  9.2672e-01  7.8058e+00
-#>  -6.4940e+00  3.4464e+00  4.2818e+00  1.0922e+01  1.1316e+00 -3.7089e+00
-#>   5.0213e+00  9.4807e-01 -2.1677e+00  5.5849e+00  1.1946e+01  1.0366e+01
-#>   1.1274e+00  5.3332e+00  1.5231e+00  1.3328e+01 -1.3047e+01  8.8516e+00
-#>  -3.4887e+00 -7.2093e+00  1.3508e+01  3.4285e+00 -2.4273e+00 -2.3980e+00
-#>   1.1403e+01 -1.4971e+01  2.1474e+00  2.2323e+00  5.8092e+00  5.7479e+00
-#>  -3.5859e+00  1.2391e+01 -3.6762e+00  5.5437e+00 -1.0012e+01 -1.2154e+01
-#>   6.0459e+00 -9.5193e+00  1.4791e+01 -2.6113e+01  7.2202e+00  6.5619e+00
-#>  -3.8888e-01  8.4880e-01 -5.9651e+00 -1.3199e+01  1.5273e-01 -1.0037e+01
-#> 
-#> Columns 43 to 48  1.2266e+01 -2.8834e+01 -8.0280e+00 -2.0428e+00 -4.9001e+00  9.2407e-01
-#>   9.0277e+00  1.7013e+00  5.1573e+00 -5.6153e+00  2.1339e+00 -2.9950e+00
-#>   7.0761e+00  1.5237e-02 -1.3783e+01 -5.3301e-01  3.7376e+00  1.3496e+01
-#>  -1.1369e+01 -1.5329e+01 -7.7484e+00  5.5542e+00  1.0380e+01  5.9031e+00
-#>   1.7468e+00 -3.4204e-01  7.3675e+00  4.2309e+00  6.3147e+00  4.0213e+00
-#>   1.1571e+01  1.5676e+01 -6.5670e+00  6.0116e+00 -9.0544e+00 -1.0544e+01
-#>  -8.3220e+00 -1.5512e-01  1.5130e+01 -5.3928e+00 -4.0473e+00  1.3477e+00
-#>  -1.3731e+01 -9.9624e+00  1.8703e+00 -3.4599e+00 -1.0661e+01  6.7202e+00
-#>   9.7868e+00 -5.0907e+00 -1.8674e+01  2.8963e+00  2.7677e+00  1.6540e+01
-#>  -2.8286e+00  2.6729e+00 -7.6072e+00  9.1315e+00 -3.8775e+00 -7.6609e+00
-#>   1.5656e+00 -6.6805e+00  9.2606e+00  1.1768e+01 -1.4334e+01  2.7171e+00
-#>  -7.4580e-01 -3.4155e+00 -4.1006e+00  8.6670e+00  9.1879e-01 -1.4253e+00
-#>  -4.0258e+00 -1.1773e+01  4.4728e+00  8.1287e+00 -1.2450e+01  6.1224e+00
-#>  -4.0809e+00  1.3555e+01 -3.5414e+00  8.0674e+00 -5.2967e-03  6.7733e-01
-#>  -8.5613e-01  8.3502e+00  4.0190e-01  6.8232e+00  1.3272e+01 -1.0222e+01
-#>   6.6294e+00  3.8793e+00  6.6421e+00 -3.5255e+00  1.1354e+00 -1.6485e+01
-#>  -1.1456e+01 -6.4231e+00 -6.0706e+00 -1.6325e+00 -9.0896e+00 -3.1131e+00
-#>   6.9775e+00 -1.0652e+01  6.8487e+00 -4.7825e+00  9.5261e+00  5.9431e+00
-#>  -5.6337e+00  2.5851e+00 -1.9914e+01 -2.2390e+00 -8.6217e+00 -3.2185e+00
-#>  -3.1673e+00 -7.3470e+00 -8.4587e-01  1.4544e+01  4.8756e+00 -1.0150e+01
-#>  -1.8867e+01  1.3802e-01  2.3707e+00 -4.2196e+00  5.3611e+00 -7.3660e+00
-#>  -1.3701e+00  4.8789e-01  8.8058e+00 -1.4194e+01  5.8139e+00 -2.0546e+00
-#>  -1.4534e+01  3.2997e-04  8.3650e+00  4.9874e+00 -1.5715e+00 -2.5045e+00
-#>   7.4516e+00 -1.2611e+01  3.1515e-01 -3.5712e+00 -2.8321e+00 -1.1929e+01
-#>   1.7093e+01 -5.9745e+00  2.7888e+00  1.9365e+00  1.1775e+01 -1.8040e+01
-#>   2.6475e+00  1.1220e+01 -6.5698e+00  4.0266e+00 -2.2043e+00  3.9965e-01
-#>  -8.9387e+00  1.1510e+01 -1.3995e+01  6.5618e+00 -8.3189e+00  2.2141e+00
-#>  -4.2109e+00 -1.1936e+01 -7.9612e+00  5.2782e+00  5.8308e+00  1.4404e+01
-#>  -3.3679e+00 -5.2825e+00  9.1787e+00 -1.5533e+01  2.4056e+00 -6.0696e+00
-#>   1.0202e+01 -1.0092e+01  1.2604e+00  6.0711e+00 -6.1118e+00 -7.1285e+00
-#>   6.6391e+00 -1.0309e+01  9.7536e+00  9.0842e+00 -5.4748e+00  6.3011e+00
-#>  -1.3415e+01  3.2043e+00 -8.2222e+00 -7.4362e+00  7.4519e-01  1.9304e+00
-#>  -3.7220e+00 -4.5726e+00 -2.2351e+00 -1.9753e+00 -1.2845e+00 -7.2767e+00
-#> 
-#> Columns 49 to 54 -7.8283e+00  7.6700e+00 -6.4372e+00  6.7907e-01  7.5293e+00 -2.7664e+00
-#>   6.8952e+00 -1.7029e+00 -1.9042e-01  3.6731e+00  2.5942e-01  4.6866e+00
-#>  -1.9880e+00  5.6396e+00 -7.6415e+00 -3.5026e+00 -3.4724e-01  4.2378e+00
-#>  -2.9653e+00  3.0887e+00  4.5493e+00 -7.4611e+00 -1.0166e+01  9.6342e+00
-#>   6.3533e+00 -2.0191e+00 -3.0445e-01  6.5535e-01  6.8669e+00  2.7846e+00
-#>  -3.0310e+00  1.6618e+01 -1.5621e+01 -2.1432e+00  7.1358e+00  5.5269e+00
-#>   5.7609e+00 -4.8679e+00  7.3759e+00 -9.2898e-01  2.1645e+00  8.0198e-01
-#>  -1.5698e+00  9.1562e-01  4.2051e+00 -5.0667e+00 -6.5677e-02 -1.8864e+00
-#>  -1.6002e+01  4.7873e+00 -2.8521e+00 -6.0687e+00 -2.3322e+00  8.8416e-01
-#>   1.2803e+01  2.7640e+00  4.7702e+00 -3.9992e+00 -2.7644e+00  5.7228e+00
-#>   1.3082e+00  4.6939e+00 -4.8850e+00  1.8737e+00  7.4326e+00 -8.6145e+00
-#>   1.6210e+00 -7.7442e+00  7.6699e-01  3.9052e+00 -7.2906e-01  8.8425e+00
-#>  -3.1784e+00  1.6128e+01  7.4539e+00 -7.2732e+00 -2.3566e+00 -4.6841e-01
-#>   1.8711e+01 -2.0399e+01 -1.7557e+00 -6.4544e+00  7.8731e-01 -3.3787e+00
-#>  -3.0505e-01  1.3782e+00 -2.6054e-01  2.7923e+00 -9.2549e-01  1.0817e+01
-#>   1.4942e+00  7.8411e+00  1.3749e+00 -8.3084e+00 -1.3907e-01  7.3425e+00
-#>   3.9848e+00 -4.5565e+00 -1.3949e+01  4.5808e-01  4.6363e+00 -5.0544e-02
-#>   4.7378e-01  2.4725e+00  8.0652e-01  8.6825e+00  3.1991e-01 -3.7330e+00
-#>   2.5099e+00 -7.2661e+00  4.7621e+00  3.0879e+00 -5.1366e+00 -1.6244e+00
-#>  -1.0469e+01  7.9192e+00  2.0401e+00 -1.2957e+01  3.4234e+00 -2.3505e+00
-#>   6.6999e+00  7.5522e+00 -2.8123e+00 -3.0786e+00 -1.0235e+01 -2.7743e+00
-#>  -1.6822e+01 -1.2617e+01  9.4496e-01  2.7541e+00  1.7316e+00  6.2106e+00
-#>   1.0395e+01 -2.0956e+00 -1.2255e+01 -4.5416e-01 -3.9487e+00  4.3195e+00
-#>   4.6914e+00  3.1649e+00  4.1611e+00  6.1286e+00  5.0255e+00  7.1065e+00
-#>   7.5021e+00  6.8406e+00 -9.6136e+00  1.7223e+01 -1.3089e+01 -3.5942e+00
-#>  -5.5354e+00 -5.7653e+00  4.2610e+00  2.9217e+00  7.4059e+00  1.9449e+00
-#>  -3.5394e+00  8.6149e+00 -2.3641e+00 -5.7500e+00  1.5863e+00 -2.8873e+00
-#>  -1.4569e+01  4.0575e+00 -9.7085e+00 -3.8323e+00 -1.4526e+01 -3.2623e+00
-#>   4.0992e+00 -5.9664e+00 -5.2388e+00  7.7750e-01 -5.2460e+00  1.2358e+00
-#>   6.7283e+00 -1.1768e+01  4.6034e+00  1.5952e+01  1.1168e+00 -9.1776e-01
-#>  -3.8493e+00 -2.4761e+00  5.4825e-01 -8.8532e+00  1.0645e+00  9.2228e+00
-#>   1.3682e+00 -4.5516e+00  8.4050e-01  9.9312e-01 -2.7629e+00  3.6249e+00
-#>   1.8553e+01  3.0052e-01  3.0743e+00 -8.5578e+00  2.4675e+00 -3.0139e+00
-#> 
-#> (10,.,.) = 
-#>  Columns 1 to 8   4.1819  -2.7002 -18.1760  -1.1440  -2.9460   6.0683  -1.4644  -8.8791
-#>   -5.2517  -2.8884  -7.8021   1.1429   1.3565  18.4330  21.5672  -6.8964
-#>    7.6318  -6.9620   8.9294  -8.1504  -0.3623   0.9137   4.5002  -3.5007
-#>   -5.7043  -0.1398  -5.9344  14.1726   4.8862   3.5814   5.0882  -6.4967
-#>   -0.8601  -6.4114  11.4836  -7.3367  -7.5688   7.5060  -5.5756  -1.5425
-#>   -8.2925  -6.0803   6.6156 -13.9416   1.9437   3.9939   1.1387   7.1872
-#>    2.5452   2.3789  -6.3848   1.8546   1.7084 -12.1336  -3.9954  13.7758
-#>   -1.8303   3.3219   5.7164  -3.2491  -1.4877   9.1777   8.8536   5.8444
-#>    8.2554   5.8091  -6.1897  -8.2495   1.8647 -10.6258  -0.8845  -2.1385
-#>    1.7795   7.0254  -9.9362   6.2154  -3.4837  -0.7500  20.6577   2.2969
-#>    2.0167  -1.8900   0.5460  -1.2813  -1.4822  -3.7381  -4.6472   6.2117
-#>   -2.2446  -0.4886  -4.3657   6.1555 -10.2198   4.8470  -7.4376  -2.0771
-#>   -5.2713  -1.6133  -1.1467  -4.1871   5.4227   9.1644  -3.4754  -5.3385
-#>    0.3023  11.8181   1.3283 -12.7279  -7.5569  10.1936   6.1990  11.6441
-#>   -3.3104   3.9960  -2.4172   2.2504  -7.6548 -12.2355  10.1682   7.0920
-#>   -6.2455  -2.9252   1.0112  10.4484   1.2471  -0.9377 -12.7561  -5.3247
-#>   -3.9030  -9.2627   1.0247   1.9701  -8.9632   0.5715   2.5766   1.9814
-#>   -2.1366   5.8496  -6.1677   8.8440  -5.9129   0.9059  -8.7046  -2.5762
-#>    5.2249   5.9455  -8.3770  -7.0487   1.7812  -7.4599   0.4756  18.2719
-#>    2.1648  -0.6042   5.4310  -3.9076   1.8279   3.3609   8.3237   4.8889
-#>   -3.8500   8.1186   3.1748   0.4045  -3.5455   1.0117  -5.7007  -7.4015
-#>    2.6253  10.7632  -3.3398   4.9756 -20.4394   3.7262  11.1934  17.5135
-#>   -0.7312   4.0369   6.8090   3.6341  -0.8025   1.7529  -4.8715  10.0601
-#>   -1.1232  -2.8163  -2.6153   0.1292 -13.4160   3.9119  -7.7021  10.4302
-#>    2.2927  -2.3197   3.5505   8.4843   7.7590   6.3685  -6.6163 -17.7623
-#>    2.3017   5.6381   0.4991 -10.5783  -4.1324 -13.1374   5.4518   6.6804
-#>   -2.0478  -7.5091  11.3796  -7.9909  10.7743  -3.7296  -2.3580 -15.9901
-#>    4.0426  -9.7816   8.3604   2.4883   8.6378   9.9463  -3.2728  -0.6286
-#>    1.2885  -0.2170  12.5709   1.1797  -8.4373  -3.5454 -11.9339  -8.7778
-#>    5.4442   1.5278  -9.7229   1.7396  11.1748  -4.8533  -4.4837  -3.0608
-#>    3.1077  -2.4470   7.1603   5.6823 -21.7576   0.4642  -6.7589  10.2302
-#>   -4.3704   2.2297   0.9107 -17.3731   2.7938  -0.4466  -1.6011   1.0906
-#>   -1.2622  -2.4540  -1.1643  -8.5674   9.3976   2.6191 -14.8639   1.3717
-#> 
-#> Columns 9 to 16   1.8556   0.0946   0.1899  11.7107  -5.4183  11.5789   9.7279  -2.8268
-#>   -3.3962   4.7751 -14.7356  11.8734   2.1015   2.1329   2.1923   3.2486
-#>    1.4306   0.3409  -9.0292  -0.4911  -3.6641  -0.5287  18.8181  -1.0346
-#>   -9.8678  -5.0219  15.4837 -13.4747  -5.8500  17.1277  -0.1490   2.6720
-#>   -6.8133  -7.6474   5.9233  -0.4694  -1.0901  -7.4239  10.4323   3.4986
-#>   -5.5519  -0.0618  11.3379   8.0623  -3.5955   1.7862   7.5723   2.4541
-#>    1.3072   0.6549   6.0913 -18.9477   1.6956   7.7507   1.1176  -0.0589
-#>    6.1180   3.5580   2.0954   3.3366   4.7224  -3.3512   9.1941  -3.5992
-#>   -8.3560  -4.7540   1.6382 -13.4815   3.3727  -1.9814  -0.0188  -3.9449
-#>    4.9662 -12.4013  19.1299 -15.2008   8.3711   3.6924   4.5674  -2.7297
-#>    0.6927  -5.4917  -5.8683  -2.1278  -9.2548  -8.4762   9.8112  -6.1467
-#>    0.5953  -0.1684  -3.7758  -3.3063   3.1845 -12.9019  -5.7863  -2.8776
-#>   -9.7528  18.2416  19.8239   8.8325 -16.4710 -13.3338   4.2949   7.3125
-#>   -3.6096  -7.3529   4.0815  -0.1361   4.7710  -6.8037 -22.0151  11.4496
-#>   13.0295 -16.0956  20.0523   0.4107  -3.8567   6.1604 -10.1262 -17.2949
-#>    3.1475  -0.2741  12.1048  17.4119   4.1416  -7.9165  10.1866   7.0605
-#>    7.9490   6.5653  -0.1566   6.7108   8.9105   4.9730 -11.4455   1.9475
-#>   -7.0497  -1.4302  -0.2031  -0.7717  10.2553  -5.8846 -12.2485   5.1642
-#>   -1.4445  -1.0975   4.7951 -10.3304   3.8726 -11.5951  18.1794  -1.8722
-#>   -3.5374   1.9384   7.5930  -1.7373  -1.3728   0.1740   9.8508 -17.4885
-#>    5.2514   1.7544   7.5342  21.3597   2.5470 -13.2217  -2.4640  15.8839
-#>   15.8121   1.9745 -12.0076   9.3390 -10.4766 -11.5816   5.4715  15.8169
-#>    2.7863   4.0846   3.5614  11.5852  13.7292  -3.9139  -1.5637  -4.1002
-#>   -8.6593   5.3422  -7.6377  -6.7198   2.7386  -2.0494  -7.7664   1.0160
-#>   -7.9545  11.3175   5.0165   2.6514  -4.5948   4.2351  -5.6257  -0.6661
-#>    2.0352   8.5044 -11.0207   3.9361  -3.1783  -5.0761 -10.5843  -6.2035
-#>   -9.2982   3.4657  -8.0456   1.1406  -5.7957  -0.0046  -2.1324   8.1229
-#>  -16.6553  10.8810  -0.9489  13.3421  -8.0725   9.1832  -4.2330   7.3050
-#>   -8.2410   7.6877   4.3861 -15.3545  -0.3129   0.7203   1.5005   6.7024
-#>   -6.5903  -1.3819  -6.9875  -4.7477   4.7515   0.0780 -13.0780 -12.4752
-#>    0.5467   0.2573 -11.7447  11.3582   0.7454 -11.0607  -4.9295   6.2202
-#>    0.9685   0.8027  11.2603   1.4244 -10.0601  -9.5701  14.1312  11.6304
-#>  -11.8415   2.6648   0.9239  -9.2368  -4.5917  12.4541   8.8730   4.3792
-#> 
-#> Columns 17 to 24  17.7241  16.4431  -4.4976   4.1706 -12.8776 -15.2067 -10.9706   4.8594
-#>   -2.4854   4.0536   0.9395   0.8457  -2.1911  -0.2284   2.7090  -5.2006
-#>   -1.5690   2.2409  15.2344   2.3106 -15.5675   4.6727 -18.2378  -8.5262
-#>  -19.8752   9.8127 -10.5200   8.9852 -14.8782  -3.1370   4.1668 -10.8551
-#>   -3.6712 -12.0466   7.5911   7.8817 -14.2461  -1.8762   6.1248  -4.2913
-#>    7.4452  -0.9572  -3.7197   1.3354   6.7883 -13.2608 -13.4355  14.7262
-#>   10.5368  -0.6395  15.0316   7.2175  -6.9461 -20.6147   1.1905  13.3449
-#>    9.9587  17.1341 -11.5921  -1.7717 -12.4661   6.2366  -9.3802  -1.1792
-#>    0.3752   8.5826 -13.2740  -0.0306  13.3570  17.4113 -12.8242   9.3084
-#>  -11.8180   9.5951   6.7905  12.8367  -8.0408 -15.6782   6.5608 -18.7112
-#>   22.3179  -7.4422   1.5256  11.6816   5.9146 -10.3219  -3.7901   7.5836
-#>    3.8820  -7.6058   8.6871  -4.0585   8.9200  -4.1044  -1.5731  -3.6648
-#>   -1.7718  -1.6068  -6.4439  11.8292  -2.6022 -16.4736 -11.8727  11.3917
-#>  -17.9264  -4.7390   6.3397  -8.9043   8.8302 -16.6837   2.7382 -14.5637
-#>   -1.9014   7.9378   1.7300   0.9099  -7.4772  -0.5940  -6.2970 -17.5786
-#>    1.4715  -0.3907  -9.3192  10.1859  11.9984 -14.9277  12.3849  17.7064
-#>    2.3705 -17.2180   4.6233   4.2500  -4.6967 -11.6569  -5.3638 -11.5411
-#>   -1.3751  12.4701  -7.0619   4.3700  -0.2691  -0.2517   6.5075   2.4046
-#>    9.9320  -0.5399   3.7089  -8.3076   4.6503  -6.4728 -24.8052  24.1327
-#>    1.9584   7.0046   1.5319  -5.6831 -14.7582   1.4350  -3.2149  16.2855
-#>   12.0401  -0.7776   3.8262  -6.7161   0.6593   2.0989  -4.3371  12.7094
-#>  -19.7485   3.2476 -10.9042  14.1623   8.4786   7.8983   0.4614 -12.9178
-#>   -7.0698   1.4244 -11.8563   1.1273  15.1136  10.7658   2.1623  -7.2914
-#>    4.7051   3.1767  -8.4571  -4.8364  14.5448   8.8465  -1.5965   9.6834
-#>   -4.4673  -7.8511   0.8354   5.9707   3.3181  12.2863  11.1134 -10.8450
-#>   -7.2237   0.3425  17.3627 -14.8729   2.3539 -13.4249   3.3829   4.3209
-#>    1.4075  -4.3673   1.7913  -1.9425  10.2997  -7.9903   1.3192   5.3896
-#>   19.7331  -9.6629  -7.2495  -9.8141   5.0245   8.9532 -18.5641   8.1088
-#>   -7.1797  -1.4011  -7.3396   2.2222  -7.3143   7.9236   2.3504 -12.2137
-#>    6.8541   2.4263   0.0412   8.6535  -3.8287   4.7261   8.2146  -1.9722
-#>    0.9613   2.0930  -8.1847   8.7364  11.5909   4.4428 -15.8480   2.7165
-#>  -14.3310   3.1957  13.1065  11.7513  -4.6397   4.3785 -23.1525   2.6458
-#>    8.8166   9.1156   0.1519  -5.9758   6.7014  -3.0737 -10.1020   1.6576
-#> 
-#> Columns 25 to 32  10.5292  -3.9645  12.8190   2.1494  10.7930  11.1448   9.9790  12.4497
-#>   11.4912  12.2822  -0.5843  -8.5035 -10.4811   7.8215   5.3842   1.7837
-#>    9.6353  14.6377   3.8013  14.2778  -5.8059  19.4480 -11.1792   9.9659
-#>    1.2615 -12.5058  -1.1939  -1.1402 -15.7723   5.3684   1.1663  -5.7907
-#>    2.3630  -5.0501  13.3084  18.5605   1.1618   8.6902  -2.5698  -1.1850
-#>  -13.9340   4.3304   5.8433  -7.3367   4.6003  -6.4036   4.8493   5.0689
-#>   -1.3893  14.5972  16.2413  14.9188  -0.5374  -7.7302  -0.7117  -2.1723
-#>    1.4423   2.1201   1.2344  -0.5692  14.9963   3.6928  -3.9090   0.6049
-#>    0.7095  -7.0357  -2.1930  -3.7832  -5.9221  -8.0483  -6.4077   2.4676
-#>   -1.5388   0.4704  12.8991  -6.0182  14.1894  -0.9701   9.0996 -16.5178
-#>  -10.3212 -10.7765   4.8461   4.5388   4.3799 -10.6147   0.8329  -6.4080
-#>    7.8106   2.1017  -9.8434  -0.4331   5.9101  -3.3945  -2.3179  -2.2783
-#>  -13.8799   0.6262  -4.3279   3.8890  -3.8753  -8.0789  -0.7328  -4.0891
-#>   14.3206   7.7849   3.6460  -4.0085  -7.5287 -11.1466   4.8729 -11.5882
-#>   -0.0881  -2.9461   2.1459  11.0899   3.5483  -0.2515   2.0340   1.7700
-#>    9.2189 -13.4063  -2.4231   3.0836   5.5822   2.4684   2.2476   1.8075
-#>   -2.4374   2.6486  -5.4516   1.9154   5.0113 -13.6252   9.9636 -11.3138
-#>    2.4418  -4.0569  -8.3882 -19.7733  -2.0071   4.8646   2.7268   6.5430
-#>   -5.4768  -4.4051  17.2406   1.0203   7.2529 -12.8022   1.9017   4.4126
-#>    5.6344   7.9751   3.8124  -4.3246   6.1148  15.3043 -18.8127  10.1895
-#>    1.9013   4.9514   4.0854  -3.0271  19.8358  -0.8026  14.0764  -0.8553
-#>   -2.2414 -11.0523  18.3026  -1.5447   0.4390  -9.4300   0.1453  -6.5077
-#>  -12.4334   6.4966 -14.2762  13.4793   6.2123   0.7264  -8.5034  -7.8488
-#>    3.6735 -10.9852  12.3695  -3.4940   3.7098 -15.8378  -2.1686  -8.8639
-#>   -6.1870   6.3384 -17.9945   4.0391   5.4411   3.9668 -13.1839   6.2893
-#>  -12.5335  10.5731  -9.3001  -6.2328 -10.8204   4.7457   3.1881   6.8758
-#>   -9.5590   4.8932  -1.7767  -4.7540  -8.1867   0.3086  12.7548 -12.7002
-#>    3.6840 -12.3161  -6.0951  -1.2251   4.7612  -1.1004  -1.5123  -2.2366
-#>   -3.0114  -9.8084  -9.4691   0.2194  -9.6904 -13.4815  -1.6036  -6.8736
-#>  -13.0158 -10.7943  -6.4755   4.5214   0.5924  -7.7346   8.6752  10.1047
-#>    7.4194 -13.7749   5.0595   5.8050   5.8419  -0.0406   9.7345  -1.6591
-#>  -12.7815   0.2335   2.2811  -2.3682  -7.0829 -12.3729  -3.2147   1.8516
-#>    8.9504  -2.9962   8.6639  -3.1886  -5.3588   5.4321  -9.9454  -5.3727
-#> 
-#> Columns 33 to 40  -2.6904  -3.1793  -3.9982   2.2110 -15.0660  -7.8316  -4.5374  -3.6815
-#>    6.3429  -0.5526 -12.4770  10.9454  10.4932   2.0863   7.1767 -15.6242
-#>    9.9699  -2.9062  -7.5707  -4.7522  -8.1896  -2.9347  -0.8152  -4.8127
-#>  -12.2653  11.1087   0.8183  -3.0673   5.4707  -7.3395   8.2259   0.0790
-#>   10.7333  11.7357  -0.5174  -2.7349 -11.8983   4.5970   4.7187   7.6155
-#>    0.8824  -0.0948   2.7482 -16.1985  -3.4809 -13.0749  -5.6538   1.3078
-#>   -4.6005  10.9233  -8.3767 -23.2996  -9.9544   4.9645   6.6196  -1.6382
-#>   -4.8016   1.2827   2.5463  -9.7602 -11.3491  -6.9045   8.6490  -3.3517
-#>   10.8844  -4.3575   4.9099  25.9703  -7.6978  -8.9655 -17.4221   1.0042
-#>   -4.2847   2.7185  -2.9281  -5.9636   7.4574   0.7742  -1.9685   3.4123
-#>    9.2045   5.2980  15.7517  -5.8331 -15.0311   7.7835  -9.3464  -1.9853
-#>    6.4127  -4.6117  19.0878  -3.0422  -1.5535  -2.1965  -1.4020   1.2183
-#>  -11.4244   7.5634   8.4587  -1.8272   3.1815  11.3702   1.6303   8.1204
-#>    1.2992  -2.9209  -2.0812  11.5782  22.2623   7.6336  -5.1908   5.6859
-#>   -2.1399   3.7254  -9.4623  -7.0854  -2.7738  12.2496   1.6377   2.5113
-#>  -12.5850  -4.9262   3.3072  -5.5994   4.4245  -1.8885  -0.8919 -10.0518
-#>   13.9544  -2.2759   4.0960 -12.4514 -14.1780  -0.7708  -2.8591   2.4331
-#>   -0.4444  -4.2921   7.0387   6.5836   9.4148 -14.9592   4.8886   7.0435
-#>  -19.0743  -3.3144 -13.8473  -2.4842  -1.5997  -0.7481  15.9668   0.6583
-#>  -10.1261  -2.2332  -7.1975  -2.3450   9.0592  10.7116  -0.8987  -8.9685
-#>  -11.9545  -8.5190   4.8090  13.1186  10.6559  -4.5238  -2.1798  -8.8657
-#>   -1.9783   8.6018   3.2554 -16.5278 -14.0102  -3.3953   9.9409   3.1031
-#>  -13.6221  -1.6317  17.8017 -13.5058  -0.0398 -20.7438  -2.9618   4.1430
-#>    0.8359  -2.0096  -1.9416   4.9894   4.1868  -2.3571   5.3722   7.2971
-#>    2.0086 -12.1338  17.6228 -10.7163   5.5852  -7.9251   1.4832 -14.7635
-#>    5.7109   5.6109  -7.2849   1.9554   5.1607   2.8878   1.7395   1.9746
-#>    1.1878  -8.7317  -0.0576  13.3530   1.2356   4.8184 -11.7238  -1.7304
-#>   -5.7051  -4.8007  10.1910  18.0028 -12.7282 -13.6544   2.4402  -9.5052
-#>   -2.6643  14.4130  10.9647  -9.3912   3.2169  -7.6773 -12.9542   0.5395
-#>   -7.9634  -6.3668  -8.8339   3.0626   3.5328  -5.4092   6.0550  -2.6830
-#>   -5.5458   1.8084   3.9153   6.7767   5.4337 -10.1347  -0.7966   9.2530
-#>    5.2944 -15.6789  16.6287   0.8781  -8.6488  12.3779 -11.0757  -2.0095
-#>   -1.9817   1.1022   3.5618  -4.4506   0.1626  -8.8249  -2.2472  -2.0069
-#> 
-#> Columns 41 to 48  17.5704   3.3890 -14.7766   8.8433  -8.1115  18.5641   5.5488 -15.7888
-#>    1.3682  -0.3895 -13.8270 -15.9155  -4.3090  -8.9518  10.9437 -12.4455
-#>   17.5254   1.6004   1.1251  -3.3617   6.1294  14.9661 -12.5143  14.6155
-#>    2.5869  11.4185  -0.2229 -15.8317  16.1505  20.4406 -10.4021   4.9527
-#>   -4.8332   2.2539   2.9690 -19.8020  -0.6848  10.3750  -3.9140  -7.7243
-#>    9.0005  -4.9784 -14.8227  -5.6539 -22.5182   3.3849  -6.7069  -9.1133
-#>  -13.7044   8.7026  15.7930 -21.8754  14.5765  -6.7201  -2.8081   2.8092
-#>   -8.3891  -2.3845   8.5504  12.8548   1.4612  -3.3912 -12.1353   5.9169
-#>   -0.5118   0.2147   5.5487  -9.9571   7.8578  31.2956  -7.6141   6.0582
-#>   -4.1179  17.5565  -1.9947   7.3969   1.8679  -1.0288 -10.8080  -6.1185
-#>  -12.9663  -2.5697  12.2865  13.0955  -2.8212 -13.4418  -3.3537  11.4065
-#>    0.6306  -6.9157 -22.3353   7.5794   5.3888  -1.9895   6.8287 -15.9672
-#>   -3.7175  -7.1140  -7.8213   0.2052   7.8420  23.6224  -1.2615   5.3317
-#>   -2.8885   7.7248   5.9880 -12.1741  -8.5161   3.1150  20.8556  -8.3987
-#>    2.4623   2.5093   8.3615  -4.6639   6.6286 -13.4688  -6.7489   9.2497
-#>   -9.4874   0.9945   5.8155  -4.5071  -3.0293 -14.6485   0.9375  -9.3388
-#>    5.6274  -2.6494   2.2440   6.4652  -6.9652  -8.7051  -4.4503 -11.7992
-#>   -7.1215 -11.7251  -0.0189  -7.7000  20.6908  -9.6292  17.5161   0.8517
-#>   -1.1748  16.3685  17.1124  -0.1526   9.9417 -15.2438  -2.5329  -3.7418
-#>    9.6564   9.9983 -10.8354   6.3171   5.9041  -1.7046 -10.1057   1.9377
-#>   -8.3920  -5.5874  -3.4125   9.7132  11.8479   8.2080   4.9230 -10.1816
-#>    9.3801   3.2812   4.9593   2.1379 -10.3081  -8.9848 -15.6181  -5.7878
-#>    1.7352  14.5038  -3.1819  -9.0198   9.3146   9.1462 -12.0767  -4.1429
-#>   -7.9687   1.5227 -10.3528  -6.1670 -12.7228  -8.4186   9.4608  -7.8613
-#>   -0.3986  -0.5350 -20.1001   7.9296   2.2427  14.5613  16.7416  -7.5759
-#>   -4.4657   1.9143  -2.0119   9.2431  15.5990  -9.8887  -2.0631  -5.6964
-#>   -4.3807   2.3498   2.3197   2.3938  -1.6953   5.2110   4.9993   2.3942
-#>    8.3853 -10.0567  17.5188  -0.2648   6.5264  23.4170   0.1300   4.5117
-#>  -16.0887   2.2805  16.1597  -5.0918  14.6784   0.1509  17.1091  10.7455
-#>   -5.0488  -1.5862   3.4866  -7.5867  12.9261   1.0782  -9.2606 -10.5018
-#>    4.2590  -8.3886  12.6921  14.9392 -12.0440  -5.4093 -14.8714   9.9903
-#>   10.3134   7.1416  -2.7204  -8.9261  -3.6273  29.5756  -9.7316  12.0568
-#>   -4.3794 -11.0773  10.8934  -7.9329   5.4392  -4.8761   4.0482   7.3628
-#> 
-#> Columns 49 to 54 -14.5902  -0.2382   4.3159  -2.6877   5.0703   2.4193
-#>   -7.1263  -7.5356  -5.7498   7.8019  -0.7866  -2.3049
-#>  -12.3100  -1.9089   0.3939   9.3053  -5.7823   1.2514
-#>  -10.8058   5.7169   6.6433  11.4855  -1.1410  -4.5655
-#>    8.8070   5.1104   8.8327  12.1930   5.0258   5.7235
-#>  -25.6938  -3.5064  -2.6577 -15.0395  -2.9256  -3.4516
-#>   -6.6522  -0.1651 -11.4028  -6.1774   2.9720  -3.9651
-#>  -11.4142 -13.1631  14.7867   5.8335   5.6973   0.3327
-#>  -24.5765   5.7319  12.5413  -2.6588   1.5041  -1.9145
-#>    6.3423  -8.7484  -4.6776   1.1261  -4.7972   0.9373
-#>   13.7444  -5.1163  -2.2804  -7.3522   3.7646   6.7636
-#>   -6.6618  -3.2632  10.3643   0.2059  -1.5956   2.7000
-#>  -20.9447   4.3667  -0.4386 -11.0839   1.1866   3.1471
-#>   10.5578  -6.5905  -5.1642   6.4413   0.7218  -5.2311
-#>    1.3020   9.0326   7.8362   0.2074  -1.2691  -3.3912
-#>    0.0193  11.9974  -4.4291 -10.2958 -13.7476  -0.6264
-#>   -7.6380   2.1646 -13.6631  -4.7001   5.4647  -4.1669
-#>    5.9041   4.0855  -2.6119   2.6817   6.5380  -0.4950
-#>    6.0665   3.0488   7.5467 -16.5522   1.2797  -2.5643
-#>    2.4455  -9.6235   7.8516  -1.5448   2.5017   3.9083
-#>   10.5918  10.5633   4.1331   3.3853  -0.5743   0.0424
-#>    6.7426  -6.8340  -1.2416  -1.1304 -13.0831  -2.6119
-#>   -9.2593  -1.9201  -3.0986  14.1559  -0.0596 -11.1140
-#>  -11.5428   2.5954  10.6733  -4.6600  -3.8252  -3.4938
-#>    1.3032  -0.2393  -5.7651   3.2347   1.5240  -3.7687
-#>    4.9202  -6.3740 -17.5235   4.7132   2.0002  -0.2020
-#>   11.3402   5.2820 -17.9162  -0.2594  -7.4186  -2.8355
-#>  -19.4105   5.8406   7.7503  -7.5794   1.5874  -2.1943
-#>    1.6365  -4.2029  -6.0806   1.8311  -1.0763   0.0894
-#>   17.7446  21.0740  -0.0280 -10.0488   5.2612   4.8506
-#>   -4.6811   9.2956  18.5009  -6.1713  -1.4305   0.4427
-#>  -10.9019   7.2243 -14.8779  -8.3666  -7.9722   0.3536
-#>   -8.8841   2.5430  -2.5944   1.4092  -9.3180  -2.0870
-#> 
-#> (11,.,.) = 
-#>  Columns 1 to 6 -3.7849e+00 -5.0196e+00  2.1432e+00  1.7021e+00  2.1228e+00 -4.3782e+00
-#>   4.1938e+00 -3.9595e+00  4.0635e+00 -5.5167e+00  5.6394e+00  4.2589e+00
-#>  -2.8545e+00  1.2807e+00 -6.3746e+00 -3.4508e+00  1.5402e+01  5.1446e+00
-#>   2.8870e+00  3.8449e+00  9.1714e+00  6.2121e+00  8.3932e+00  8.3983e+00
-#>  -2.2077e+00  8.2990e+00 -1.5067e+00 -5.9537e+00 -1.3866e+01 -6.3206e+00
-#>  -3.9909e-01  4.0141e+00 -1.2573e+01  8.3191e+00  2.9710e+00 -2.4224e+00
-#>  -1.8828e+00 -2.7120e+00  4.2654e+00 -1.2382e+00 -1.4503e+00  5.6398e+00
-#>   3.4461e+00 -6.0116e+00  2.6112e+00 -4.3507e+00 -6.3859e+00 -4.9195e+00
-#>  -1.6851e+00 -9.8991e+00  5.0588e+00 -2.4422e+00  6.6687e+00  1.7778e+00
-#>   4.5697e-01 -7.3823e-02  4.4258e+00 -4.7368e-01  1.0315e+00  3.9583e+00
-#>   1.1417e+00 -1.6804e+00 -2.8100e+00 -2.1802e+00 -3.9562e+00 -1.0400e+01
-#>  -5.8564e-01  1.0935e+01  2.6781e+00  4.0670e+00  8.1068e-01 -3.8475e+00
-#>   4.1610e+00 -5.0571e-01  2.9245e+00 -7.9322e+00 -1.6292e+01 -3.1278e+00
-#>  -1.4342e+00 -2.3585e+00  4.6606e+00  1.9605e+01  2.9582e+00  1.8324e+01
-#>  -5.5404e-01  7.1800e-01 -1.4267e+01  4.0446e+00  5.6713e+00 -1.4284e+01
-#>   4.1463e+00  7.7104e+00 -1.1590e+01 -4.9941e+00  6.1806e+00 -1.0090e+01
-#>  -2.4521e-01  8.6493e+00  3.0875e+00  1.5020e+00  7.0473e+00 -7.6942e-01
-#>  -1.9347e+00 -1.0450e+01  3.0863e+00  2.8111e+00  1.0976e+00  1.5948e+01
-#>  -2.6669e+00 -1.8782e+00  2.9217e+00  1.3300e+01 -1.6403e+00 -8.4687e+00
-#>   1.7010e+00  6.6925e-01 -2.5950e+00  1.3026e+00  8.2395e+00  4.8721e+00
-#>   2.0192e+00  2.7153e+00  2.1160e+00 -7.7253e+00 -8.3825e+00 -3.1612e+00
-#>   3.9173e+00 -6.8199e+00  1.5681e+01  8.2122e+00  2.8327e-01 -9.1162e+00
-#>   1.6034e+00  3.3712e+00  1.5284e+00  1.3668e+00  5.2616e+00  4.2150e+00
-#>   9.3535e+00  5.1441e-01 -5.8771e+00  6.4237e+00 -7.5995e+00 -1.9427e+01
-#>   1.0015e-02  5.9925e+00 -4.5338e+00 -4.7200e+00  5.7863e+00  3.5718e+00
-#>  -3.8353e-01  1.7491e+00 -1.1367e+01  1.3499e+00 -7.0512e+00  1.4846e+00
-#>  -6.6765e-01  6.4164e+00 -7.2331e+00  8.6983e+00 -4.7788e+00  9.2212e+00
-#>  -3.8685e+00  3.6740e+00  4.8216e+00  1.3614e+01  2.4227e-01  1.1748e+00
-#>  -1.2426e+00 -6.0361e+00 -5.0742e+00 -1.0331e+01 -1.2833e+01  7.2044e+00
-#>   8.5433e-01  3.0204e+00  6.8311e-01 -1.3152e+00 -1.2573e+00  1.1791e+00
-#>   1.0658e+00 -1.8278e+00  2.1841e+00  1.3825e+00  5.6781e+00 -1.4014e+01
-#>  -3.4659e+00 -2.0254e-02 -6.6509e+00 -2.2007e+00 -1.0925e+01 -3.6717e+00
-#>   3.3764e+00  2.2750e+00 -8.4900e+00  7.7153e+00 -4.4704e+00 -1.6245e+00
-#> 
-#> Columns 7 to 12  6.0973e-01 -1.4816e+01 -1.6339e+01 -3.2286e+00  4.9790e+00  3.0585e+00
-#>   6.8249e+00  3.8479e+00  1.2093e+00 -4.8146e+00 -1.4413e+01 -2.8921e+00
-#>  -1.7814e+00  6.0191e+00 -1.1324e+01  5.8392e+00 -5.0403e+00  3.6101e+00
-#>   4.6124e+00  2.8248e+00  9.4244e-01  2.1540e+00  5.7656e+00  1.0509e+01
-#>   1.6468e+00  8.3349e+00  1.0383e+01  1.0796e+01 -1.1249e+01  4.1644e+00
-#>  -1.0202e+01 -1.2100e+00 -1.3321e+01 -1.6383e+01 -1.6935e+01 -4.5484e+00
-#>   8.5930e-01 -5.8933e+00  7.5169e+00  5.9695e+00  1.0066e+00 -6.7549e+00
-#>   2.3309e+00 -4.0142e+00 -2.3207e+00 -2.2595e-01  3.0484e+00 -2.7808e+00
-#>   2.0428e+00 -6.2656e+00 -7.2521e+00 -5.2489e+00  9.3829e+00  1.3080e+00
-#>  -6.5883e-01 -4.0317e-01 -1.2260e+00 -1.4979e+01  2.1824e+00  2.7155e+00
-#>   7.3020e+00  9.7149e-01 -1.2680e+01  9.1245e+00  1.1231e-01 -1.4746e+01
-#>  -8.0618e+00 -2.8863e+00 -1.4427e+01 -2.7446e+00  3.0282e+00 -7.5802e+00
-#>   7.9672e+00 -3.7802e+00 -5.4363e+00  7.7521e+00 -1.0090e+01 -7.8178e+00
-#>   1.2089e+01  5.3239e+00  4.9814e+00 -5.6864e+00 -3.6484e+00 -4.6010e+00
-#>  -2.8823e+00 -1.1789e+01 -1.1138e+01 -1.0029e+01  1.0326e+01 -1.0063e+01
-#>  -5.3812e+00  3.2276e+00 -3.6145e+00 -3.5016e+00 -4.2648e+00 -4.8598e+00
-#>  -8.2244e+00  2.2045e+00 -5.3808e+00 -9.6569e+00  1.8693e+00 -3.7419e+00
-#>   4.7899e+00  4.2961e+00 -9.5148e-01  1.1050e+01  2.0492e+00 -6.1241e+00
-#>  -9.6661e+00 -1.4662e+00 -1.4761e+01  4.7909e+00  9.8864e+00  5.9882e+00
-#>   1.0315e+01 -3.5138e+00 -1.0110e+01  8.8079e+00  6.5133e+00 -5.1630e+00
-#>  -1.4707e+01 -1.8352e+00  8.1046e+00  3.2276e+00  5.4078e+00  1.4720e+00
-#>   4.0316e+00 -4.7272e+00  2.2854e+00 -1.0523e+01 -9.7445e-01 -2.0957e+00
-#>  -8.9254e-01  4.7154e-01 -3.2894e+00 -4.5754e+00 -3.8137e+00 -7.3894e+00
-#>  -2.3613e+00 -1.3534e+00  1.3022e+01  4.2130e+00  3.0146e+00  1.2287e+00
-#>   6.0422e+00 -2.9257e+00 -8.1346e+00 -5.4069e+00 -6.5148e+00  1.1687e+01
-#>  -4.2277e+00  6.4720e+00 -2.5719e+00 -5.1238e+00 -6.9550e+00 -1.9845e-01
-#>  -4.8493e+00 -3.1173e+00 -3.0814e+00  4.2641e-01 -2.9877e+00  4.9414e+00
-#>  -6.0841e+00 -4.0842e+00 -1.1773e+01  4.2059e-01 -8.0204e+00  2.0235e-01
-#>   3.8129e+00 -5.0090e+00  8.0143e+00  7.2605e+00 -8.3539e+00  1.9997e-01
-#>  -5.2110e+00 -2.1361e+00  5.1565e+00  5.2963e+00  7.4855e+00  8.4635e+00
-#>  -6.7656e+00  1.0277e+01  3.9130e-01  1.8962e+00  5.5895e+00 -7.6272e+00
-#>   4.1073e+00  4.8892e+00  1.0822e+00 -4.1000e+00 -1.1186e+01  1.5630e+00
-#>   2.1621e+00 -1.8975e+00 -1.0544e+00  7.2115e+00 -5.1892e+00  1.8350e+00
-#> 
-#> Columns 13 to 18  1.2055e+01 -1.5823e+00 -4.0961e+00  4.4132e+00  1.6397e+01 -7.4848e+00
-#>  -8.9138e+00 -1.2013e+00  8.9636e-01 -2.3079e+00 -9.8273e+00 -3.5474e+00
-#>  -4.1584e+00  9.4283e+00  3.3601e+00  1.1121e+01 -2.0212e+00 -7.8219e+00
-#>  -5.1769e+00  3.6503e+00 -1.1306e+01  4.6026e+00 -6.1186e+00  4.3157e+00
-#>   7.2794e-02 -3.5959e+00 -6.4562e+00 -4.8976e+00 -8.0606e+00 -6.6448e+00
-#>  -2.9705e+00 -1.6580e+00  9.4389e+00 -1.0084e+00 -1.6057e+00  5.3935e+00
-#>   1.2491e+01 -4.7120e+00 -5.2667e+00  1.6226e+01  3.9891e+00 -1.4046e+01
-#>   1.3069e+01 -3.1612e+00 -9.8630e+00 -2.3612e+00  1.6367e+00  2.9848e+00
-#>   2.8621e+00  1.2066e+01 -8.5787e+00  1.0951e+01 -5.8907e+00  8.6545e+00
-#>  -3.6769e+00  8.8309e+00 -4.3361e+00 -2.2998e+00 -5.8985e+00  1.7773e+00
-#>   5.7171e-01  4.1866e+00 -3.4568e+00  2.9769e-01  1.8227e+01  2.3260e+00
-#>  -4.9518e+00 -4.5572e+00 -1.9396e+00 -3.8327e+00 -4.9439e+00  1.4059e+01
-#>  -1.8056e+00 -1.0121e+01 -1.8802e+01 -3.8292e+00  1.4599e+01  7.9980e+00
-#>  -5.1516e+00 -6.4763e-01 -1.7462e+00 -1.9015e+01  5.6483e+00 -3.0024e-01
-#>  -1.0154e+01 -8.1130e+00  3.3937e+00  6.0944e-01 -1.4344e+01  9.7953e+00
-#>  -4.8702e+00  2.4856e+00  7.8282e+00  3.4010e-01 -1.4870e+01  1.3035e+00
-#>  -1.1545e+00 -3.1283e+00  5.7233e+00  2.2559e+00 -1.8827e+00 -2.2008e+00
-#>   2.7410e+00 -2.7028e+00  4.8085e+00 -5.9716e+00 -6.9213e+00 -6.9172e-01
-#>   1.5735e+01  4.2947e+00 -3.2594e+00  7.8355e-01 -4.0335e-01 -2.7748e-01
-#>  -1.2516e+01  9.3661e-01  8.7588e+00 -1.0998e+01 -1.0668e+01  3.7740e+00
-#>   2.1174e+00 -1.1302e+01  2.5620e+00 -1.2488e+01 -3.4072e-02  1.3111e+01
-#>   1.3338e+01 -2.0520e+00 -1.5102e+01 -2.9870e+00  5.0300e+00  6.6853e+00
-#>   9.5776e-02 -1.1173e+01 -7.8260e+00 -7.9570e-01 -4.0905e+00  3.1859e+00
-#>   9.8249e+00 -8.4933e+00  6.2898e+00  1.8266e+00  4.9972e-01  1.1476e+00
-#>  -3.1772e+00  5.4599e+00  9.6864e+00 -9.3830e-01  1.3744e+01  4.6697e+00
-#>  -7.9928e-01  9.6497e+00  1.1326e+01  5.6546e-01 -4.4016e+00  1.2161e+01
-#>  -4.6892e+00 -2.7207e+00  1.5347e+01 -1.3501e+00  1.0858e+01 -1.9357e-01
-#>   3.9911e+00 -2.1637e+00  3.5168e+00 -5.6583e+00  4.2209e+00  4.6337e+00
-#>   9.9214e+00 -7.2976e-01  1.0678e+01  1.8799e+01  1.7413e+01 -8.4330e-01
-#>   2.3533e+00 -5.9228e+00  1.3134e+01 -1.4971e+00  3.8762e+00  1.5080e+00
-#>  -7.7518e-01  1.4636e+00 -7.8376e+00 -1.2756e+01 -3.8406e+00  3.6612e+00
-#>   2.5242e+00  8.6385e-01 -6.2545e+00  4.3963e+00  9.6608e+00  1.5203e+01
-#>  -4.8494e+00 -6.9702e+00  4.1564e+00  4.8794e+00  1.7624e+01 -7.4009e+00
-#> 
-#> Columns 19 to 24 -1.1275e+01 -8.8870e+00 -1.1503e+00 -1.3656e+00 -1.4318e+00 -4.2171e+00
-#>   1.2225e+00  6.3358e+00  1.4911e+00 -9.2090e+00  2.2159e+00 -3.0175e+00
-#>  -6.2561e+00 -6.9127e+00 -1.5702e+00 -1.1056e+01  3.5933e+00 -2.8720e+00
-#>  -8.9248e+00 -5.0138e+00  1.0471e+01 -5.2612e+00  5.8908e+00  1.0312e+01
-#>   9.3393e+00 -1.1216e+00 -9.9527e+00 -5.3251e+00  3.2107e-01 -2.6164e+00
-#>  -1.4038e+00 -2.8797e+00  3.0678e+00  1.7291e+01 -1.7825e+01  1.5292e+00
-#>  -1.1628e+00 -1.6507e+00  4.8153e+00 -1.1936e+01 -1.5914e+00 -1.2476e+01
-#>   5.0162e-01 -2.5429e+00 -3.8954e+00 -1.0717e+00 -7.4392e-01 -8.1214e+00
-#>  -2.0364e+00 -3.6251e+00 -1.8497e+00  4.7399e+00  1.0754e+01 -1.1310e+01
-#>  -4.8160e+00  1.7677e+01 -2.3417e+00  1.8043e+00 -1.5608e+01 -3.7794e+00
-#>   1.0231e+00  9.7939e-01 -4.3045e+00 -5.1458e+00  3.6919e+00  4.1636e+00
-#>   3.0280e+00  6.1041e+00  9.0048e-01  4.3932e+00 -1.2203e+01  2.1182e+01
-#>  -2.6326e+00 -5.6641e+00  6.3962e+00  8.7000e-01  1.0775e-01  1.2407e+01
-#>   9.1635e+00  2.8067e+00  2.2553e+00  2.4540e+00  5.8933e-01  1.7804e+01
-#>   3.9390e-01  1.5576e+00  2.1263e+00  3.5894e+00  7.0876e+00  1.6979e+01
-#>   2.2544e-01  4.1435e+00  1.8176e+00  3.9213e+00 -1.5278e+01  2.8989e+00
-#>  -4.3914e-01  1.2226e+01 -4.4174e+00  2.6144e-01 -5.7790e+00  1.1095e+01
-#>  -1.0954e+00  3.1233e+00 -4.9339e+00 -5.5239e+00  1.2868e+01 -1.3096e+00
-#>  -1.7473e+01 -3.2913e+00 -9.9370e+00  1.6746e+01 -1.0182e+00 -1.9162e+01
-#>   6.0977e+00 -7.1840e+00  9.8135e+00 -1.5442e+01  4.1905e+00  4.2416e+00
-#>   8.6695e+00 -1.1826e+00 -1.3659e+01  1.9926e+00  3.4558e+00 -8.9040e+00
-#>  -7.1326e+00  3.0948e+00  5.0819e+00  8.8750e-01  8.3385e+00 -1.4594e+00
-#>   1.3645e+01  5.1997e+00  3.6020e+00 -8.2307e+00  2.1137e+00  1.2046e+01
-#>  -4.8727e-01 -7.2430e+00 -2.7028e+00  1.1680e+01 -7.5035e+00 -5.5648e+00
-#>   7.9166e+00 -2.5712e+00  1.0126e+01 -5.6492e-01 -1.6257e+01  8.2240e+00
-#>   1.1700e+01  1.9525e+00 -1.7176e+00  7.5046e+00 -9.1546e+00  6.5096e+00
-#>   5.9320e-01 -5.0605e+00 -1.0223e+00  1.9240e+01 -1.0675e+01  2.9595e+00
-#>   1.3353e+01 -1.2302e+01 -2.9855e+00 -1.1250e+00  5.4672e+00 -7.9218e+00
-#>  -2.6932e+00  6.7082e+00 -1.1248e+01  5.8509e+00  1.2224e+01 -1.1559e+01
-#>   6.9015e+00 -6.2887e+00  1.2609e+00  3.2606e+00 -1.2129e+01  2.1034e+00
-#>   5.9783e+00  2.8002e+00 -1.0461e+00  2.0890e+00 -1.5872e+00  4.5608e+00
-#>   2.3425e+00  9.2480e+00 -1.0278e+01  8.0220e+00 -4.5289e+00  1.0035e+01
-#>   1.3217e+00 -2.8492e+00  4.2375e+00 -8.9814e+00 -8.6686e+00 -8.7894e+00
-#> 
-#> Columns 25 to 30  8.3360e+00 -3.7383e+00 -1.0755e+01 -4.4215e+00 -5.5599e+00  4.4972e+00
-#>   9.7614e+00  3.3047e+00  8.1004e+00 -1.7579e-01 -2.6047e+00  6.5980e+00
-#>  -4.3140e+00 -9.9903e+00  3.5220e+00 -1.1364e+01 -2.4828e+00 -1.2487e+01
-#>   7.3264e-01  1.2492e+01  1.8758e-03 -1.5940e+01 -1.7799e+01  7.2498e-01
-#>  -6.0802e+00 -2.9302e+00  5.6184e+00 -7.8101e+00 -3.4038e+00  3.7225e-01
-#>  -3.1649e+00 -8.9998e+00  2.3312e+00  7.6182e+00  8.3816e+00  9.3550e+00
-#>  -1.1552e+01  2.3792e-01  1.4321e+00  3.3447e+00 -1.7611e+01  8.7142e+00
-#>  -8.7107e-01  8.1181e+00 -1.4570e+01  6.2605e+00  7.8271e+00 -4.8895e+00
-#>  -2.9786e+00 -2.9680e+00 -2.1442e+00 -1.3693e+01  2.4202e+00 -1.5083e+00
-#>  -3.8475e+00 -6.6983e-01  1.0256e+00 -1.1677e+01 -4.5688e+00  1.2044e+01
-#>  -1.4389e+01 -1.1644e+01 -2.4801e+00  2.1499e+00 -5.2939e-01  3.5510e-01
-#>  -2.3637e+00 -3.1054e+01 -6.8584e+00  9.6814e-02  1.5122e+00  9.6570e+00
-#>   4.2619e+00  5.8534e+00 -1.5839e+01  1.4565e+00 -1.7065e+00 -9.5515e+00
-#>  -8.0406e+00  5.8786e+00  7.9152e+00 -6.5251e+00 -1.3515e+01  1.7494e+01
-#>  -5.5358e+00 -1.6714e+01 -1.0522e+00  5.3414e+00 -1.4394e+01  7.4968e+00
-#>   5.9624e+00  7.6743e-01  4.9624e+00  9.7006e+00 -5.8005e-01 -2.2949e+00
-#>   3.7649e-01 -1.2126e+01  1.4155e+01  8.3035e+00  1.8507e+00  4.5304e+00
-#>   2.6175e+00  1.0547e+01 -5.2089e+00 -1.7335e+00 -1.1900e+01 -3.6790e-01
-#>  -2.6824e+00 -1.0599e+01  1.6807e+01  1.3984e+01  1.0876e+01  1.8125e+01
-#>  -6.5827e+00 -2.4784e+00  2.5894e-01 -8.1518e+00 -5.2204e+00  7.2081e+00
-#>   9.8621e+00  4.5915e-02  2.3873e-01  1.2638e+01  1.6099e+00  3.0094e+00
-#>   3.0689e+00  5.8365e+00 -1.1364e+01  3.0868e+00  1.1472e+01 -3.2644e-01
-#>   4.4706e-01  6.1634e+00 -8.9733e+00 -1.2721e+01  6.9967e-01  1.8322e+01
-#>  -5.6052e+00  6.5106e+00  8.6718e+00  4.4310e+00 -9.5444e-01 -6.2647e-01
-#>   4.7944e+00 -5.0659e+00  5.1348e+00 -5.5745e+00  2.4925e+00 -1.3703e+00
-#>  -1.4523e+01 -1.3315e+01  7.2122e-04 -1.1568e+01 -4.8835e+00  4.6232e+00
-#>  -3.9652e+00  6.9381e+00 -4.8351e+00 -1.2155e+01  8.0029e+00 -8.4143e+00
-#>   6.9426e+00  3.5703e+00 -9.3369e+00  3.6161e+00  2.8410e+00  4.1312e+00
-#>  -6.9064e+00  9.6734e+00 -1.0496e+01 -3.5655e+00 -8.3905e+00 -1.7785e+01
-#>   3.1462e-03  1.9450e+00 -5.5292e+00  2.8397e+00 -6.2833e+00  3.3583e+00
-#>  -5.6672e+00  3.4510e+00 -1.5409e+00  8.8068e+00 -5.1308e+00  2.6718e+00
-#>  -3.3111e+00  1.4582e+01  5.8425e+00 -1.7964e+01  6.5661e+00 -2.8826e+01
-#>  -6.9143e+00  1.0431e+01 -5.4161e+00 -2.9465e+00  3.6363e+00  3.3457e+00
-#> 
-#> Columns 31 to 36  1.2633e-01  2.0837e+00 -1.7995e+01 -7.4558e+00 -3.4899e+00 -2.1166e+00
-#>   5.1230e+00  9.6372e+00  4.8431e+00 -4.0710e-02 -1.1583e+01  2.3542e+01
-#>   3.2173e+00  6.4572e+00  8.2509e+00  8.3110e+00 -1.1414e+01 -1.0241e+01
-#>   1.9424e+00 -8.8026e+00  1.1571e+01  6.9345e+00  2.2727e+00  1.0056e+00
-#>  -7.7589e+00 -1.0472e+01 -3.7097e+00 -1.0164e+01 -3.7912e-01 -2.1021e+01
-#>   1.9517e+01  4.7494e+00  4.4957e+00 -1.0816e+00  5.4489e+00 -6.5487e+00
-#>  -8.7826e+00  7.8509e-02  9.0676e+00  6.6272e+00  2.6549e+00 -1.5556e+01
-#>   9.7232e-01  1.4177e+01  7.6577e+00  1.4539e+00 -1.0341e+01 -4.9364e+00
-#>   5.9784e+00  5.7458e+00 -1.4109e+01 -5.6991e+00  9.1061e+00  1.4150e+01
-#>   1.6477e+01 -3.1146e+00  2.9849e+01 -1.8320e-01 -2.9251e+00  4.5001e-01
-#>  -5.6236e-03  1.4766e+00 -6.0240e+00  9.4568e+00 -1.5395e+00 -1.5155e+01
-#>   4.9929e+00  6.7127e+00 -8.6386e+00 -4.3836e+00 -5.1465e+00 -2.8905e+00
-#>  -3.2138e+00  6.1757e+00  1.0868e+01  2.0150e+01  2.3389e+00 -1.8949e+01
-#>  -3.8071e+00  9.3911e+00  1.2564e+01 -9.0786e+00  4.9773e+00  9.1801e+00
-#>   6.5899e+00 -1.0259e+01  2.5031e+00 -6.8856e+00  2.3481e+00 -2.8523e-01
-#>   7.7059e+00 -3.3388e+00 -1.0783e+01  3.0331e+00  1.0327e+01  1.5650e+01
-#>   5.5370e+00 -2.3466e+00  7.0102e+00  3.5636e+00  5.4982e+00  2.2237e-01
-#>  -4.8712e+00 -8.5445e-02 -6.6527e+00  1.0074e+01 -9.5377e+00  5.8579e+00
-#>   7.7541e+00 -5.7797e+00 -1.3062e+00 -1.3028e+01  1.2405e+01  7.4622e+00
-#>   7.9735e+00  1.0001e+01 -2.2889e+00  2.2752e+00 -8.3279e+00 -1.2083e+01
-#>  -6.8952e+00 -8.5599e+00 -8.3242e+00  5.5835e+00  2.0972e+00  5.6569e+00
-#>   1.2612e+00  1.4267e+01  1.1097e+00  1.6704e+01 -3.4209e+00  7.5645e+00
-#>   2.4637e+00  4.9550e+00 -2.2320e+00 -6.2892e-01  5.8074e+00  2.5013e+00
-#>  -1.9164e+01 -3.9155e+00 -9.6766e+00 -1.0788e+01  1.0344e+00 -4.6280e+00
-#>  -5.2133e-01 -5.1429e+00  4.8088e-01  2.4318e+00  4.7498e+00  1.5048e+01
-#>   9.6296e+00  7.1187e+00  7.5106e+00 -1.4648e+01 -6.7761e+00 -9.6407e+00
-#>  -6.2040e+00 -5.7022e+00  3.5299e+00 -7.0126e+00  2.2069e+01 -1.1930e+01
-#>   1.0790e+01  1.6324e+01 -2.4648e+00  8.3412e+00  1.5067e+01  2.6021e+00
-#>  -5.1471e+00 -6.1570e+00  1.7403e+01  9.0279e+00 -1.1493e+01 -2.4385e-01
-#>  -6.8378e+00 -2.1936e+01 -8.3022e+00 -7.4845e+00  9.9043e+00  3.4503e+00
-#>   3.5370e+00 -4.7399e+00 -9.0786e+00 -8.4300e+00 -1.1417e+01 -2.3562e-01
-#>   1.6061e+00 -6.5874e+00  6.4658e+00 -7.2971e+00  1.1293e+01 -1.7372e+01
-#>  -6.1479e+00  4.7600e+00  2.8297e+00 -4.8681e+00 -5.9525e+00 -2.5531e+00
-#> 
-#> Columns 37 to 42 -4.1066e+00  2.3703e+00 -5.9486e-01 -4.4666e-01  1.8248e-01 -8.1558e+00
-#>   8.1020e-01  1.9982e+00  1.4639e+01  4.4896e+00  7.8410e+00  9.7412e+00
-#>  -1.4223e+01 -1.3682e+01 -1.7861e+00 -6.5508e+00  8.4454e+00  7.9100e+00
-#>  -1.8200e+01  1.3744e+01 -1.3529e+01  1.0235e+01  5.1733e+00 -1.4189e+01
-#>  -6.4968e+00  1.8096e+01 -2.5983e+00  3.8399e+00  5.0918e+00  3.8648e+00
-#>   1.0602e+01  1.2529e+01  1.3578e+01  3.7652e+00 -2.2580e+00 -5.7485e+00
-#>  -7.6017e+00 -2.7962e+00  7.1716e+00 -1.3455e-01 -1.4039e+00 -4.6689e+00
-#>   3.3376e+00 -8.9963e+00  1.1831e+01 -7.4716e+00 -5.3106e+00  4.3870e+00
-#>   7.4744e+00 -2.4187e+00 -2.0261e+00 -6.3718e+00 -6.1785e+00 -9.6643e+00
-#>  -1.2513e+01  1.6945e+01 -3.6329e+00  6.4687e+00 -5.0772e+00  8.4799e+00
-#>   7.7864e+00 -1.5982e+01  9.4983e-01  4.9537e+00  3.2252e+00  7.0543e+00
-#>   1.2666e+00 -4.2042e+00 -1.7502e+00 -2.3269e+00 -1.6726e+00 -9.4753e+00
-#>   6.5305e+00 -8.1240e+00  9.4768e+00  4.4920e+00 -6.1574e+00 -5.3549e-01
-#>   2.2090e+00  4.4112e+00 -7.0229e+00  1.0717e+01 -6.2096e+00  3.4585e+00
-#>  -1.1082e+01  6.0274e+00 -2.1499e+01 -3.2891e+00  2.8027e+00 -1.1171e+01
-#>  -1.3268e+01  3.0344e+01  6.3035e-01 -4.0915e-01 -1.5139e-01 -1.0354e+01
-#>   6.5644e+00  7.3598e+00 -6.2678e+00 -1.0709e+00 -4.2123e+00  2.6543e+00
-#>   1.8287e+01 -1.2104e+01  3.0959e+00  6.8876e+00 -9.4723e-01  1.1671e+00
-#>  -4.3596e-01 -9.0169e+00  1.3417e+01 -4.2153e+00  3.0050e+00  1.0411e-01
-#>  -3.4339e+00 -1.8052e+01 -1.4217e+00 -3.8098e+00  7.3061e-01 -1.7107e+01
-#>   1.3988e+01  9.8941e+00  7.7778e+00 -1.4597e-01 -3.0472e+00 -5.8861e+00
-#>  -1.7668e+01  2.0067e+00 -1.7894e+01 -7.7294e+00 -7.6481e+00  1.0407e+01
-#>   2.3894e+00 -8.2646e+00  9.7261e-01 -2.3514e+01 -1.4048e+01 -1.6880e+01
-#>  -2.4144e+00  2.2946e+00  4.3116e+00 -5.1044e+00 -1.0307e+01  5.4957e+00
-#>  -1.0266e+01 -6.9912e+00  2.0645e+01 -1.2542e+01  2.4918e+00 -9.1484e+00
-#>   3.8444e+00  3.6482e+00  6.9221e+00 -6.6864e+00  7.9277e-01 -6.1825e+00
-#>  -7.1886e-01 -1.9885e+00 -8.9606e+00  1.2146e+01 -6.4248e-01 -6.7472e+00
-#>  -5.4758e+00 -1.1466e+01 -2.0698e+00  1.4037e+01  9.3900e+00  4.5658e+00
-#>   5.3529e+00 -4.9850e+00  4.7814e+00  1.0499e+01 -1.3354e+01  1.0404e+01
-#>  -1.2554e+01 -6.0901e-01 -4.0208e+00 -2.2119e+01  5.2214e+00 -1.0635e-01
-#>  -4.0157e+00  5.0545e-04 -4.0805e-01 -6.4190e+00 -9.8647e+00  7.2780e+00
-#>  -3.5262e+00  1.7072e-01 -1.3999e+01  4.5315e-01 -1.7660e+00 -1.6332e+00
-#>  -5.1083e+00 -4.2060e+00 -2.6048e+00  2.4064e+00  3.4505e+00  1.4462e+01
-#> 
-#> Columns 43 to 48  7.4749e+00  3.2896e+00 -6.5771e-01 -2.0536e+00 -1.5341e+00 -2.1485e+00
-#>  -1.4133e+01 -5.2030e+00  1.2760e+01 -1.2155e+01  1.1957e+01 -8.8063e+00
-#>   5.0999e+00 -6.1652e+00  3.1785e+00 -6.0359e+00 -4.3141e+00  2.7696e+00
-#>   4.1555e+00 -1.0005e+01  7.6561e-01  1.1265e+00 -5.4895e+00  7.6430e+00
-#>   4.9313e+00 -1.8493e+00  5.9208e+00 -9.9984e+00 -4.2710e+00 -1.3521e+00
-#>   5.3910e+00 -4.5412e-01 -9.8172e+00  2.3860e+01 -1.8580e+01  1.1603e+01
-#>   5.4036e+00  3.2782e+00  3.6406e+00 -7.9052e+00 -3.6935e+00 -5.4650e+00
-#>   4.7777e-01  1.8790e+01 -7.1115e+00 -1.1741e+01  5.1975e+00 -9.0889e-01
-#>  -4.3182e+00  9.0281e+00 -8.1416e+00  7.9910e+00 -9.8128e+00 -1.0565e+01
-#>   5.0018e-01 -4.6825e+00  5.4171e+00 -1.3931e+01  1.5320e+00 -8.1043e+00
-#>   1.3373e+01 -5.6451e+00 -6.6508e+00  1.5060e+00 -8.8349e-01  1.1201e+00
-#>  -5.6232e+00  3.6242e+00 -1.0497e+00  2.5834e+00 -2.0360e+00 -7.3847e+00
-#>   6.6888e+00  2.3927e+00 -3.8077e+00 -2.1639e-02 -5.8558e+00  6.8943e+00
-#>  -7.8989e+00 -2.6013e+00  5.0901e+00  2.8595e+00 -5.5578e+00 -2.1903e+00
-#>   9.7989e+00  5.4863e+00 -1.1755e+01  1.3804e+01  6.5772e+00 -2.0106e+00
-#>   4.7886e+00  4.1859e-01  1.0380e+00  1.2049e+01  3.2355e+00 -6.1523e+00
-#>  -1.0745e+00  4.7642e+00  2.3202e+00 -7.0219e+00 -1.0187e+00 -7.0501e+00
-#>  -5.4901e+00 -3.2077e+00  2.0884e+01  2.2532e+00  3.0159e+00  7.5106e+00
-#>  -1.1099e+01  1.3630e+01 -7.1980e+00 -1.1558e+01  1.6250e-01  7.4032e+00
-#>   1.2947e+01  5.4460e+00 -1.5756e+01  9.4801e+00 -1.2139e+00  6.2564e+00
-#>   1.1998e-01  5.7220e+00  1.1314e+01 -3.5625e+00  3.5268e+00  1.1134e+01
-#>   1.3069e+01  1.1677e+00 -8.4681e+00 -1.1823e+01 -1.4532e+01 -5.3117e+00
-#>   3.4807e+00  1.9474e+01  8.1345e-01 -4.3280e+00  3.3045e+00  8.0262e+00
-#>  -4.1458e-01  2.9176e+00  3.3451e+00 -2.6143e+00  8.6070e-01  2.2524e-01
-#>   4.1285e+00  9.9700e-01  9.8412e+00  3.2570e+00 -8.5901e-01  4.1803e+00
-#>  -1.1954e+01  5.9839e+00 -1.7945e+01  1.3585e+01 -3.0892e+00  6.8838e-01
-#>   2.6369e+00 -5.5210e+00 -1.5965e+01  1.8688e+01 -1.1416e+01  9.1712e+00
-#>   1.5720e+00 -3.2047e+00  3.7386e+00 -1.0319e+01 -6.0105e+00  6.2452e-02
-#>  -1.0030e+00 -3.8978e+00  8.4415e+00 -6.1983e+00  9.5013e+00  7.3072e+00
-#>   1.0870e+00 -1.7976e+00  1.0758e+01  6.4537e+00 -3.5953e-01  8.7095e+00
-#>  -1.2589e+01  1.6091e+00  4.8148e+00  3.5849e+00 -3.8170e+00 -3.7377e+00
-#>  -1.0399e+00  1.7239e+01  3.7850e+00  8.8687e+00  1.6787e+00  1.8082e+01
-#>   9.9256e+00 -5.7379e+00  6.7655e+00 -4.6335e+00  1.2040e+01  4.8663e-01
-#> 
-#> Columns 49 to 54  2.0588e+00 -1.1653e+01  1.0608e+01  2.7881e+00  5.6513e+00 -5.9351e-01
-#>  -1.5856e+01 -1.0242e+01 -7.9768e-01  2.1046e+00 -4.7630e+00 -2.5084e+00
-#>   5.8313e+00 -1.4300e+01  4.8191e+00 -3.9928e+00  3.9323e+00 -1.7959e+00
-#>   7.1854e+00  7.4052e+00 -3.5649e+00  3.0255e+00 -6.5195e+00  4.0929e-02
-#>   7.3524e+00 -4.1666e+00  1.4574e+01 -8.3825e+00  1.2557e+00  2.2931e+00
-#>  -1.6590e+01 -4.2074e+00  6.5455e+00 -2.3427e+00 -5.9171e+00 -2.4614e+00
-#>   8.4701e+00 -4.9499e+00  2.8532e+00  1.2714e+00 -1.0051e+01 -2.1670e+00
-#>  -4.7380e+00 -4.1595e+00  7.7442e-01  4.5593e+00 -3.1357e+00  1.0338e+00
-#>   1.7061e+01 -1.1289e+01  8.1655e+00  5.5929e+00  5.5192e+00  6.9233e-01
-#>  -1.1707e+01 -4.1463e+00 -4.4399e+00 -5.5704e-01 -5.4864e+00 -4.6775e+00
-#>  -1.9277e+00  4.0682e+00  1.0254e+01 -2.4243e-01 -1.0002e+00 -1.2724e+00
-#>   6.7865e+00 -1.4280e+00  1.7221e+00 -4.3760e+00  5.2218e+00 -1.1768e-01
-#>  -3.3110e+00 -9.4838e-01 -1.9602e+01  9.0612e+00 -1.4708e+00  4.8763e+00
-#>  -3.5278e+00  4.1600e+00 -1.1134e+00  7.8087e-01 -2.6953e+00 -1.4792e+00
-#>   6.3919e+00 -8.5068e+00  1.0860e+01 -2.0888e+00  4.6862e+00 -2.6934e+00
-#>  -1.3631e+01  3.2825e+00  3.6291e+00  2.1424e-01 -6.6992e+00 -2.3271e+00
-#>  -8.4119e+00 -8.5404e+00  4.4918e+00  2.3728e+00 -2.8177e+00  2.3407e+00
-#>  -5.7681e+00  4.3214e+00 -1.0287e+01  3.5729e+00 -4.2341e+00  1.4796e+00
-#>  -8.7989e+00  5.0641e+00 -4.5184e-01 -7.1411e+00  4.0992e+00 -2.9186e+00
-#>  -5.6270e+00 -6.4904e+00  3.5192e+00 -4.1002e+00  5.7519e+00 -9.8820e-01
-#>  -4.6634e+00 -5.7731e-01 -5.6198e+00 -2.6096e+00  5.3468e+00 -2.4138e+00
-#>   1.9903e+01 -1.9132e-01  3.7509e+00 -2.5854e+00  8.5715e-01 -1.6367e+00
-#>  -1.8672e+01 -6.1275e+00  6.0109e+00  6.0365e+00 -3.1170e+00  2.3840e-01
-#>  -3.7971e-01 -6.9648e+00  2.7984e+00 -2.4435e+00 -4.0945e+00  3.8353e-01
-#>  -1.4058e+01  5.9574e+00 -1.9671e+00 -5.1292e+00  1.5424e+00 -5.5177e-01
-#>  -8.7823e+00  1.3229e+00 -7.1644e+00  1.2158e+00 -3.8932e+00  3.1650e+00
-#>  -5.6919e+00  1.2080e+01  1.2179e+00  4.1779e+00 -5.4655e-02  2.0174e-01
-#>  -8.4263e+00  3.1701e+00  2.4079e-01  5.1354e+00  2.8352e+00 -2.6872e-01
-#>   4.9421e+00 -5.0250e+00  2.6910e+00  2.7623e+00 -6.1163e+00  1.4400e+00
-#>  -1.1406e+01  2.4924e+01 -9.8252e+00 -1.6379e+00 -2.1415e+00 -1.5942e+00
-#>   5.9189e+00  6.6855e+00  5.4495e+00  8.0702e+00 -6.0936e+00  1.2601e+00
-#>  -7.1622e+00  7.4579e+00 -1.6247e+01 -1.7368e+00  3.5875e+00  8.7810e-01
-#>  -5.6202e+00 -4.4285e+00  1.1572e+00 -4.1747e+00 -1.2560e+00 -6.9329e+00
-#> 
-#> (12,.,.) = 
-#>  Columns 1 to 6  2.2343e+00  2.4861e-01  1.3061e+01 -1.3344e+01  1.2442e+00  1.6980e+01
-#>   5.8112e-01 -1.1779e+01 -2.8015e+00 -1.2982e+01 -3.2822e+00  7.1146e-02
-#>  -4.3058e+00  1.0108e+01  2.0428e+00  4.8671e+00 -7.7594e+00 -1.3086e+01
-#>  -1.4351e+00 -5.7256e+00 -4.4247e+00  1.3467e+00  1.5534e+00 -1.1222e+01
-#>   1.7914e+00  6.6421e+00 -2.5117e+00  2.6416e+00  4.7904e+00 -3.9372e-01
-#>  -8.3136e+00 -1.5627e+00 -4.9528e+00  6.6515e+00  5.2495e+00 -1.7103e+00
-#>   5.3299e+00  6.1510e+00 -7.8576e-01 -3.3475e+00 -1.0934e+01 -3.2163e+00
-#>  -2.7892e+00  7.9525e+00  1.3028e+01 -3.7640e+00 -2.0863e+01 -2.6415e+00
-#>  -4.3488e+00 -8.0201e+00 -5.8985e+00 -3.7523e-01  1.3658e+01 -5.0732e+00
-#>  -7.9534e-01 -8.1160e+00 -4.3030e+00 -8.5787e-02 -1.4162e+01 -1.9195e+01
-#>   1.7526e+00 -1.3950e+00 -3.5435e+00 -3.9843e+00 -5.4550e-01  1.4017e+00
-#>  -5.9295e+00 -1.8240e+00  2.3624e+00  2.2988e+00  8.0792e+00  5.5243e+00
-#>   3.5374e+00  1.2951e+00  1.0387e+01 -7.4211e+00 -1.6104e+01 -9.4376e+00
-#>   4.2853e+00 -5.5056e+00 -4.3562e-01  1.1677e+00  6.9683e+00  3.3208e+00
-#>   7.1005e-02  8.5128e+00  3.6922e+00  7.9138e+00  5.0410e+00  3.8249e+00
-#>  -7.2560e-01 -9.5460e-01 -4.3871e+00 -4.4477e+00  5.1031e+00  5.3250e+00
-#>   9.0075e-01  9.4957e+00  5.7318e+00 -5.9370e-01  7.3959e+00  8.1217e+00
-#>  -6.2461e-02 -8.1013e+00 -2.5499e+00 -1.3845e+00  3.1540e+00  1.0728e+01
-#>   5.4301e+00  2.6902e+00 -5.4021e+00  1.0105e+01 -1.1946e+01 -8.0718e+00
-#>   1.5006e-02  5.5758e+00  4.5327e+00  3.3208e+00  3.3094e-01 -2.0927e+01
-#>   7.4586e+00  4.6525e+00  5.8436e-01  1.0973e+01 -2.3610e+00  4.2508e+00
-#>  -1.2983e+00 -1.1301e+01  1.2430e+01  4.0250e+00  4.1832e-01 -1.6513e+01
-#>  -7.8391e+00  2.4531e+00 -1.8300e+00  1.0450e+01  1.5860e+00 -2.5208e+00
-#>   5.6105e+00  5.5700e+00 -4.3786e+00 -9.1806e+00 -5.7021e+00 -7.4651e+00
-#>  -9.2395e+00  6.9638e+00  6.7176e+00  4.8742e+00  1.5557e+00 -4.7856e+00
-#>   1.3244e+00 -1.5630e+00 -6.8633e+00 -2.1048e+00 -4.5452e+00 -1.2455e+01
-#>  -3.4415e+00 -3.6602e+00 -8.8038e+00  1.2900e+01 -1.6583e+00  3.5402e-01
-#>  -5.4946e-01  4.2501e+00 -3.8394e+00 -1.1170e+00 -8.1395e-01 -3.9050e+00
-#>   2.5022e+00  8.3729e+00 -2.6832e+00  2.7693e-01  3.8717e+00 -6.5552e+00
-#>   7.8093e+00  1.3572e+00 -1.4310e+01  5.0189e+00  3.0458e+00  8.0139e+00
-#>   1.3326e+00 -6.4450e+00  1.0170e+00  5.7269e-01  1.9765e+01 -6.6270e+00
-#>   1.9113e+00  5.7325e+00  7.8303e+00 -8.4500e-01 -7.9439e+00 -6.7554e+00
-#>   2.8425e+00  8.1149e+00  6.1998e-01 -6.4198e+00 -1.3863e+01  2.7923e+00
-#> 
-#> Columns 7 to 12  1.0106e+01 -8.0998e+00 -1.6541e+00 -1.0441e+01 -6.2678e+00  1.5084e+01
-#>   4.2660e+00  5.1183e+00 -4.0667e+00 -2.3064e+01 -4.2162e+00 -1.1576e+00
-#>   1.3151e+00 -4.1282e+00  1.2276e+01  2.8656e+00  1.3460e+01 -8.0012e+00
-#>  -4.6559e+00 -1.3864e+01 -4.5554e+00 -3.6413e+00 -5.4804e+00 -1.7346e+01
-#>   9.5870e+00  7.6032e+00  8.3130e+00  1.2399e+00  2.7644e+00  3.0823e+00
-#>   7.4200e+00 -1.7400e+01 -1.1742e+01  7.4787e+00 -2.5101e+01  5.8978e+00
-#>  -1.8558e+00  2.3167e+00 -5.5227e+00 -3.7291e+00  4.4580e+00  1.3574e+00
-#>   4.9338e+00  1.0376e+01 -5.2100e+00  6.7813e+00 -3.7380e+00 -1.6642e+01
-#>   1.8014e+01 -2.5866e+00 -5.0389e+00 -3.0290e+00  1.5388e+00 -1.0736e+00
-#>  -2.3015e+01 -4.0512e+00 -2.0893e+00 -4.2346e+00 -2.2044e+01  1.4695e+00
-#>   4.4195e+00 -1.5963e+00  2.3283e+00 -7.8490e-01 -1.9061e+00  9.3110e-01
-#>  -1.0458e+01  2.6943e+00 -1.1518e+01  3.4626e+00  9.9623e-01  1.5833e+01
-#>   5.4454e-01 -1.8906e-01 -4.6633e-01  8.7036e+00 -1.3710e+00 -1.1635e+01
-#>  -3.0853e+00  1.0756e+01 -2.1256e+01 -1.3527e+01  2.0147e-01  8.2447e+00
-#>  -2.6700e+00 -2.6537e+00  3.8012e-02 -4.0746e-01  4.2514e+00  1.3912e+01
-#>   4.6423e+00 -1.3562e+01  3.3131e-01 -4.1649e+00 -7.3361e+00  4.3607e+00
-#>  -1.1995e+01  7.7007e+00 -1.9198e+00 -1.5712e+01 -1.6685e+01  9.2774e+00
-#>  -5.1702e+00  1.3754e+01  1.1154e+00 -2.0734e+00  1.8528e-02 -3.4237e-01
-#>  -7.5284e+00 -9.7365e+00 -3.6080e+00  6.4859e+00 -8.1583e+00  6.2707e+00
-#>   1.2302e+01  4.8155e+00  6.7645e-01 -9.2286e-01  7.5249e+00 -1.2391e+00
-#>  -3.8789e+00 -7.4176e-01 -3.9320e+00 -6.7518e+00  2.9841e+00  2.0185e+01
-#>   3.6928e+00 -1.8126e+01 -9.5705e+00  2.5406e+00  7.8560e+00 -1.4716e+01
-#>   1.0955e+01  3.9481e+00 -1.9212e+01 -2.1933e+01 -1.2778e+01  1.1302e+01
-#>   7.4397e+00 -7.9327e-01 -9.3109e-01  1.9953e+01 -1.2216e+00  1.0269e+01
-#>   1.7136e+00 -1.5817e+00 -5.9169e+00 -6.5075e+00  4.2173e+00  1.1803e+01
-#>   6.3738e-01  4.2163e+00 -1.4027e+00  9.7656e+00 -2.3720e+00  1.1708e+01
-#>   4.1512e+00 -6.7453e+00 -7.7274e+00  3.5501e+00  8.7879e-01  4.4375e+00
-#>  -3.6729e+00 -1.0202e+01 -6.3316e-01 -1.5827e+01 -5.6612e+00 -1.3232e+01
-#>   3.4021e+00  1.2152e+01  4.2543e+00  1.6077e+01  7.1638e+00 -9.5322e+00
-#>  -1.6027e+01 -1.2267e+01  9.8984e+00  5.8649e+00  1.5113e+01  2.3995e+01
-#>   6.6292e+00 -6.8196e+00  4.2903e+00  7.1128e+00 -1.0787e+01 -1.3526e+00
-#>  -7.1549e+00  1.3952e+01 -9.2232e+00 -6.3592e+00  6.6941e-01 -9.2557e+00
-#>  -3.7317e+00  9.6946e+00 -6.1778e+00  9.9506e+00 -5.2459e+00 -5.7252e+00
-#> 
-#> Columns 13 to 18  7.9775e+00 -3.3716e+00 -1.6398e+01  1.0290e+00  1.4219e+01 -7.9817e-02
-#>  -2.8099e+00  9.6442e+00  5.9975e+00 -8.3840e+00 -1.2924e+01  1.7883e+01
-#>  -2.9478e+00 -4.1491e+00  6.5517e+00 -3.4311e+00 -2.5219e-01 -4.7551e+00
-#>  -1.7489e+00  4.8127e+00  1.7067e-01 -1.0031e+01  4.8095e-01  1.7671e+01
-#>   3.3080e+00 -5.0196e-01  1.6195e+01  2.1735e-01 -1.1563e+01 -1.8092e+01
-#>   5.8877e+00  8.8385e+00  4.5792e+00 -1.3592e+01  3.6850e+00  3.2994e+00
-#>  -3.6112e+00  6.8737e+00  3.0362e+00  3.9409e+00  1.3417e+01 -7.3520e+00
-#>  -1.2533e+01 -5.9631e+00 -6.3714e+00  2.5638e+00  1.9757e+01 -1.3020e+00
-#>   1.8081e+01 -3.9881e+00 -2.4899e+01 -1.1685e+01 -5.0189e+00  2.7581e-01
-#>  -1.0677e+01 -1.7292e+00  6.1302e+00  9.3948e+00 -5.9134e+00  1.0920e+01
-#>  -1.5548e+01 -9.7880e+00  1.5163e+01  3.2234e+00 -1.4654e+01 -2.7111e+00
-#>   2.6025e+00  1.6772e+01 -9.5342e+00 -1.7929e+01  6.9729e+00  1.7990e+00
-#>  -4.6987e+00  1.8096e+00 -4.8640e-01  1.7515e+00  1.6400e+00  8.6115e+00
-#>   5.8794e+00  2.3319e+01 -1.2410e+01 -1.4471e+01 -2.5027e+00  1.3834e+01
-#>   3.6492e+00  1.0097e+01  6.0911e+00 -1.6952e+01  3.7765e+00  1.2470e+00
-#>   9.0154e+00 -7.9646e+00  1.0940e+01 -2.5419e+00 -1.2839e+01 -3.2985e-01
-#>  -1.0842e+01  1.9563e+00  2.8683e+00  6.0006e+00 -4.8396e+00 -3.4927e+00
-#>   5.7079e-01 -9.4869e+00 -7.2187e+00 -2.3264e+00  1.1500e+00  1.3042e+01
-#>   9.3394e-02 -1.3834e+01 -1.0763e+01 -9.9645e+00  1.9327e+01 -3.2349e+00
-#>   1.6126e+01 -7.6733e+00  7.4829e+00 -1.7533e+00 -4.0451e+00  6.5047e+00
-#>  -7.6359e+00 -1.2013e+01 -7.0419e+00  7.2818e+00 -9.0434e+00  6.7979e+00
-#>  -7.6891e+00 -3.2007e+00 -1.9260e+01  1.1613e+01  1.1378e+01  1.1982e+01
-#>   1.8505e+01  1.5860e+01  1.0207e+01 -1.2126e+01 -3.8591e+00 -5.2286e+00
-#>  -1.0642e+01  9.9338e+00 -4.5486e+00 -2.5227e+00 -4.0303e-02 -1.1448e+00
-#>  -2.2505e+00  2.7698e+00  1.3077e+01 -9.0763e+00  7.3168e+00 -5.4729e-01
-#>  -1.9906e+00 -1.5236e+00 -5.2221e+00 -6.9238e-01  9.1291e+00  2.5066e+00
-#>  -3.4526e+00  1.7163e+00 -1.1267e+00  4.2589e+00 -4.5121e-01  1.1336e+00
-#>  -8.9524e+00 -9.5551e+00 -4.5162e+00 -2.4994e+00  1.2130e+00 -2.3913e+01
-#>  -4.9009e+00 -8.7625e+00  3.8590e+00 -2.1340e+00 -7.0136e+00 -7.2723e+00
-#>  -4.2614e+00 -1.5789e+01  1.1984e+01  1.4028e+01  2.2274e+00 -9.6963e+00
-#>   1.0833e+01  1.0684e+01  3.9278e+00 -2.4064e+00 -7.7770e+00  8.4217e+00
-#>   5.8047e+00  1.9016e+01 -1.1636e+01  5.5353e+00 -4.7503e+00  1.2186e+01
-#>  -5.2927e+00  1.4010e+00  1.2860e+00  6.6502e+00 -5.9610e+00 -1.4549e+01
-#> 
-#> Columns 19 to 24 -7.8774e+00  9.3987e+00 -3.1508e+00  3.2035e+00 -1.7237e+00 -2.2337e+00
-#>   9.8753e+00 -5.1873e+00 -1.5979e-01  7.0176e+00  1.4790e+00 -2.2652e+00
-#>   2.4684e+00 -1.5629e+01 -1.0845e+00  2.5782e+00  5.0271e+00 -1.5199e+01
-#>   1.1172e+01  1.2462e+00  1.2406e+01  7.3422e+00 -1.3610e+00 -7.2763e+00
-#>   1.1630e+01 -6.2654e+00  1.3605e+00  3.8456e+00  9.3753e+00 -9.7017e+00
-#>   2.6302e+00 -4.7561e+00 -1.1254e+01 -2.3001e+01  4.0595e-01  6.5323e+00
-#>  -1.6812e+01 -1.8110e+01 -3.6096e+00 -3.5465e-02 -1.4968e+01  9.3158e+00
-#>  -2.4737e+01  8.3503e+00 -1.7397e+00  2.9828e+00 -8.2400e+00  4.5024e+00
-#>  -3.7867e+00  8.1052e+00 -8.3192e-01  8.8176e+00  4.7793e+00 -1.0590e+00
-#>  -3.2457e+00  2.4234e+00 -5.5022e+00 -5.7761e-01  5.2855e+00 -7.4730e+00
-#>  -1.1944e+01 -6.8048e-01 -8.7536e+00 -1.1724e+00  3.2814e+00 -1.1310e+01
-#>   2.9503e+00  1.9739e+00 -8.4449e+00 -1.2881e+01 -7.4179e+00 -2.1671e+00
-#>  -7.6667e+00  9.0520e+00 -4.4530e+00  3.8394e+00 -4.5973e+00  1.5565e+01
-#>   1.2824e+01 -3.3694e+00  1.1752e+01 -2.3582e+00  5.0714e+00  4.2392e+00
-#>   8.0841e+00 -2.5142e+00 -1.0000e+01  1.6647e+00 -8.9167e+00 -1.6243e+01
-#>   1.3602e+01  2.1327e+00 -1.9909e+01  2.4326e+00  1.2900e+01 -3.7971e+00
-#>   1.8914e+01  6.4058e+00  2.3033e-01 -8.2163e+00  3.4782e+00 -1.0740e+00
-#>   6.3810e+00 -4.0515e+00  1.0759e+01 -8.3499e-01  7.3965e-01 -9.1388e+00
-#>  -5.8142e+00  4.5707e-02  1.1224e+00 -4.1406e+00  6.5294e+00 -6.1837e+00
-#>  -9.5804e+00 -7.5517e+00 -4.6984e+00  1.7209e+00 -6.8727e+00  1.3254e-01
-#>   8.7612e+00 -5.2527e+00 -6.8012e+00 -9.3108e-01  6.9843e+00  1.3341e+01
-#>  -1.4546e+01  9.7236e+00 -6.3226e-01 -4.3340e+00 -5.9874e+00 -3.2339e+00
-#>   3.9066e+00  2.2284e-01 -1.4156e+01 -3.8453e+00 -8.6821e+00 -1.9315e+00
-#>  -1.5990e+00  7.5505e-01 -1.8868e+00  1.4217e+01  1.5455e+00 -2.3546e+00
-#>  -1.0250e+01 -7.7651e+00 -7.1299e+00 -3.6324e+00  3.5405e+00 -4.7819e+00
-#>   1.4602e+01  1.3240e+00  9.4274e+00 -8.6895e+00 -5.1178e+00  2.1310e+00
-#>   8.2942e+00 -2.1216e+01  9.4975e+00 -7.5825e+00  1.0006e+01 -2.7521e+00
-#>   1.1945e+01 -1.4659e+01  6.7279e+00 -6.1521e+00  4.0640e+00 -6.7216e+00
-#>  -6.6725e+00  2.7873e+00  8.7322e+00  9.3220e+00  5.0820e+00  5.8792e+00
-#>   3.6809e+00 -1.6383e+01 -1.5499e+01  1.1922e+01  3.0115e+00 -2.6662e+01
-#>  -3.1499e-01  7.1257e+00 -1.5790e+01  1.0345e+00  2.9703e+00  4.5194e-01
-#>  -1.3069e+00 -1.4656e+00 -2.5011e+00  8.5862e+00 -3.4828e-01  1.6991e+01
-#>   2.6757e+00 -2.0333e+01  1.2204e+00 -5.4096e+00  6.6630e+00 -6.8385e+00
-#> 
-#> Columns 25 to 30 -1.5597e+00 -4.3700e+00 -8.6704e+00 -4.0963e+00 -1.6321e+01 -4.3423e+00
-#>   2.9396e+00 -5.9848e+00  2.3327e+00  9.8268e+00  1.4889e+00  1.8608e+01
-#>  -3.4125e+00  9.3013e+00  1.5506e+00  1.0031e+01 -5.2014e+00 -6.4869e+00
-#>  -3.6186e+00 -5.2734e+00 -8.8278e+00  1.1643e+01 -1.0888e+01 -7.8657e+00
-#>   8.9388e-01 -3.4935e+00 -1.1399e+01  8.2926e+00  1.1714e+01  1.8860e+01
-#>  -2.4677e+01 -1.4618e+01  1.2835e+01  2.7846e+00  3.1225e+00 -7.5194e+00
-#>  -3.2098e+00 -1.2272e+01 -7.2635e+00  2.9061e+00 -1.4480e+00 -7.6684e+00
-#>   6.1910e+00  1.7474e+00 -1.5868e+00 -1.4324e+01 -3.1537e+00  2.5270e+00
-#>  -1.4938e+01 -2.9654e-01 -5.9241e+00  4.5311e+00  5.1367e-01 -4.2442e+00
-#>  -1.3926e+01  1.4841e+01  1.2362e+01  3.4737e+00 -3.6818e+00  6.5808e+00
-#>   5.6785e+00  4.2856e+00 -4.1781e+00 -4.9512e+00  1.3393e+01 -4.2968e-01
-#>  -5.5229e+00 -1.6487e+01  6.4846e-01  7.7945e-01  1.2717e+01  1.9683e+00
-#>   4.8212e-01 -1.0946e+01  5.6029e+00  1.0808e+01  1.0435e+00 -4.6982e+00
-#>  -8.2592e+00 -5.3752e+00 -5.2363e+00 -5.4708e-01 -7.7221e+00  1.0334e+01
-#>  -5.4707e+00  2.1817e+01  6.3952e-01  2.8722e+00 -7.4404e+00 -9.4587e+00
-#>  -9.4711e+00  3.9907e+00  2.3350e+00  6.3608e+00  1.4797e+01  8.6591e+00
-#>  -1.5397e+01 -1.2832e+01 -1.2764e+01 -9.1527e+00 -1.3297e+00  3.4214e+00
-#>   2.7281e+01 -1.0290e+01 -8.7485e-02 -3.3851e+00  6.6595e+00 -2.6885e+00
-#>   7.5860e+00  4.2211e+00  1.8520e+01 -1.6380e+01 -5.6852e+00 -1.8309e+01
-#>  -4.2865e+00  1.0817e+01  6.2852e+00 -7.2758e+00 -4.1168e+00  3.6913e+00
-#>   2.4672e+00 -1.1057e+01  2.4086e+00 -4.3137e+00  9.9728e+00  1.6034e+01
-#>  -6.7063e+00 -6.8820e+00  4.8363e-01  1.1105e+01 -1.0333e+01 -1.6367e+00
-#>  -2.1280e+01 -4.1415e+00  6.0902e-01 -1.1766e+01  9.4662e+00  1.6602e+01
-#>  -1.0490e+00 -9.4947e+00  8.6725e+00  3.2525e-01  4.2604e+00 -2.7379e+00
-#>   5.9630e+00 -5.8807e+00  1.1127e+01 -7.1153e+00  1.0029e+01 -1.1379e+01
-#>  -2.1555e+00  6.7623e+00  2.2071e+00 -1.9433e+00  1.1161e+01 -7.5301e+00
-#>  -1.9755e+00  7.1774e+00 -2.1621e+00 -4.2744e+00 -1.2805e+01 -1.0321e+01
-#>   1.6206e+01 -9.0574e+00 -1.3386e+01  1.4836e+00 -3.6717e+00  1.9019e+00
-#>   1.8684e+01 -1.0844e+01 -3.7766e+00 -1.4817e-01 -6.8664e+00 -1.0706e+01
-#>   4.5852e+00  1.4066e+01  9.4515e+00  7.7569e-01 -4.8496e+00 -1.4866e+01
-#>  -2.0469e+00 -4.9087e-01  7.5441e-01 -2.6077e+00 -3.6706e-02  1.2476e+01
-#>   1.8071e-01 -7.4970e+00 -3.3489e-01  6.6271e+00 -2.0375e+00 -1.4445e+01
-#>  -1.8088e+00  4.1301e+00 -2.2527e+00  2.1262e+00 -3.4904e-01  6.9723e+00
-#> 
-#> Columns 31 to 36 -6.5040e+00  5.8752e+00  3.1383e+00  1.1837e+01  6.4853e+00  1.0323e+01
-#>   4.4193e+00  2.0463e+00  5.9618e-01  7.4200e+00 -1.3114e+01 -1.4656e+00
-#>  -1.0508e+01  3.8826e+00  5.1851e+00 -9.6904e+00  1.1859e+01  4.9136e+00
-#>  -4.1396e+00 -7.9817e+00 -5.4383e+00 -1.7414e+01  7.0854e+00 -9.6429e+00
-#>   2.2758e+00  6.2357e+00  9.3515e+00 -6.7403e+00  1.8872e+01 -5.0694e+00
-#>   5.8505e+00  4.4309e+00  1.3236e+01  1.0722e+01  8.9095e+00 -2.9298e+00
-#>   6.5115e+00  3.5489e+00 -9.1628e-01  2.5802e+00  7.0845e+00  1.8733e+01
-#>  -3.0491e+00  3.2534e+00  1.1997e+00  8.8833e-01 -2.1584e+00  2.3641e+00
-#>   1.6468e+00  4.4843e+00  3.3471e+00 -1.8669e+00  5.9386e-01  8.3609e+00
-#>  -2.0089e+00 -1.9370e+01 -2.9176e+00 -5.4528e+00  5.9043e+00  8.6076e+00
-#>  -9.3280e+00  6.0964e+00  3.1025e+00  1.0701e+00  1.0776e+01  9.5514e+00
-#>   6.1485e+00 -3.3274e+00  8.4756e+00 -4.8317e+00 -2.7427e+00  1.5232e+00
-#>   4.6530e+00  9.2008e-01 -6.9505e+00 -6.6205e+00  7.9752e+00 -1.4570e-01
-#>  -2.1591e+00 -1.0310e+01 -1.2721e+01 -2.1465e+01 -1.4941e+01  5.3263e+00
-#>   8.0429e-01  9.5050e+00  5.3638e+00 -5.6013e+00  2.7263e+00 -3.2744e+00
-#>   2.6271e+00 -7.3372e+00  6.6152e+00  2.8711e+00  1.1847e+01 -3.4755e-01
-#>  -3.4368e+00 -9.0752e+00 -1.5055e+00 -3.1626e+00 -9.8245e+00 -4.7415e-01
-#>   2.9191e+00  2.4938e-01  1.8888e-01  9.8061e+00 -1.7531e+01 -6.0103e+00
-#>  -4.7865e+00  1.6937e-01 -5.0390e+00  4.5317e+00  2.9974e+00  1.9773e+01
-#>   2.9439e+00  4.0795e+00 -3.9380e+00 -6.3246e+00  1.0128e+00 -4.5317e+00
-#>   2.6851e+00 -5.0098e+00 -6.2676e+00  6.7208e+00 -1.0663e+01  5.7691e+00
-#>  -1.2311e+01 -6.5510e-01  7.9312e+00 -1.9693e+00 -6.8400e+00  1.4329e+01
-#>   9.2061e+00 -5.5078e+00 -5.9201e+00 -1.1016e+01 -1.0204e+01 -9.2475e+00
-#>   1.3350e+01  1.9800e+00 -9.1722e+00 -3.6948e+00  8.2696e+00  8.0762e+00
-#>   4.6315e+00 -7.5339e+00  1.5947e+00 -1.1322e+01 -8.3186e+00 -6.6386e+00
-#>  -5.7139e+00 -1.3925e+01 -2.3077e+00  1.1494e+00 -7.2648e+00  2.1355e+00
-#>  -4.2203e+00  2.6027e+00  1.7255e+00 -5.4968e+00  3.3107e+00 -7.9746e+00
-#>  -3.7495e+00  1.9044e+00 -5.6979e+00  1.2216e+00  2.1435e+00  4.0822e+00
-#>  -4.3472e+00 -1.2315e+01 -5.7846e+00 -9.3757e+00  6.5540e+00 -5.8813e+00
-#>  -4.1403e+00 -3.7812e+00 -4.1460e+00  3.9681e+00  1.9608e-01  3.2650e-01
-#>   1.4559e+00 -1.7231e+00  1.6940e+00 -5.9361e+00  9.1474e+00 -6.8446e-01
-#>  -5.8992e+00 -3.6607e-02 -4.8166e-01  4.3366e-01 -1.3624e+00  8.6887e+00
-#>   5.1203e+00  4.1813e+00 -9.7441e+00 -2.3176e+00  6.0868e+00  8.1336e+00
-#> 
-#> Columns 37 to 42  1.1062e+01 -2.0296e+00  8.4705e+00 -3.9911e+00  8.3573e-01  1.9506e+00
-#>  -1.1974e+01  5.3647e+00 -1.1954e+01 -5.3444e+00 -1.2646e+01 -1.6711e+01
-#>   2.1595e+01 -4.4778e+00  1.0384e+01 -5.3072e-01 -9.7432e+00 -1.0414e+01
-#>  -1.0581e+01 -4.4263e-01  4.7731e+00 -2.6030e+00  6.7749e+00 -1.6233e+00
-#>  -9.3047e+00 -1.5634e+01 -1.5564e+01 -1.4220e+01 -1.4309e+01 -6.0601e-01
-#>   5.9982e-01 -4.9061e+00  3.9769e+00 -6.1671e+00 -1.0324e+01  4.0731e+00
-#>  -4.3588e+00 -5.6208e+00  6.1008e+00 -9.9672e+00  1.3759e+00  4.8355e+00
-#>   7.6434e+00 -1.1719e+01 -4.9626e+00 -5.4398e-02 -9.2290e+00  5.2844e+00
-#>   1.2625e+01 -1.1581e+01  1.8633e+01  5.7880e+00 -1.1121e+01  2.0442e+01
-#>  -3.0447e+00  5.1596e-01 -1.4120e+01 -2.8625e+00  5.8997e+00  4.8108e+00
-#>  -1.3048e+00  1.6969e+00 -6.9976e+00 -2.0704e+00 -1.4876e+01  1.3682e+01
-#>  -1.0865e+00  3.1554e+00  1.2313e+00  8.0548e-01 -2.0513e+01  7.1541e+00
-#>  -1.4557e+00  2.9544e+00  2.4083e+00  1.6820e+00  7.4094e+00  2.2584e+01
-#>  -2.5237e+00  1.0195e+01 -2.6441e+00 -3.7556e+00 -3.9881e+00  2.7679e+00
-#>   1.6696e+01 -7.1908e+00  2.1827e+00 -2.7923e+00 -1.9770e+01  5.8063e+00
-#>  -1.1795e+01  1.0671e+01  2.6017e+00 -8.2290e+00 -1.6968e+00  7.3746e+00
-#>  -1.9721e+01  3.6066e-01 -2.9877e+00 -8.9195e-01 -2.4205e+00 -1.4037e+01
-#>  -1.7180e+00  2.5656e+01  1.3118e+01  2.6885e+00  2.4311e+00 -1.9627e+01
-#>  -1.2209e+00 -1.7275e+01 -1.0422e+01 -1.7955e+00 -3.1315e+00  2.8352e+01
-#>   5.5952e+00 -1.3472e-01 -1.9804e+00 -2.7732e+00 -3.5788e+00  1.0023e+01
-#>   4.8103e-03  1.3171e+01 -7.5450e-01  8.6808e+00  7.5673e+00  1.6787e+00
-#>   7.9544e+00  8.2651e+00 -1.1695e+01 -1.4135e+01 -3.2763e+00  6.9574e+00
-#>  -4.4499e+00  4.4580e+00  1.5868e+01 -4.1087e+00 -8.7055e+00 -4.7099e+00
-#>  -9.6114e-01  9.5251e-01  1.4457e+01  2.6207e+00 -2.6928e+00  1.6989e+01
-#>   1.2193e+01  7.8603e+00  5.9201e+00 -2.7639e-01  9.1031e+00 -1.2982e+01
-#>   5.6224e+00  6.9850e+00  1.0509e+01  1.2624e+01  1.2922e+01 -1.3763e-01
-#>   1.1296e+00  5.3891e+00  2.9965e+00 -7.8478e+00  1.1889e+01 -2.1520e+00
-#>   1.0341e+01 -1.0417e+01  7.8661e+00 -8.1170e+00 -4.0068e+00 -7.5216e+00
-#>   4.2167e-01  9.3623e+00  6.6411e+00  2.1331e+01  1.4457e+01  8.5066e+00
-#>  -5.5189e+00  5.7353e+00  8.0404e+00 -3.5269e+00  1.4370e+01  8.0252e-01
-#>  -5.0023e+00  8.6836e+00 -1.6432e+00 -3.7779e+00 -1.8732e+01  7.3776e+00
-#>  -2.2673e+00  1.3026e+01  1.6185e+01  5.9499e+00  8.0218e+00  1.3399e+01
-#>   4.0683e+00  4.2663e+00  1.6034e+01  2.7645e-01  7.4202e+00 -2.4128e+00
-#> 
-#> Columns 43 to 48 -1.2115e+01  1.1046e+00 -1.2515e+01  2.2028e+00 -7.2036e+00 -4.0879e+00
-#>   3.2179e+00  1.7676e+00 -1.1806e+00  1.7817e+01 -4.8552e+00  4.6124e+00
-#>  -4.8195e+00  9.0433e+00 -4.6589e+00  7.1531e+00 -1.0476e-01 -2.9681e+00
-#>  -9.5588e+00 -2.9691e+00 -3.7199e+00  8.4445e+00  5.2903e+00 -3.8193e+00
-#>  -5.4232e-01 -3.8415e+00 -7.7434e+00  2.5880e+00  1.6367e+00 -1.3412e+01
-#>  -8.0844e+00  3.3727e-01  5.1215e+00 -1.5609e+01 -5.1258e+00  8.3984e+00
-#>  -2.3635e+00 -3.6531e+00 -5.6427e+00 -8.5791e+00  5.5740e-01 -1.5238e+01
-#>  -4.3731e+00  1.1792e+00  1.4618e+01 -2.5758e+01 -1.0487e+01 -2.2543e+00
-#>  -1.5963e+01 -1.3927e+01  2.1588e-01 -3.2920e+00  9.5727e+00 -5.3125e+00
-#>   2.9978e+00  8.8854e+00  6.2099e+00 -4.2905e+00 -9.6987e+00  1.4991e+00
-#>  -2.1656e+00 -7.8803e+00 -4.0464e+00 -1.5159e+01 -1.1318e+01 -4.2191e+00
-#>  -6.2049e+00  5.2066e+00  1.2489e+01 -7.5448e-01  7.2532e+00 -5.8020e+00
-#>  -5.8869e+00  1.3076e+00  1.9979e+01 -9.7389e+00 -1.3736e+01 -1.6782e+01
-#>   5.1603e+00 -6.6834e+00  5.8808e+00  9.0616e+00  1.1712e+01 -6.1902e+00
-#>  -6.4048e+00  7.1058e+00 -1.6456e+00  2.8912e+00  3.1004e+01 -1.2531e+01
-#>   6.8006e+00  1.9610e+01 -9.1857e+00  4.4045e+00  2.9447e+00  1.6091e+01
-#>   1.6478e+01  4.0959e-01 -2.3223e-02 -7.2793e+00 -2.9199e+00 -1.1647e+01
-#>  -9.9995e+00 -8.1696e+00  1.4737e+00  6.5148e+00 -1.0667e+01 -3.2807e+00
-#>   2.2444e+00 -2.0464e+01  3.0094e+00 -2.6361e+00 -3.3323e+00  1.1333e+01
-#>  -1.0368e+01  7.5013e+00  1.0117e+01  1.3035e+01 -7.9614e+00  8.9583e+00
-#>   5.1484e+00 -3.3187e+00  4.4564e+00  4.0747e+00 -1.4273e+01 -4.7209e+00
-#>  -6.8290e-01  5.9370e+00  1.9618e-01  2.6352e+00  8.7978e+00  1.3552e+01
-#>  -3.7305e+00  2.9977e+00  1.2201e+01  8.4139e+00  1.1424e+01 -4.4993e+00
-#>   1.6751e+01 -8.0573e+00  1.2008e+00 -3.8915e+00  1.2050e+01  1.0548e+01
-#>   6.2575e+00  1.6294e+01  3.5308e+00 -1.2285e+00  4.1870e+00  8.4701e+00
-#>   3.4958e+00  1.4690e+01  8.2981e+00 -1.0359e+01 -1.0257e+01 -7.3372e+00
-#>  -6.5664e+00  9.0567e-01  1.2777e+01 -3.5199e+00  2.0997e+00  4.1423e+00
-#>   8.9526e+00 -1.3091e+01 -1.2533e+00  6.2042e+00 -7.8835e+00 -1.1464e+01
-#>   9.2127e+00 -3.9388e-01  7.7766e+00 -1.4394e+01 -1.3689e+01 -1.4557e+01
-#>  -3.7454e+00  3.2210e+00 -1.3385e+01  4.2240e-01  8.1938e+00 -5.3303e+00
-#>   2.7591e+00 -4.6040e+00  2.7475e+00 -5.0884e+00 -1.3997e+00  4.3406e+00
-#>   4.6150e+00 -9.1322e+00  2.9752e+00  1.5551e+01 -1.3454e-01 -1.5530e+01
-#>   1.6372e+01  1.1288e+00 -3.9405e-01 -3.8099e+00 -6.5638e+00 -7.1677e+00
-#> 
-#> Columns 49 to 54  2.5190e+00  1.1175e+01  1.1601e+01 -7.7790e+00 -5.6342e+00  3.8789e-01
-#>   7.7460e+00  1.1136e+01  1.7623e+00  6.0582e+00 -1.1285e+00  2.3664e+00
-#>   3.6941e+00  5.5731e+00  1.9186e+00  4.5717e+00 -1.0011e+01 -3.9924e-01
-#>   1.0815e+01  2.3231e+01 -4.9671e+00 -4.7148e+00  7.6496e-01  2.7236e+00
-#>  -8.7334e+00  7.1061e-01  2.8245e+00  7.4412e-01  1.5410e-01  5.2671e+00
-#>   9.0524e+00 -4.5650e+00  6.4491e+00  1.6855e+00  7.0050e+00 -9.4149e-02
-#>   8.5457e+00  1.3220e+01  7.7125e+00 -6.8548e-01  3.0141e+00 -5.4146e+00
-#>   4.9509e+00 -2.3485e+00  1.4033e+01  9.0368e+00  3.3652e+00  5.8068e-01
-#>  -2.0652e+00 -5.7014e+00 -3.8886e+00 -5.5111e+00 -5.6780e+00 -3.0847e+00
-#>  -2.7895e+00  3.7967e+00 -1.0079e+00  1.4597e+01  4.4697e+00  3.1462e+00
-#>  -5.1495e+00  2.1150e+00  5.2226e+00 -1.7447e+00 -4.7373e+00  2.6336e+00
-#>   1.9226e+00 -1.4160e+00 -4.5898e+00 -6.5440e+00 -2.6794e+00  3.3858e+00
-#>  -9.6982e-01  4.6653e+00  9.2560e+00 -5.5485e-01 -1.1931e+00  1.4302e+00
-#>   3.4949e+00 -6.4405e+00  9.6045e+00 -2.4561e+00  5.1457e+00 -4.2440e+00
-#>   4.8166e+00 -3.9878e+00 -3.9815e+00 -1.3134e+01 -1.0717e-04  3.4807e+00
-#>  -6.2559e+00 -4.7228e-01 -6.3469e+00  6.4103e-01 -3.5107e+00  3.2496e+00
-#>   2.0834e+00  1.0204e+01  1.1521e+01  1.6283e+01  6.7450e+00  4.0019e-01
-#>   5.8669e+00  8.3477e+00 -2.4140e-01 -5.7797e+00 -2.8879e-01 -3.9505e+00
-#>  -5.4113e+00 -2.5000e+00  6.8480e+00  2.3189e+00 -2.7195e+00  3.8497e+00
-#>   3.4269e+00 -3.1728e+00 -1.1029e-01 -9.0684e-01  1.3045e+00  1.5835e+00
-#>  -8.3938e+00  4.9229e-02 -2.7664e+00  3.1438e+00 -3.9460e+00 -2.1360e+00
-#>  -4.3670e+00 -3.5643e+00  2.9556e+00  8.9076e+00 -6.1031e+00  1.0562e+00
-#>   4.2641e+00 -9.5336e-01 -3.9316e+00 -3.7728e-02  7.7775e+00 -5.3253e+00
-#>   2.4582e+00 -7.1300e+00 -3.4889e-01 -3.3291e+00  2.4103e+00 -5.9407e-01
-#>  -2.3643e+01 -9.1169e+00  2.0369e-01  1.8650e+00 -5.5027e-01 -2.4543e+00
-#>  -2.8845e+00 -4.8286e+00  8.2757e+00 -1.1491e+00  7.9805e+00 -1.8078e+00
-#>   6.6483e+00 -1.0365e+01 -8.0959e+00 -8.7907e+00  1.4690e+00 -7.9068e-01
-#>  -8.7006e-01  1.8929e+00  4.0245e+00  1.9152e+00 -2.2162e+00 -2.2415e+00
-#>   3.6591e-01 -5.5330e+00 -8.4923e+00  1.8120e+00  5.9263e+00  3.9024e+00
-#>  -6.2291e+00  8.7485e+00 -1.9414e+00 -7.9282e+00  4.7886e-01  4.3102e+00
-#>   5.4525e+00 -1.2424e+01 -7.3165e+00  2.8767e-01 -1.4393e-01  4.6123e+00
-#>  -1.2733e+01  4.2735e+00 -2.6760e+00  1.1882e+01 -5.1786e+00  5.2276e+00
-#>   6.1855e+00 -8.2518e+00  8.2563e+00  8.2125e-01  1.9322e+00 -2.0311e-01
-#> 
-#> (13,.,.) = 
-#>  Columns 1 to 8   3.3697  -1.6417  -2.5881   1.4601   4.3656  -6.9630  -4.3418   0.4763
-#>    1.1765  -1.5559   2.3349  -1.8196  -0.6485  10.1627  -6.8879  -7.2488
-#>    0.8502   1.8956 -11.6411  -5.9180  -5.4087  14.6186 -24.2376   0.7743
-#>   -1.4274   3.4590  -9.0911   8.5080  -2.2069   9.8465  12.1775   6.1672
-#>    2.4107   3.6783   0.7586  -9.3225   4.0492   3.9703  -6.0808   0.2643
-#>   -2.4323   1.7145   6.8121  -6.2586   8.6515  -8.2777   9.1493  -2.4481
-#>    4.9859  -0.4675 -15.7742   6.5457  -1.6402   0.0901  -6.2409  -7.5844
-#>   -4.2467   2.7866  -5.5029   6.2417  -0.0762  -4.2140  -9.9406   8.5973
-#>   -1.1883   7.0216  -1.1023  -6.5494  -6.9241   4.7190  -8.5682   6.3939
-#>   -0.1639   5.8292  -8.6185 -16.1194   3.0383   6.4299  13.7305 -25.3119
-#>   -3.3431   1.2020  -3.0795  -5.2280   8.8131  -1.5852  -3.8657   6.2194
-#>   -0.8473   0.6341  -5.4182  -2.6758  -2.1707  -6.6575  -0.5795   0.3148
-#>   -0.2078  -1.1004  -7.5125   7.7188   4.9043 -11.1906  -0.4454   6.0457
-#>    2.4375   2.4327  -6.1229  -7.1326   7.1260  -7.1742   4.7441  -3.1286
-#>    0.3591  -2.6065   1.6676  -8.6926   7.5336   5.8167  -6.4093  -9.9438
-#>    0.9972   8.1101  -0.7143 -10.3995  -6.7773  10.8206  11.4318  -4.4148
-#>   -1.1437  -0.7181  -3.4500  -4.8707  -1.9937   5.6570  16.0217   5.5252
-#>    2.4227   8.3839  -2.4364   3.3629  -5.9968 -12.7720   7.5399  -6.6289
-#>   -1.8409  -6.0536   1.2139 -12.4322   8.8837  -0.7812   8.3287  -2.8039
-#>    1.9115  -4.3994  -9.7979  -8.3191   7.2758   4.4275 -16.5625  -4.0027
-#>    3.4311   2.6624   2.9892 -10.4464  -2.2261   1.0225   0.3791  -8.0550
-#>   -4.4709  -0.7903   0.7995   3.1568  -1.2654  11.3205  -1.4539  17.6271
-#>   -1.7305   9.0243 -13.2431  -1.9026 -16.2815   0.5502   5.2861   7.0291
-#>   -4.2350  -1.8609   0.3718   4.0760   0.5236  -8.7421  -6.9448   7.3357
-#>   -1.3744   3.5020 -12.2297  13.4129   1.6466 -11.8234  -8.9527   1.1651
-#>    2.0638  -4.1548   8.5688  -6.7970   5.9547  -7.3101   3.1767 -17.7759
-#>   -1.1274  -3.6897  10.8214  -4.8866  15.7748  -7.5586   4.6709   0.7997
-#>   -4.9754  -8.4870  -0.9146  -1.8098  -2.3627   9.0146  -2.1974  12.3374
-#>   -0.2340  -3.5367   2.8041  15.3531   0.9489 -13.3056  -0.0813  -1.9383
-#>    0.3238  -4.0122  -1.6916   9.1604   8.2029  -4.6214  -4.9758 -12.1864
-#>   -3.2851   6.1557   6.3449  -3.0137 -10.8634   5.8261   7.5779   5.0891
-#>    7.6729  -5.9596   2.6869  -4.4836  21.3971  -9.8410  -1.4419   5.5365
-#>   -2.3335   1.0808  -6.9140  -9.3190 -13.7158  -4.9634  -1.6641   9.4183
-#> 
-#> Columns 9 to 16   1.4272   6.9005  -8.1758  11.1291   1.9131   0.3419 -16.7107   2.3946
-#>    2.9979   9.6438  -1.1152  10.2243  10.0655   2.6768   0.7035  -7.3764
-#>    0.7594 -11.4686  -0.8667  -6.9460  -3.0995 -10.4431   9.9436  -4.9980
-#>  -12.8362   6.3509   8.7563   1.5577  -6.4292  -1.0006   0.2837   2.4161
-#>   -0.8063 -11.6076   5.4064  -1.6572   5.0197  -0.9882   6.2821 -11.3196
-#>   -0.4334  -5.9995   8.1809   9.3257  -7.2008  11.0680   9.0294  11.7098
-#>    1.7393 -10.8395  -3.5343  -2.6829  -6.3353   9.3118  -4.4164  -0.7906
-#>    3.3009  10.0124  -0.3810   5.4467  -6.9107  -2.5141   2.8867  -7.0754
-#>    9.0013   2.4283   5.4354   6.2346  -2.8392   3.9953   0.3989   7.1242
-#>   -1.1859   9.7886  -9.4103   4.9508  -3.2787   7.1588   0.6914   9.1727
-#>    3.9983  -4.8992  -4.3854   5.9145   8.3597   1.4387   5.1238 -11.0169
-#>    6.1850 -18.2773  16.1798  -0.8564   4.5464 -12.6405  16.6850  -5.5169
-#>  -14.1683  -3.7150   8.3266   3.9403 -10.1845   3.7448  14.6025 -10.5982
-#>   -2.1483  -1.9087  -4.0616   4.0809  -7.5876  -1.6227   6.5654 -12.7282
-#>  -12.4211  -2.9235   4.4465  13.7550  -3.7480 -11.1861  -4.5634   0.1438
-#>   -4.0812  -0.1487   1.5998  -2.6823  -1.8722   3.0849   0.9512  -3.3778
-#>   20.1330  -4.9240   0.1899  -1.7735  -2.1026   5.9774   1.7584  11.2253
-#>    2.7877   9.6033   2.8122 -10.5991  -4.5545   7.3662   3.2882   1.9574
-#>   -1.0704 -11.4845  17.3092   0.6163   7.4801  -0.4920 -11.8827   3.2849
-#>  -13.0745   8.8030  -2.1587  -2.8709 -12.8417 -10.1367  -7.5423  -9.6992
-#>   -3.0761   7.7702  -2.4629  -3.4838  -3.1326  -6.3012  -0.3314   0.4033
-#>   -0.9980   0.8173 -13.4021   5.3338   4.5769   0.6703   7.1546  -5.3624
-#>    6.4712  -7.9052  -8.2864 -10.3298  -5.9160  -9.6548  -6.2402  -3.2102
-#>    8.4025 -13.5483   4.8272  -1.4405  -7.0647  -2.4812  10.6723  -6.7507
-#>   10.1343 -10.1757  -5.0400  -3.4735  -3.4416  -8.9320  -2.7094  17.5644
-#>    9.3466  -0.2112   6.1917 -14.8537   7.7931   7.1170   8.1069   0.8100
-#>   -0.9844  10.4327   9.0464  -0.4420   0.5409  -7.9996   4.3075   0.8815
-#>    4.1023  -5.1221   1.8569   2.7524   1.9987  -3.7410   1.0878   3.2000
-#>    4.1913   2.2717  14.7937  -8.9496  -7.7735   1.2565  10.5878   8.3647
-#>  -19.0380   2.2726  -6.1166   9.6046   5.4028  -8.7725  10.7857 -15.7318
-#>   -6.8082  -8.1418   2.6750   3.2514 -13.1861   3.7538  14.1491 -20.7132
-#>    2.5634  -7.6944  -1.8655  -0.1787  -5.3450   5.7111   1.7207  -2.0251
-#>    2.7810  -2.1352 -14.3000 -14.9511  -4.4896   2.1630   4.6799 -10.1312
-#> 
-#> Columns 17 to 24   4.1233 -17.0780  -3.9538  -2.8758  -3.5483  -7.9644  13.6918   7.0413
-#>   -4.7687   0.5470   1.2882  -1.0949  -0.6276   1.5143  -9.6125  -1.6341
-#>  -15.2562  -1.1686 -16.4718   8.9318   7.9628   9.2988  -2.5182  -9.5424
-#>   -0.6619   5.0462 -12.8017   1.7106 -15.0936  14.4036 -13.6283 -22.3025
-#>   -8.0790  -0.9867   3.7640   7.9434   1.0856  11.9036  15.1968 -13.2958
-#>   -4.3092  -4.7185  -0.5972   7.3328   5.9813   0.2981   7.5770   5.4590
-#>    9.5222 -13.4433  -6.4562  -3.4667   3.6051  19.5683  -0.8224  11.0804
-#>   -7.5286  -8.2387  -3.8117   9.0431  -1.1148 -11.5416   2.0163   2.2055
-#>   -2.1770   6.3546  -7.6080  -5.0374  -6.2474  -7.7696  18.1866   6.6824
-#>   -7.2299  -4.4548  -7.4621   2.6389 -14.8909  -8.9010 -13.5682  -8.0851
-#>   10.6522  -5.2483   4.8124 -14.9675  13.2051   7.2541  20.9926  -3.5390
-#>  -17.9549  14.9953 -11.2778 -11.2486  -0.9213   1.1407  15.7856   1.4826
-#>   13.3178  -6.0633  -3.3993 -16.2574  -7.6736   4.8620  11.2303 -21.1926
-#>   15.5492   3.6144   6.5018  -6.6918 -13.9382 -14.1719  -3.3112  -1.1664
-#>  -12.5781   6.3902 -10.8792   6.3373   8.9104  -0.7249   0.7847  -9.6297
-#>    5.4024   6.1987   0.7445   6.3244  -7.0532  13.4351  -0.4791  -7.8003
-#>   -8.9566   4.4521   3.5503   0.7959  -2.4220  -4.2371 -16.6129  -3.5669
-#>   -5.0858  -3.2332   4.4177  -2.7194  -5.7067   0.9085  -0.5359  23.8047
-#>    6.6007   1.4694 -14.1240   5.0184   2.4091 -14.1085 -10.9890 -13.9345
-#>   -4.3680  -6.0325   5.6439  -3.9697  12.0833  -1.6750  -1.3681  -2.9893
-#>    3.2750   1.4166   7.3507 -13.6353 -13.9985 -15.8404   9.4659   5.0599
-#>    7.0789   1.9307  -7.8050   5.8272  -6.3961 -15.0296  10.2564   8.1358
-#>    2.6946  -0.3610  -6.2785  15.8479   1.6669  -1.4408  10.4534  14.8757
-#>    7.3278   3.8470  -8.3954  -5.5899  -7.4549  -2.1057   4.4085  16.0832
-#>   -4.0996  19.4165  -3.9743   0.4099   4.5863   8.4134  -2.0642   4.1411
-#>   -7.2185  -5.7259  -2.9592  -1.7083  -2.6126 -13.4320 -22.0603   7.3150
-#>   11.2710  -4.9157   6.0592  -2.3185   2.4728   6.5344   0.5997  -0.1768
-#>    1.0739   4.0677  12.0173  -1.5614  -6.8807   1.5222   8.9047  -7.8059
-#>    1.2063   7.1509  -8.9546   8.0253  -1.3432  -5.8292  -8.4954  -1.0355
-#>    7.4239  -4.5134   1.7243  -5.2007 -10.9802  16.0628  11.1323  -8.3712
-#>    4.0806  -0.9152  -2.2090   5.6934  -4.1941  -4.5412   9.3974  -0.6221
-#>   15.5935   0.7072 -10.0603   5.7345   1.6642   8.9193   5.1053  -2.7519
-#>    7.1138 -12.1894   0.6080   7.8461  -8.7989  22.0221   6.3918   9.6505
-#> 
-#> Columns 25 to 32  -2.6800  -5.7408   5.5992   2.6666   5.3303  -4.8828 -11.9063   3.5820
-#>    6.6322  -2.1588  -2.1263   1.4245  18.6545  -7.0352 -17.6687  -3.6482
-#>   12.9900   1.3050 -11.1928  -9.5810  -5.0537   8.9391   2.4537  -3.9121
-#>   11.7591   9.5109 -13.7638  -6.9739   6.2391   7.8595 -18.5428 -13.9119
-#>   -7.2973   4.5046  -8.6841   0.8250  -1.5067   9.7065   7.1418  17.4973
-#>   -5.0957   4.2618   6.5113  10.0076   2.1287   1.5233  13.4246   1.1008
-#>   13.6883  12.8369   2.2686   1.3899  -3.6863  -4.7827 -18.9132   0.2136
-#>    0.2509  10.7896  15.7100  -2.1480   2.9210 -11.5030  -2.3106   1.9727
-#>    1.2314 -13.5424 -16.2364 -15.6695  -8.0330  -4.8299  -2.7412 -10.9407
-#>   -7.7083   9.8008  -1.3087  -8.5017 -15.5218  -5.4738 -14.0310  -5.9766
-#>    3.9822  -3.2992   4.6238  -6.0679  -6.0140  -5.3986  -3.0122   7.2745
-#>    0.5601   2.7388 -11.0853  -0.6145  -2.6784  -3.7761   5.2345 -10.7856
-#>   -4.9741   3.9108  19.4934  -4.8232   9.6452   1.3108   0.9940  -2.7460
-#>    1.2843  -7.5330  -7.7228  12.3964  -4.2323   1.3258  -1.2630 -16.1458
-#>   -1.2016  12.5133 -14.4585   5.6997  18.9993  10.7756   5.3936   7.2677
-#>   -1.4915   2.3885   3.8869  -7.1487   7.6098  -3.9885  -3.6000   1.5721
-#>   -9.4018   6.0380  -8.7007  15.1696  11.1714   3.3346   8.8122  -1.4180
-#>    8.4743  -7.6606   2.2928   6.4204  -2.3149   1.3092   0.0668 -23.7015
-#>  -19.6935   7.9098   4.7952  -0.2914  -5.0775   5.1258 -13.0558 -19.8275
-#>   14.4639  -7.6469   1.4158  -1.6512  10.6585   7.2336 -15.5307   4.4070
-#>  -20.4726  -3.5960  16.8774  12.4712 -10.9533 -14.0708   0.3716  -0.7920
-#>   -6.4765   1.7232  11.5085  -4.0904  -4.2319 -14.4870   3.9247  12.0280
-#>    8.0161  -5.6225  -4.3256  -0.7993   5.5562  -0.6457 -10.7856  -8.2703
-#>    5.5643  -2.1936 -12.2500   1.4277  -7.6654  -3.2104  -5.2612   9.9382
-#>    5.8360  -5.2719 -11.2102   3.7008  -4.7814   4.4835  -3.3795 -11.0323
-#>   -5.5010  -5.6203  -4.4000   7.3807  -1.5143   0.2017   1.5784  -7.5409
-#>    2.7717   3.4020 -14.4807   5.5100 -13.9762  -4.2596  -3.8514   7.4326
-#>  -16.6801  -2.2176  -3.4166  -3.9260  -0.4806   1.3265 -18.2089  -8.6009
-#>   -0.0372   9.5898 -11.3903  -1.9474  -2.1608   0.1678  10.6816   0.2370
-#>    7.6345   9.2515  17.8873   3.6420  -7.1994   5.8380  -6.9410   6.9322
-#>    7.5231  -5.2615   4.9636  -4.4058  -2.2565  -5.9325   5.9708   1.2859
-#>    4.2971  -4.3269  -0.0417   0.7345  21.4405   3.9511  12.8331   0.6572
-#>    6.0391  -3.2032  -9.0262  -4.9702  -5.1867   6.1670  -3.9718   0.8178
-#> 
-#> Columns 33 to 40  10.8348  14.7557   3.3659  -0.1846 -11.8096   4.6553   4.4372   3.0805
-#>   -1.4661 -23.1553   9.9883   2.0956   4.4233 -18.6846  -8.7162  -8.2316
-#>   -7.0449   2.0095   3.8353  -5.8263  19.1000 -13.2257   3.6385   7.9960
-#>   17.4574 -10.0265  -0.9992 -17.1924  -1.2527 -11.1004   3.5991   4.4013
-#>    0.8899   2.4346  -4.6935  -5.2829   3.8203  -5.5734   3.9520  16.4752
-#>    4.7666  15.8362  -2.0676  -1.8818   1.2573  20.5019 -14.3063  17.9730
-#>   17.2786 -12.8405  -3.7920   5.0189   1.6979   0.9932  12.1372   0.0466
-#>    3.7036   6.8519  -9.6157   6.8130  -9.7540  -3.0379   8.6012  -5.9710
-#>   -5.5511  15.5132  -6.4415 -14.1951   3.1228  -9.8643  -1.0604  10.2775
-#>   14.2856 -10.0348 -12.9017  -7.8481   0.7524 -14.6779 -16.8381   8.3535
-#>  -10.8446   4.1617  -6.2735   5.5409   2.7467 -14.3498   7.3312   9.8202
-#>   -3.1855   6.0698   9.7219  -3.7233  -9.9178  -3.9497   1.5279  18.1157
-#>    2.7665   3.5974 -11.1750  -2.1840 -10.3358  -0.4430  14.4890  -2.0752
-#>   -7.7241  -2.5540   8.6497   8.3299   2.8415 -20.8746 -24.1512  18.1693
-#>    9.1557   9.8385  -3.6903   7.4441   0.4999  17.7083 -16.9265   3.5991
-#>   23.0757  -8.6160   6.5006  -4.3907  10.3004   0.7045   1.5834 -10.7031
-#>   -4.5615  -7.9168   2.6719  -0.5778   5.2735   1.1896  -2.8907  10.4492
-#>   -9.2825  -7.2149  -0.4448  14.7885  -2.6618  -8.3046   4.2973  -6.7991
-#>    6.1126  20.5788 -26.1840  -1.7718 -11.1458  -7.2249   2.3674  -9.6824
-#>   -1.3163   5.0976   3.1525   6.4770   6.3339   0.9214  -2.7082   5.1429
-#>   -3.1457  12.5447   8.5838   2.9981  -7.9624 -12.5725 -10.0365  -9.2533
-#>    0.9133   5.9405  11.3644 -18.7311   4.3938  -7.6613  12.7711  -2.6682
-#>   30.8365  -7.4982   0.9603  21.9556  -6.2884 -11.5021   4.4946  10.3176
-#>   14.4685   0.7428 -16.6316  -8.9628  -4.4752   3.2429  -1.9568  11.5486
-#>   15.3273  -3.5033  -3.1176   4.2342  -2.5407  10.6061  -6.7412   0.8499
-#>   -1.9486 -13.6522   1.0336   7.3432   0.7010  12.5901  -9.1793  10.5238
-#>   -7.0167  -0.1492  -8.6312   5.7787   2.9533  14.0871 -16.4314   2.4016
-#>  -18.2783  16.0457   8.4394  -4.0634  -8.0765  -7.8682  -6.6131   3.6371
-#>    3.8207  -4.2039 -16.9315   3.1379  -6.0499   1.3322   9.1753  -1.0800
-#>   15.7705 -12.9205 -13.6020   8.6659   6.2298   7.5931  -2.9307   1.7454
-#>    3.2957  16.8939  11.6730  -2.9389   8.2990 -14.7993  -2.8298   9.3996
-#>    1.9768   2.8376   3.7889   3.5501 -10.1581  -5.4513   8.9801  -7.9636
-#>   10.2112 -16.3559  -7.1759  -0.9930   9.6437  -1.5389  -1.1243  10.5625
-#> 
-#> Columns 41 to 48  -3.3578  -1.1139  -2.4762 -11.2209   9.3577  -8.8033  13.6489   4.8650
-#>    6.5548   1.7133  -0.1935   5.5874 -10.3480  -6.8775   3.3956  -6.4977
-#>   13.0252  -6.4013  -1.1414  11.4570  -7.0205   6.2467  -4.4751  -4.3536
-#>    1.8241  28.0728 -12.6101   8.6788   2.0485   6.1478 -12.4244 -16.7476
-#>   -8.6532  -8.1284   6.3043 -12.8859 -12.4685   9.6203 -18.7328 -18.8922
-#>  -15.2930  -5.9777  -0.0329 -28.5118  -3.1383   2.8168   0.3067 -13.5621
-#>  -14.5134  -3.2274  -0.0055   4.0627   2.8727   1.3799  -6.3114   0.0714
-#>   -0.6586   5.8188  -6.9768  -4.4756   2.2888   2.8688 -13.7075  11.0588
-#>   10.5744  13.1215  -2.0282  11.4851   2.1987   9.1812   3.6154  14.4257
-#>    3.2649  20.1229  -6.1985 -15.9335  -3.7413  -2.8519  -9.2608 -15.2161
-#>   -6.5173  -7.6655  -0.2815  -4.8623   2.1578  -1.2280   9.3485  -0.0405
-#>   11.4033   7.2183  -8.9260   1.6441   0.8568  -7.4824   1.2641   3.8432
-#>   -6.9809   6.6826  -9.4452  -2.2702   3.1859  -2.7236  -9.9576   1.2124
-#>   -5.0526  25.2570   3.2705  -8.0824   0.9330  -8.9063  14.1930  -0.4507
-#>    1.8832  -4.0172   0.1316 -12.6761  -5.1909   9.1281 -10.9429  18.3555
-#>   -3.4178   0.2063   1.9687   3.5427 -13.6490   3.5390  -2.1170  -7.5645
-#>   -4.9405  -9.4943  -8.6500  -7.4028  -4.2744 -15.9314  10.8900  -7.9999
-#>   -5.6152   0.7457  -8.4452  15.5308  -9.3691  -2.9904  10.8867   2.9986
-#>  -20.7752  -1.4896   7.0495  -8.1634   6.6833  18.6335  -6.1095  18.3737
-#>   10.9329   1.9044   6.1540  -2.9243  -0.0544  -3.7527  -3.9820  10.0857
-#>   -1.5593  -0.9560  16.8549  -3.6897 -10.3189  -0.4447  11.5176  -5.2639
-#>    6.5579  13.6265  -3.9144  10.4601   3.4433   9.1518   6.4529  -4.3566
-#>   11.3136  12.5980   7.1762  -5.4726 -20.0146  10.0288  -6.0000  -3.1094
-#>   -6.4761  -7.8402   9.1078   1.4559  -1.1450   2.7579   4.0419  -2.2974
-#>    3.5403  -6.0334  -3.7099   3.2879   6.2914  -9.1560   0.7688 -11.4910
-#>    4.6251  -0.9901   1.4971  -9.3215  -6.6628  -6.7178   8.7838   6.6056
-#>    2.6455   0.9620  13.9202 -10.0320  13.9161  -1.2996   6.1395   2.7685
-#>    0.3381   3.3796  -0.1898  -3.0536   7.6887   1.7112  16.5639  -2.5761
-#>    5.5124  -6.3711  -2.4787   7.4375 -13.7518   3.8744  -4.3973   2.8493
-#>   -2.6076  -9.5805  -3.0601 -13.9946   4.2460  -6.1053  -2.4415  -2.1601
-#>   -3.5259   8.7255  -4.6512   9.3553 -13.5940  -4.1108  14.2099 -19.3789
-#>   -9.5535   7.4221  11.2101 -15.1180   6.3322  -0.1481 -11.3736  12.0167
-#>   -4.7469   3.2653  -3.6165 -11.0855   5.2520  -7.3072  -6.8884  -0.6938
-#> 
-#> Columns 49 to 54  -9.5841  -3.1883   4.8819   0.9323   2.6546   3.5816
-#>   -0.0657  -3.6876   6.0528  13.6310  12.1754   0.8895
-#>    6.4921  -1.6472   0.3413  -4.9985  -5.0039  10.1445
-#>   17.9599  -1.9585   2.2322   8.4005  -2.2082  -4.5209
-#>    1.2629  -0.2790  15.5301   7.7452   7.6565   8.6430
-#>   -8.3178  18.0239   1.7017  -4.8925   6.3538   1.4147
-#>    1.9469   6.5868  -6.3767  11.1967  -4.1276  -3.9215
-#>   -6.0379  11.6491   8.1192  14.2532   2.8171  -3.9183
-#>   -7.5273  -5.7284  -2.2543   5.3219 -13.6635  -1.2019
-#>   11.3169  16.1229   5.3380   5.9345   7.8532  -1.9193
-#>   -1.1561   0.6397  -5.8041  -5.6023   3.9126   4.1689
-#>  -11.1197   9.0098   0.2090  -4.2656   5.6267   2.1441
-#>    0.2229  23.9966   4.7711  -3.8389  -2.0389   0.9057
-#>   -0.7366  -1.9622  -0.0843   5.3703   7.7059  -5.1786
-#>    7.1206  -9.2183 -13.2013   2.0726   5.0482   0.8396
-#>   14.2594   7.7721   3.2311 -13.0711  -4.3741   0.9782
-#>    3.7075  11.6253   2.4502   1.5064   0.6112   1.8650
-#>    0.5343   4.5191  -2.2040  -3.6395   3.1832  -3.9657
-#>  -10.2922   0.4036 -10.7387  -0.4683   7.3636  -4.6882
-#>    5.1110   2.4479  -8.0885  -0.8889  -1.2094  -0.3844
-#>   -4.1646  14.5772  16.0076  -7.5377  -3.2482  -3.9920
-#>    0.1385 -18.6015   0.7036 -10.1109  -3.2682   3.0401
-#>    4.0277   3.9081  -8.8889   2.1189  -7.2020  -7.7930
-#>   -5.4221  -2.3047  -2.3612  -5.3930   1.0088  -1.1463
-#>   10.4664  -0.2838   2.3142  -2.4206  -3.5280  -7.2229
-#>   -5.9612  11.5086  -9.4708   9.8642   0.9766  -3.3587
-#>   -0.9830  -5.6997   0.4194  -5.3989  -7.7813   0.5598
-#>    5.4461 -11.4660  14.4418  -5.9451  -5.2957  -3.8965
-#>    9.3387   3.2802  13.7992  -1.7918   2.2744  -0.3253
-#>   -5.5320   6.5413   0.8090   0.4279   1.2553  -0.8095
-#>   -6.7395   4.1986  -5.4247 -12.3497   4.4859  -1.4505
-#>   -0.5004 -13.5893  -4.6648 -16.0753  -8.4585  -0.8199
-#>   11.9082   0.3645  -0.3096  -5.3894  -1.3357  -2.1738
-#> 
-#> (14,.,.) = 
-#>  Columns 1 to 8   0.3770  11.4530  12.3254  -8.1717   2.0064   9.2537  -6.0489  -2.5420
-#>    3.3567   0.3686  -2.1060  -8.9118  -3.2569   8.7885  -6.6666  -0.7948
-#>    1.4497  -6.2063  -8.3565   5.7694   0.5264   6.2934  -8.0693  -3.4899
-#>   -3.0604  -2.0974   3.9263  -2.7745  -3.1499  14.5660  10.1938  -4.8976
-#>   -5.2067  -7.2476  -3.7690   2.6146  -5.7421 -14.2176  -5.4898   9.6478
-#>    2.7384   9.4170  -1.8324   9.9095   6.4603  -9.8938  -1.7702   5.1115
-#>   -4.0225   2.1869   4.1249  -6.0901   2.7495  -9.6204   1.9513   6.8786
-#>    6.3655  -0.8163  -3.4434   8.3752  -2.3094   6.4114  -4.9764   0.9664
-#>   -0.0141   2.9467   9.7410  -3.6164  -2.9697   3.0740   1.8683  15.1830
-#>    4.5287   7.7451   1.3771  -5.9612  18.3195   3.0365 -13.9349   0.0934
-#>   -2.5749  -7.0213   3.1655   8.8867   7.6434 -14.2088   6.4947   0.1312
-#>    0.2012   0.2631 -14.1806   9.5833   2.1844  -8.8791   6.7946  -6.6629
-#>   -1.3827  -1.3199   4.2433  13.1405  -5.5283   5.0318   7.1572  -6.3521
-#>   -1.7635  -9.2385   1.3914  -0.1005   3.9263   0.8530   2.5017  -0.9691
-#>    6.6711  -7.8012  -4.7570   8.3643  -3.3958  -8.0918  -3.4167   2.7507
-#>   -0.3402   0.0913   6.1923  -0.4942   9.8246 -10.3545  -3.3388  -2.1711
-#>   -0.1663   2.2720  -4.4339  11.3540  13.9740  -2.5846   2.5917   8.9986
-#>   -7.9547   3.4470   2.9946  -0.8519  -3.0121   5.5671   2.6166  -6.7560
-#>   -2.8500  14.7417  11.9077   0.6807  -3.3666  -5.2585  -1.2208   5.3802
-#>    1.9767 -10.0252 -11.7052  -0.6985   8.8981  -7.7196  -6.4009 -11.7299
-#>    0.5899   8.1488  -1.2487   3.1090  11.6321  -1.5359   4.3982  -9.4264
-#>    4.3843   3.0785   5.5632  -9.2855  -6.0409   0.6462   0.5483  -0.7019
-#>   -1.0347  -1.7948 -18.4069   4.8440   3.1485 -12.7971   2.4637  13.6364
-#>    1.7668   1.3412  -1.5615   5.8986   1.3487 -17.8216   3.9517   0.0958
-#>    0.1322   0.1879 -17.0802   7.1159  13.9271  -5.1603   1.4898   8.4073
-#>    1.5698   9.0832   4.5893   6.2004   6.9843  -1.5408  -6.6988  -3.6267
-#>    3.2580  -1.6388  -6.9130  -4.1207   3.7113   7.9904  10.7252  -1.3340
-#>   -2.4975  -2.1435   4.3989   9.1296   7.1549  11.2097   6.9807   4.6930
-#>    0.9505  -3.5208   3.4347  18.2746   4.8642  15.6792   8.6931   6.0316
-#>    2.9614  -2.0693  -0.3657  -4.5516   2.1412  -5.9561   0.8259   7.0303
-#>    0.2068  -7.9595   0.9035  -1.1417  -6.3314  -5.8986  -2.8805  -7.1754
-#>   -6.0468   1.5532   7.6197   4.5963  -9.7855  12.0498   4.3925  12.1035
-#>   -1.9815  -5.6976   4.5649   0.0077   1.1741  15.6724  -8.5468  13.0375
-#> 
-#> Columns 9 to 16  14.8517  -7.5338   2.0898   7.6614   9.8378  -4.6831 -10.0600  -2.1377
-#>    0.2623  -4.5503  -9.4165   8.9472   5.1810   1.5789  -4.1377  10.3595
-#>  -11.9591 -22.1948  -3.1148   9.7955  11.0973  17.6188 -16.4940   6.3169
-#>  -28.6606  -0.8674  -1.7593  -0.1612   7.4730   0.9168   9.7108   4.6269
-#>  -14.9942  -7.2785  -5.0295  -3.6556  -1.4412   3.0655  -4.7327  -2.4419
-#>    7.7481  20.4307   6.6899   3.0492  14.3508   6.8140 -18.2257  -9.2996
-#>   -6.0070   6.3098  -6.6324   9.5148  -2.5147   7.1132   6.0873 -21.6238
-#>    6.2963   4.4553   4.4620  15.4355  -2.1113  13.9785   5.0768   3.0637
-#>   -0.0940 -11.8444   1.7085   7.9298 -21.2709  12.9068  -1.5473   1.5120
-#>    3.5864  -1.8245  -3.1646   3.0038  17.3009   3.9642   4.0612 -17.3035
-#>    6.0550  -2.8465   6.7859   5.7545  -3.3479   7.4782  -0.4281   0.6236
-#>   -7.2762  -8.2545  -3.9465   1.5299   2.3039   3.3165 -10.5609  16.3569
-#>    1.4526   8.4141  -3.7688  -0.0326   6.0030 -10.1505  -4.4883   0.2529
-#>   -8.2809  -3.0374 -28.4246  -4.4572  15.7466 -10.8104  14.7449  -3.9600
-#>  -15.1718   4.2424   9.0816  -0.5463  -4.5732  -3.3233  -6.8652   7.9014
-#>    4.3162  15.7039  20.4732 -12.0980   7.2798  -3.6740  -1.9739 -12.1692
-#>   -2.6370   0.7528  -9.7031   1.6575   8.0723   8.4717  -2.0160   3.2745
-#>   -4.5273  -0.0800   3.9357   9.6299 -14.8624  -5.6028  -1.8641   6.1496
-#>    8.7650   8.5841   8.5647   4.0912 -27.8494   6.4642  -8.0686   4.8601
-#>   -5.1022  -7.4816   0.6639   0.6033   2.0572   4.6621  -1.3301  -3.6485
-#>   12.1442   3.4068  -2.5187 -10.2796 -14.7906   5.9140   0.7293  -5.2769
-#>    8.1061  -4.8040  -2.1649   0.6224  11.7789  16.9427   2.5081   6.6288
-#>    4.9209  -7.8456 -26.9416  -0.1341   0.9170  12.5050   8.5160  16.3678
-#>   -3.3597   8.2125   5.1914  -7.7349   7.0574  -6.7789  -8.3004  -8.7751
-#>    5.3466  -3.3574   6.1179 -13.2464  14.9734  -9.8865 -19.7086  19.1938
-#>  -11.9796   6.3637   5.0602   7.1964   1.0075   5.9998   6.7007  -9.5336
-#>   -4.5889  13.0579   5.0471 -13.9253   1.0684   3.8819   6.7362   5.6673
-#>   -5.1198  -4.9673  10.4607   0.9514  -6.6400   6.9641  -0.0188   8.3754
-#>    2.1722  15.0798   6.2502   5.0564   9.8680  10.7211  16.0995  -4.0884
-#>   -9.4826  -2.3942  14.4484  -1.4258   4.7605 -15.5945  -9.1665  17.3647
-#>   -9.1650   1.6061  -2.9621 -12.2872  -5.8349  -4.7291  15.9064   3.0888
-#>    8.6125  -0.0677  -1.3918 -11.1935   5.1555 -11.0749   7.4689  -3.5442
-#>   -2.2461  -0.5258  -7.8684   1.0981  11.2391  -1.9608   1.9035  -7.2017
-#> 
-#> Columns 17 to 24  -4.9053  -2.2667  -1.0283   9.6957   0.2477 -10.0500  -4.8617   0.0063
-#>  -13.0942   1.2088  -5.3956  -1.5164  15.8247   4.6247   3.8747  -0.5828
-#>  -10.8099   4.3983  -8.7463  -3.5179   2.2671   5.8489   0.7554  -5.0317
-#>   -1.0272   8.7758  -6.7657   4.8201   6.0469   6.8353  -1.8373  -8.9972
-#>   -4.4041   5.9650   6.4497  -1.7451 -12.9368  10.2491   8.8281   8.5340
-#>    9.2032 -14.1359 -18.1122  13.0184   6.0143  -9.3855   0.8192   7.0896
-#>    3.8926  -9.6341   4.3712  14.9302 -10.4555   2.5968   7.1624  -5.0525
-#>   -6.0445  -0.9045  10.0906  -2.3694  -1.8475  -3.1114   7.8476  -1.0478
-#>   13.0187  10.4528   4.5314   4.6289  -6.1119  -3.1832 -17.6575  -9.1404
-#>   15.6379  -0.9983 -15.1494  14.3846   1.5959   3.0012  -1.9903  -2.3397
-#>   10.5089  11.0922 -10.5190   3.0268  -6.0590   8.4724  -7.9977   6.2165
-#>    2.7381  -5.1110   5.9924  10.3621 -14.0646  -2.0999 -11.6789  -1.5407
-#>    2.9158  -4.4157   1.3596   9.1929  -6.0317  -2.0577  -0.3624   8.0497
-#>    0.9831 -17.8929  11.4422  15.9151  10.2596   0.3767  -3.8407  -6.7646
-#>    8.4754  -5.1325  -5.9244  -4.3481  -2.2820   9.5119  -3.3970 -18.9483
-#>    6.4051  -5.7837  -3.7830   2.3501  -1.5908  -1.0771  -7.2282   1.1143
-#>   -4.5312  -6.2030   1.3716   0.0105  -0.7657  11.8283  14.8129   4.3418
-#>  -20.4536   6.8824   3.8435  12.0614   7.8767  -3.0026   5.1493   6.4343
-#>    6.8865   8.7946  -7.3705  10.7327  -6.2733  -3.3637   3.3572 -12.2461
-#>    6.0896   1.9243  -4.6876  -1.3111   9.2689  -1.3074 -17.4840  -7.6172
-#>   -6.5077  24.6859   1.1896   6.5526   5.2203  -8.3758  -0.7854   2.7145
-#>    0.3053  -0.3795   5.7081  -9.1353 -10.9340  -7.9618  -7.4539   0.8218
-#>    1.8895   0.8432   4.5600  -7.0166  -5.9432   3.2469  -1.9923  -5.0270
-#>    2.7993  -9.1268  -3.3086   0.9909  -9.4538   3.3323 -20.2697   5.6602
-#>  -11.5537  -6.1840  -5.3921  17.5635  -2.1432   1.7829  -1.8565   8.2781
-#>    0.6469  -8.2766  10.0507 -10.9065  -8.3207  -8.7234   2.6877   1.4146
-#>    3.9395 -10.7987  -2.3744  -0.4842   9.9037  -6.3236 -16.1055   1.0521
-#>    0.3880  21.7167   8.6837  -1.8329   9.5305   7.4091  -7.5914  -9.3135
-#>  -11.2178  -0.9932   4.0680 -18.4544   7.8578   4.7529  -9.2014   3.2285
-#>   -5.4010   8.5502  -7.7846  -0.5676  -3.9020   7.2746  13.7788   4.3461
-#>    7.3959   3.2083  -5.1978  -0.5509   8.3545  -5.1124 -16.1492   1.5927
-#>   -5.4056  -7.4793  -1.7978  -0.9334 -20.6932   3.2113   8.4319 -12.3685
-#>   -1.3231  -0.5456  -8.2329   1.9113  -5.4219   1.5084   1.4032   7.3739
-#> 
-#> Columns 25 to 32  -4.0028   4.8457  -5.0337  -5.2634   5.4246  -7.4923  -6.1144  10.9337
-#>   -5.0902  -2.3824   0.2125   8.0339   1.0811  -8.2466  -3.7509  -5.0870
-#>   -9.6053 -12.6881   6.7873   7.1377  11.8440  -9.8080   9.0595  12.2712
-#>    0.4937   4.3730   8.4381  12.5765 -13.4349  -1.9148   0.0590   5.0614
-#>    5.8021  -0.0977   7.5855  16.1178   5.8871   1.0429  -3.0092  12.8243
-#>    2.4016  -4.3190  -6.2966   2.1748  -1.2029  -5.1658 -15.5438  -2.8358
-#>   -2.3391 -11.2238   2.7683   0.0513  -6.0206  -8.7212   1.2179   9.4017
-#>   -4.2383  -3.6747  -0.1034  -1.2900   2.7896   3.6094  -5.0779 -11.1294
-#>   -0.4860  -1.2960  -7.0285  -3.7450   0.6067  -2.7440  -2.7454   2.7484
-#>    5.7242   6.1890  -6.7480  10.1540  -5.7611  12.0798  -8.7057   7.2471
-#>   -4.3361 -14.2966   9.0196  -4.6395   7.8449   8.9978   2.8720  -6.5707
-#>   -2.1374  -4.8998  -0.3253  -1.8154  -4.7756   0.1213   4.8799   0.6199
-#>    3.0059 -20.9024   2.4299  -4.7674  -3.9045  -1.9628  -6.2359  -4.9126
-#>    4.3799   7.5832  -0.6820  13.5544 -13.8275   6.0846 -12.6547   9.0053
-#>   -0.1091  13.7221  -3.1154   7.0069 -11.3168   7.5186  -4.2445   2.5850
-#>    1.4133   1.5797   0.1667   4.9029 -14.3867   0.2739   0.6428   3.6051
-#>    1.4212 -10.1993 -11.1234   5.9852  -9.9299  -4.6147  -6.8913  -3.7936
-#>    1.9547   2.7585   2.8571  -8.3228  -8.9590   4.0832  -1.1072   3.0466
-#>   11.6103  -2.7016   6.9391  -3.3514  -8.2382   7.7049 -13.1237   2.9195
-#>    6.5049   0.7257  -1.6754 -13.8499   6.7940   6.9524  14.0003  -0.5845
-#>   15.9858  10.4259  -1.7270   4.0562   6.4757  -2.5022  -2.3366  -8.7249
-#>  -20.8702   7.3881 -10.0172  -4.7277  -2.9041   6.2343   0.9083  -6.3604
-#>   11.2073 -18.9086 -10.6599  -6.8272  10.4249  -2.8861   9.5068   3.4434
-#>   -3.0630  -3.0321  12.9202  -5.8672   4.3428 -16.0467   6.4028  16.5198
-#>    2.1288   4.1729   1.9260  -6.8011  27.9381 -14.9567   5.8274   0.3583
-#>   -0.3553  -4.5055   6.6590 -16.1631  -3.7251   4.1277   3.0556  10.5262
-#>   -1.7344  -8.8326   2.0646  -6.5968  -3.3695  -2.0245  -6.8629   4.3947
-#>   -3.8936  -4.9729  -9.2678   7.3137  13.4529  -1.2636   0.7179  -4.3780
-#>    7.6871   2.5940  -2.2730  -2.1216  -6.6604   2.8323 -16.7128  -0.9180
-#>   11.0534   0.7482  -5.2661   6.5815   3.0277  -9.2092  12.3954  17.9892
-#>    1.9958   1.8362  13.7950   4.5558 -13.7289   8.9340   4.6768   2.5235
-#>   15.4579  -0.8263  -0.8164   9.3055 -23.9530   6.0654   5.2620  -3.2954
-#>    7.7474 -13.7643   0.0998   3.7900   0.5168   5.0000   1.4169  17.5255
-#> 
-#> Columns 33 to 40   3.7398  11.9164  -2.1994  17.3545   7.6919  18.2535  16.2565   3.0534
-#>  -22.1746  -2.5972 -10.5328  -5.6384  17.1634   0.1492   2.6765  -8.7797
-#>   15.7078  13.5311  -1.9342   4.0391  -6.3492   0.9819   9.8864  -8.1232
-#>   -7.4723   1.2027   5.8385 -15.9131 -16.5342   1.8749   7.4802 -13.5046
-#>  -14.5844  -6.6044   6.7171  11.3866  -1.6240   4.8207  14.8608  -5.6832
-#>   -5.3270  -8.5717 -11.8968  -5.8006   8.5304   6.1275   5.4905  -1.5662
-#>   20.9067  -3.2596  16.0589  -8.4640  -4.7471   0.1439  -9.1014   1.8498
-#>    6.4862   0.3508   5.7172  -7.2685  16.5987  -5.4466 -11.0539   9.2751
-#>    8.8171   3.6017   4.7839   6.9964 -12.3529  -3.2227   6.5608  18.1055
-#>   -1.8451  -7.4598   2.2739  -2.4590 -23.3320   3.6801  -1.7517  -0.7305
-#>   -2.1438  -0.6471 -15.6980   9.3079  -3.2956   3.1527   1.0967   6.6124
-#>   -9.3272   5.5347   5.8071 -15.6016  23.7244  -3.1802   2.5827   2.6769
-#>   -1.8480   3.5504  -9.2644 -16.1763   7.2894  13.7280  -0.3345  -6.5412
-#>   -6.9716  -2.2138   7.0869 -13.8287   5.5485 -20.5346  -9.7861  -2.8131
-#>    0.6799 -17.7559   2.9644  -3.0908   8.0651  -5.7967  17.4581  -7.6221
-#>    1.4858  -4.2815   9.1155   3.8075   5.8086  -0.4867   3.1360  -3.1349
-#>  -10.7100  -4.7337  -4.7890  -9.3010  11.4710   5.1431  -6.5741  -6.5903
-#>    1.1697   2.8402   0.9476 -12.3607  13.7122  -3.1704 -12.2792  -3.6520
-#>   -1.7845 -10.8223   1.3488  -4.9230  -6.2594  -8.1701   8.8945   7.6211
-#>    9.5109  13.8461  -2.3755  12.3814  -1.8163  -7.5476  -0.9768  -0.7303
-#>   13.7919  13.6527  15.5735  10.0841  10.3817  10.6021   9.0033   1.7628
-#>   13.4732   5.4684  -8.3357 -10.4179  -7.9186 -15.1649  -8.5268  13.5443
-#>    7.3305  -0.4604  10.7282  -6.5767   3.2823  -7.0912  -6.9034  -7.9658
-#>    3.3321  13.0385   3.1378   1.2988 -11.9930  11.8531  -7.0118  -3.9382
-#>    2.0293   9.2086  -0.8242  12.7483 -19.4353   0.8570  -3.8385   5.4355
-#>  -11.7978   4.2577  -3.3972   3.6214  11.9412 -15.4736  -8.1722  -5.0488
-#>   -9.0630   1.7378  -9.9009   3.4573  -3.5296   6.7400   1.7715  -6.7341
-#>   -8.6042  -2.0558   0.2023  -2.2278 -11.8652   1.5105  11.7174   2.7020
-#>    7.3962  -5.1905   4.9489  -4.2631   3.5248   0.8108  -4.9632  -8.9748
-#>  -14.8573 -14.3859  14.9262  -2.3562 -20.1310   4.1151  13.4385 -11.9751
-#>   -5.2578  13.2722   0.9739  -5.3776   3.9516   3.5788  -3.9147  -5.6340
-#>    7.1877   3.0640 -10.6718  -2.7446  -0.6904   8.7616   6.3383   7.2771
-#>   15.3132   2.0735   4.8753   0.2851   4.6359  -0.4220   9.4668 -14.1132
-#> 
-#> Columns 41 to 48  -2.7489  -8.7637   3.4835   1.0170  -0.7160  -1.7874   4.3668  18.0269
-#>   11.4088  -3.2957  -4.7056   7.7861  -8.8527  -3.9654   9.5578  -0.8020
-#>   -2.7851   4.7159   8.3065  -5.3754   2.2233   9.1180   4.2429   3.4977
-#>   -3.0859  -0.8083 -13.5866   6.5434 -10.6219 -11.0563  -5.6027   2.0945
-#>    6.7164   0.0129   3.6310   3.6835  -9.5113  12.8007  -0.6328   5.7456
-#>    3.5358   2.2530   1.3245 -20.5415  -1.0149  -3.6555 -11.8244   4.6888
-#>  -13.7008  -0.9236   1.7582  16.2404   3.8166 -10.3989   1.1112  -3.0338
-#>    1.4287  -1.9917  -9.1448  -2.0913  10.1029  -1.3184   1.9773  10.6877
-#>   -2.1908   5.9729  15.2308   7.5356  -2.5093   7.4100   4.0253   4.1294
-#>    5.3162   4.4217   3.4451  -7.0947  -6.7558  10.9257  -5.3132  -9.3164
-#>   17.2181   6.2738   1.8721  -4.4210  12.1152   8.9523 -14.2943   5.3058
-#>   10.6419  -0.2886  10.8269  -5.1502   0.3061   0.6123   6.4105  13.0537
-#>   -6.0470  -2.5747  -5.8937   1.1997   6.6017  -6.8245  -4.9771   8.0405
-#>    8.6029  -4.6612  -0.0321   4.4581  -7.2621  -3.3948  -4.2746  -0.9244
-#>    3.5538   9.7629  -6.2850  -0.0550   0.5231  11.7813   1.8676  -2.0707
-#>   -7.6222  12.5437   7.6741  -3.4922   1.6843  -1.0553   5.5238  -7.2334
-#>   -2.4571  -6.9917  -8.7695 -11.9418 -12.1425  -9.5730  -2.0288   7.2764
-#>   -0.2375  -6.6255  -1.9505   2.1925   2.3345  -6.5458  12.4992   6.5844
-#>    9.2619 -10.2640   2.0741   7.4089   4.0091   7.8756 -13.4588   0.8496
-#>  -10.6017   9.2146   6.2082  -4.6360  -7.2165  18.5288  -2.6394  -6.6095
-#>   -1.1088   4.6886  14.3204  -1.6789   2.7128  12.7741  12.5627  -7.0749
-#>    3.5440  10.7971  -7.8047  -4.8617   6.3549   1.0599   7.3522  -7.8855
-#>  -16.5681  12.9976  14.0391 -18.0699  -2.0621  -1.0039  -7.8445  -5.6196
-#>   -3.3615  -7.7826   2.4972  -9.4898   7.0115   2.9747  -2.6037   1.7966
-#>  -16.6786   0.8662  10.1673 -12.8165   6.5936   3.3037  -1.5560  -8.5763
-#>    3.9353 -11.2481   4.8464 -17.0819  -7.2871   3.1291   1.1024  -1.7613
-#>    4.4568  -2.9537  16.3754  -1.8688   1.7240   0.7697 -11.0913  -7.0720
-#>   15.3738   2.0966   1.3261   8.6218   5.8933  -2.8206  -0.0873   7.9319
-#>   -2.3210   3.2512 -13.6099  -1.0325  -2.7013 -14.6160  -2.2551  -1.8460
-#>  -10.7967   1.6568  -6.5915 -11.5341  -9.9567   2.6287   4.8549 -18.6987
-#>   17.4152   9.9368  -4.7196  -2.0201   7.0575   1.9245 -10.3459   5.4035
-#>   -9.8628  -1.2139   0.5197  -9.0915  -9.3666 -17.6017 -11.3053 -11.6865
-#>    5.1867  -0.8220  -2.7756   3.3252   5.9480  -6.7896  -3.5799   0.5004
-#> 
-#> Columns 49 to 54  -0.6389  -7.4733 -14.9716 -11.8120  -1.9556  -5.3680
-#>   10.7929  12.4003 -14.2223  -7.3193  -3.7665   0.6283
-#>    8.6197   2.0170  -6.0246  -6.1153  -0.3049  -7.7032
-#>   11.1266 -22.0888   0.6137   2.3653   1.6914   6.3109
-#>   -0.6324  -5.6238 -12.4644  -6.6811  -6.2189  -8.1869
-#>   -2.6817 -15.2342  -6.2547  -0.4114  -9.7645  -5.8096
-#>   -2.0674 -12.6191 -15.4729  -7.7481  -3.3053   3.9714
-#>    4.4544  -6.5520  -3.6067  -3.6903  -1.3885  -1.8380
-#>   10.1238  23.3828   6.2061  -0.9916   9.4449   1.7633
-#>   -1.6249 -16.9761   3.6877  -4.3206  -9.6594  -4.2910
-#>    0.2759  -0.0149   6.6960  -3.9407  -4.2733  -0.9428
-#>    8.0797  -1.3322   3.3517  -1.2963  -1.9725  -2.5632
-#>   -3.1325  -3.4439  -1.0952  -1.0389   5.6795  -2.8524
-#>   10.0878  12.1763  -3.0694  -2.4060  -6.0147   8.5415
-#>   -1.3119  -6.9195  10.8808  -0.8992  -4.8843   1.3601
-#>   -4.5548 -20.3759   1.4613   8.7679   9.1140  -1.0856
-#>    0.3173   3.1479  -8.8415  -2.1779 -12.6805  -1.6526
-#>    6.4048   6.2706   4.1337   3.4527   3.3755  -1.7199
-#>   -0.6586  -5.9617  12.3254   5.0271   8.6379   6.4460
-#>    4.2861 -12.6578   9.2266   1.6816  -0.8269   4.5423
-#>    9.5541  -5.7773   2.6169   3.7411   8.2549   1.6539
-#>    5.7854   0.5161  -1.5401   2.0400  -0.6564  -5.9205
-#>   15.7457  -0.5584  -4.0106   2.0536  -0.5748   2.0987
-#>   -1.1142   8.8274  -2.3402   0.8552   8.2448  -0.9579
-#>    4.9794   2.9381  -5.4608   0.4388  -2.2405   1.1353
-#>   -1.2917   1.1117  -4.4265   3.8347 -12.0188  -2.6317
-#>   -6.2574   2.3697  -2.6166   2.7013   3.3212  -0.2425
-#>   -3.8129  19.2050  -4.5047  -5.3278  10.3334  -2.1496
-#>   -0.3943  11.1524   5.1407   9.3953   3.7767   0.9381
-#>  -13.8334  -2.3486   1.6269  -4.3620   4.8251  -1.9973
-#>    0.3519  -7.7991  -6.3443   3.6354  -2.9999  -4.0078
-#>   -1.0298   7.8492  -4.6288   1.7104   0.7321  -3.8820
-#>   -2.9105   6.6443  -1.8968  -0.9760  -1.5631   4.2653
-#> 
-#> (15,.,.) = 
-#>  Columns 1 to 8 -10.0990   9.5713   1.5318 -12.0500  10.1011  -0.1370  13.3657 -12.0867
-#>   -2.0152  -2.9317   2.3648   0.7224   7.9930   5.1094  10.4382   2.3264
-#>   -0.3481  -1.4523  -7.0102   3.4108  -2.3078  -5.3298 -15.2268   9.4144
-#>   -2.6055  -9.8104   9.6568  12.2556 -10.5023  -2.5403  -8.1588  11.3824
-#>    4.8885  -1.2973   0.0989  20.7426   3.1836   2.7362  -4.7865  21.5729
-#>    4.6923   1.6641  -1.2218  21.5251   2.9839 -10.0373  15.2247   6.9074
-#>    0.9719  -3.3859  -6.3710   0.5712 -14.3356   8.1015  10.3925  -7.1472
-#>   -5.1478  -0.4332   7.1794 -11.7585  11.1017  -0.9514  -0.9117  -7.5852
-#>    0.8523   4.0596  10.2042   8.8919  -6.7690  -3.1127   2.1410   5.3746
-#>   -0.7264  -9.5159  13.2099   0.0314  -0.9318 -12.5969   2.5364  13.7676
-#>    3.2831   2.4765  -0.7692   0.4533   3.5371  11.6717   4.3137  10.3082
-#>    1.7459   3.0613   4.4691   3.0374   1.8916  -4.2943  -4.3574   2.6327
-#>   -5.2546   0.7602   5.5456   2.5400  -7.4997   3.5122   8.7494  -3.9388
-#>   -4.8197  -7.6028   1.1249   9.1811 -12.9616  -2.8928   4.7218 -12.8941
-#>    7.2280  -6.4193   0.2792   0.6822   6.5073  -7.9635  -0.0429   5.5636
-#>    7.6099  -0.7829  -5.8236   3.8139   2.2075  -0.5448   3.2761   4.8688
-#>    3.5679   0.7753 -15.0284  -4.2804 -11.6611  -5.5340   8.6002   5.5089
-#>    3.7578  -1.4981   0.1193  -5.3650  -1.1050  12.6927  -6.2565  -1.5469
-#>   -0.8684  -3.1628   2.5079 -10.9548 -12.2026 -11.2409  -5.4725   6.1974
-#>   -4.8493  -5.0138   4.2155  -2.2080  -8.8043  -5.2325  -5.2402  -5.7408
-#>    3.4794   1.2526   1.7075  -2.2464   5.5673 -13.7139 -11.0148  -7.4443
-#>   -8.2279   0.0555   3.0568 -11.7474  12.0560   3.7196  -3.5746  -5.4535
-#>   -1.3951   1.4963  -0.4092 -11.0516 -22.7744   3.8872  -2.3623 -20.0583
-#>   -4.2623  -9.1239  -3.2667   3.5099  -9.2813 -14.1512  11.7609  -1.7458
-#>   -4.4801   2.6365   7.5302  -6.0688 -19.6205  -5.4619   2.6827 -14.5390
-#>    1.4995   4.9967  -4.6511   2.8508  -1.2898   4.5070   0.4309   7.5099
-#>    1.0764   1.9196   0.5463   3.8593  -3.8163   9.3600   7.4160  -3.2611
-#>   -3.6800   4.9303 -10.1005  16.2065  -2.8412  -9.7141  -9.2764  13.5852
-#>    2.1092  -2.0972   6.2155  -2.3583  -1.2559   1.8911 -17.7634  18.2820
-#>   -0.5072  -1.4840  -1.8773 -13.4366  -9.4894  -5.1573  19.5537  -5.9230
-#>   -3.0255   3.9880  -7.6719   6.5883   9.1100  -8.1304  -5.2865   6.3455
-#>   -1.7676  -3.3332   2.5160  -3.1491 -18.3137  -9.7269  15.7431 -19.0633
-#>    3.7121  -0.4173  -9.2082   0.8137 -20.6655  -6.5320   1.3618  -3.3141
-#> 
-#> Columns 9 to 16  -5.0178   2.8376 -13.4590  -1.7824  -3.2256   6.0948  10.5693  -7.3420
-#>  -13.4998   9.7929   7.6963  -5.6667   5.6835  -5.7594  17.1983  -8.0135
-#>   -6.6259   1.8900   0.4777  -2.9068  -1.9392   2.3020  12.7189  11.0737
-#>    9.7699  -1.4176  19.3781  -8.8971   1.0619   1.5661  12.3683   6.8381
-#>   -8.8433   6.7435   5.3905  -2.1816   7.6027 -13.1408   9.6943  18.9325
-#>   -3.7578 -20.4967 -12.1749  13.7635  -8.2955  10.1202  -9.7523   0.7279
-#>   -1.5795 -27.3397   6.2327   4.8520  -6.9096   7.8854  11.5034  19.2436
-#>   21.0009  10.6960   0.3235  18.1828   1.3996   1.6413   9.7474 -12.8684
-#>    3.1225   3.5732   4.3682  10.1331  -2.1940   1.4707   6.3428   7.3753
-#>    5.8743  -4.9510   7.6673   3.3482   0.4906   4.1957  21.5036  -0.4971
-#>    4.3325   4.0696  -7.1543   4.7685   9.2816  -8.0765  13.1303   2.2542
-#>    1.3199   8.9509 -14.1791   0.3937  12.9080   2.3078  13.6441  -7.4704
-#>   14.1569   5.6918  -2.8820   5.4940   5.2787  -3.9716  11.0201  -9.0025
-#>   -1.4026   0.8986   0.0281  -1.1187   0.9862 -23.6242  12.1266   2.6198
-#>   -5.9391  -5.4533   2.9304 -14.2286   8.5711   6.9260  -5.7363   9.8813
-#>  -13.7160 -13.2488   6.5590   0.5550  -0.6141   0.2143 -11.7267  -2.9618
-#>    8.3337  -8.7911  -4.3042   0.2995  -3.9446   2.9244 -13.8378  13.2256
-#>    8.2129  -0.0243  -2.5634   4.2056   7.3335 -13.9953 -12.1880 -10.9778
-#>  -15.5198   3.0817   4.3212   2.8397  -4.5304  -2.8506  18.1735   0.9459
-#>   -0.1223  -5.3029  -0.6803   1.7909   8.2157   9.3911   3.0801 -20.6149
-#>    6.4214  -5.0558   1.5302  -1.3221   6.9508 -19.6272 -11.9980  -1.2132
-#>   10.1410  15.7525   0.6530 -15.2663  -6.4518  -4.8851  16.6364   9.8039
-#>    2.4659  -9.5107   5.0274   3.2727 -20.7507  -2.8210   0.5230   9.5059
-#>  -17.5149   7.4447  -0.9244   5.0719  -8.1361   2.1893   7.6107   2.9166
-#>  -15.9416   3.8260  -6.9479   2.5022 -14.7595  12.8274 -20.2647   1.7912
-#>    7.4356  -4.8601  -4.9859  13.4862  -3.5921  12.9788  -8.9852 -11.1844
-#>   -9.6747   2.3929  -1.8941  -0.1248   1.6079   8.6074  -6.0711  10.0755
-#>    5.3568  16.4666   6.2013  -1.3843   0.6055   2.8061 -12.3449  17.9458
-#>   11.9440  -0.8353   3.4883  10.3915   7.5584  -1.4332  -4.6735  12.5461
-#>  -22.4059  -4.5566   7.3376  -4.3847  -1.5582  -7.5045   7.5009  -1.9745
-#>    5.6077  -5.3056 -13.0279   1.4611  -4.6591 -15.2655   4.6496 -10.6508
-#>    0.5694  -4.6875  -1.8906 -10.3570 -15.9350  -3.2111  -2.0236  -6.8917
-#>  -11.4327  -4.4946  11.8158  -3.7137  -5.7878  -3.2148   2.5095   5.8133
-#> 
-#> Columns 17 to 24  23.9548   4.4337  22.5554  10.9288  -2.2020   3.8517   1.3465  15.9809
-#>   -2.5955   9.0179  -3.8016  -8.0012   1.7868  -3.1316  -5.1833 -18.7513
-#>  -11.2852  13.7604   9.7052  -4.6647   9.1436   1.4140  15.3762  -5.4790
-#>  -30.2964   9.0454   3.9029 -30.8797  -6.0035   6.8617  12.3477  -8.1561
-#>   -7.8357  -2.4154   0.1894 -17.0568  12.7654  -1.0467  -1.8494  -2.1127
-#>    9.1948   0.0944   3.3596   1.7337   7.1465  -3.6636 -13.6780   2.3150
-#>    1.4789  11.8322  15.1057  13.5677   2.5585  10.9729   1.6040 -10.0423
-#>   18.6681   6.5529  -2.8593   3.3970   6.8450   0.3297 -10.0932  -0.2324
-#>    2.9418  -4.5070  17.7137  -1.9664   2.8364 -10.0479   6.7950  10.6355
-#>   -3.8491  14.8961   0.6555 -14.6044   7.5461  19.3217  -9.9193  -8.7238
-#>   13.7032 -13.1803  -1.7174  11.4773  12.4560  -9.4533   6.6293 -14.7780
-#>   10.5144   6.1302  -7.4889  19.8148   5.8770  -0.9864   6.1714  -4.5893
-#>   10.9235 -19.2067  -7.2074   1.9999  -0.9868   0.1538  -3.3172 -12.9561
-#>   -8.1066  -9.5891  -5.8863  -4.7349   3.4509  -1.9967  -0.9951   2.1819
-#>   -0.1153  -1.2667   7.1105 -10.0341 -10.6734  25.9604  11.4678   6.4583
-#>   15.4342   3.8854  -0.8302  -5.8226 -13.3946   1.0479   6.4934 -15.2569
-#>    7.5051   3.4195   1.0269   7.6924  13.4544  -7.2745   0.9785  -1.0435
-#>   -7.2453  10.1602   2.5436 -12.7391   0.8896  -1.8076  -5.2475  -9.5786
-#>    0.9025  10.1723   8.4099  17.3126  -3.8429   0.2009  18.8467  17.4271
-#>    5.6315  15.4214  -6.9537 -16.5940   5.3793  19.0690  -8.0471 -13.2905
-#>   11.9089   3.0548  -5.6932   7.0078  -2.3373  -3.9093   5.7294   4.5638
-#>   11.4385 -25.3665   0.1245   4.7662   6.2247  -4.7778   0.2985   1.8013
-#>   11.1348  11.0020   0.1509  -5.2465  10.6244   8.7351  -2.8962  -4.9217
-#>    4.3799  13.4892   4.0956  -2.8105  -4.9192  -1.0366   1.2922   0.6402
-#>   -8.3492  24.5169 -10.7443  -2.3853   8.8224   1.1678   9.0221   0.4602
-#>   -0.2645   2.5483  -1.9621   6.9814   6.4197  15.2534 -17.7681  -2.2454
-#>   -7.4653   4.4360  10.7323  -7.0816   3.3647   9.4408   9.3030   8.1634
-#>   -2.1583   1.4271  -0.0035  -8.9739   5.9935  -9.9652  15.8939   2.9064
-#>  -22.6930   3.8092  -0.5091  -9.1934  10.3580  13.3964  -6.4586  -0.6747
-#>   -6.0896   5.1857   3.3376  -4.1952  -0.2352  -1.3639  27.2648  -4.6405
-#>   13.2255  -4.4454  -0.9535  -1.1588   5.7364  -5.5923   8.2029   1.3131
-#>    8.5942 -18.0943  -3.8576  14.8559  -0.2151  -0.5652   5.3782   5.7814
-#>   -2.4000   7.8375   8.4794  -0.1797   8.0042  -5.4103  -2.8932  -6.7073
-#> 
-#> Columns 25 to 32   9.5944  -3.9121  -0.6456  -4.2796   4.8669  -3.1430 -16.6505  10.3947
-#>    6.2774  -4.8735 -10.7184  -1.3617   8.1813  -6.5940   9.6497  11.8253
-#>   -0.8060  -7.0873  -5.1033   3.6967  -5.9224  -1.5106  -3.2493 -22.3877
-#>   -2.1715  16.7673 -14.4711  -6.9998  -2.7722  -5.2056  -5.5870  -3.6650
-#>  -10.9918   9.5981  -4.8545   1.3297   1.6327   6.8057  17.8017 -10.4679
-#>   -5.9841   5.0507  -1.8719  16.6006  -8.6540  18.5502   6.4155  -0.4553
-#>   -1.2806  -4.6073   1.7010  -2.0524  21.2555   1.6784 -14.3942  14.7029
-#>   -6.6802 -10.5134  -5.3347   1.4647   6.6026   7.7703  -8.3399  -1.4836
-#>   -6.5434   0.1531   0.9292   2.4402 -17.5561   5.7259   9.4887  -2.3714
-#>   -0.8271   9.9857  -0.1178  -1.8616  -7.1793   6.0239  -7.9769   7.4283
-#>    7.1402   4.4461  -0.9369  -2.3136  -0.0208  10.2257  -4.8433  -9.9684
-#>    5.9708   1.4622  -4.8579  -8.3934  -7.1991  14.0736  -2.2637   0.5849
-#>    4.8735   8.7754  -5.3562 -14.4586  11.7871   3.8676 -26.7182   6.0868
-#>   -1.8051  -0.7627   7.4101 -11.8195   9.1956   3.0181   1.0301   0.4012
-#>    2.3346   5.5885  -6.8960   2.6455 -11.1043   1.1019  -3.3641   1.5160
-#>    0.4052   2.8550   9.9087  -9.2560   4.5426   3.6349   1.1024   1.1567
-#>   -3.4402   4.1977   2.2295   3.9754  -5.8424   2.3379   9.0788   1.3631
-#>   -0.3071   4.4000   0.4199   6.6844   9.9604  -3.5819  -0.1863  -0.4231
-#>  -12.1294  12.6241   4.2895  -2.8436 -20.4353  -1.1826 -21.5068  -3.8060
-#>   -1.0920  -4.3573  -8.0907   1.3174  -1.0410  -4.7343   3.5406 -20.6220
-#>   11.1171   5.0765  12.8293  -2.8107  -9.1112  10.5481  -5.7296 -10.1943
-#>    2.2845 -14.3124  -0.5741 -18.6881   7.3470   8.4301 -16.0972  -4.9773
-#>   14.1851  15.5705  -8.0232 -14.2519  -8.7095   5.3573   8.2457   3.0091
-#>   -8.0929   0.0361 -15.0765   1.3317  -9.0183   0.3250  -2.7328   4.0589
-#>   -0.5257   6.8122   2.5629  23.2483  -1.5479  12.1290   3.9113 -14.1497
-#>   -3.3007  -0.4385   0.9678  11.9494  -6.6841  -8.8127   8.3065   2.3145
-#>   -2.0953   4.7034  -6.3555   4.3592  -7.7332   2.2606  10.0530  -0.9074
-#>   -8.5917   8.7902  11.5992   4.3040 -12.4374  -3.4665  -0.0276 -18.1506
-#>  -13.3506   1.1399  -7.6358   3.4748  12.0200   1.2798   1.8368   6.4288
-#>   -1.7819  13.8193   9.8684  -1.6199  -5.4542  -4.0736   1.5121  11.8665
-#>    3.2625  10.5987   1.5617 -10.7859  -7.1992   1.7146   5.2890  -3.0862
-#>    6.1506   0.8400   9.1340  -6.4293 -11.4344  13.4539 -25.8874 -12.8097
-#>   -0.2576   2.2089   0.5780  -3.6492   9.1683  -5.4479 -13.5015  -8.2683
-#> 
-#> Columns 33 to 40   8.8396  -4.0908   3.3033   5.9178  -2.1426   4.7535  11.7253   2.1089
-#>   -7.8518  -0.6864   5.4062  -0.0704   2.2593   1.2757  -3.3115  -2.8540
-#>    8.0880   8.8613  -0.2046  25.4886  -0.0231  -0.6460  -3.0581   3.7206
-#>    1.3567   2.0511   3.5708  -3.5456  -7.6781  -1.4215  -6.5459   3.4979
-#>    8.6816   6.3005  -2.1095   7.5814 -10.5794   2.5243   2.6340   2.6379
-#>    2.2035  10.3515 -11.2097  -9.3520   7.2193  16.8672  -1.1143  -6.3585
-#>   -6.3557   2.2419   4.5310  -5.1427   4.8296   0.9774  17.5645  -2.1679
-#>    0.7955   3.9828  19.1163   7.6492  -5.3007   4.0388  10.3170 -10.0017
-#>  -10.3534   6.4167 -19.4180   3.1978   5.7691 -21.6791  -2.2751   3.2430
-#>   -1.7144  -2.9233  -4.6692   2.2329  24.4980   3.3718  -0.1364   3.9223
-#>   15.9999  -5.0894  -0.0592   5.6503  -1.0933 -18.6162  13.1086  -2.5467
-#>   -8.7221  -2.9894   1.3795  11.1901  -4.6743  -4.0438   6.3607 -14.1750
-#>    6.4703 -12.0389  15.9626 -14.4179 -11.5250  11.3063  18.6204  -6.1838
-#>   -9.8206  -8.7003   8.7307   6.8307   5.9919  -0.1534 -13.9388   3.1807
-#>   -5.1773  -2.5748 -13.1049  17.7876   2.0671   7.2958   1.2341  24.2162
-#>    2.9031  -0.5510  -8.2014 -11.4646  -0.3466   5.5836   5.9189  -2.3536
-#>   -7.3025   9.3635  -6.8624   3.7640   1.4827   4.0607  -1.9772   0.5987
-#>  -12.8694  -6.1116   8.6849 -18.8294 -10.3818  -5.0519 -12.2981   6.4965
-#>   22.5084   4.1215  -7.5574   0.8125   7.2067  -9.4430  14.0021   5.2206
-#>    2.0273   4.4054   6.1284  13.6395  14.3388  -8.0690   6.9133  -0.9929
-#>   -6.8994   3.5835   4.0496 -10.4464   2.8428  10.3485   3.0069  -3.1604
-#>    2.0785   9.0722   3.1944  16.6751   0.8630   4.7099   2.2799 -12.4083
-#>  -14.1698 -11.5007  -2.5575  18.4120  12.5671   0.3347  14.5842  -6.3854
-#>   -0.4839   3.4926  -0.2347  -4.1269  -8.5848 -17.0848   5.6182 -10.1792
-#>    6.5714  -2.6046  -5.7125   6.4672  -3.9295 -19.8987   6.2386  18.6289
-#>  -18.1906  13.5562  -6.0317  -7.6967   9.0732   5.7860   3.1072  -0.3426
-#>    2.8831  -2.1550  -8.5623   6.4899  -9.2266   7.8874 -10.9110  -7.0155
-#>    0.4335  12.8337  11.4560  11.9060 -20.1756   0.3952  -4.7485  11.1188
-#>   -0.0864  -0.7748   5.9271  -9.5802 -24.4697   3.9133  -9.2256 -10.1374
-#>    9.4869 -26.5448  -4.7492   1.5490  -0.8644   4.6959   0.7020   6.8054
-#>  -11.8460  -2.2999   8.3583  -4.7632   5.6372  12.6364   4.8529 -11.5818
-#>    7.3621 -14.5742   5.1122  -4.9590  -6.8797   0.0120  15.1780  -1.1519
-#>    9.1480  -8.6119   5.6794   1.8353  -1.2049  -3.6436 -15.5298   6.1394
-#> 
-#> Columns 41 to 48  -0.9185   1.0292   2.3816 -19.0016   3.2346   5.1427  -9.2602   3.5470
-#>   -4.6642  -7.5193  -6.2972  -3.6340  -4.3782   3.5069  12.3738   3.8607
-#>   -1.7061  17.2409   0.5289  -6.2155  -9.2250   7.7128   2.6069  -4.1813
-#>  -22.3711  15.8590 -12.1630  -1.9723   3.4912   0.7936  -0.4754  -4.8637
-#>  -10.7078   8.7035 -17.7855  -6.9211   9.6944  -0.4565  14.0562   7.5147
-#>   15.2351   6.8293  11.9645   9.6385  16.1149   5.4100  13.9070  11.3935
-#>  -18.9558   4.3913 -11.3429 -11.7447  10.8551  -1.7770 -10.4569   2.5707
-#>   -5.0401  -9.8150   1.7656  -1.7277 -14.3881  -3.8345   2.5442   7.6936
-#>   -3.8712   7.5262   5.4346  -3.8012  -4.1631   3.5016 -10.7002   2.8133
-#>  -12.1691   9.2626 -20.9391  -0.4648   0.0210 -11.2584  -9.1199  24.2553
-#>   -4.7094  -2.3166  -9.8245   9.5187  -3.5064  16.1850   3.9799   2.8132
-#>    6.3814  -3.9819  -1.0964   3.3634  -8.4593  17.8737   5.7258  -2.5306
-#>   13.5904  -5.1623  -4.3888  -9.1726  -5.7060  13.3960   9.3317  11.0526
-#>  -15.8183   3.6202   8.2642  -3.5512   7.4538  -7.7091   6.9085 -20.2944
-#>   -1.1283 -18.0089   9.1768 -19.2064  13.2384   0.6168  -3.4532   1.7254
-#>    1.0596   4.8809  -7.4798   9.9955   2.8880  -0.7054  -1.5500   3.2815
-#>    3.6454  -0.9967   1.9723   7.5453  10.3257   9.4845  16.9860  -0.8021
-#>   -7.3195  14.6915  -0.9854  -4.1521  -0.8937 -13.6568  -3.7865 -23.4725
-#>  -14.4901  -3.1384 -17.0815  -4.9651   5.3681  -3.0924 -18.9295 -15.6902
-#>   -2.3697  -4.0433 -11.8967  13.1645   5.7599   3.2860  -4.1032   8.4708
-#>    5.5353  -7.1651   5.3395   2.2028 -13.1871 -10.0166 -14.7682   1.3285
-#>   12.3502   2.8922   8.9898  -1.6964 -13.0801  -0.3030  -7.0138  11.3802
-#>   -8.0877   3.5271  -2.7746  -7.2920  11.5408  12.2708   5.6332  15.9228
-#>    7.5764  11.6671  -3.1646  -0.9293  -8.9805 -10.0701   3.1878   3.2721
-#>    3.0567   9.6918   0.9289   9.2551 -17.5344  -4.5718  11.4749  -8.6895
-#>    9.8700  -5.0108  11.2222   2.5289  14.6404 -18.5415 -10.1208  -7.4381
-#>   -5.1425  -8.1582   5.6891   1.3202   9.9968 -10.1401   9.7812  -5.5545
-#>    6.0983   3.3249   4.1080   3.2006   5.4616   6.3398  14.2389  -3.0887
-#>   -5.4333   9.9757  14.0214   0.1371   9.8365  -4.8653  -3.6701 -17.4995
-#>   -2.9224   1.7300  -6.4158  -4.8144   3.8159 -14.3869   1.6443   3.7402
-#>    5.6625  -1.9869  -0.9393   2.7615   0.5217   1.2785   0.9142   7.9043
-#>   13.3689  -3.6108   0.6681 -16.7220  -0.2150   9.0452   3.9819   8.0725
-#>  -11.0557  21.5327  -4.6524  -2.5301  15.3207  -1.6733  29.7789   0.5129
-#> 
-#> Columns 49 to 54   1.5166   8.2477   6.7136   6.3118  -4.9695  11.5162
-#>    1.4363 -12.9781   0.1371   0.9093  -3.6536   4.9203
-#>  -18.0463   5.2483  -0.5983   5.6387  -2.6682   1.3394
-#>   -2.6935  -6.1874  -6.5942  -4.2045   6.6405  -9.5767
-#>    0.8123   0.8789 -17.2362  -1.3923   9.8621  -4.7030
-#>    0.9047  25.0544   4.3639   7.7567  19.5197   8.8219
-#>   18.4562  11.7296   5.6156   4.0337   8.2729  -3.8409
-#>    6.9517  -5.0020  -3.8453  -0.1510   1.8457   4.7716
-#>   -7.2966   8.9733  -7.9880  -5.7693  -1.0765  -3.2241
-#>   19.3973  -9.3490  -5.5793   2.3670   4.4611   6.6473
-#>    2.0844  -0.0334  -8.0439   5.7844   1.4116   4.2696
-#>   -0.1985   5.3961   6.0418  -0.2969  -0.8843  -2.1523
-#>    2.0317  -4.4195  -3.6542   0.5706  -2.4710   0.7280
-#>    7.4199  -3.0333  -4.4803  -2.6479   6.0699  -2.7246
-#>  -11.5188  16.6455  -0.1031  -2.7591   3.5070  -0.1705
-#>   -3.0831  -4.5135  12.9683   1.0926   1.7067   2.5385
-#>    5.8785   6.7963   7.6884   5.5664  11.5878   2.8810
-#>   13.8876  -7.7859   1.2779  -7.9931  -4.2584  -0.1300
-#>   14.8720  15.5157   3.7080  11.8443  -3.9134  -2.1870
-#>   -5.7535   7.5996  -4.3604   2.5150  -6.0650  -3.4270
-#>    3.9493   1.7726  -3.3017  -2.0998  -8.4011   8.5350
-#>    0.7961  -3.1373  15.8146  -4.7486  -1.0243   4.7894
-#>    8.8234  -8.4761  -7.7498   9.6531  10.3125   0.0289
-#>   11.9533   9.9974   0.7059 -16.6528   1.1749  -7.9864
-#>  -10.4745  -5.0384  11.4629   2.7173   4.2650  -1.2628
-#>   11.0660  -5.0848  -0.7776  -6.1537  -0.6618  -0.3784
-#>   -9.9422  -1.2817  -1.6077   5.3577   3.3337   0.4067
-#>  -12.0042   5.7682  -6.7125   4.8503   6.7699   2.9700
-#>   -3.3479  -7.8093  -2.1469  -7.6515  11.3454  -7.3593
-#>   21.3429   2.5380 -10.8956   2.5114  -3.3077  -1.5169
-#>    1.3157   5.7816   2.1688  -8.4880   6.3563   1.6823
-#>    4.5685  -1.2746   8.4463  10.5379 -11.6546   5.8264
-#>    9.5908 -13.6034   2.6211   4.8941   5.8864   2.5896
-#> 
-#> (16,.,.) = 
-#>  Columns 1 to 8   5.7149   2.6410   0.4331  -1.4741  -3.6267  -1.4720  -1.6433 -14.4904
-#>   -4.1965  -2.3145   2.4730   7.7392  -2.5778  -1.9739   5.9185   6.2945
-#>   -0.3824   2.3342   5.6595  -3.6659  13.7773  -5.9059  -3.7858   3.5306
-#>   -2.4134  -1.4280  -7.1424   2.0948   2.2874  -1.0853   5.7903  10.8578
-#>    3.2607   0.0906   0.4323  -3.3052  13.3146  -0.6935   5.2667  13.1755
-#>    1.6097  -0.1559   1.9115   5.1168   6.6133  13.6786 -12.4811   5.9716
-#>    7.3616  -5.4578   2.2418   8.3101   8.4899   7.1793   4.6639  -8.8759
-#>    1.4888   3.3524   4.0891   7.2668  -7.6339  10.0442   3.4466   1.9793
-#>    2.9624  -0.7599  -4.4550 -16.5763  -7.2150  -9.3511 -14.4847   3.8877
-#>    5.4535   2.1113   0.6556   8.3213  11.4949   4.0772  -0.9157   6.0573
-#>    1.3422  -2.3339   2.4545   2.0971   8.9639 -10.1366  -1.9312  11.8052
-#>   -1.4407  -5.1037  -0.8735  -7.5622  -6.1879  -0.0690  -6.2049   6.1181
-#>    1.4357  -3.5222  -1.9755   9.4500  -1.6771   4.4376   0.3270   3.4174
-#>    2.5677   0.9643  -0.4635  -1.1444  -5.9775 -13.5607   9.5219  -1.5825
-#>   -3.4731   4.3262   7.8018  -1.1242  -0.3613  -4.4380 -17.4636  11.9620
-#>   -4.2367  -0.5903  -4.0691   4.3360   6.9549   5.8410  -7.6657   5.2381
-#>    0.2624 -11.5379  -1.8299  -6.6303  17.5205   6.7721   7.8004   2.3381
-#>   -3.8974   1.7662  -4.8138   9.5514  -9.4977   0.3022  -1.0301  -7.1786
-#>    8.0423   3.0451  -5.4620  -1.9462 -11.0921   2.8610  -8.4385  18.6906
-#>   -0.7504   8.3239   2.5793   2.7778  -8.9122   6.5091  -6.5768   5.6288
-#>    4.3457  -1.8958 -10.0769  -3.2070   0.9191  15.5076   1.3231  -1.1706
-#>   -3.5223   0.8839   0.7376   4.0680  -6.0472 -10.2105   2.2769   2.2336
-#>    3.7377  -5.7436  -0.2260   4.7655   3.2836   9.0940   3.0380   4.9916
-#>   -0.6492  -7.3590  -3.8933  -4.5155   3.0945 -17.9669   0.7992  -2.5830
-#>   -4.1239   3.6299   0.7977   1.3964   0.1331  -2.0033  18.6986  -2.7015
-#>    0.3150  -1.0361   4.5186  -2.9666  -1.6695   4.2238  -9.6508  -5.4337
-#>   -2.3472  -2.5659   5.6535  -6.8268   0.4131 -15.7032   4.3612  -4.8776
-#>    5.7511   1.5074   4.6386 -14.9877  11.4655  -3.4371   6.8665   4.7969
-#>   -1.2161   2.2089  -1.8261   3.9251  -1.3776  -0.7568   0.8804  -8.0807
-#>   -0.7410   0.6941   2.5636  16.1539  17.1623   4.5810 -12.6779   0.4777
-#>   -0.5473   0.5947  -0.6145  -0.4223   4.8203  -2.9468  -8.0156  16.5324
-#>   -2.8870  -0.4101  -8.1284   8.2239 -12.4741   9.3738   0.8917   2.3292
-#>   -0.0762  -4.8836  -2.4877  -2.8065  11.9033  -6.2954  12.8217   4.6448
-#> 
-#> Columns 9 to 16 -10.8486  19.0040  15.5205   6.3897  12.7509  18.7076   6.7341  24.7735
-#>  -12.0438  12.6606  -0.3553  -4.9212  12.0861   4.6795   6.6952   5.5794
-#>   -4.6007   0.9672   8.7212  11.1086   0.4787   8.4304  -7.2332  13.4946
-#>   -8.7545   2.6975  -7.5476  -0.5051 -26.9210   7.1168   7.2227   7.0064
-#>    5.0392  10.5801   3.8094  10.7401   4.8611   6.8573  -2.9970  10.8328
-#>    2.7897   2.6428  -3.0699  -1.1935   1.6653   3.4466  -6.4703  15.9608
-#>   -4.0934 -13.0422  30.0203   9.2027  -0.0284  -8.5338  17.6166   5.7459
-#>    0.6521   6.9662   3.7325  13.7420  22.0283   3.8541  -6.6730 -10.8181
-#>    8.4981  -0.7829   1.4430   7.1065 -20.1671  -2.2740   4.0409   1.3867
-#>    1.0489   7.8033 -10.2676   6.4569 -12.2644  27.0497 -12.3430  -4.7140
-#>   -4.2990  -7.0005   9.4127  10.5111   5.6435 -16.3177 -19.4785   4.6994
-#>    7.1485   0.1711  -2.3173   8.7568  13.6645  15.3494  -2.7278  -6.3752
-#>  -22.2274 -13.7903   2.3464   9.1934  12.2973   1.7982  -8.8251   1.1781
-#>   -1.8933 -11.6445  -5.1614   2.8326   7.9728  -9.9083   1.7724   6.6256
-#>   -0.4213  -2.8628  -9.6671  12.5749  -0.2841  17.4236 -15.0692  12.7773
-#>   -2.8915   4.1267   3.7853  -2.7139   6.6256  11.3734 -20.9926  -4.2265
-#>    2.4341  -8.6435  -2.8338   0.5613   7.2049   6.5807  -2.2237  -2.7587
-#>  -10.2696   0.0926  -3.1129 -10.9694   7.9198  -8.8719  13.5763  -9.7744
-#>  -15.7203  -4.0138  12.2160  -7.0447 -18.8194 -24.3269  12.7430   9.2565
-#>    1.8916   2.4190   4.8938   8.2787  25.5384  11.2692  -9.0044  -2.9264
-#>    4.7253  -1.1566  -7.7629   6.2313  19.2309  12.9958  -1.3933 -23.9209
-#>    4.0398   3.1965   1.0156   1.8936   0.3614  -6.0729  -5.9055  -5.6367
-#>   12.6499  -8.3206  -7.9043  -2.4800  19.1821   5.0694  -7.8344 -22.8667
-#>    1.9533  11.7377  11.7026  -3.2078   4.5977  -9.8745  -2.6903  -0.1726
-#>    0.5964   2.2094 -20.7317  -7.7429   8.8774  -9.4002  -8.3394  -5.2582
-#>   -7.7434   2.7373  -0.4027  -2.3704  12.3224  -3.0172   3.5563 -13.3000
-#>   -6.1673   2.1377  -9.6263  -7.3032  -8.1092   4.8288   0.9269   6.6680
-#>    7.1307 -11.4082  -1.8733  -3.4725   0.7833 -11.1207   1.6904  -3.3928
-#>  -13.8459  -8.0944  -4.5687  -1.3178 -12.7660 -14.1142   2.3761  -1.5851
-#>   -4.4088  -4.7370 -13.4434 -11.3929 -10.0721   0.3293  14.4283  -5.7123
-#>    1.6628  -4.8135   4.1967  -3.6642   2.6072   8.6424  -5.3487  -5.5875
-#>  -15.1982 -16.2075 -11.1081   2.1837 -15.1037   2.5881 -16.0889  19.7138
-#>   -1.3740  -0.5834  11.2036  -2.6599   8.2420 -12.5969 -12.3959   1.4875
-#> 
-#> Columns 17 to 24   2.0507   2.6598  -8.2677  -0.3819  -9.7047 -13.0155   0.8416  11.0928
-#>    9.5006 -16.9117 -12.5434  -9.1153   0.4889  -1.8486   0.6586   7.6950
-#>   15.1938   3.1147   0.5621   1.7471 -14.8611  -2.8270  -0.4675  11.0833
-#>  -21.2756 -17.3433  -3.7241   2.3021   1.9242  17.5036   2.0321  11.3387
-#>    4.0239  -4.6242   1.3642   0.7304  -4.2136   6.3788  -5.1951   1.0344
-#>   -0.0384  -2.6888  -6.1012   6.7180 -12.0463 -12.4807  -5.0917  -9.4530
-#>    3.7491  -1.2887  10.0114   0.4980 -19.6182 -11.8975  -6.1498  -9.0285
-#>    3.5560   4.8208   2.8959  -9.5669  -5.5017   1.9822   8.5510  -2.1044
-#>   -2.2116   2.3504   2.2195  14.9792  -9.9029  -1.9384  -0.5256   3.0722
-#>  -22.9213  21.1300  -9.5968  13.4872 -10.8303   0.4232 -12.2590  -4.7347
-#>   19.0331   2.7374   4.2416   4.2413  -0.5785  -2.3002  -2.3245  -8.5098
-#>    3.4308  -4.5625   2.0586   6.0886   5.3807  -1.9928  -3.4943   0.4703
-#>   -1.6577 -15.8600   8.2561   7.0638   1.7736 -16.2779   7.8722  -3.1787
-#>   -0.3687 -16.4942   6.5590  -1.1787   3.5540 -14.2099  12.5565   4.2178
-#>   13.0011   0.9513   4.4265  10.9985  -0.1402   4.5048 -13.1231   7.3450
-#>    7.5692  -4.8010 -20.2641  10.6312   6.1431   9.6376  -9.9602  -9.9894
-#>   11.8609   6.2776  -2.6604 -13.9893  -0.5079  -3.7566  -5.1813   4.9594
-#>   -5.7975  -2.5242   2.6223 -15.3010   8.8385  15.4189  13.6916   2.9766
-#>   15.4826  14.5025  -1.2235   9.1774  -3.8551  -1.9364 -10.3641  -6.1060
-#>   -4.3016   0.8259  -4.8363   0.1938  -5.1897  -5.0624  -3.6538  14.0120
-#>   -5.6653  12.6322  -1.3424   7.4415  10.4254   5.4954   5.7740   5.2493
-#>   10.3612 -11.3761  -4.5315  20.6901  -4.6147  -4.6538  -6.1524  -4.4536
-#>    1.1449  -1.2692   4.8713  10.5046   4.0514   1.0056   1.6884  -9.5293
-#>   -4.5487  -7.4370   0.9980   0.2414  -9.4703   0.0699   6.6126   1.3606
-#>   -7.5593  -4.8113   8.1871  15.3340  16.2282   0.7743 -10.5867  -0.0072
-#>   -7.5182   2.6224  -0.3788  -3.3806  -4.6903  -6.0372   5.0801 -11.2094
-#>   -0.7491  -4.1805   6.0432  10.6777   0.9205 -10.8444  -2.9452  -1.8480
-#>   10.1078  -2.1582  -1.2274  -9.9255  15.0571  17.5290  -3.8803   3.5488
-#>   -7.4319   5.0795  13.8641  -1.4426   1.3595   0.4998  -3.2554  -2.9333
-#>   -3.0166  -4.6155  21.9036  -3.6327   0.2526   4.0147  -3.2307   2.0131
-#>    2.7417  -0.6201   4.8743   2.8442   3.4621   6.0141   0.4053 -16.9837
-#>    0.8861   1.6744  -1.4065  14.6046   1.5714 -24.4373   0.3676  -3.4827
-#>    4.0054   0.5725   8.7190  -8.5844  -1.3806  -6.2709  13.3831  -4.2304
-#> 
-#> Columns 25 to 32  -4.1254   9.3742 -11.9406  19.7302   6.2971   2.7894  13.8389   6.6408
-#>   -1.9426  -1.7817 -12.3767   1.1051   3.3208  -1.9366   2.5308 -11.9070
-#>    8.8551  -2.2768  -3.4851  -2.1931   4.0168   3.8313   7.3140  11.3613
-#>    5.8380   1.4407 -14.6187  -6.7719 -12.8975 -12.0475  -1.8910  -8.9767
-#>    1.2515  -0.0015  11.6763   6.6943   3.9679  -4.1267 -11.1167   6.0731
-#>   -7.4304  -0.4224  -1.8240  -6.7562   3.3597   3.2047  14.7926   3.0720
-#>   18.2255  -5.5199  12.2591  12.3209 -10.4560  -6.3152   0.1159 -13.3165
-#>    3.0514  -6.1623   1.4615  -2.6432  -3.0623 -16.2089   3.5304  -2.6365
-#>    1.8521   3.1686   1.3424   0.4103   7.6215  -2.8771   5.7098 -15.4748
-#>    7.0573   8.4287 -15.0557  -2.5131 -13.4608  -2.3850   1.0915 -15.5075
-#>   -7.8992   0.6289  -4.1602   5.5693  -4.8073  -8.5766   4.1574  -1.1716
-#>   -7.6856   4.9891   0.0264   8.6593  12.1591 -10.1124   4.3597  10.4212
-#>   -2.5367  -9.9828 -11.1830   3.8636  -6.6999  -9.8037  14.4445  18.1645
-#>    3.0433   8.5416  -6.2895  -0.2634   7.1224 -22.0405  -3.1111   5.2978
-#>    2.5170  -4.6569  -3.9122 -10.7506  13.1106  -1.4986  13.1161  14.7422
-#>    1.7151   5.4397  -4.4998   0.5734  -3.2140   8.3545  -6.4990 -22.1587
-#>    1.0871  -0.3733   6.6648  -1.9736  -3.1345   9.0372  -7.9000   3.6416
-#>   -5.7346  -5.5697  -7.7569  -6.2873   4.6999  -4.1704   1.8441  -8.9156
-#>    1.9548   6.3252   3.0557  -9.0061   5.7293  -4.4918  10.9426  -2.5873
-#>   15.6885  -2.0274 -10.3964   4.3669   5.7090 -10.1337  -2.0274  -5.9175
-#>  -10.9127   8.2897   1.2689  -2.7881   5.2909  -7.6923  -7.8837   2.5822
-#>  -12.6344  11.2309  -6.9455   4.0729  -0.8880 -14.4394 -11.9550   5.4840
-#>   11.6747 -18.4813  -0.9224  14.7047 -13.1532 -16.9584 -14.5604  -3.2588
-#>    7.1224   7.6081   7.8043   6.9435  -4.3951  19.4718   0.1496  -0.0237
-#>    3.6916  -3.1680 -14.2709  12.8420   5.4446   3.6627 -16.5807  15.2178
-#>    5.9516  -3.0243  14.8711  -3.7670   6.9118   9.2722   8.1439   7.4384
-#>   -5.9544  -2.2944   6.3805  -9.6320  12.2830   6.9966  -4.4074   0.8192
-#>  -18.1687 -16.2847 -10.2525  -7.6339  -7.2074   9.0036 -15.0035   4.3509
-#>    1.0724  -6.5366  11.6397  -5.2278  -2.8765   4.4298   5.9241   6.1974
-#>    3.5181   1.3551  -1.8297  -4.7495  -8.6474   3.3282  16.5622  -4.5502
-#>   -8.1147  12.4193   6.6285   7.0230  -7.9623  11.4584  -7.5197 -14.5441
-#>    4.9385  -5.5366 -15.0028   8.5349   0.1998 -13.5030   7.5491  10.4168
-#>    6.9734  -9.1558   2.4214   2.2550  -6.5530   0.7602   9.4171  -5.7679
-#> 
-#> Columns 33 to 40 -16.2546 -23.7417 -17.0193  -1.3142   2.8238   7.1652  -5.3280  -1.3377
-#>   14.1420  -1.5059  22.5524 -21.2378  13.9141   8.4371   9.6989   5.8117
-#>   -7.3609  -3.2967 -12.9083 -11.0725   4.1133  11.0140   9.3464   7.2597
-#>  -10.0718  16.3289  -1.2712  -3.5266   5.6203  -5.0587  -0.1206 -14.1769
-#>   -5.1636   5.6576  10.7469  20.5651   8.4264  10.4495  -3.8374   5.2713
-#>   15.6951  14.4955 -11.6119   9.4762  -3.1124  -2.3191   2.1196   3.5569
-#>  -10.7368 -10.5437 -12.2213  10.9230  -4.4011  -1.6173   0.0974  -0.5739
-#>  -14.4136 -15.9359 -10.0872  -3.8252  16.9249  -4.7211   7.9440   1.1553
-#>   -3.7608  -0.2942 -17.8180  -3.6190   8.0015 -14.3660   1.6582 -10.8504
-#>   21.1495  16.3938   9.4025   1.9902  -3.1828  10.3133  -0.6980 -12.5243
-#>    3.1599  -1.0148 -10.7063   4.5622  -6.2987 -11.1163   8.4843   1.3144
-#>    3.5440  13.5956   5.0636 -11.6090   9.5306  -3.8764  -3.9018   7.6265
-#>    5.8936   0.2157  -4.4326  12.8327   8.0861 -25.1949 -14.3138 -12.4501
-#>    5.9666  15.9317   3.8190  -4.7217  11.1876   5.2509 -10.9786  -0.5133
-#>    0.6909  18.5410   4.4026  -8.1224   8.0649  18.7591  18.1166 -18.0383
-#>    9.0911  14.8643  11.4708   4.9207  -8.1068   7.9500   4.9009  -6.7330
-#>    9.7842  -4.6255  20.4290  -4.4621   1.0605  -4.9756   0.1691  15.5058
-#>   -2.8511 -16.5230 -13.0380  -6.8763  -5.0870   5.3321  -3.4553  10.2239
-#>   -4.1570 -19.2809 -12.6503   4.1567   6.9719   5.7000  -1.0137   5.4218
-#>    3.9844  23.7523 -20.5564   2.7967  13.0207   1.8161   4.7416  -9.0043
-#>    0.8970 -10.7307   9.0940  10.2688   1.0261   2.0990  -3.9108   3.1225
-#>   13.4368 -10.2608   8.3270  -0.6738   3.3290  -3.8392  10.7277   0.5851
-#>   -2.8274  21.8511  17.5014   7.3997   2.9204  -2.4399  -3.8114  -6.8817
-#>  -12.0053   9.8954 -13.6067  10.2075  -0.5364   4.7130  -2.0404   2.1682
-#>    7.2322   1.0930  -6.5927   1.4633  -1.6856  11.1873 -12.6404  -4.7624
-#>   21.7513   1.6566  -7.0826  -9.0371 -14.3815 -19.6564  -6.0599   8.9391
-#>   -3.6946  -2.1719 -14.3045   3.9593 -11.4235  -0.6905 -10.8632  -6.2279
-#>   -7.9132   4.1077  -7.4057   3.9989   2.7405  10.1765 -11.8403   0.2625
-#>  -12.8951  -8.4539  -3.7227  -8.7636 -19.2086 -20.4250 -23.3362  -6.3952
-#>    9.0696  -1.4293  -5.9031  -7.8074 -13.0599   0.1288  -5.4286  -6.5128
-#>    3.3542  21.9397   1.2557   0.1547  18.7915  -2.2035  12.2932   4.3940
-#>   19.6359  -5.6027   3.4627  -4.9635   4.0945 -17.2492  -8.7759  -7.4648
-#>  -14.4994  13.8843  -3.1236   9.1751  -7.8411  -6.0328  -1.2764   5.8990
-#> 
-#> Columns 41 to 48  12.4684   1.5413  -0.9921  -2.4225  15.1364  11.2646   9.6565  18.3759
-#>   -2.2483  -9.2219  -9.1508   8.3533  -7.1090  -0.2778  -0.5484   4.5380
-#>    6.2917  -1.6471  -0.3049  -6.6333  -9.6016  11.6996  -4.1160   8.6289
-#>    6.0482   2.3693  -8.5061   6.0234  -1.0929   8.3549  -6.7385  -3.8282
-#>    3.0921  -0.1369   2.3729  -4.0608 -12.5768   6.9210  -2.2395   0.3691
-#>   -3.9794  -9.2462  -3.3190  -5.6941   5.3892  -5.3677   0.8627   1.9602
-#>    5.0847   7.7314  -8.0740  -0.1435  12.0844  -1.2089  18.1352  -6.2714
-#>    4.8262   2.1688   6.4618  -0.6966  14.3354  -0.1032   7.7291   7.0555
-#>    3.2609  14.0137  -2.2330 -13.2423  -4.2445   1.9784  -8.1714  -6.3727
-#>   -4.8374  -6.5268  12.9251  15.1205  -8.1213  -1.3245 -10.2242 -14.8910
-#>   -6.9816   8.6871  -3.6590 -14.3809  -9.3692   2.6268  -1.2327   8.6051
-#>    1.6206  13.5033  -5.4795  -2.0907  -5.7077 -14.7999  -5.7586   4.2999
-#>    8.7203   3.3506 -14.3992  -5.7758   3.1561 -16.5495   5.3469   3.9362
-#>   -4.2642   8.1815   4.7881   2.3056  -6.4157 -12.4372   8.1750 -13.3807
-#>    9.0167  12.7056  11.6007 -11.0262   4.2971  -3.2547   1.4945   4.9691
-#>  -16.8074  -6.4873   3.3881  13.5651   3.2480   2.2724  -7.9075  -0.5044
-#>    6.9120   5.2717   2.6873  10.3306  -8.6791   4.5743  11.1047  -7.8470
-#>    3.4473  -9.3260 -13.5324  -2.1988   3.6893   9.5879  -2.7626  -3.1742
-#>   -1.0179  15.3903  16.5882 -15.5050  10.5391  16.1257  -4.2574  -0.6911
-#>    0.2554  10.1870  11.1322  -0.8859   7.5715  -6.3537  -6.4601   8.0480
-#>    1.9401  -4.3159  13.1100   7.9316  -0.6930  12.7543  -0.0876   2.2636
-#>    1.8901  -1.4288   1.4151  12.0978  -8.4133   1.3448 -13.1126  -4.5250
-#>   14.9244  15.4586  22.7820  14.1672  -0.7533  -3.9422  -0.6175  -6.9593
-#>    2.2502   8.3303  -2.0124  -6.4444  -1.9069  -9.1721  -9.1885   0.4218
-#>   -1.2529   4.6876   3.5248   0.3786   6.1484  -2.2346 -12.1829  25.1356
-#>   -3.8372 -10.7091  -7.9372   2.7616  -1.8327  -8.7472   3.2606 -12.9457
-#>   -0.8361  -8.9647  -5.8202   0.8926   5.8276   0.1345  -3.0728   7.0463
-#>   -6.0486  10.4944  -4.7435  -7.4233  -1.6023   2.5923   0.8755   7.7830
-#>    6.3221 -10.5719 -15.1768  -0.5792  -4.6111   5.8933   1.8873   1.3806
-#>    4.9719   2.2166  -5.4620  -3.4779   8.0047 -12.0724   4.7335  -3.8603
-#>   -5.3593  -5.7105   7.5636  13.7879  -7.3514   4.2796  -1.3703 -14.9948
-#>    1.2680   6.3434   4.6993  -5.8200   6.6031  -3.4956   5.4723   8.7672
-#>   -8.4542   3.9480  -2.9003  -7.2767  -2.1211 -11.5426  -1.4058  -4.2086
-#> 
-#> Columns 49 to 54   6.9345   6.8547   3.0552  -3.7366  15.4697   0.1524
-#>    6.2922  -0.7933  -6.4809   2.7974  -5.4409   0.0367
-#>   -3.3257  -0.0392   2.6232  -1.5331  10.6490  -2.2028
-#>    4.5382  -9.6036  -9.4649   6.2032  -3.8240   1.4407
-#>   13.5199   6.7690   2.6443  -3.3702   5.4460  -3.5104
-#>  -16.6280   2.0464   1.1608  -5.5702  -3.6791  -5.3750
-#>    6.2361  -2.5801  -0.8141   5.3224  -2.5808  -0.4248
-#>    6.5722   4.9813  13.0259   3.5532  -0.2108  -0.3481
-#>   -8.5610  -4.7566   0.1911 -19.4216   8.7068   1.1852
-#>   16.1358  11.3908   0.7024  -5.9319  -4.2298  -3.7900
-#>    0.7456  -2.5285   2.6793   0.9100   4.2093  -1.8958
-#>  -14.6449  -7.2198   2.0372  -3.0012   0.7265  -2.1829
-#>    4.9887  -4.5485  -7.1482   0.7946  -0.2220   1.1991
-#>    7.3561  -6.5854   0.4942  -9.0523 -14.6778  -0.6867
-#>    1.8704  -5.6577  14.4754   0.7415   2.1388  -1.6480
-#>    1.0187   7.2594  -7.2277   2.4263  -2.5897  -0.5953
-#>    5.8552  -3.0989  -4.6741   2.1087  -6.3685  -5.4360
-#>   -4.7954  -3.0337  -2.6234   9.1441   4.1686   1.3206
-#>    0.5679   9.4099   5.8011   0.1202  -0.3942  -0.2886
-#>   -2.6996  -0.6632   0.7895  -1.5725  -0.1924   0.0885
-#>   14.5838  10.1875   0.2721   3.2883   1.1273   2.9354
-#>   -8.6839   4.6683   4.7676  -4.0084  -3.8531  -2.5571
-#>   -2.7880   3.9429   3.5918   2.5719  -6.0650  -0.8717
-#>  -10.4243  -2.6173  -0.4466  -6.1775  -4.4496  -1.5418
-#>  -13.9890   3.4637 -13.8115   7.9119   2.7131   0.7817
-#>   -2.7265  -7.1634   4.9050   0.6368  -4.2845   0.3284
-#>    6.9923 -13.7115   2.1192  -0.0309   3.4392  -0.0764
-#>   -2.5682   6.7601 -11.6196  -0.8245   5.3279   2.1712
-#>   -1.3624  -9.8982  -3.0243   3.6376  -3.2003  -0.3407
-#>   -1.5863  12.3002 -15.6227   9.8221  10.8348   1.3270
-#>   -0.2349  16.1233   7.4516   5.6809  -5.4817  -1.3903
-#>    5.5133   7.0013  -9.5816   2.0696   2.0649  -0.7826
-#>    4.8998  -2.3120  -3.3847  -3.6179  -0.9100  -0.9852
-#> 
-#> (17,.,.) = 
-#>  Columns 1 to 8   6.1443   6.9194  -1.0562  -4.3711   4.4654   0.5805  -9.1360 -14.1596
-#>    2.9841   6.6422  -2.7177  -1.1271   0.7274  -5.4302   2.3300  -5.4333
-#>    1.9327  -5.6299  -6.9753  -3.4866   7.1639   4.7113  -8.2368   9.1127
-#>    4.4792  -8.6570  -7.9390 -12.0866  -1.9326   4.4371 -24.3383   4.0757
-#>   -0.5831  -6.2944  -0.5927  -0.3604  11.4115   2.1684  -7.3916   3.0542
-#>    4.4370  -0.6599   3.2872   9.1564   2.6958   6.7784  -1.4288  -2.8477
-#>   -2.1455   4.1315 -10.8879   1.0214   7.5736   2.6349 -12.0602 -16.0574
-#>    1.5223   4.4186  -2.2786  -1.9884   0.6818 -11.9779  -7.7711   0.4184
-#>   -0.6403   9.8892  -0.0395  -1.9764  -3.8331  12.8968  -8.1312  12.7658
-#>    4.0269   4.3379  -2.1609 -17.5246   8.5467   2.9727  -6.4218  -1.9910
-#>   -3.8391   2.7261  -0.3629   0.9506   2.2703   8.2623  13.7309  -7.8259
-#>    2.4645  -2.5920 -12.7258   0.0863  -2.0993  -6.0942   0.4621  13.7128
-#>    2.4760   1.2588  -9.4273   8.8638   4.6503   1.9964   4.9189  -3.0370
-#>   -0.5484  10.3669  -6.2906  -7.2585 -16.6554  -2.5892  -7.9406  -4.5755
-#>   -0.7397  -8.5692   4.6649  -3.7718   0.4087   4.4101 -11.1031  -4.4849
-#>    0.1002  -2.6376   2.6019  12.8042  -1.8125   2.6265   8.1862   2.4251
-#>   -1.0834  -3.4857  -4.6050   9.3387  -5.0314  -9.9726  -9.3422  -8.8175
-#>   -1.5390   0.1313   9.5713  -3.0821  -2.9337  -3.8771   3.7128   4.3213
-#>   -7.7632   6.8190   6.7207 -13.4798  14.6956   3.4435  -2.8333 -21.4868
-#>    3.2687  -4.6805  -4.7812  -3.1980   1.6897 -13.9128  -9.4960   3.1243
-#>   -1.6829   3.7035  14.6709  -2.2077  -3.2603  -1.1370  10.1571   1.2648
-#>   -1.0883  11.0762   1.3416 -12.7511 -21.1933   2.9799   1.6736  15.2179
-#>    4.4480   2.8421  -5.4820  -4.7026  -5.0694 -11.4562 -23.3585   7.1575
-#>   -2.9082  -1.0902  -8.4298  -1.1375  -2.0630  -7.2376   8.0495  15.6140
-#>    7.1673 -10.4637  -7.5146   0.0550  10.0286  -4.0913  -6.3031  20.5785
-#>   -5.4081  -0.1117   2.7705  12.1973  -8.7742  -0.2077   8.2314  11.1816
-#>    1.1246  -5.8824  -3.4459   2.8865   3.2704   4.4080  -1.6483   5.7580
-#>   -0.6552   3.7956  -3.3537  -1.6918   3.8882   4.0538   3.1555  -8.2176
-#>   -2.3748  -6.2723   5.2245   4.1894  10.9072  -3.6395  -0.2955  -1.0144
-#>   -3.5075  -5.5204  -6.1432   0.8693   9.1727 -10.3702   6.5089   3.1676
-#>   -2.1095   6.3571  -2.2335  10.9228 -13.9975  -2.3345  22.6371  -9.1804
-#>   -0.1568  -0.9048   5.5829  -2.9037  13.8580  -8.1319  -1.9552   3.5291
-#>   -3.8153  -0.3087  -7.4113  -8.1043   7.0150   4.7633  16.0928   3.2886
-#> 
-#> Columns 9 to 16  -3.1810  -3.2627   1.0612   4.0951   1.5249   5.4468  -0.3204   7.8181
-#>   -4.6727  -5.7859   0.5801   3.7623   0.3317  -1.4330   1.5571  -0.2559
-#>   -7.3271   5.7157  -5.1694  16.8466  -2.7004   2.6971  -3.5376 -10.4199
-#>   -6.5211  11.6065  -8.2419   5.3917  -1.8777  -7.2023   5.5177   7.2373
-#>    6.9915 -10.4649  -8.8021  -0.0459  -0.1659  -1.2228  -1.3253   8.1643
-#>    3.4482   6.1002   3.7333  -9.9588  11.2046  11.5108  -6.2545  -9.3857
-#>    8.2876   1.7361 -12.5369   2.4079  16.4401   5.7171   2.8861  13.5347
-#>    3.4647 -11.3662 -21.3331   7.0156   6.9562   6.3768 -10.0717   0.1385
-#>   15.1363  16.1886  -0.6350  11.5681  -6.6108  -8.0296   2.9866  -5.0846
-#>    3.5970  18.8378 -11.3572  -4.2139   0.3005   7.6319  18.2322  -4.4625
-#>   -0.8092  -5.0381   4.6445   1.8130  -9.0166   8.6400   7.3903 -17.6204
-#>   14.0589  -4.6007  10.7953  -7.4705 -12.3759   9.3497  -3.1502  -5.5020
-#>   -6.2404  -2.1810  -2.7716   0.7559   2.4455  11.5034   4.8724  -9.3390
-#>    1.5732  -4.2609  14.5878 -21.0616   2.1890  -5.8857  -4.9004   8.9875
-#>    1.0933  -3.6500   3.7066   5.4885 -13.8248  -0.8643 -16.2240   7.1034
-#>  -17.7197   5.6153  12.2955  10.4517  -3.1934   4.9312   5.7033  13.4370
-#>    1.3269 -10.6556  -6.0935   6.4030   2.2019  -2.4704  -3.2745  -7.5166
-#>   -6.9070  -1.5901  -3.8122  -5.1475  -3.1792   1.8839  -6.9131   7.7553
-#>    9.1986  -4.2436  -2.6430   4.5851 -18.2240 -13.6614   2.8320  -6.5608
-#>   -3.7126  -0.7393  12.5292   5.0482  -8.4647  -1.1843  15.3833 -12.8543
-#>  -13.4071  -6.8954  -6.4824  -6.8973  -8.1641   7.5570  20.1569  13.7861
-#>    7.7510  10.6749  -6.2694   0.7353   1.9931   3.6837  -7.3125   1.7338
-#>   -0.2942   6.2285   5.4998  -8.2894  14.3482  12.4478  -3.6528  -4.6218
-#>   13.3558  -4.2591  18.4636  -8.0938   5.3546   8.5646  -4.5114   3.6857
-#>   -1.7329 -15.8077  12.7673 -10.2201   9.5951   8.8423 -11.7141  -9.4775
-#>   19.4649  -6.8292   9.3490  -7.6591   8.2599  -1.1958  -1.2143   9.2001
-#>   -9.5506  -3.6073  14.2369  -3.8460  -1.9205   5.9566  -4.4946  -9.4718
-#>    2.9816  -0.1607  -3.5225  14.3550  -7.2591 -11.0014  -2.8438   3.6171
-#>    3.2333  -0.8371   2.9425   2.9499   2.0615   9.2706  -8.6544  -7.0034
-#>  -16.4620   2.1289   0.1354  -8.4689  -1.2499   4.8122   2.5817  13.4833
-#>   -0.5567   1.8226  -2.1932 -11.4949  -8.9891   7.0014  11.6330   0.0430
-#>  -15.2057  -1.4595 -11.9441  16.5283  16.2629  -5.4902  20.6133  -0.9546
-#>   -7.3588  -4.2087  -0.1877   4.6742  10.1569   4.0011  -0.8341   8.5255
-#> 
-#> Columns 17 to 24   4.7876  17.2720   3.3702  -1.0435 -10.8395  -8.1406  -7.5049  -5.5539
-#>   -9.9691  -1.1988  -0.3827   8.6837 -16.2213 -13.0124  -2.5975  -7.0430
-#>    4.5661  17.4118   7.9166   2.5864 -10.3547  -1.7683   9.1435  10.5543
-#>  -23.2334  10.3631 -25.5210 -12.3129  -4.8079   9.2390  -4.7016  -1.0820
-#>   -2.2913  -9.7322  -9.6260   5.5965  -2.5656  -0.5326  -6.9418 -10.7495
-#>   -3.2222   8.0219   5.9834 -14.8087  -6.2356  -2.7075 -13.7779 -38.2064
-#>    1.0871   0.2784   4.6760  -6.9322  15.0898 -14.0151  -7.9154 -12.2438
-#>   20.5265 -13.0739  -4.6356 -14.1243   1.4672  -5.4715  -0.2433  19.5680
-#>  -12.5312  -2.4462  12.4738 -14.6028  17.5852   3.3055   0.7279   2.9435
-#>   -9.0759   7.4424 -13.4984  -3.4864  10.9933  23.4347 -12.4106  -6.8116
-#>    0.2945  -2.3298  -7.9317  14.3155  -0.0490  -8.9765  -2.0218   7.3739
-#>  -13.2542   5.0205  -2.1731   1.3804   1.0738  -4.6849 -13.5458   2.3084
-#>    7.1460  -7.8515   2.2562  -6.2295  11.3502  -1.0303  -5.2992  15.8323
-#>  -15.0881   0.1767   6.4603   2.2481  10.8668  -1.9336  -3.0059  -3.9025
-#>   -3.4863   0.9929  17.7379  -0.4459   4.1941  -0.3456   7.3584  -9.6679
-#>   -7.0207   0.9989 -17.1677  12.6743  -3.4119   5.4517   4.1034  -7.5619
-#>   -6.6729  -7.0634  -8.5295  -6.7227   7.5573 -20.6982 -17.9593  -6.2486
-#>   -0.9564  16.8376 -17.7000   8.7197  -8.0258   3.5434   3.8272   8.6623
-#>    4.7000  -2.5202  -4.5670  -4.7674  -4.1558  19.1030   0.3822  -3.8329
-#>   -4.2104  18.9013  -0.6283  -5.7242  -0.6763   4.5063   4.3415  -4.9077
-#>   -6.0676  -5.2026  -9.6267  16.3435  -8.3280  16.4242  10.7939   5.0139
-#>   -6.8391   1.9071 -11.6442  21.1259 -11.9573  -2.9056  -0.0309  -0.9524
-#>  -15.3838 -14.4158  -5.6303  10.7407  18.0120  -5.8079   3.7584  -4.3958
-#>   12.5217  -0.7652  -7.5056   1.5211 -10.9556   9.6789   1.6204  -0.5635
-#>    8.5732  20.9318 -13.5805   8.2473  -2.1256  13.2180  -0.0080   2.3703
-#>   12.6771  -0.0184  15.6403   1.7141  19.6619 -13.3052  17.5606  -7.7566
-#>    2.2863   0.1965   7.5713  -2.2330   0.8285 -10.7955   3.7508  -3.1906
-#>  -12.9624  -5.9707  -4.7057  -9.0526 -14.0223  20.6805   0.6815   6.7069
-#>   11.4943  -6.2175   7.6624   8.1907   3.7794   3.9137  16.4800   9.7840
-#>   -6.1348   4.4264   4.3409  11.0234  -1.3618   5.7269 -19.5008  -0.8807
-#>    3.5661  -2.3396  -6.4453   4.2679   7.8474  10.3957  -0.1799   1.4265
-#>    3.1961  -9.3156  25.0814   8.3016   9.0736  -3.5149  -4.4687   4.5316
-#>   -2.1196   1.2412  -5.3532  -1.9141   1.0356   2.0371   3.8051   3.0251
-#> 
-#> Columns 25 to 32 -12.7784  -3.1151  -4.1067 -17.6687 -13.4600   6.3249  -9.3811  -7.3388
-#>   -0.7619 -11.6666  12.7568  -7.8023 -13.0570   0.8027  15.5756  -0.4485
-#>    0.7670  -3.6165   1.0185   6.6977   0.9419   0.6155  -6.7409   9.6993
-#>    5.1670   0.1816  -5.8841  10.4241   8.7812  -0.6064  17.1802   5.0143
-#>   -4.9780  -2.7439 -14.4820  -3.7913  -4.8708  -2.0714  -3.2589  12.7400
-#>  -11.4598  -1.2145 -10.0119 -10.2576  -8.2680 -11.5806  -3.2187  -2.6170
-#>   -2.6593   3.2811 -11.8429   6.7609  -2.9523 -20.6242   1.8380   5.0370
-#>   -0.6210 -23.1475  -2.7063  -5.0878   3.5943  -1.2283  -3.5178  -7.9973
-#>    3.6137   9.2104   9.5769  -2.2702  11.1843  -0.4034   1.9130  11.4164
-#>    7.6460  -0.3827 -20.7806  -0.2912  -2.5550   2.5483  -0.9074   3.8046
-#>  -10.1698  -1.4984  -7.6352   1.1972   3.4435   4.5729   2.1873   2.6382
-#>    2.0410  -4.2316   1.8286   4.2103  -2.9651   6.1363   9.6191  -2.9329
-#>   -2.6176   1.7511  -8.1758  -4.8099  -9.2014   3.0600   4.1247  -6.5042
-#>   -0.7695  -2.7357   6.5961   0.2550  -8.4879   5.1099   7.8306  -3.8784
-#>    7.1554  -8.0673  -4.7431   4.5292  12.5880   0.7548  17.6900  -0.6163
-#>   -9.0725   9.8556   5.5272  -7.2525  -4.6794  -9.3108  12.0713   6.8277
-#>  -15.2457 -11.3464  -0.8299  -5.3889  -7.3931 -14.3264  -2.2502   4.6353
-#>   10.7940   4.0557  14.0390  -9.8524   2.6743  -4.7332   5.4225  -1.7788
-#>    5.1810  -9.9234   5.5246   1.2374   7.2331  -0.2130  10.8272  -0.3448
-#>    4.7325   4.7821  -2.9912   4.3711  -2.0756  12.5127  -0.9461  -8.1639
-#>    8.8977   8.8578  -9.6155  -7.2892  -4.1856   4.3503   5.4361  -3.7193
-#>   -3.5420 -11.7217   5.6519  -1.5706   1.9119   4.2155   4.5332 -15.1637
-#>    9.5145   6.2907  -9.3909   7.1549   4.9775   3.4711   7.4276   7.5582
-#>   -4.8574   7.1038   5.1234   8.3762  -4.2527  -6.3355   6.4191   1.9055
-#>   10.1519  12.8104   4.5860   0.7293   0.0193  -1.5512  -3.6323  -3.6445
-#>    9.6038   3.8469   3.8026  -2.4492  -5.6320  -7.9020  -0.5373  -3.5500
-#>   -3.2464   6.0656  -0.9350   3.5552   3.2878   8.9093   4.0319  -3.2805
-#>   -0.9716  -4.9932   1.2699  -2.3636   9.6565   2.8870  -2.7157   6.6711
-#>   -4.0200   2.8403  11.0463   5.4527  13.8217 -11.7949  -6.8832 -15.9980
-#>   20.2676  10.8881  -7.9728 -13.7948   4.5471  -4.2364   4.6383  12.1830
-#>  -13.1566  -7.2088   3.0935  -5.6241  -1.0032  -1.1888   5.7101   9.9611
-#>  -16.2755  11.5573   6.1171  -1.1188  -9.2500   7.4123   6.8200  -2.3877
-#>    0.3761   1.2744   1.5478   4.8301   3.0607  -1.7451  -8.4018   5.3828
-#> 
-#> Columns 33 to 40  -2.6798  10.7408   5.4904  -4.9847  -6.8072   5.9453  -7.2387   9.5456
-#>   -1.6163  -9.3078   7.8399 -18.4102   7.3172  -3.7820 -10.7607   6.2403
-#>  -10.8727   3.4745  -0.8042  18.6595   2.5602   7.4274  -2.0725  -3.4180
-#>   -4.3281  11.4767   6.3640  10.9480  -6.1661 -18.5870  -4.1403  10.0234
-#>   -2.4974   1.1080  -6.0992  10.2662  -2.3050  13.6306  16.0703   9.8415
-#>   -3.2326   1.2055  -2.1907  -1.9525  -3.4973   8.5366 -14.1278  -2.0794
-#>    1.5418  12.4918  11.6216   2.1373   6.5673   4.2851  -1.3019  15.2588
-#>    6.4475   4.5054  -8.5098  -8.8409  -0.7175  -3.4654  10.5672   2.5838
-#>    5.9579   0.1376   4.9462   1.3211   9.5206  -8.4694   0.2855  -4.4919
-#>    5.8723   8.9215   9.1594  -7.8355   5.6397   2.0405  -0.8956  -3.4817
-#>   -0.3586   0.7275  -0.6278   6.3590  13.4949   3.0733   3.7876   0.0623
-#>   12.0649   0.3028   0.3616  -4.0420  10.4038  -0.6742   2.6561  23.8164
-#>   -9.3511  -1.2811   1.8103  -9.9004   8.7360  -3.9421  -0.2291  15.3043
-#>   -2.3802  -7.0159  11.3137 -13.9367   6.5566  -6.2087 -11.2825   7.4017
-#>    5.6427  -5.7506  -0.9135   1.0892  -2.6045 -13.5775   4.7687   0.4022
-#>  -10.2733 -16.6287  -0.3390  -2.6910   0.1103   0.9711   2.0200 -10.0394
-#>    0.4027  -8.1790  -1.2274  -1.8224   6.4739   0.3178 -10.8276   5.8462
-#>    5.6329   0.4834   1.0717   2.2724  -4.3132  -6.7506   1.2790   9.4726
-#>   -6.6045  10.1189   6.9280   1.8852  -2.8820  -9.5778 -18.9174  -7.5127
-#>   -4.6235  11.4260 -15.3983   4.8237   0.5430   3.1613  17.0547  -7.7714
-#>    0.9165 -15.7357  -2.9726 -16.5025  -4.2553   8.5064   4.5877   6.8933
-#>   -1.0045  -1.5503  11.9903 -16.7153   1.7860  -8.8464  -1.3527  -5.7166
-#>    1.9460   4.4425 -13.1039   0.3578   8.0725   8.0274   9.0462 -14.6979
-#>   -1.4275   7.5097   6.2892   0.8070  -3.7205   0.7155 -12.4836   7.3437
-#>   -3.1733   9.4923 -12.6845  17.5650  -1.7871   8.4914  -5.2970  -6.5126
-#>    5.3399   6.7639   7.6913  -3.0564   8.4512   2.6952   8.8310  -0.6163
-#>   -4.9734   1.6869   3.8372  11.4282 -10.7808  -0.1684   8.9313  -4.8800
-#>   -8.7450  -6.8279  -6.4785  21.0190 -10.2943  21.6078 -29.3649   4.0950
-#>   -2.7746  -2.4019   1.7970   8.5955  -9.3389  -1.3879  13.5859  -5.6466
-#>   11.1821  -5.2228  13.2041   1.8326  -6.4913   5.4415 -16.9786  -5.4153
-#>   -2.8319  -4.7344   9.0530  -7.0999   4.7814  -6.2008  -4.4767  28.7513
-#>  -19.1089  -8.6855  13.1355  -9.2310   6.0743  -9.0933  11.9112 -10.9186
-#>   -9.4442   0.6115  -4.7476   4.9595   2.3222  31.3597  -0.4587  -3.3852
-#> 
-#> Columns 41 to 48  13.2335  18.4072   3.8452  -6.9916  16.0191  -1.4387   7.3699  14.8535
-#>   -4.2993  10.6820   7.0353   1.3130  -8.7711  10.1553   4.1372  -0.5599
-#>    3.2517  12.1267 -15.7250   0.9289 -12.1262  -4.4340  -5.4977  10.2717
-#>    2.3578  -1.1384 -13.6929   1.9523  -9.0392  16.9055   5.2991 -21.7345
-#>   20.1297   9.7540  -4.4430  17.1155 -10.7662   3.5038   7.8592   3.0781
-#>    8.2474  -7.9042   2.5285   4.0943  11.3423  -2.9282  -3.7371  -5.7120
-#>    4.6633  -6.2643  -2.6094 -16.2379   1.1435   4.4389  17.2909   8.6930
-#>   -0.7847 -14.8175  14.3593  -0.9707  13.6857   8.9951  -1.7974   5.3307
-#>   -2.7323  13.8403  -4.1151  -8.8232  -5.2754   3.0921 -12.9723  -7.8679
-#>   12.0113 -14.4334  -7.5176  -4.4632   3.3497   4.9875 -13.5565  -1.4509
-#>    2.6535   2.2753   3.7532   1.5156   2.9532 -11.4222  15.0870  12.4004
-#>   -0.8589   2.4802  -6.1565  -4.2074  -2.5356 -13.0278  -3.9227 -18.4011
-#>    6.0597 -10.3485   1.7872  -6.2897   0.9800   3.4572  17.1559  -0.8167
-#>   -3.0977  -4.0392   5.5219  -7.0214 -15.3351  -7.9001   5.0116 -29.1513
-#>   24.2113  14.1920   3.8048   5.9459  -8.5433 -11.8677  -0.5119   5.3183
-#>   -1.5524  -1.9231 -10.8412  14.6299   6.4453  -4.2846  -3.5581   6.6648
-#>  -17.1757  -4.2006  -2.9053  -7.5145  11.6744 -18.1325   5.4189  14.1761
-#>  -13.5461   6.5545  18.4231   9.6028   6.9905   7.2333  -4.9760  -7.3707
-#>    4.2127  -3.3927   8.6950 -22.2378  -4.6125  -7.6642   4.6457  22.9384
-#>    1.9780 -16.4488  -4.6691  -0.0215   3.3830   1.5566 -12.8181 -11.4244
-#>    4.7052   4.0998   3.6235  -2.1365  11.1460  -6.0001 -11.2796  -2.0067
-#>   -9.5723   6.5852  -8.2466   3.4642 -19.0371  -1.4680 -10.6882  -6.0754
-#>    1.5706  -2.1467  -8.1451   4.2954  -5.1098  -9.6729  -8.2277 -16.7589
-#>    4.9486  -7.8326  -9.1857   4.9922  -6.8312   2.1194  13.4255  -7.9282
-#>   -2.9973   4.8711 -14.2213  12.5082  -8.9272 -13.5373 -10.6331  -1.7991
-#>  -20.9875 -15.0079  -0.1355  -4.0733   3.4722  -1.1602  -1.9319 -10.5716
-#>   10.5513  -6.3437 -10.0204  -5.1811  -7.3454   3.2951  13.2194 -10.6875
-#>   -9.0411   9.6437  -0.7267  -8.0317  -3.7791 -14.4714   3.3345   0.9388
-#>  -12.0010 -11.0004 -18.0084  11.7813  -4.8742  17.4474  13.8801   2.0438
-#>   31.7265 -13.4833  13.5332  -2.5016  -7.9255   4.0851   1.6553  21.2569
-#>   -1.0274  -0.3764   3.9221   0.6137   4.0974  -6.4963  -6.7259 -11.1689
-#>    7.8031   0.8675   5.7429   2.3580  -2.4164   2.3665   5.2437  22.7314
-#>   -4.2878 -10.5383  -9.9768   1.3139  -9.4152  -6.3573   9.6883   5.9264
-#> 
-#> Columns 49 to 54  -0.2671   9.7701   6.0322   4.6811   2.9762   2.2722
-#>  -14.4196  -1.0555  10.7776  -8.0051   1.9966  -1.7877
-#>    3.2285 -12.2791   6.1325  -8.0227   6.4722   2.3322
-#>   -9.7100 -12.2404  12.3004  -3.0280  -5.8205  -3.4416
-#>    1.8904   1.2133   7.7791   2.6255   1.2190   0.2403
-#>   13.7775  11.0335 -12.7964  -3.6531  -5.7224  -0.2613
-#>    2.1140   0.6198   8.4620   2.8346 -13.2978  -4.7245
-#>    6.4016   6.4218   5.0143  -7.5094 -10.5561  -1.5737
-#>    5.6732 -21.9615   9.2310   6.7673  -3.2778   0.9200
-#>   10.3036  -3.3432  -9.5236   6.6128  -3.7361  -1.9285
-#>   -3.4186  -3.4549  -3.8103   2.1477   0.9592  -0.1993
-#>   -4.8919  10.2478   8.8289   1.0471   5.5893  -2.8752
-#>    1.7449   1.9779  -4.1833   2.3643  -0.7714  -1.5880
-#>   -7.5838  -4.1429  -6.4595  16.2080  -8.8949  -4.2480
-#>   -7.4641  -0.9506  -3.1015   8.1307  11.6222   0.3097
-#>   13.7682   8.3304   1.3823 -18.3744   4.5722   3.6273
-#>  -11.2872   3.8188   0.5564 -11.4159  -6.8905  -0.0975
-#>   11.2198  -1.0655   6.9872  -7.0629  -3.6281  -0.3584
-#>  -10.5131  -2.6208  -5.8299   0.5071   9.3178   3.8768
-#>    3.3297   8.2219 -13.3294  -0.0814   1.1981  -1.7593
-#>    7.4771  22.3231   3.6667  -4.9007   1.9783  -1.0990
-#>   11.3080 -15.7802   1.4606 -12.4734   2.3892   1.7499
-#>    0.4649  -9.0525   7.7570  -7.6014 -11.8574   0.6533
-#>    5.2104   1.1293   6.9246   6.9781  -3.4111   0.2557
-#>    6.8321  -5.3332   0.8903  -1.9140  -0.1111   0.7168
-#>   11.2020  13.3858  -6.2562  15.5736  -7.2203  -3.6565
-#>   -2.5946  10.7949  -9.5117  13.1883   0.9728   2.0467
-#>  -16.0799 -13.1516   0.1700 -12.3154   1.6330   4.2724
-#>    1.1213   8.2665  14.9941  -4.0737  -4.9093   1.5110
-#>    4.0347  -4.5001  -0.1521   7.3414   6.3084  -0.2847
-#>    3.4297   8.9231  13.8903  -1.4625  -3.8002   0.3066
-#>  -15.8880  12.6141   4.3672  -4.6317  -0.2330   2.2429
-#>   10.0463   3.7442  -3.5000   0.3014  -3.8686  -2.9811
-#> 
-#> (18,.,.) = 
-#>  Columns 1 to 8   1.6420   1.2879   5.3164  -7.8571  12.7048   5.8433  11.3036   3.7826
-#>   -2.5109   0.0431  -1.9696   4.5404 -18.1270  -4.8284   9.7173   6.8896
-#>    3.3734  -1.0133  -1.5317  13.5069   8.2392  -2.2201  18.2343   8.3940
-#>   -0.6494   1.1275  -0.7992   6.7130   6.8398  -0.1860   3.1122  -4.9259
-#>   -0.1896   1.4126  -7.5902   8.6638  -6.4634   2.9422   6.8588  -8.1542
-#>   -2.5423   1.3982   8.7002  -5.7975  -3.0473 -11.9556  13.9886   8.3534
-#>    0.0925  -1.9056   4.1929   9.6189   3.0107  18.1900  -4.0006  -0.3840
-#>   -1.9065 -12.0112   0.1455  -5.1230  -5.6154  -0.0627  -6.9732   7.9400
-#>    3.1135   9.0919  -4.3371 -19.9986   6.5665 -11.0074  -8.8907  -3.8055
-#>    7.1416   3.7126  -1.0907  10.3849  11.9761  -1.6818   9.6157   1.0954
-#>    1.6181  -7.9607  -6.3046  11.3903 -19.3071   0.1217   6.8742   7.1179
-#>    3.1030   0.0477  -3.0999  -4.6140  -9.5560  -5.0644  -0.4219   9.1221
-#>   -0.3628  -5.0311   7.3550   5.5133  -4.0872  -4.3923   8.5265  11.5909
-#>   -2.2950   5.0372   6.1929   0.2985  -9.3952   2.1639  -2.5889   0.1801
-#>    0.5811  -7.8022  -0.1743  -1.9489  -9.0799  -2.7091  12.2969  -0.8607
-#>    2.0249   1.1647  -2.1831  13.7438  -3.9637  -1.8503   8.7329  -7.0915
-#>   -3.8079  -6.8342  -6.9091  -2.8699  -1.6679 -16.3355   2.1067  -1.9638
-#>   -8.4592   8.9154   0.8565   0.4704  -4.0822   7.5768 -11.9635  12.3542
-#>    3.6177   3.4600 -10.9865   1.7910  -2.5461  10.9428 -11.2756  -9.3878
-#>    2.6046  -3.8795  -1.0403   8.5378   3.9353   3.5416  -1.2939  14.9872
-#>    2.5174   1.1112  -1.4658   5.9015   6.6519  -5.6944  -8.8193  -4.0332
-#>   -1.5362   0.8535  -5.1453  -6.1017  -5.8288 -13.3209 -13.5367  -0.0891
-#>    5.5770  -5.0353  -3.9072   9.3719 -15.7994 -14.6314   3.9531  14.4180
-#>    4.4054  -8.4694  10.0511   8.5610 -10.1093   4.8425   1.0703  -1.3042
-#>    6.2056  -1.5487  -4.2585   6.9884   1.3044  -2.5759   5.4127   9.3555
-#>    2.2354   6.1777   8.6935   0.8330   7.0591   9.0817   4.4968  -2.9316
-#>   -0.3657  -2.1088   1.9124  -2.1532   0.5189  -4.1221  10.8716   4.1775
-#>   -0.4629  -0.4061  -5.8569  -5.7979   8.3717  -0.3196  -4.8337  -5.8717
-#>   -6.2881  -4.1826   0.7540   6.1380  -4.3152  -7.9107   6.6223   2.5199
-#>    8.2005   6.7751  -7.6609  12.9753  -3.5936  10.3625  -2.7662   0.3874
-#>    2.6145  -1.6590   1.1084   6.4919 -10.4815  -7.4845   5.0912  -3.3561
-#>   -5.6482   6.7101   0.6589  -2.6008   9.2927  -2.4136  25.8413   4.2583
-#>   -1.5522  -0.6731   3.7094  13.6084  -3.4717  14.0455  18.0751   9.0139
-#> 
-#> Columns 9 to 16   5.3804 -12.1351 -10.3660   0.5842   8.7693  14.6643   1.6818  -4.7866
-#>   -6.3455   3.6485  -5.2393   8.7314 -12.4487   4.7045   7.5412  -7.6969
-#>    8.4230 -14.6234  11.1057 -14.9826   4.4700  -5.2711  19.1685  -4.3796
-#>   17.0186  -6.2625   7.5988  -5.7124   6.4860   5.1624   2.9079  -1.0623
-#>    5.3565  -3.7169  29.1445   0.5628  -6.2952 -15.5886  -2.4912   1.7751
-#>    4.5112 -12.6647  -5.2051  -5.0987  -2.6762 -12.0308 -17.8213   2.3569
-#>    9.6305   7.1402  -9.4125   4.1921   5.6178  -4.1310  -7.4195  20.3406
-#>  -23.4526   6.1727   4.8096   6.1797  -0.8832  13.5739  10.5601   1.5159
-#>   11.5793   3.3354  -4.7421  -2.3175   7.3162  -5.4094   5.8834  10.9601
-#>   21.4171   3.9900   5.2486  17.4438   4.1869 -10.7578   9.4085  15.2720
-#>    3.6663  10.5914   9.0197 -12.5738   3.7574  -8.8465  -1.0220   3.9162
-#>    8.9307  -4.4353   2.4044   3.1607  -5.1845  -4.3000  -9.6301 -17.7922
-#>    4.1468  -5.9985  -0.0510  -0.9246   8.0400  -0.8469 -19.3457  -0.0925
-#>    2.5744  -1.3962  -4.2692   6.4907 -18.3116   3.7004 -19.8901   2.7946
-#>   -0.8892  -3.9821  -2.5982   1.1824  -8.8158  -0.4670 -10.6899   7.9036
-#>  -11.7663  -8.2280  10.0735  12.8687  -3.1658  -1.1699   5.9914   4.3675
-#>   -2.5636 -13.9672 -10.1241   6.0991 -17.4340 -10.4532  -0.0374   6.1176
-#>   -7.6453   9.5692  -7.7252 -12.5117   6.0954  -0.0058   2.8967 -21.3386
-#>  -13.3735   5.3355 -13.9365  10.3804   8.2995 -10.9733  33.1979  27.0988
-#>  -12.7816 -11.8679  11.4570  -3.9373   5.9092   7.1102  -6.8729  -6.7231
-#>   -6.3768   3.6131   2.9764  -1.4754  15.5975   8.9042  16.6951  13.3923
-#>   -0.5956   8.4284   9.3541   6.6690   3.1478  15.4808  10.6831  -8.6146
-#>   -2.4373 -18.7960  18.9351  15.9599  11.4891  -7.5023 -15.6213  -6.1754
-#>    3.8959  -1.9634   2.8737   4.6599   4.4457  -1.1856   4.8629 -12.7711
-#>    6.0529 -14.6800   2.7329  -1.2511  -7.1787  -5.7072   6.1981 -25.1700
-#>    4.8224   1.9774   8.1258   1.0748 -27.5141  -2.1715  -2.2420   0.7899
-#>   11.7260 -18.2582   6.0380  -6.0932  -3.7677   3.0523   4.9454 -11.8659
-#>   -0.3142  -1.3074  -2.0408  -8.9408  15.0563  -0.6220  19.6902   1.4908
-#>   -6.0120   9.0143 -10.1494 -11.0386  -5.0090  -5.1131  -9.2287  -8.1093
-#>   14.9981  18.3641  -4.4207  -3.9998 -10.9331 -15.5465   0.6011  -2.4611
-#>    5.7311   2.3118   7.1591  16.2519  -0.1902   6.0938  -4.1115   0.3160
-#>   16.1710  -7.7254   5.3234  -6.6723   0.1106   3.8326   3.5976  -9.7607
-#>   -1.7634  -1.1571   2.4107  -5.2687  10.5207  -9.7934  -5.3621  -4.9413
-#> 
-#> Columns 17 to 24  12.7919  13.8965   7.2914   7.1090   2.6619 -11.1852  20.6129  -0.3397
-#>   -6.1425   4.7137   0.3191  12.4660 -12.7929 -10.1171   1.9036  -9.9026
-#>   -2.9329   5.4450   8.1227  13.2214   2.8128 -23.6955  13.6789 -10.9361
-#>  -21.1594   3.2425  14.4772 -10.3611  21.0172  -5.6483   9.4607   6.3364
-#>   -8.1113   2.5801  -8.5287  -0.6597 -14.0022  -0.5884   3.7643 -12.5078
-#>   15.2535  -7.8894  21.2285 -13.4478 -13.0378  -9.4753   6.7139  -6.2283
-#>  -18.2535   2.6662   2.6795  -2.7393   4.5147   2.3993 -10.3161   9.5086
-#>    9.5280 -14.5483   4.6656   3.5450 -12.7685   5.6188  -4.8503  11.8668
-#>   -8.0276  17.9217  -7.9395  -7.7207  20.0763 -10.4341   0.3116   6.6646
-#>  -12.0671   3.4923   3.1873  -8.6960  13.8439 -15.9396  -5.4830   9.7459
-#>    6.6190  -1.9811  -8.0694   9.4840 -16.2822   2.9730   1.0627   3.1966
-#>   20.4468  -0.6983   4.6333   5.5524 -14.3997  -1.8202  -4.0220 -12.3155
-#>   11.8901  -5.3156   4.3961  -4.3514 -17.1259  10.1295  -3.1806  -3.7633
-#>  -22.2255   5.1331  19.4986   9.8792   1.8949  14.8938 -12.4817   3.8372
-#>   -2.5785  -4.3861   9.4344  -4.7644   2.7722 -10.0445  -4.9544   2.5280
-#>   10.1179   4.8086  -4.7714   6.3682  -4.8846  -8.6565  14.4215  -2.6060
-#>    2.7266  -6.6072   7.6766   8.2032  -2.2043   1.3790   2.5085  -2.5679
-#>   -9.4860  13.6252   0.5499   4.3637  -0.8586   3.5431   2.8470   5.7581
-#>    8.7090   8.6967 -25.1840 -12.8700  -0.1018   8.5919   1.0733  10.8271
-#>   -6.4616   3.3153   5.3792   7.9816  -1.5986  -3.9117  -6.9535   3.2914
-#>   19.0527  -3.1730   3.6325   3.6042   9.1775   6.4150   2.8763   3.6961
-#>    7.6531   1.3698  -9.0558  13.6710  -0.5876 -10.8151  11.8866 -19.2419
-#>   -8.6815 -17.6992  12.5567  12.1497   6.0738   3.7060  -3.1910  -0.9991
-#>    4.7524  -5.6268   2.4945   1.2878  -8.5590  -1.7245   2.4662 -12.2543
-#>   20.1751   1.5843  12.8122   2.2665  -0.8076  -7.3859  17.5358 -16.9012
-#>   -4.6932   9.3886   0.5833  -5.7472  -2.2443  -9.8603 -19.1601   3.3404
-#>    0.8012  -0.8414   2.3527  -1.0912   8.3260   2.9054   2.3910   3.4724
-#>   22.5107   3.1786  12.0413   1.5933   7.6991  -9.4353  13.6481  -2.4569
-#>   -3.8804 -11.3571  13.3741   1.0029  -6.2457  12.8943   0.5813   3.3167
-#>   -0.2584  -1.0563 -17.1628 -12.4293   1.2945  11.4786  -6.6795   6.9568
-#>   -6.6891 -13.8328   2.5390  -0.3918  -2.6157  -3.9123   7.7240  -1.6569
-#>   -9.8826  -1.3782   1.5560  13.3592   0.2965  17.9120   7.0649 -11.1058
-#>   -5.5006   9.0927   8.0763   2.2972   0.9185   5.0729  -2.5752   2.4860
-#> 
-#> Columns 25 to 32   2.6157  12.1656  -3.4293  11.2619  29.0579   9.0954  -7.2592   2.0037
-#>    0.8193  -0.0767   3.5949 -10.6917  -5.6788  11.6697  -5.4333  -6.3205
-#>   -3.6810   1.5525  -5.6771  -0.1137   4.2578   9.2257  -7.5001   1.5330
-#>   -0.0569  -4.2848   9.9154   6.3289  -5.2351   7.7988   4.8216  -2.9912
-#>   -3.8000   6.5655  -2.0004   1.7156 -17.9381  -4.2242   3.4018  -1.0467
-#>   -9.4344  -9.8031 -12.0416   8.2862  13.2678  -0.7302   0.4335  -8.4655
-#>   -7.8939   3.8292   9.9277   0.7376   1.4882   4.2665  10.4770  -8.0951
-#>   10.8728   9.1898   1.6196  -2.5027   1.6465  18.7318  -5.1867   4.6003
-#>  -11.2066  -9.4481  -5.4285  -4.1906  -7.5989  -2.8678 -16.7686  -2.0709
-#>  -17.2278 -10.1598  10.4174  -3.8264  12.3131  -2.5120  18.8831  -7.7288
-#>    0.3052   5.7338  -7.8968  -1.5548  -9.5453   4.5017  -5.5146  12.2713
-#>   -9.3960   3.2968 -12.8012   4.0913 -15.1407  -0.3459 -14.7093  -7.6451
-#>   -1.7817   4.8734   5.0182   4.4158  -6.2907  18.4289 -16.1908  10.3766
-#>    0.4862   5.4331  -6.9836   3.9721  -4.2630   9.7002   2.3095 -14.5059
-#>   -4.1786  -5.7285   2.5321   6.2680  -4.6553  -2.1756   6.8678   2.6513
-#>   -8.6665  -6.0416  -0.5409   0.5216   4.5455  -1.9902   0.3366   0.8028
-#>  -10.9652   6.0440  -2.8763  -5.2005  15.1445   1.7218  19.8993  -3.4327
-#>   10.6043  -3.2512 -10.1111   4.6157  -4.0006  -2.1343  -1.4302  10.6029
-#>   -0.7327  -0.6984  -2.8468  22.2596   8.2914   2.4505  16.9699  -3.4296
-#>    2.0949   2.8143   5.6126  -0.4120  -5.6877   3.9536 -10.9128   0.8354
-#>   16.6468   7.1970  -5.7594   1.3074   7.8698   0.9892   6.8667  -4.1368
-#>   -2.2990   3.1578  -5.4826  -1.8124   2.9626   8.2702 -14.0820  -6.5329
-#>   -5.5275  -8.0167  -3.2093  -3.3525  -9.3943  13.0748  -7.8546  -4.9310
-#>   -1.6238  -7.5335  -0.1831   3.1611  -7.2427  -1.5588  -5.7129  -0.7068
-#>    8.0404  -3.7601   4.9198  -5.5980  -1.6015 -12.4899  -1.4713  10.5558
-#>   -7.2427   2.0596  -5.3566  -7.9559  -4.5101  -5.5048  18.3094   4.4071
-#>   -1.3398   2.4388   6.1397   1.8313   3.2439  -6.4362   6.1027   1.0680
-#>    9.4973  -1.7636   0.4137   8.9540  -8.8187   0.1385  -0.4692  -5.5934
-#>   14.0594  -1.4364   3.2645  -2.2201  -1.2013  -4.2073  11.8217   9.4594
-#>    7.2463  -7.3954  14.7617  11.0248 -11.8653  -7.9344  -2.0461  17.9101
-#>   -4.8893   0.5295 -10.6414   2.5047  -1.8547   8.1770 -11.2773   3.5899
-#>    6.6188   5.2276  17.5914 -12.3284  13.5262  15.2202   2.1484  -6.2322
-#>   -3.2337  -6.4030   2.5477   0.6266  -1.7585   4.5958  -3.8904  -2.0269
-#> 
-#> Columns 33 to 40   0.0029  -3.3509 -12.6342   0.0077 -13.8816  19.4735   6.4888  -4.3073
-#>    1.5395   0.2632  -5.4625   0.9212  -9.7440   5.6657  -5.8855  -1.8879
-#>    5.9266   0.1231  -6.3651   0.3660  -1.8050 -17.6215  -0.0248 -11.7501
-#>  -10.3363  -1.2273   6.9968  -4.5623  24.2431  -8.3985   7.2473 -31.0217
-#>    1.2550   9.8227   3.1240 -18.6046 -13.7359  -3.0107  -1.1276  -2.6916
-#>   -8.9907  -9.6516  14.6502   8.2522   3.6585  -7.0430   2.7615   4.5177
-#>  -26.4023  -3.6227   6.8321  -8.3929 -11.2319   2.2254  -2.6124  -8.0238
-#>    6.9105   7.0635  -1.4849   2.4072 -19.7923   6.6321   3.8779   2.5356
-#>    6.3276  -8.5924  -5.0837  13.8570  12.6960  10.3987  11.6815   2.7757
-#>    2.8557  -1.6411  -8.9849 -14.0671 -11.5407  -7.7375   5.4139 -15.6535
-#>    0.7287  -7.0148  -4.2302  21.0221 -13.1672  11.1729  -5.9700  -4.3374
-#>   10.4731 -10.1309   3.3727  20.1838  10.8264   5.5776 -10.2286 -10.2985
-#>    3.3903  -8.1233  -0.6413   4.7653 -13.4528   3.2653   1.5537  -0.2125
-#>   -4.8917 -23.8506   8.8801  -4.8572   4.5650   8.1567   8.8407  10.9621
-#>    2.9578   9.7805  -9.9187   2.7422   9.3902   0.2009 -16.6340  10.2006
-#>   -3.8805  -6.7501   7.9872   2.7867  -0.0815  -7.9427  -3.5837  -0.0629
-#>    1.6030 -13.3641  11.1395  -1.3434  18.6885   3.3939   9.9179  -1.1933
-#>  -18.0162  -8.5544  -0.6633  11.2242  -0.5546 -10.2144  -3.5952  -0.8228
-#>   -0.0304   3.3971 -13.0760  12.1379 -18.7442   6.0984  11.3735  13.1533
-#>   -1.3424   7.4699 -16.1672  15.2030  -1.1397   1.4846  -9.6994   0.7061
-#>  -17.6452   7.4832 -12.2968  20.7037  -5.9765   6.0838  -3.7101  14.0650
-#>    9.8086  17.2381  14.2677   5.9241 -12.9382  15.8107   0.3432  13.0134
-#>   -7.9321  19.8009   6.0164 -11.8849  21.6689   7.9013  -0.1456 -14.9485
-#>    3.3430   3.6445  10.6610  -2.1489 -17.0001  -7.2675   6.2436  -3.5449
-#>    3.4334  -2.1735  13.1118 -14.9150   6.0767 -11.2966   4.3785 -12.3123
-#>   -2.0132  -6.5839  11.9638 -18.3315  -3.5849  -8.2416  -3.7682   6.0796
-#>   -0.0251 -11.1150   7.8212   1.7854   7.5301  -4.5684   0.8110   4.1804
-#>    8.2421  -1.7128  -1.8108  12.8005  17.7682   4.3181   8.1897  -3.9628
-#>    0.6283   2.0559  12.3124  -3.8224  -3.3201 -12.3931   6.2987   3.5806
-#>   -2.0786   6.6837  -3.2043   3.0021 -12.1649   0.8647 -19.2996 -11.3476
-#>    3.8575 -17.6842  13.0417  20.2883  -6.0133 -10.4405  -0.8185   3.9150
-#>    6.9854  10.4905   7.3135  -9.4315  -7.8653  -0.3362  -3.7238   3.9443
-#>    7.3930  -1.5779   0.2267  -8.8062   2.0794  -3.5313  10.0632 -14.0367
-#> 
-#> Columns 41 to 48   4.0510  -1.2702 -10.7254  -0.1518   6.2255 -16.2182   0.0292  13.3147
-#>   -2.1117   7.7726   4.7524  -6.3753   9.8414  -0.9707  -1.7454   8.6750
-#>   -0.9473  -6.6488  25.7680 -21.5618  -1.7513   8.2230  -4.7134  -9.3620
-#>    2.9891  -9.4650  26.0017 -24.0466  -3.2031  16.5410  -1.8369  21.9893
-#>   -1.7402   8.2179  11.1601  -4.2580   2.8483  13.6395   5.4053   2.7420
-#>    8.2215   6.4315 -10.5551   6.0965 -29.5574   9.0842   4.3430  -8.8682
-#>    4.0511  -4.8678  -7.8091  -7.2168  -3.2019   0.7052   2.3832 -13.7279
-#>    3.7785   7.8156  -3.6508  -6.4915   9.9509  10.5353   0.7613  -1.9243
-#>   -3.4082  -3.2314   7.5694  -9.4198   1.2483 -10.8518  -0.4544   5.1223
-#>    2.7928  -4.2954  13.6518  -4.1961 -32.9449   8.2825  10.6235  -6.5547
-#>   -2.1706   0.5347  -4.1825   9.5515  -1.3363  -0.9141  -8.9849  -2.0354
-#>   11.3627  -0.6651  -7.9567   1.5583 -23.5124  15.5929   0.6318   0.4863
-#>    9.5106  -0.9316  -1.4384  -7.9514   3.3732   2.6686  -4.9989  -3.0646
-#>    1.5673  -3.4351   3.2348   5.1354 -16.7270   2.7156 -10.4553 -14.2158
-#>   -2.6128   9.3098   6.3065  -3.1190  -4.8262   8.8057  -9.8614  -5.6171
-#>   19.3820  -7.3091  -1.5507  -3.3476   1.9809  10.8549   2.1270  -3.7262
-#>    7.5950   5.9518 -14.4187   1.6114  -7.7032   7.1640  17.7226  -8.4070
-#>    0.6154   5.7930 -13.4261   5.2674   9.2733   3.7338  -7.8101   1.1300
-#>   -8.1761  12.1908 -21.7284  13.0551 -16.3943  -5.5680 -10.0639   3.2579
-#>    1.8726   4.2444   4.9620  -6.5421  -9.1609  10.7864  -0.0778 -10.7919
-#>    9.7009  -2.5378 -15.6215   8.3960  -7.2251  -3.9734   8.8126  -4.3367
-#>    2.8564 -32.7612  16.8721   0.2182  11.6929  -9.1474  -5.9281   3.8051
-#>    1.7788   1.4127  -1.8833  -6.9912 -12.5033  18.4125   3.6896 -15.0041
-#>   -5.9166  -7.0237  -7.5080  -8.2371   7.0100  13.8009 -11.6932  -1.6627
-#>    4.9167  -2.6230   0.3647  -3.4979 -20.9022  16.2120  -6.7058 -14.1063
-#>   -1.9939  -0.7363 -17.2596  14.2294 -19.4004   8.7278  -0.1109  -9.6073
-#>   -4.8946  -0.0505  -6.9824  15.6053  -5.0343  -2.1242  -2.9937   0.5938
-#>   -4.5974  10.0005   5.7879 -12.7138   4.0694 -22.5153  -8.6040  19.5947
-#>   -7.7176   9.4326  -0.3602  -1.8544   9.7682   2.2847 -11.9673  -4.2708
-#>  -16.3190  -5.7646  -6.2865  -0.0257   2.4428  12.3580 -13.9446   2.4780
-#>    8.4663  -8.2000   9.8663 -13.7863  -1.1844  13.4650  -4.9049   5.9373
-#>    2.4027   0.5915 -21.0702  -1.0956   6.1958 -18.1889   5.0352 -18.4239
-#>   -9.9235  -7.0443  -2.4528 -12.1563   3.5734  -3.8522  -9.3329  -9.5187
-#> 
-#> Columns 49 to 54   2.4848   8.9600  -6.6746 -12.3711  -0.5049   2.8786
-#>   10.6893  -5.4225   9.1650 -13.3324 -13.8708   3.7911
-#>  -16.2479  27.7164   0.7434 -11.4350  -8.6765   0.6951
-#>   -0.2513  -5.2872  -8.1689  -6.9968  -0.3131   2.7240
-#>  -20.2858  15.1985  -7.0279  -9.8774  -2.8880   7.5356
-#>   -5.0441  -5.9110  -3.6796  10.4714  -2.4352  -5.0220
-#>   -2.9365   3.6362  10.7610  -0.0537   1.7815  -1.1395
-#>    1.9543 -10.6093  -5.5753  -3.9769  -3.0863   1.6045
-#>    3.8588  11.3072   2.7108  -5.5991   4.0178   0.1168
-#>  -22.2331 -13.1465  -2.4980   5.7941  -6.1919  -8.3823
-#>   -0.9479   7.0255   0.0698   1.3584   0.6481   0.1080
-#>    4.3669   5.3655  -2.4001   3.0138 -11.6708  10.0307
-#>    5.1871  -7.0168   0.4818  -0.0774  -0.6100   2.0541
-#>   17.7132   6.4588  10.1605 -10.7837  -6.7728  -7.9383
-#>   -7.6739   6.9783  -1.2624  -0.2311   1.1638   3.9497
-#>  -17.5456  -9.7086   2.0341   9.9349  -3.0648  -0.5089
-#>   -3.1795   4.3665  17.0024  -8.8965  -1.5841  -8.8056
-#>    2.2149  15.9212  -4.4968   0.0624  -3.1900   2.9269
-#>   11.9618  -2.4289 -17.1797  17.8757  -0.4401   7.4848
-#>    0.8993   9.7873  -2.5482  -0.0111  -6.4800  -1.8389
-#>  -12.8026  -5.3223 -10.4207  -7.1540  -3.7932  -0.5174
-#>    3.0748  -8.7669   3.6482  -0.7108   2.5877   2.6914
-#>   -1.5469  -5.7033   1.4735   4.2395   1.8257  -3.1212
-#>   20.0404  -0.7424  -2.4644   9.7535   1.2855   5.3932
-#>   -4.9952   6.6275  -9.0983  11.9380  -6.4644  -2.4656
-#>   14.4117 -11.4367   6.1705  -2.5055  -4.0090   1.6790
-#>    1.7852   2.5159   5.6664   7.5982  13.7174  -5.9481
-#>   -0.8873  -0.4220 -12.1559  -7.4173  -7.3533  -0.6071
-#>    3.2437   1.4743  -7.6123  13.1501   8.1817  -3.4226
-#>    3.7093 -13.1205  -7.3952   9.8814   4.7580  10.3219
-#>    3.5487 -23.3564   5.8674   6.2407   0.5246   4.0238
-#>    8.0665  28.6886   2.2886  -3.1141  11.1435  -2.9868
-#>    4.3882   5.3014  -3.7234   8.4248  -0.0245  -8.0384
-#> 
-#> (19,.,.) = 
-#>  Columns 1 to 8   2.2563  11.1571   5.1803   2.0074  20.4880   1.9959 -11.7897  -3.3816
-#>    1.6023   4.9350  -9.8570   2.5549   3.4270  -6.4630  12.5979  -3.4586
-#>   -3.7042  -7.2734  10.1350  -2.1168   4.5737   4.7926  -6.3151  13.1638
-#>    6.8103  -6.0863   3.9821  23.3459 -23.1046   3.2218  -2.8127  -2.0219
-#>   10.0323  -1.5816  -0.3515  -3.7162  -1.0843  -8.9585  -9.5895  -1.1016
-#>    4.5691   0.3556   6.5799  11.4361  -5.5251  -0.7265  -4.4567   7.4877
-#>    3.7387  -5.0253   6.2016  12.1069   1.3906  -3.1792   5.3994  -8.8015
-#>   -6.3128  19.6994   0.8121  -5.3457   2.7312  -7.0969   0.8557  -5.9668
-#>   -0.6554 -17.8413  -3.6626   7.7413  -3.0338   1.9055   2.3498   4.4129
-#>    2.2045 -17.2951  14.3557   5.4896 -13.9269  13.9771   0.1688  13.7065
-#>    1.4103 -10.9335   6.4542  -7.1951   0.4260   6.0540  -8.6265 -10.6153
-#>   -2.4367  -3.0208   1.7582  -3.9473  11.5795  -9.8707   8.2718   3.9334
-#>    2.6910  13.7177 -12.4472  -0.7891  -4.1189  -4.9260   1.9796   6.3284
-#>   -2.2626   0.8278 -16.8472   8.7072  -1.5437  -2.5874  18.4442 -12.6984
-#>    0.2770  -5.7886  -1.6831  -6.2280   0.3892 -10.7264   1.6883   0.5731
-#>    5.9888   2.8220   5.1822 -10.9491   2.9278  -1.2400   6.9684  -3.0588
-#>    2.0853   6.4697   9.1610  14.2869   3.9100   7.3812  -5.6622  -2.6049
-#>   -2.8214  -2.8717   3.4763  -4.1231   4.3919  -7.9700  14.0622 -10.4217
-#>    0.0360  -7.2489   7.1088  -5.2261   7.6209  -6.4602   3.4370  -3.3627
-#>    3.0146  -8.4109   7.3443   6.8336 -16.3648   7.2043   7.3347  -7.1156
-#>   11.6654  -2.6083  -1.8932 -11.5787  -7.3925 -12.9529  -8.5728   6.7741
-#>   -7.3200  10.8262  -1.1320 -12.8965  17.0946  -4.7953   1.3419  -5.9471
-#>    0.9198  -4.3271  -5.4991   3.7542 -11.3688   7.0314  -0.1410  -5.9312
-#>    5.9632   8.4848   2.1999   4.6207   0.4360   1.6097  17.6682   7.0791
-#>   -3.5695  -2.5270  -0.5916  -8.6880  -8.8335  14.8022   4.9419  -2.7857
-#>   -5.3153  -5.2006   1.6115   9.8521  -2.7637  13.6420  20.2294   2.9587
-#>    0.1992  -6.7249   2.8040   3.9956  -7.6735  16.6353   0.2961  -0.4600
-#>   -0.9324   4.6584  -9.8605  -3.0216   3.8279 -18.3609 -14.6428   2.9325
-#>   -2.3640   2.2242  -7.8729  -2.3704  11.0849  -3.0016  12.9536   4.4597
-#>    0.6707  -3.5127  -1.1921   7.2875   5.6279  -5.3606  16.7637  -0.5393
-#>   -6.0561   6.4866  -2.1347   2.0663  -9.1948  -4.9570   4.9678  -2.9562
-#>    1.7940   8.1519  -9.7820  -8.1634   8.2479   7.6515  -7.6138  20.4446
-#>    1.0375   8.2653  -6.3321   4.4135   7.8574   2.1843  10.2095  -3.0581
-#> 
-#> Columns 9 to 16  -6.7446 -19.2839  -5.2996  10.6448  -2.2137  -9.8432  16.7385   1.8806
-#>    2.7721  -9.6319  15.9549  -3.9562  -2.7533  -1.4063  17.1808  -3.8886
-#>    0.8954   3.4040  -4.2748  -7.6926  -1.9848  17.2263   0.3864  -4.7782
-#>   13.6218   3.0880  -9.0650  13.7402  14.2595  14.3139   8.1423 -10.8839
-#>   -2.8056  -1.5642 -10.9384   2.4840 -14.6773  15.3640 -11.8735   8.1836
-#>  -12.2327   8.0373  -9.9830  -7.0531  -2.4499  -2.4429  -9.1589   7.2055
-#>    6.7662   1.5342   1.1651   4.3661  -5.1278   7.6165   2.6917  -7.9761
-#>   -4.9940 -12.7029  -0.5018   4.1232   7.7914  -3.3340   3.7179 -15.3366
-#>   -2.8687  11.0463 -19.0638   0.3108   8.7410  -5.9274  -4.5910  10.8263
-#>    2.7317   2.1383 -13.6276  31.3021   5.1940   6.0216  -4.0457  -5.5339
-#>   -8.8773   1.3894   6.0943   8.2040 -22.9392   6.0407  -5.8266   9.3254
-#>   -5.3709   6.6576  -0.3668   0.5234  -0.9117   8.5379  -2.6543   5.2700
-#>   -4.0529  -2.2297  -3.6299   8.0911   7.2778  -1.6554  15.2207  -8.6982
-#>    2.9273  10.0753   1.0093   2.4645   6.0291   2.2682   1.5076   3.4788
-#>  -16.8549   8.1650  -5.1286  -7.7697  -3.0778  13.3012  -7.2841   0.8980
-#>   -4.7963  -5.0447  11.0234  19.7087 -13.5973  -1.4497  -6.0398   3.9615
-#>  -11.6929 -12.9310   3.0861   3.8349  -0.8498   0.3676   7.5970 -15.8111
-#>   10.5982  -8.7752  -4.8810   3.8208  -2.7940   0.2052   2.5400   8.1477
-#>   -3.8651  11.3115 -15.3378  -0.9253   7.8896  -0.5798  -9.4853  21.2292
-#>    1.5519   2.7537   7.5006   5.2048   3.9256   3.6954 -14.5668  -1.0811
-#>   -6.4954 -15.8022   2.8659   9.0508  11.4639  -2.6907  -8.2854  -1.4667
-#>    7.8183  -1.5043   9.2190  -3.2909  -3.6400  -7.3343   4.0588   3.0736
-#>    1.6113  -7.8219   7.8065  20.6163  -7.6171 -11.7764   8.7982  -8.1107
-#>    4.7483   9.2704  -0.8942   2.4555   7.9658   2.8396 -11.1332   6.9196
-#>    9.2414   3.9240   7.5839 -10.3013  -8.0429  -0.9105   7.5698   5.5207
-#>    9.1179   9.3893  -2.6667  -0.5141   6.6394   2.4894  -3.3466 -12.0105
-#>   -7.6842   7.3149  -2.5730   4.5251  -6.0077   4.9602   5.9953   2.1281
-#>  -10.2351  -0.4251   2.1078  -6.7500   6.6732  -0.6379  -4.8256   7.8820
-#>    7.6989   5.7581  -0.8032  -1.8760  -1.4233   4.0420  -9.7446 -10.8131
-#>   13.1536  -3.2820  -5.9773   0.4688  -7.2200  -1.9802   6.8597   7.1834
-#>   -6.9597  -6.3075 -10.8629   6.5933   5.1967  -2.3144 -13.2033  -6.3124
-#>  -15.1643  11.3891   1.7059  -1.9721   6.4254  -8.3010  13.8574  -8.0904
-#>   -0.6400   0.8845   9.1436  12.9765  -0.8867  -5.4252  -2.4219   2.5684
-#> 
-#> Columns 17 to 24 -11.6251  -3.3042  -9.5897 -19.7662 -11.3639   2.9764  -7.6847   5.3094
-#>    5.5535  12.3224  -0.4621  -1.7751  -6.2082  -2.8243  -3.3639   4.4086
-#>    4.9465  -6.1456  13.8044  -2.3682   0.1131  -1.0469  -0.6537 -18.7963
-#>   -0.2615   3.3738  12.8766  -0.7729  13.0538  -3.7030  21.3555  -0.0547
-#>   -5.4725  -2.8746   3.7941  -7.9571 -14.6536  -3.4342  -0.7045   1.1588
-#>    3.2876  -3.8150   2.2217  -7.6296  -6.1589   9.3530  -2.3499  17.2963
-#>   -8.1793   7.5804  12.0472 -14.9795   2.5250   4.4984  18.0541   8.3617
-#>  -10.3372   2.5190 -17.6749  -9.9759  -7.6969  -6.4078  13.4594  11.9478
-#>   -5.4648  -6.1133  -3.1051  15.8897  -0.9202   8.7116   5.8395 -20.7721
-#>   -4.9633   7.0661   1.5125  -9.6190  -6.7895   3.5231  -0.0329  23.3810
-#>  -15.8677  12.4208   2.0475   6.4192  -9.7269  -0.6899 -15.3368  17.5331
-#>   -7.8009  13.4144  -4.8032  15.5606 -10.1968   9.3907 -12.3619   0.2271
-#>  -17.6470   3.5627  12.0824  -4.9767  -6.5277   0.2517   1.5120  17.2585
-#>   -7.6810   2.1964  14.6920  -5.9527  10.4770  -6.3552   7.7612   9.4945
-#>    0.4429  -3.4509  -2.8073   0.4457  -8.6056  -3.4702 -23.0327   0.9529
-#>    0.8179   4.0997   2.5110 -15.3957   3.0363  10.0195  -9.8834  -8.2598
-#>    0.9310 -15.8169  -2.7237  -3.8845 -11.1570   3.2370  -4.0259  -0.8774
-#>   -5.0513  18.1145  -1.9949  18.7507  16.7231   7.7012  -4.9110  -4.4177
-#>  -32.6358   0.2473  -4.0207   2.8901 -18.4278  11.5049 -11.9611   8.3352
-#>    3.0942   8.3002  -8.3140  -5.4769   9.5909  -5.1340   1.8389   9.0214
-#>  -16.4604 -16.0312 -13.7154   5.3215 -13.6139  12.0238 -12.3383  -9.2225
-#>   -5.7482   1.7096  -0.1163  14.3944   0.9409 -12.6108  -5.2439  -6.4736
-#>   -2.8593  -0.6078   5.9682  -8.9257   6.8423   6.6952   1.2522  -8.5496
-#>    0.1467   9.9529   4.9536   4.2114  14.9164   8.9636 -11.2608  -6.2331
-#>    7.4674  -1.1952  -6.9978  -3.2490  11.2160  -6.2838  -1.1349   5.1366
-#>   11.8175   3.2338   2.4789  -7.7572   9.2989  -6.1385  -4.1347   4.6263
-#>    3.8211  -2.9451  12.8165 -13.0431   2.9364  -0.6053  -3.3417   0.7684
-#>   -5.6006 -21.6590   0.9646  13.6898  -1.2653 -14.3905   4.7676  -5.7793
-#>    3.3701  -2.2316  -6.6199  -1.5730   0.6153   2.5474  14.7909  14.1248
-#>    1.4418   6.3772   0.3450   6.5393   9.4165  15.8391  -5.4509  -7.6689
-#>    9.1360  -3.4296   4.2381   0.1421  -6.0637 -10.6114 -10.5209  -1.9886
-#>    8.9036 -12.5969  22.4951  -0.5840 -12.5121  15.3569 -18.5965   9.6492
-#>    1.6620  -3.6564  29.1041 -15.0301  24.3040   2.8217   5.5570  -3.0949
-#> 
-#> Columns 25 to 32   0.6754  -5.4351  -1.7427  -2.1690 -12.6507  -7.9582  -2.0196  -5.9914
-#>    6.2957  24.2517  -0.6646   5.8407   4.2708   0.6678   0.8753   7.4855
-#>    9.4350  -1.2963  -3.6902  -5.5026   2.7794  -3.1165  -1.7660   1.7011
-#>   10.9539 -17.5292   4.3171  -8.1743 -14.3399   9.7072  -7.5103   9.4845
-#>   -3.8901   7.8685  11.0553  -7.6921  -0.4697  11.2485  -3.0967   7.2322
-#>    5.9094   3.8987   8.5515  24.0678  -3.1706   2.4028   0.1868  -4.1452
-#>   -4.8616   5.1777  23.9718 -11.5407   7.5438   3.0655 -15.7713  -8.2458
-#>   -0.8644   5.6632  -3.6068  -3.0043  14.2817   7.5471   5.6906  -4.2427
-#>   -7.6225 -10.5315  -8.6553  -2.2209 -15.1343  -2.7813  14.5287 -10.1209
-#>    8.1794  -1.0438  17.5802   1.3836   6.7183  10.8328  -2.5115   2.8458
-#>    2.9136 -10.5741  12.6177   0.9100   2.0254   3.3510  -2.8068 -13.4172
-#>  -13.1170   1.6400 -13.0929 -10.3412   5.5702 -11.1293   2.2765   3.6344
-#>    1.1890  -1.9082  -2.3892  -8.1190   2.6411  -3.1444  -6.9475  -6.7670
-#>  -14.5015  11.7778  -2.8347 -13.3463  -5.1831   4.0238   0.1387   2.6846
-#>   11.0696 -13.9293  -6.1347   7.4526  -5.8511  -3.2405  -0.3918  13.2905
-#>   17.0813  -5.0977   6.4885  12.9523   8.3253 -10.0022  -6.1836  14.6672
-#>   -9.5167  -0.1452   8.0347   9.8185  -4.8826   7.0202 -13.3114  -7.3593
-#>   -3.5866  -2.1113   6.1158  -4.5314   0.5352   3.9646  -8.2554   1.0600
-#>   -6.3913  -3.4983  -2.0942   2.1771   1.5844  -3.6043   4.8755  -9.8164
-#>   11.0548 -20.8779 -16.4557   5.0648  -3.3323 -10.0792  18.6829  -5.9163
-#>   -2.9553  -3.4663   5.8928 -17.9741   7.4210  14.4276  12.8937   3.8324
-#>    6.7334  12.8358  -6.2878  -5.9928  12.9341   1.9918  -0.3133   0.8225
-#>   -8.5431  -4.5959   5.7668  -6.6540   6.5829   6.1331   1.0447  -7.4261
-#>   -5.5379   0.1180  -2.7121  -5.2564  -3.0441  -9.8881   4.7905  -2.0531
-#>    1.8131  -4.7013  -3.3520   6.5667   3.2757   5.0640   9.5182   2.7596
-#>   -7.9812   6.5552 -21.8246  15.2338  -1.6433  -0.1308   4.5212  -3.7304
-#>    1.0232   1.4512  -3.1897  -7.0585 -15.2776   3.4033 -12.1728  -0.5707
-#>    6.9714  -8.0632  -3.9023  -1.7853  -7.5021   9.3818   9.8124  -7.2047
-#>   -8.3226  -3.2363   5.5813  -9.9950  -1.7240  17.5094  -6.7417  -5.7870
-#>    9.7854  15.8732  13.8283  -0.1509  -6.0653   9.1286  -3.0200  -1.7873
-#>   -1.2083  -4.4204  -5.7270  11.8766  -5.3162  -0.7704   9.3954  -1.7334
-#>   -2.2619  11.5894 -10.0027  -5.0707   4.5968  -5.3793 -13.2128  12.2694
-#>   -9.4825  -1.5247  -0.3769   3.1203   5.3710  -5.7538  -8.5316  -1.2781
-#> 
-#> Columns 33 to 40  -9.2212  17.9095   8.4176  -3.7203  -6.5719  -1.9328  -3.5606   4.5551
-#>   -1.8634   6.8070   5.5377   7.7612  -0.3723  -8.5310 -11.2221  12.8836
-#>   -0.5835 -20.5723   6.2568 -13.6192   6.2027   2.5340   8.3277  -1.0690
-#>    9.4813 -13.0169  -6.5955  -9.6504   3.9302 -10.4059   0.3368  11.0317
-#>   13.2242  -6.7998  -7.4696  -4.5022   7.5813  -2.4320  18.8617   6.3256
-#>    5.4512   5.7200   4.0642  -1.2367  -0.7577  13.4109   1.9488   2.7067
-#>   17.0755  -5.8967  -4.9994   2.3772  -3.3988   7.8152  -7.2953  -7.5021
-#>   -8.0343  -4.6180   2.3566   9.2143   5.1217  18.9750   7.0116   0.8720
-#>    8.8868 -12.8316  32.2617   1.5173 -29.2014  -9.1697   1.7793 -11.5384
-#>   -8.6272  -4.4338 -21.9003   4.1937 -13.0200   8.8821   0.1906  13.0268
-#>   11.8491  11.2913  -2.7882 -20.0160  22.0333  16.8525  -3.0531  -2.2457
-#>    3.9485  -1.9158   2.2538 -21.1789  -3.8281   0.7445   2.8729   5.1457
-#>    4.8669  -7.2527  -3.9915 -13.5313  13.7822  12.5903   8.1406 -10.5367
-#>   -2.3059   2.7685  -3.8104  11.5816 -24.4048 -18.5444  -8.2961   3.2680
-#>  -16.0640 -19.7496 -16.1403  -6.2305  15.5044   0.6679   0.0522  19.7908
-#>   11.9814   5.5630  -0.8154 -19.5417   9.2743   8.0217 -15.5096  15.4365
-#>    4.4094  14.3800   3.7023  -2.6637  -6.9884  15.1600 -10.9028   8.2918
-#>    8.1691  17.8184   3.8099   3.3481 -12.3396  -3.6603  -0.4540  -1.5425
-#>  -15.0172 -12.5406   2.6438   1.7852  10.4317  16.6561 -11.3111 -20.8243
-#>  -12.4438 -10.9364   3.7333  -6.6735  13.5679  -1.7383  -2.9847   6.8149
-#>   -1.8302  17.7674   5.9496   1.7236 -20.1008   2.4569   4.4831  -5.7613
-#>   -0.7342  -9.7959  -7.8026  -6.3387  -2.7167  13.1393   7.8315   9.4000
-#>    9.4051   9.6574  14.3628 -27.0737 -19.5745 -15.6395  -3.8150   2.3063
-#>   -9.8362   7.6502   0.7495   5.7295  -0.4509 -10.2346   3.5372  -6.7736
-#>  -10.6270  14.9071   3.1708 -15.4192   2.3833 -13.9320  -9.1408   3.5938
-#>   -9.1457 -11.3525  -5.3337   4.7483  -4.7272  -3.0666   0.1602   2.4069
-#>   -8.1658   3.7447  -3.9244   9.3006  -1.0673  -7.4670  -3.5135  -1.1845
-#>   -3.0652   9.2276  17.3474  -4.8320   0.7358  -4.0601  13.5146 -17.2049
-#>  -12.7639   1.8174  -5.2838  10.5123  11.9389   1.5286   5.6593   0.2460
-#>  -12.6807   2.0698 -15.8524  -7.2091  -7.4212   0.1610  13.4838   2.1601
-#>    7.2512  -1.3859   0.5661 -15.6594  -3.3170  -2.9406   0.2242  -3.9722
-#>  -25.7610  -6.7176  10.3714  12.6513 -11.1680  15.6171 -19.9599   1.9016
-#>   -2.6267  20.8037  -2.0124   4.1648   1.2791   6.2882  -3.8924   3.8411
-#> 
-#> Columns 41 to 48  12.6592 -29.1091   4.9542   6.5500  -6.5881  -9.1306  -3.5637   6.9740
-#>   12.5785   4.9390  -7.1512   9.5374  19.1770  -2.1619  -8.8319  -6.1387
-#>    1.6069   0.0530  -7.3822   0.7928   4.1775  -0.2300 -11.3976  15.9961
-#>   -0.9627   5.4817 -15.4045  10.0725   5.5603 -11.5714  12.0004   1.3725
-#>   -2.4268  17.9863  10.2603   6.4608  12.7709   1.9074  -2.4812   2.6753
-#>    6.0541   0.7232  12.2480   5.2808   1.4006  10.6693  12.6639  12.8870
-#>   -6.6617 -14.0500 -14.0936  -3.0179 -11.9357   5.9934   3.5963  -6.5327
-#>   -1.3355  -2.3099  -5.0284   8.5370   3.2054 -19.0103  -6.5679   6.2471
-#>   10.6122  11.9086  -0.1262   6.2764  -3.3073   0.9882   0.3496  13.5524
-#>    1.8818   3.3550 -19.4137  -0.5076   8.5231   5.5520   7.0628  -4.7189
-#>    2.0409 -11.5721   1.2794   4.2566  -1.2600  11.6436   8.4207   9.9688
-#>    7.2530  -0.5176   9.8714  18.3778   1.0340   4.8684  10.6200   8.9634
-#>   -1.2232  -8.9204  -2.1755  -1.1896  -3.3770  -2.9910   0.3296  -5.7852
-#>   -7.5940   5.8494 -15.8490 -10.0028   6.6867   3.4989   2.2816  -7.2253
-#>    6.5804  -0.6134  -2.1385  15.5190   8.1329 -11.0707  11.2124  -2.0956
-#>   20.2437  -4.8852  -6.5738   2.1046  -1.0111  -4.7278   1.7603   7.4301
-#>    4.8448   1.4679   3.6642  -7.8998 -14.3289   8.8894   7.9565   2.9185
-#>   -2.0559   3.1594   5.7790 -13.0144 -14.4777  -5.6680 -10.7241  -4.0730
-#>   -2.1987  -0.4621 -29.1733  -3.3608 -10.2317  -5.5618  -2.7433  16.9024
-#>   -2.5571 -18.0090  -6.6635  17.0925   7.7247  -9.7994  -3.9254   9.3806
-#>   -2.6549  11.5325   1.9270 -18.3873  -4.6849   3.4927  -5.3377   8.0902
-#>   -6.9130   0.0039   0.7236 -23.0587  10.3118  17.0714  10.3968   9.7206
-#>    7.9891  -6.5689 -13.5171   4.7222   1.5046  -7.6745  -7.4960  19.0716
-#>   18.9985   2.7868   8.8201   3.8623   4.7100  -1.8755   1.1543   0.4049
-#>   -1.9884 -16.9054   6.6718   6.4880   5.4618 -16.1783  -5.3956   5.4232
-#>    2.3665   6.1933  11.9676   5.5544  -6.5978   9.0037  -4.2192  -6.4146
-#>   -8.1628 -11.2495  -7.0179  -6.8217  -1.2933   6.0335  -4.5630  -6.4349
-#>   -9.8572 -11.5498  -4.4926  -3.8380   0.7976  -1.0803   8.9509  24.5363
-#>  -16.4516  13.6568  10.2520 -10.9182   7.4908 -13.2014  -1.2110 -18.3038
-#>   -0.4914   4.0439  -2.5239   7.8569  -3.8332  -2.2241   4.7996 -14.7188
-#>   10.2891   6.7213  -2.7195  -8.7093   0.4224   8.2590  14.4842   5.9226
-#>   -2.3089 -15.3783  -6.7664  -7.4489   5.3329  -1.4130  -3.1034  -6.9561
-#>    0.3502  -1.1984 -10.8877  -5.4941   5.8245  -7.2559   2.3815   5.3173
-#> 
-#> Columns 49 to 54   1.2747   5.2395  13.4698  14.2738  12.2964  -1.5817
-#>    1.6910 -12.1397 -12.7877 -10.0018   7.3331   0.6355
-#>    0.7433  -5.8941  -6.2776   4.1104   2.8945   0.8537
-#>  -12.5226  -6.5539 -10.7138   7.6140  -1.7271  -7.8163
-#>    3.8362  -6.7615   2.8636  -6.1179  -3.3340  -9.7257
-#>   -1.7788   4.3488  22.8753   1.8028   5.4304  -1.8112
-#>   -1.4235   3.3501   5.2342   4.2336 -14.7682  -7.2064
-#>   10.1970   3.3946  -1.0360  -1.1210  -4.4042  -4.1440
-#>   11.1277   0.9043  12.8983  13.0145   5.9844  -1.5443
-#>   -4.2875  -5.3223   3.4411  -5.3851  -1.3105   0.6720
-#>   -6.5519  12.1854   0.0411  -1.8376   0.8744  -3.2010
-#>   -0.1420   7.3235   7.9048   7.8784   4.8304 -10.4523
-#>   -3.6911   4.2254   5.7063   8.9996  -4.4160   0.4081
-#>    8.5811  -7.6967  -5.5423  -5.7741  13.3399   1.0483
-#>    7.6063  -7.0702   3.5762  -6.5361  15.6316  -0.6609
-#>   -9.8814   1.9607  -0.4300  -0.0419   2.0097   9.4041
-#>   13.4482  10.9558  14.7211   0.1087  -0.7896   2.0498
-#>  -16.6676  -4.7743  -7.8918   1.7374  -4.2081  -5.8012
-#>   -8.2581  -6.0684   8.2776 -10.0404   9.0940  -0.6857
-#>   -3.9399   6.9471  -3.0039  -4.1868   0.1530  -1.3818
-#>    5.2030   3.0198   0.3190   4.3111  -2.7122   9.6921
-#>    8.5626   9.0417  -1.1888   6.9106  -3.1359   5.2059
-#>   11.7007   7.5871  -0.9854  -0.6513   0.4028  -3.6121
-#>   -9.2621   4.3883  -7.0322  -1.0245  -1.7525  -3.5827
-#>  -20.7991  20.7254 -10.1940   0.9200   5.2781  -3.8300
-#>    1.3659   0.4175  12.2263 -13.9632   9.0599  -8.4849
-#>   -4.6873   1.5768   7.5582  -0.5245  10.7458   5.4537
-#>   13.1642  -0.6127  -1.5006  20.3494   3.8420   6.7404
-#>   -3.1472 -10.0608   7.6488   4.2702  -4.5538  -2.1601
-#>  -27.4567   4.2973 -14.8549  -6.8283 -10.6547  -6.2378
-#>    4.6752  -3.8682   5.5952   1.8841  -0.3845  -1.9298
-#>    5.4148 -13.7245   6.6207   5.3980  -1.0152   7.5767
-#>    4.4342   1.4319   0.4299  -7.3339  -1.4901  -0.7209
-#> 
-#> (20,.,.) = 
-#>  Columns 1 to 8  -0.1621  -1.1462   5.5624  -3.2040 -15.1401   7.7637   2.6860   5.4238
-#>    0.8396  -3.5203   9.1231  15.4953  11.7898  -1.6306  -9.3022   1.3270
-#>   -1.3684  -3.0398 -10.3844   3.6498  -5.7445 -19.0700  -0.8349  10.7056
-#>   -0.0485   5.6198   3.5489   9.6158   1.7576  -4.0273 -13.8786 -16.4744
-#>   -2.5919  -8.6451 -13.4473  -2.8219   6.3921  -3.4349   3.5949  -5.9716
-#>   -4.9905   4.9731  10.2412   1.4691  -4.3466   7.6242  16.1750   6.4483
-#>   -1.5315  -0.1067   0.8477 -12.7635   2.7935  15.6729  -8.6513  -7.0986
-#>    4.7082  -2.4865  -1.2656   4.2381   1.8470  -8.7176  -0.1376  -1.4295
-#>   -4.5697   1.0897   4.0721 -16.2454   0.7888 -14.9746  -1.8295  -3.1963
-#>    0.1823   1.8564   6.2780   3.6687   7.3347  -1.5156  -4.9225 -16.4798
-#>   -1.4463   1.9402  -1.1858  -8.6737 -12.8343  -5.3619   6.5962  -7.5694
-#>   -2.6235   4.2898  -3.5294   4.9846 -10.0827 -30.8690  -0.3993  -3.9279
-#>    0.2410  -3.7194   5.1257   8.5350  -3.8493   3.5762  -2.3895 -23.1393
-#>    1.4401   2.6178  11.1458  10.0722  10.3905  -0.3786  -1.1106 -14.5141
-#>    1.5678  -0.4691  -5.4716 -15.0261  -3.4612 -16.8536  10.2880   0.9811
-#>   -5.6570   0.1170   5.6482   8.2850  -9.1239   2.9178  14.5901  -0.7696
-#>   -1.4326   3.0316   5.5369  10.9070  17.6759   0.0452   7.9348   3.0246
-#>   -1.1936   2.5122   7.0014   1.9653   0.1634  -1.7728  -1.8913   9.4284
-#>    1.6027   5.4183  -3.9848 -12.1217  10.1409  13.7953  21.0640   2.8829
-#>   -1.6356  -3.6014   0.0734  10.9810 -12.7295 -14.2390  -3.9708   6.6003
-#>   -2.8815  -3.3364   9.8267  14.1771   5.8635  -0.1086  -6.7500  11.7749
-#>    7.1709   8.3964   1.4076 -13.0953  -0.5085   4.6746 -12.6335   1.4956
-#>   -0.1749   3.9424   3.2652  16.9313 -10.2269 -11.3709 -13.7632  -4.8729
-#>    2.5850  -0.2872  -5.2173  -7.3208 -12.8036   9.8185   4.2883  -4.7945
-#>    2.4509  10.4656  -9.4248   8.9418  -7.5186   1.9619  -2.0981   9.3907
-#>   -2.6993  -4.5462  -2.2453  -4.4655  -2.1707  -3.4057  -9.1211   7.5554
-#>    2.3860   0.4197  -4.8111  -3.5332  -5.9333  -0.6345  -5.2828 -10.2688
-#>    1.8418   5.4025   3.4547   8.0757  -2.6860  -1.5656  -6.3146   9.1767
-#>    3.1711  -3.4898  -6.5597   0.5113   0.2847  -9.3321   5.6376  -4.6058
-#>    8.5215   6.1679  -2.6810  -5.5771  -6.6683  12.2519   2.2518 -16.5943
-#>   -0.2038  -2.1986   2.9182   1.5978  -9.3678  -4.8177   3.7600  -1.1258
-#>   -2.5421  -3.1019  -1.8657   3.2041   3.9845  -5.3289   8.3623  -6.0053
-#>   -2.2026  -4.4154   1.7093   1.2705 -11.6828   5.0229   7.7633  -4.8920
-#> 
-#> Columns 9 to 16  -9.6379  -8.8353  -2.0164  -3.9967 -14.9024 -13.2522   5.6158  -8.8205
-#>   -1.7650   3.1118  -1.6893  16.0329  11.6731   0.8990   9.0909  -8.5504
-#>   -4.1310  -3.4250   2.2022  -3.6775  -2.3736  -1.0813  -7.3223   2.0234
-#>   -7.7183   5.4651  -2.1186  -0.2175   2.3976  -5.4979  -1.3650   7.4051
-#>    0.3214  -4.2397  -1.5178   7.8210  -2.7930  -4.5397 -10.7320  11.6219
-#>    3.3722   8.3514  12.6042  -6.2435   9.9449   6.8076  -7.1994   7.9202
-#>   -0.7087  -3.7693  -5.8845   2.2852   4.7547   7.6629  -0.9728  -5.5279
-#>    0.5844 -13.7592  -3.8658  -4.3545   1.7405  10.1863   3.6145   7.0898
-#>    1.1853   1.7078  11.3930  -4.2465 -10.0186  -9.8318   6.1848   2.0908
-#>   14.1360   3.3278   8.2308  20.9610  -2.2449   2.7065 -13.6995  12.0065
-#>    8.1762   5.8692  -1.0152  -1.7100  -3.0871   7.8233  -0.6612  -0.3667
-#>    0.7398   2.9508  14.0272 -14.8148  -6.7409   8.5475  -8.1818   7.2170
-#>    3.1297  11.1152  16.2606 -10.3462   4.2060  -4.3647  -0.5266  -2.5029
-#>    6.4961  -8.4803  -3.9518   3.8498  10.7788  -2.2223  -5.9765   1.3549
-#>   -5.1063   4.6187  -2.1797 -22.5261  -7.0005   6.2757  -9.0270  25.0982
-#>   -0.1420   1.3720   7.4795   9.6153   4.3483   7.4302  -9.9186  -5.4024
-#>    3.4592  -8.3573 -17.2632  -6.2594   4.1314  10.3204   7.1232   6.3911
-#>   -5.9489   2.3011  -0.3504  15.1394   0.2342   6.0624   4.9731  -7.1409
-#>   -6.4556  -3.4254 -15.0925  -5.4326  -7.9049   5.4858   1.3068  17.6305
-#>  -11.9726  -0.1709  -2.0448  -8.8095  -3.7666  -1.9334  -3.4856   8.0408
-#>   -5.5254  -2.6180  -5.0557  11.0289  -9.9996  10.3354  -9.6572  -0.4148
-#>  -10.8925 -12.7567   0.0669   4.5049  -2.1159   7.2722  -7.0065 -12.0945
-#>    0.5809  -2.2126  -3.6979 -12.0575  -1.5567  -1.7114   6.3236  -0.1161
-#>   -9.8552   5.4610   0.4954   8.4186  16.3263   8.1864  10.1633  -7.8072
-#>   -5.9334  17.6178  12.9692   3.4399  -1.6990  -7.9805  -3.4114  -7.6213
-#>   19.0645  -4.7203  11.0164  -0.9063   4.8046  -8.6888  11.7567  -3.8675
-#>   11.7528   2.8265  -1.7707  -4.1104 -10.3084  -4.3146   2.8402  -6.7274
-#>  -16.2602  11.0432  -2.8828   0.1409  -2.7798  -4.6135   4.9228  -6.8063
-#>   13.7149  12.8582  -4.4341   4.5278  10.9037   6.1661  -4.7417  14.1037
-#>   -0.3279  21.5061  -1.1329   3.3908 -11.6120  -4.9290  10.7559  11.6238
-#>    4.9558  -5.5311  -8.7086 -15.1959   8.1782  -3.1170 -10.8156 -11.8758
-#>    9.2265  -7.8700   8.2888  -2.7848  -6.6362  -3.8733   6.7170  -9.3142
-#>    7.6552  11.7999   4.7896   7.7007   8.5952   2.0664  11.5924  -1.0443
-#> 
-#> Columns 17 to 24  -0.5321  11.0464   0.0856   0.5655  14.5991  12.9319   0.4836   7.6641
-#>   -6.7579  -5.3101  19.7148   3.8677 -14.5008  -9.3260  -5.3839   3.1493
-#>  -11.6755  -0.6071   2.5071  10.7974  -6.7518   1.3822   3.6109   4.1592
-#>   -2.6031  -5.6818  18.6989  -2.4348  -9.8791   6.2369 -15.8756   9.3603
-#>   -8.1665   6.7317  12.1462  16.9517 -15.2266  -5.7444   1.5100   6.8900
-#>   14.4074   8.8029  -5.4545  15.4322   6.9543   0.2443   2.1472  21.9676
-#>   -1.3419   6.2277  11.9838  -7.4835   2.0090   2.4590   2.9050  11.5184
-#>   -4.5372   1.9943  13.6520  -3.9550 -19.4712   0.5986  25.6229   7.6501
-#>  -12.0105  14.3773   0.4855  -3.1355  12.9980  17.6869  -7.5678 -20.4467
-#>    5.4924  -7.7566 -11.8491   7.2381   3.8651 -11.9726 -13.3154  30.7087
-#>    4.9715   3.6618  -5.9609   6.0388  10.1822  -0.1636   2.9248  21.1720
-#>    4.4321  13.4352   5.0641  11.5861  -9.4310  -1.9816  -6.3792  13.6577
-#>    5.9904  -4.2367   2.1718   3.8697  -2.1954 -12.3323   0.6447  11.7598
-#>  -12.3906  -5.8361  15.9661  -4.0532 -19.7730  -7.9416 -13.6731 -14.6051
-#>  -11.5482  -6.3060   0.4562  10.2639  -7.1850   2.3008   8.4813   4.6316
-#>   16.3523  -1.7841 -12.6560  -0.8742  14.8849  -1.3381  -6.6948  20.4880
-#>   15.8185   0.9278   5.1805  13.5886  -8.4595 -12.5742  -5.5707   6.3068
-#>   -1.7460  -6.1560  -0.1155  -1.3805 -11.9903   5.2742  -2.6962   1.2403
-#>  -20.6755   8.6058 -19.3932 -14.9239  14.5644  24.0100  -0.9756  -3.3321
-#>    2.8825   1.0881  -2.3569  -6.5990  -1.0636  -9.5998  -2.6571   2.1721
-#>   -0.2178 -13.1473 -11.7695 -11.7659  11.5251   6.0879   0.9299   4.1882
-#>    9.5220  -7.2763   9.1356  -7.0332   5.8918  -4.9225  -2.3877  -2.8981
-#>   -3.8195 -11.3067  17.5316  14.3856  -2.0751 -10.7862 -10.6612   0.7596
-#>    4.8557  -3.7417   2.7421  -3.9924  15.1623   5.9950  -3.4114  -5.8673
-#>   12.6678 -10.7893   5.1291   5.0086 -12.7731  -7.5149   1.4797   4.7836
-#>    6.8793  15.9510  -0.8191 -11.1323 -21.8725  -1.1746   9.8490  -1.9807
-#>   -6.6938  11.1216  -5.1915   9.9398   6.9210   0.1600   7.1468  -3.6307
-#>   -9.6642   2.1299  -1.9093   4.7243   2.9268   8.8768  -1.7267  -6.9847
-#>    5.6409   0.9974  22.4437   3.0573 -12.4774   7.3325  14.7486  -7.2137
-#>   11.1331  -3.1796 -14.4943  -6.3280   4.9811  12.2918  -0.0304  -3.3065
-#>   12.5455 -14.9207  -2.9549  -0.1376   7.9348  -4.7665 -13.3366   7.2516
-#>  -17.0092   3.3592  12.2121   0.9171   9.6731   1.8164  10.6970  -3.2334
-#>   -6.6542   7.6337   4.5601   4.0772   9.9508  10.3802  -0.0720  10.9570
-#> 
-#> Columns 25 to 32  14.7814   2.0855 -11.5443  -8.8636  -3.2921  -8.8975  -2.2202   2.0542
-#>   -4.3575  14.1877  21.3428   7.5668  -0.0204   0.6640  -1.0708 -12.3276
-#>   -5.2300   6.1240   7.4403   2.9730   2.8976   1.5309  -4.3767  -3.9842
-#>   10.1208   5.2596  -2.1734  -1.7602   5.0769 -13.1904   3.5458   7.7316
-#>    2.8890   4.8204  -0.0606  -1.4020   6.3128  -0.8638  10.7315   4.2823
-#>   12.1047  -4.3720   4.1811  11.6347   9.3276   9.7779 -12.5684  12.7005
-#>    8.1034  -5.7556   1.0095   7.1351  17.4726   3.1717   7.3035   7.1441
-#>   -1.0227  13.4178  12.7260  -4.6267 -11.1069   3.4204  -7.7886   0.5724
-#>   -1.9399   3.8624  -1.8930  -7.9153  -3.2695  -4.7378  16.7488  10.2630
-#>   17.3373  11.5215   4.4709   2.6323  -5.6626 -22.0692 -10.8594  -5.0825
-#>    8.9432  -0.7131  11.7734  -3.8360  12.8653   9.0661   5.6882 -10.2663
-#>    3.6119   5.3201  -1.0089   7.1640   6.3922   5.7196   4.8207  -6.0330
-#>   16.9539  -9.5322  -8.6009 -10.1206  11.5182   2.3165 -12.3131  -6.9663
-#>   -7.6772   5.6404  11.2296  -6.5533  -3.4842  -1.0550 -11.3338  -2.7795
-#>   -3.0589   2.6489   0.0237   3.8240  -3.4378  -0.5842  12.3299   5.6363
-#>    1.8475  -3.6678  -7.2582   2.2023   7.2895  -5.9783  -3.0408  -9.8344
-#>   -3.9609 -16.0397  -2.3379   9.4380  -2.0241   6.7547   4.8268  -3.5794
-#>  -10.1694  -6.8895  -1.8487  10.7999  11.4416  -8.0720  12.5670  -3.0657
-#>   -5.8882  -6.5137 -14.9760  -9.7027 -10.1098  -4.3701   2.7771  -3.2866
-#>    4.8151  -0.7031  13.0597   0.4731   2.4569   5.2606  -7.8424  -6.6531
-#>    4.7037  -5.5518 -13.4641 -14.5033  -9.6714  -9.2366 -10.1225 -18.6002
-#>   -4.9474  23.4492   7.2622  -3.3553   0.5726   4.0995  11.6019  -6.0000
-#>    7.2762   6.6382  14.5240   2.3849   1.0163   9.1811   4.0564   7.0639
-#>   -7.9470   1.7570  -2.2795  -8.6340 -10.8866   4.2379   2.3539   4.4703
-#>   -1.2421  11.0018   1.2535 -10.1757 -10.0624   4.5975 -10.5913  -5.5541
-#>   -6.7632  -9.0438   3.3362   3.3541  -7.8718  -7.5242 -13.5980   5.6186
-#>   -0.8035  -7.1194   3.7788   0.8013  -3.8723  13.9522  -7.8633   2.2792
-#>   13.8338  21.8256   3.2970  -3.9498 -10.6964   2.5834  -0.7092  -9.7314
-#>   -6.8580  -4.3072 -12.3198  -4.7211 -11.5913   0.8492  -7.2394 -14.1186
-#>   11.9156   4.7210  -0.4337  -4.2159   6.3054  -5.5326   2.0532  23.0029
-#>    4.6552  -2.2351   4.9680  -2.8215   2.8679   3.2595  -1.1829   7.9291
-#>    3.0608  -3.2694  -4.6975  -0.3494  -3.4043   1.4205   2.0749  -7.1137
-#>    0.9281   0.6515   3.0646  -1.4669   3.3878   3.2400  -1.6345  -0.2212
-#> 
-#> Columns 33 to 40   9.4695  -4.2720   5.6363  -3.0229   1.0772   4.3404   2.7501   8.8858
-#>   -0.6602  -6.7124   0.2431   9.1193 -10.9290  -5.7383   0.3658  -0.0696
-#>    4.7950  -5.0814  -4.8913 -10.7810  17.6417  -3.0259  10.1834   9.6151
-#>  -12.9637   1.7315   1.6938   0.9733   4.8371  -3.4247   5.4597  -3.4403
-#>   11.6400  -6.0170   0.6291   4.2209   8.5032 -11.3803   3.6310  -2.7111
-#>   18.9459  -0.5712  -1.3361  -2.9936  -7.7226   4.8759   3.8769  -4.6880
-#>   -6.3998   2.6967   1.3923  18.0601   2.7739   5.9172  -8.6638 -17.0653
-#>   -0.1278   1.9916  -3.3368   4.4702  -4.1882  -0.3656   0.7066 -15.3845
-#>    7.9409   5.3684  -7.0261  -2.6445  -0.9552  -8.4829  -3.8520  -2.4927
-#>   -6.1056 -10.0198   1.6371   9.4007   2.8853   0.1508 -15.6866  -5.7417
-#>   16.0239   3.5886   2.7665   3.9043 -11.8213  -0.2662  -9.6202   2.5200
-#>   22.2557 -10.0216  14.6203  -1.1390 -17.5313   8.1871   0.2831   6.0888
-#>    1.0272   0.8753   7.7316  15.5033 -12.1568  -7.3910   1.0686 -14.9231
-#>  -10.8576 -14.7991   0.4624  17.4074  -3.3230  12.4914  -1.6869  -6.4875
-#>    2.1269  -1.6013 -18.9873  -4.1625   1.2891   0.9499  10.2098  -8.4856
-#>    5.0698  -2.1100   4.8530  -5.0091  -0.4301   4.8984   1.7890   1.5331
-#>   -0.8267  -1.6853  -1.6022  -2.2362  -6.0568  11.0617   0.2674   8.6695
-#>   -0.0252   9.0893  -7.1012   0.4103  -7.2297   3.1142   4.9596  -2.1848
-#>   -3.7477  -5.8867 -15.8268   7.3226   8.2175   2.2897   1.7719  -8.0674
-#>   -5.4004  -0.0574   4.5258   0.9831   2.4997   3.1957   0.6281   5.7712
-#>   -4.2081   5.1848  -1.3553  12.6855   7.1704  -0.9142  -4.2758  -1.6022
-#>    0.9877  -2.9343  -1.7928  -5.7885  14.6740   1.5706  -5.1352   1.3063
-#>    4.1812   1.9999  -0.0702  -3.6045 -12.3558  -4.5112  13.6995  10.0074
-#>   19.6984 -15.9481  -2.8311  15.3651   4.1220  -0.2127   5.8846  -8.1123
-#>    4.0772  -9.8475  13.4586  -2.3518  -0.8136   2.3596   2.0469   3.7419
-#>   -9.5606   2.4306  10.7676 -10.6189  -7.2922   4.6915  -7.9171  -0.7868
-#>   -3.5749   1.9010   0.1177   1.1493  -5.8188  -1.8577   6.5739   0.6710
-#>   -7.3241   4.7530  -3.0966   2.7295  13.0151 -16.4036   2.9880  -4.5060
-#>   12.3952   3.8545  -4.4082   4.3266  -0.9524 -16.2677  -0.3679  -8.6198
-#>   -1.7516  -3.5273   2.3961  16.9297  -8.4036  -5.4464  -2.6408   2.6277
-#>    8.5223  -5.4636   8.0825  -4.6614   0.9806   6.6712   1.4723   6.5970
-#>   -8.9293   8.8014   4.4584   9.0321  -5.9880   0.2031   1.6938   2.3761
-#>    2.8712   1.0475  -3.0858   9.3976   3.4289  -2.6453   8.2908  -2.6215
-#> 
-#> Columns 41 to 48   1.0282  13.6111  -7.0692  -6.5561  -6.1215   4.2179 -15.8656   1.0944
-#>   -6.8862  -1.9006  -0.4087   1.5739  -1.7935  -7.1098 -11.9831  -3.2336
-#>   -8.8803   7.4065  10.8403  -8.0686  -6.7777   1.9696  -6.3820  10.2839
-#>    6.9485   3.3956   3.3277   3.0010  -5.4892   9.3176   2.4230  -8.8501
-#>   -1.8685  -6.0866  -7.0440   1.3316   0.4566 -10.7386 -12.7538   2.4884
-#>    1.0826   3.4354  -2.3901  -2.5510   5.8950 -18.2303  -2.8210 -15.3365
-#>   12.4550  -8.2105  -6.3106   9.6198   2.0183 -11.0000   7.1958 -12.3621
-#>   -6.6099  -6.4649   5.2101  -5.0436  -0.9363   1.8431   7.5087  17.3076
-#>   11.8163  -9.2981  12.9937  -7.0190   0.4832   7.5841  13.7286 -11.5256
-#>    4.6048  -7.5720  -0.0066  -1.8987   7.8794  12.4534  -2.0041 -15.7481
-#>   -6.0992   1.4299   0.5921   7.1506   3.3198   2.5087  -5.8267   5.3067
-#>   -7.0652  -5.6709   8.6364   1.6091   8.9214  -6.8446  -8.9418   2.2526
-#>   -4.1896   4.0452  -8.5550   5.1426   8.1493  -5.5370  -0.8277  20.2468
-#>    0.6847  -4.9379   5.2159  13.2771   9.5256  -5.6407   5.5542  -9.0216
-#>    1.2668 -10.6842   7.2327  -6.9216 -10.3167  -6.8236  11.4308  -6.7491
-#>  -16.9219   8.2406 -14.5830   4.0330  -4.7996  -2.9334 -18.2929 -10.6340
-#>    6.6933 -19.0212   1.3064   5.3452  19.1179  -4.2772   6.7768   6.4978
-#>   -2.2510  -1.6161  -3.3499  12.3447  -8.1975  -5.6954   0.0561   4.9685
-#>   -2.0489   4.4903  12.4049 -11.0199  -7.8862   3.5034  22.8815  -1.3020
-#>  -11.0295   7.3003   9.1650   0.5886  -5.0501  16.9966   3.6578  -1.9543
-#>  -12.0994  -8.8277  -8.1078   9.0297   0.4771 -14.6433 -13.8196   8.1879
-#>   -8.8032   8.7606  12.5391   3.2308  -3.6012  13.6578  -6.5386  11.2676
-#>    1.1408 -15.7426  -9.8566  11.0532  18.8391  -5.4709  -7.6888  -3.2865
-#>    9.8008  15.5037  -2.0344  -2.3890   6.5953   4.7175 -13.4495   5.5906
-#>   -8.6769   9.2517  -4.9664 -12.0861 -10.7764  16.0101 -19.5229  -1.9862
-#>    4.7044   0.4214  14.4001   0.7839   2.9285  -1.7922  27.9028  -4.0251
-#>    4.2268   2.6263   4.2395  -9.2513  -2.2608  -0.8726   6.1136  -7.9764
-#>   -8.6987   3.2674   1.5147   1.2253   1.7308   8.3466   3.8393   8.1229
-#>    3.5640   1.9986  -0.7368  -4.6264  -1.0220   6.6309  12.8355  23.6585
-#>    9.5552  13.7640 -17.8714  -0.0075   1.6017   6.0655 -11.4160  -1.1558
-#>   -8.3849   9.1110  -1.7346   7.3672  14.7431  -1.6898 -14.6558   5.7885
-#>    5.6095   2.8757  10.7389   9.7179  -6.1931 -11.3480   3.8011  14.7688
-#>    9.2174   6.8806  -3.9769   1.5860   5.7901   7.8094  -5.1846  -2.5478
-#> 
-#> Columns 49 to 54   2.4065 -13.3140   0.6704  -2.4768 -12.8549  -1.7246
-#>    0.2845 -18.0647  12.4616  16.8168  -3.2826  -5.1261
-#>    3.2000 -17.7489 -12.3529   4.3966   3.3123  -0.0723
-#>    5.7643   6.9179  -1.1015   0.8025   1.3063  -1.2388
-#>   -2.1739  -6.4094  -4.2746  -3.4872   1.8942   5.8524
-#>   -0.2877   8.1071  -7.1430  -2.0073  -0.5958  -0.6888
-#>   -2.2713   1.2798 -17.8757  -5.1527   4.8490   2.7732
-#>  -15.9087 -18.2601  -2.4891  -6.5220  -4.8303   1.9908
-#>    6.6733  13.3821   3.4212  -9.9961  -7.5872  -0.9345
-#>   13.2082  -5.6533  -6.0926   4.7916  -0.9141  -4.4621
-#>   -5.1544 -13.5698  -5.2283 -10.9659  -3.0882   5.9742
-#>   12.7492  -2.8567  11.5335  -9.9027  -8.9687  -0.3478
-#>   12.4571  13.0040  -9.6064 -15.1820   0.5775   4.7391
-#>    8.7508   7.6705  18.0860  12.4494  -0.2345  -5.2390
-#>    1.8744  -3.1752  -9.6094   7.4359  -3.3472  -4.7954
-#>   10.9020   9.8056   1.9755  -6.1540   1.8149  -2.7576
-#>    9.7128 -10.5345   5.5649  -0.0743   1.4130  -2.4817
-#>   -0.7271  -1.5640  11.7662   1.4352  -6.7959  -1.4346
-#>    2.9927  -5.6591  -1.1774  -1.0120  -0.9716   9.8057
-#>   -7.2276   4.0850  -0.8801   5.2085  -4.5947   4.2419
-#>    6.9749  18.1678  -2.7030 -12.8955  -7.5763  -2.7017
-#>   -1.0681 -17.7503  -5.8964  -6.9143   6.0632  -0.1159
-#>    1.6390   6.9608  -4.7435   4.3092  -2.7255  -7.1128
-#>    3.2927   4.4731   4.8954  -4.1874   1.2480   2.9463
-#>    8.6057  15.0453   2.8538   2.7735 -10.3994  -0.4426
-#>    4.0538  -6.3148   3.5170   7.2334  -4.1120  -6.4150
-#>   -0.8619  11.2716  -4.3405  -1.9576   5.3423  -2.5746
-#>  -11.5102   4.1975   6.4558 -12.8352  -8.9418   0.9216
-#>   -6.5220   5.2047   4.1922  -4.8530   7.3112  -0.0121
-#>   11.3263   9.6909 -11.5103  -4.5442  -2.5944   4.6614
-#>   -5.3980  -7.5770   7.9005  -7.3473  -2.6545  -0.8043
-#>   -1.5698   1.8889  -9.2245  -2.3570   1.1759   0.8253
-#>    1.0218   1.3002  -6.2019   3.4486   0.1029   2.7112
-#> [ CPUFloatType{20,33,54} ]
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-Conv_transpose2d — torch_conv_transpose2d • torch
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    Conv_transpose2d
    
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    Conv_transpose2d
    
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    input
    NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)
    weight
    NA filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)
    bias
    NA optional bias of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or a      tuple (sH, sW). Default: 1
    padding
    NA dilation * (kernel_size - 1) - padding zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      (padH, padW). Default: 0
    output_padding
    NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple (out_padH, out_padW).      Default: 0
    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
    dilation
    NA the spacing between kernel elements. Can be a single number or      a tuple (dH, dW). Default: 1
    
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    conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor 
    
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-composed of several input planes, sometimes also called "deconvolution".
    
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-
    .. include:: cudnn_deterministic.rst
    
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    Examples
    
-    
    # \dontrun{
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-# With square kernels and equal stride
-inputs = torch_randn(c(1, 4, 5, 5))
-weights = torch_randn(c(4, 8, 3, 3))
-nnf_conv_transpose2d(inputs, weights, padding=1)
    #> torch_tensor 
-#> (1,1,.,.) = 
-#>   3.1282  4.7949 -5.5622 -1.0866  3.7899
-#>   3.4914  4.8807  4.3209  0.8437  7.1147
-#>  -5.0276  7.5935  2.4507  6.2129  4.9112
-#>   2.9302 -4.0742  1.0907  0.0252  4.4256
-#>   4.1733  6.6849 -0.1333  0.7716  1.2488
-#> 
-#> (1,2,.,.) = 
-#>    4.0475   8.1682   9.4413  -3.4628  -2.3695
-#>   -3.0556  -4.0963   5.6845   2.0032   0.2438
-#>    1.7169  -2.3353  -2.5287  -5.3750  -4.0894
-#>    1.7329  17.4464  -1.9850  -1.2224  -1.0126
-#>   -8.3888   0.5081  -5.4379  -7.7908   1.4902
-#> 
-#> (1,3,.,.) = 
-#>  -4.6889  1.3331 -3.8890 -4.2812 -1.6408
-#>  -5.7474  6.4888 -0.3864 -0.5556  3.3423
-#>  -0.5830 -4.7014  0.4339 -4.4822 -0.9338
-#>  -3.2573 -5.3475 -6.7339 -4.1705  5.4993
-#>   1.4175  5.6303 -1.1562  5.8984  3.9368
-#> 
-#> (1,4,.,.) = 
-#>   2.7974 -1.8220  1.8960 -2.4363  9.3931
-#>  -0.0791  9.0332  2.4753  6.5632 -1.9094
-#>  -0.4198 -4.7226  4.5077 -6.0814  0.9503
-#>  -0.6672  3.4472 -9.0451  1.0115 -4.7566
-#>   6.7951  6.0656  9.2166  3.3023  1.2087
-#> 
-#> (1,5,.,.) = 
-#>   4.9513 -5.1344  0.4485  2.9806  0.6510
-#>   2.6860 -0.6071  5.0654  6.0352 -0.5143
-#>   3.0599  2.8382 -1.2406 -3.1389 -6.3846
-#>  -5.3770  3.7280 -12.7695 -4.8459  3.4087
-#>   1.6425 -3.5262 -2.5308  5.2363  0.8194
-#> 
-#> (1,6,.,.) = 
-#>    1.6877  11.4338   1.1768  -0.3375  -1.5256
-#>   -3.2510  -1.9791   0.4848 -15.0722   2.7618
-#>    0.3499   4.9010  -0.0095  -6.5474  -4.1558
-#>    3.7509   8.8759   8.5394   2.6775  -2.9372
-#>   -6.3247   2.4670  -8.6403  -6.7153  -1.6774
-#> 
-#> (1,7,.,.) = 
-#>   4.5894  2.9587  3.1108  0.5861 -1.6432
-#>   0.2160 -2.0561  2.0616  0.3782  0.4569
-#>  -0.6198 -5.6256 -4.3481 -0.5680 -6.8947
-#>  -1.1064  3.6694  3.1163 -0.8316  5.3151
-#>  -2.6586 -10.2829 -6.7049  1.4970  0.0378
-#> 
-#> (1,8,.,.) = 
-#>  -0.6657 -4.0047 -1.7929 -2.8312 -5.8341
-#>   0.8324  4.9875 -2.9101  4.2271 -0.9717
-#>  -2.1073  2.4393  0.2561 -7.6416 -2.0861
-#>  -0.1300  5.5534 -0.9693  0.1627 -0.2768
-#>   6.7889 -0.3533  3.2501  3.3338 -0.3276
-#> [ CPUFloatType{1,8,5,5} ]
    # }
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diff --git a/docs/reference/torch_conv_transpose3d.html b/docs/reference/torch_conv_transpose3d.html
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    weight
    NA filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)
    bias
    NA optional bias of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or a      tuple (sT, sH, sW). Default: 1
    padding
    NA dilation * (kernel_size - 1) - padding zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      (padT, padH, padW). Default: 0
    output_padding
    NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      (out_padT, out_padH, out_padW). Default: 0
    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
    dilation
    NA the spacing between kernel elements. Can be a single number or      a tuple (dT, dH, dW). Default: 1
    
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    conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor 
    
-
-    
-
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    Applies a 3D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution"
    
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    .. include:: cudnn_deterministic.rst
    
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diff --git a/docs/reference/torch_cos.html b/docs/reference/torch_cos.html
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-
    $$
-    \mbox{out}_{i} = \cos(\mbox{input}_{i})
-$$
    
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.6755
-#> -0.8212
-#> -0.0338
-#>  0.3401
-#> [ CPUFloatType{4} ]
    torch_cos(a)
    #> torch_tensor 
-#>  0.7804
-#>  0.6813
-#>  0.9994
-#>  0.9427
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_cosh.html b/docs/reference/torch_cosh.html
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    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.5781
-#> -0.2866
-#> -1.7790
-#> -0.1226
-#> [ CPUFloatType{4} ]
    torch_cosh(a)
    #> torch_tensor 
-#>  1.1718
-#>  1.0414
-#>  3.0465
-#>  1.0075
-#> [ CPUFloatType{4} ]
    # }
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    (int, optional) Dimension of vectors. Default: 1
    eps
    (float, optional) Small value to avoid division by zero.        Default: 1e-8
    
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-$$
    
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    # \dontrun{
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-input1 = torch_randn(c(100, 128))
-input2 = torch_randn(c(100, 128))
-output = torch_cosine_similarity(input1, input2)
-output
    #> torch_tensor 
-#>  0.0628
-#>  0.0734
-#>  0.0535
-#> -0.0163
-#> -0.0151
-#> -0.0383
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diff --git a/docs/reference/torch_cross.html b/docs/reference/torch_cross.html
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    # \dontrun{
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-a
    #> torch_tensor 
-#> -1.1643 -0.5561  0.7230
-#> -0.0220  2.0844 -0.3671
-#> -1.4814 -0.9811 -1.6675
-#>  0.4909  0.6285 -0.6379
-#> [ CPUFloatType{4,3} ]
    b = torch_randn(c(4, 3))
-b
    #> torch_tensor 
-#> -1.5896 -1.4300 -0.5647
-#> -1.4339 -1.2857  0.3288
-#> -0.9875  0.1254  0.1272
-#>  0.6789 -0.6983 -0.5560
-#> [ CPUFloatType{4,3} ]
    torch_cross(a, b, dim=2)
    #> torch_tensor 
-#>  1.3479 -1.8068  0.7810
-#>  0.2135  0.5336  3.0171
-#>  0.0842  1.8351 -1.1546
-#> -0.7949 -0.1601 -0.7695
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    torch_cross(a, b)
    #> torch_tensor 
-#>  1.3479 -1.8068  0.7810
-#>  0.2135  0.5336  3.0171
-#>  0.0842  1.8351 -1.1546
-#> -0.7949 -0.1601 -0.7695
-#> [ CPUFloatType{4,3} ]
    # }
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    dim
    (int) the dimension to do the operation over
    out
    (tuple, optional) the result tuple of two output tensors (values, indices)
    
-
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    cummax(input, dim, out=None) -> (Tensor, LongTensor) 
    
-
-    
-
-
-
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-elements of input in the dimension dim. And indices is the index
-location of each maximum value found in the dimension dim.
    
-
    $$
-    y_i = max(x_1, x_2, x_3, \dots, x_i)
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#>  0.4011
-#>  0.0282
-#>  1.4709
-#> -0.3322
-#> -1.1082
-#> -0.6219
-#> -1.1143
-#>  0.0945
-#> -0.5687
-#> -0.1941
-#> [ CPUFloatType{10} ]
    torch_cummax(a, dim=1)
    #> [[1]]
-#> torch_tensor 
-#>  0.4011
-#>  0.4011
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#> [ CPUFloatType{10} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  0
-#>  0
-#>  2
-#>  2
-#>  2
-#>  2
-#>  2
-#>  2
-#>  2
-#>  2
-#> [ CPULongType{10} ]
-#> 
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor.
    dim
    (int) the dimension to do the operation over
    out
    (tuple, optional) the result tuple of two output tensors (values, indices)
    
-
-    
    cummin(input, dim, out=None) -> (Tensor, LongTensor) 
    
-
-    
-
-
-
    Returns a namedtuple (values, indices) where values is the cumulative minimum of
-elements of input in the dimension dim. And indices is the index
-location of each maximum value found in the dimension dim.
    
-
    $$
-    y_i = min(x_1, x_2, x_3, \dots, x_i)
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#> -1.1300
-#> -0.0916
-#>  1.2476
-#>  1.1859
-#> -0.8123
-#> -1.0110
-#>  0.5914
-#>  1.0707
-#>  1.3137
-#> -0.0139
-#> [ CPUFloatType{10} ]
    torch_cummin(a, dim=1)
    #> [[1]]
-#> torch_tensor 
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> [ CPUFloatType{10} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#> [ CPULongType{10} ]
-#> 
    # }
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diff --git a/docs/reference/torch_cumprod.html b/docs/reference/torch_cumprod.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor.
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    (int) the dimension to do the operation over
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to dtype before the operation        is performed. This is useful for preventing data type overflows. Default: None.
    out
    (Tensor, optional) the output tensor.
    
-
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    cumprod(input, dim, out=None, dtype=None) -> Tensor 
    
-
-    
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-dim.
    
-
    For example, if input is a vector of size N, the result will also be
-a vector of size N, with elements.
    
-
    $$
-    y_i = x_1 \times x_2\times x_3\times \dots \times x_i
-$$
    
-
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#> -1.1828
-#> -0.2114
-#>  0.2918
-#>  0.5649
-#> -0.9559
-#> -0.9795
-#> -0.7152
-#> -0.2064
-#>  0.5134
-#>  0.5777
-#> [ CPUFloatType{10} ]
    torch_cumprod(a, dim=1)
    #> torch_tensor 
-#> -1.1828
-#>  0.2500
-#>  0.0729
-#>  0.0412
-#> -0.0394
-#>  0.0386
-#> -0.0276
-#>  0.0057
-#>  0.0029
-#>  0.0017
-#> [ CPUFloatType{10} ]
    # }
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diff --git a/docs/reference/torch_cumsum.html b/docs/reference/torch_cumsum.html
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    (Tensor, optional) the output tensor.
    
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-dim.
    
-
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-a vector of size N, with elements.
    
-
    $$
-    y_i = x_1 + x_2 + x_3 + \dots + x_i
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#>  0.2718
-#>  0.1169
-#>  0.3449
-#> -1.6346
-#> -0.0393
-#> -0.0197
-#>  0.0704
-#>  0.5245
-#> -1.4307
-#> -0.8103
-#> [ CPUFloatType{10} ]
    torch_cumsum(a, dim=1)
    #> torch_tensor 
-#>  0.2718
-#>  0.3887
-#>  0.7335
-#> -0.9010
-#> -0.9403
-#> -0.9600
-#> -0.8896
-#> -0.3651
-#> -1.7958
-#> -2.6060
-#> [ CPUFloatType{10} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-      
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    (Tensor) the input tensor of size (*, n, n) where * is zero or more                batch dimensions.
    
-
-    
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-
-    
-
    Backward through `det` internally uses SVD results when `input` is
-not invertible. In this case, double backward through `det` will be
-unstable in when `input` doesn't have distinct singular values. See
-`~torch.svd` for details.
-
    
-
-    
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-
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-
-    
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-    
    # \dontrun{
-
-A = torch_randn(c(3, 3))
-torch_det(A)
    #> torch_tensor 
-#> -0.485994
-#> [ CPUFloatType{} ]
    A = torch_randn(c(3, 2, 2))
-A
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.0024 -1.0185
-#>   0.3236 -0.2787
-#> 
-#> (2,.,.) = 
-#>   0.4021 -0.3008
-#>  -0.8884  0.5782
-#> 
-#> (3,.,.) = 
-#>  -0.3821  1.3521
-#>  -0.3608  0.3485
-#> [ CPUFloatType{3,2,2} ]
    A$det()
    #> torch_tensor 
-#>  0.3302
-#> -0.0347
-#>  0.3547
-#> [ CPUFloatType{3} ]
    # }
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diff --git a/docs/reference/torch_device.html b/docs/reference/torch_device.html
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-    Source: R/device.R
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-    
    
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-    
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-is or will be allocated.
    
-    
    
-
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    Arguments
    
-    
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-      
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    type
    (character) a device type "cuda" or "cpu"
    index
    (integer) optional device ordinal for the device type.  If the device ordinal
-is not present, this object will always represent the current device for the device
-type, even after torch_cuda_set_device() is called; e.g., a torch_tensor constructed
-with device 'cuda' is equivalent to 'cuda:X' where X is the result of
-torch_cuda_current_device().
    
-
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    Examples
    
-    
    # \dontrun{
-
-# Via string
-torch_device("cuda:1")
    #> torch_device(type='cuda', index=1)
    torch_device("cpu")
    #> torch_device(type='cpu')
    torch_device("cuda") # current cuda device
    #> torch_device(type='cuda')
    
-# Via string and device ordinal
-torch_device("cuda", 0)
    #> torch_device(type='cuda', index=0)
    torch_device("cpu", 0)
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-# }
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diff --git a/docs/reference/torch_diag.html b/docs/reference/torch_diag.html
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-
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-
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-with the elements of input as the diagonal.
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-the diagonal elements of input.
      • 
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diff --git a/docs/reference/torch_diag_embed.html b/docs/reference/torch_diag_embed.html
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    input
    (Tensor) the input tensor. Must be at least 1-dimensional.
    offset
    (int, optional) which diagonal to consider. Default: 0        (main diagonal).
    dim1
    (int, optional) first dimension with respect to which to        take diagonal. Default: -2.
    dim2
    (int, optional) second dimension with respect to which to        take diagonal. Default: -1.
    
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    diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor 
    
-
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-
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-
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-dim1 and dim2) are filled by input.
-To facilitate creating batched diagonal matrices, the 2D planes formed by
-the last two dimensions of the returned tensor are chosen by default.
    
-
    The argument offset controls which diagonal to consider:
      
-
    • If offset = 0, it is the main diagonal.
      • 
-
    • If offset > 0, it is above the main diagonal.
      • 
-
    • If offset < 0, it is below the main diagonal.
      • 
-
    
-
-
    The size of the new matrix will be calculated to make the specified diagonal
-of the size of the last input dimension.
-Note that for offset other than \(0\), the order of dim1
-and dim2 matters. Exchanging them is equivalent to changing the
-sign of offset.
    
-
    Applying torch_diagonal to the output of this function with
-the same arguments yields a matrix identical to input. However,
-torch_diagonal has different default dimensions, so those
-need to be explicitly specified.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(2, 3))
-torch_diag_embed(a)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -2.3038  0.0000  0.0000
-#>   0.0000  2.0129  0.0000
-#>   0.0000  0.0000 -1.6884
-#> 
-#> (2,.,.) = 
-#>   0.8534  0.0000  0.0000
-#>   0.0000 -0.5520  0.0000
-#>   0.0000  0.0000  2.2299
-#> [ CPUFloatType{2,3,3} ]
    torch_diag_embed(a, offset=1, dim1=1, dim2=3)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.0000 -2.3038  0.0000  0.0000
-#>   0.0000  0.8534  0.0000  0.0000
-#> 
-#> (2,.,.) = 
-#>   0.0000  0.0000  2.0129  0.0000
-#>   0.0000  0.0000 -0.5520  0.0000
-#> 
-#> (3,.,.) = 
-#>   0.0000  0.0000  0.0000 -1.6884
-#>   0.0000  0.0000  0.0000  2.2299
-#> 
-#> (4,.,.) = 
-#>   0  0  0  0
-#>   0  0  0  0
-#> [ CPUFloatType{4,2,4} ]
    # }
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diff --git a/docs/reference/torch_diagflat.html b/docs/reference/torch_diagflat.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Diagflat
    
-    
    
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    Arguments
    
-    
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    input
    (Tensor) the input tensor.
    offset
    (int, optional) the diagonal to consider. Default: 0 (main        diagonal).
    
-
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    diagflat(input, offset=0) -> Tensor 
    
-
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-
-
      
-
    • If input is a vector (1-D tensor), then returns a 2-D square tensor
-with the elements of input as the diagonal.
      • 
-
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-2-D tensor with diagonal elements equal to a flattened input.
      • 
-
    
-
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    The argument offset controls which diagonal to consider:
      
-
    • If offset = 0, it is the main diagonal.
      • 
-
    • If offset > 0, it is above the main diagonal.
      • 
-
    • If offset < 0, it is below the main diagonal.
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3))
-a
    #> torch_tensor 
-#>  1.2456
-#> -1.0479
-#>  0.2374
-#> [ CPUFloatType{3} ]
    torch_diagflat(a)
    #> torch_tensor 
-#>  1.2456  0.0000  0.0000
-#>  0.0000 -1.0479  0.0000
-#>  0.0000  0.0000  0.2374
-#> [ CPUFloatType{3,3} ]
    torch_diagflat(a, 1)
    #> torch_tensor 
-#>  0.0000  1.2456  0.0000  0.0000
-#>  0.0000  0.0000 -1.0479  0.0000
-#>  0.0000  0.0000  0.0000  0.2374
-#>  0.0000  0.0000  0.0000  0.0000
-#> [ CPUFloatType{4,4} ]
    a = torch_randn(c(2, 2))
-a
    #> torch_tensor 
-#>  0.5628 -0.2248
-#>  0.2077 -2.6745
-#> [ CPUFloatType{2,2} ]
    torch_diagflat(a)
    #> torch_tensor 
-#>  0.5628  0.0000  0.0000  0.0000
-#>  0.0000 -0.2248  0.0000  0.0000
-#>  0.0000  0.0000  0.2077  0.0000
-#>  0.0000  0.0000  0.0000 -2.6745
-#> [ CPUFloatType{4,4} ]
    # }
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diff --git a/docs/reference/torch_diagonal.html b/docs/reference/torch_diagonal.html
deleted file mode 100644
index 5a4719b5eed2807bd01ddcdc225a02208a700339..0000000000000000000000000000000000000000
--- a/docs/reference/torch_diagonal.html
+++ /dev/null
@@ -1,268 +0,0 @@
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-Diagonal — torch_diagonal • torch
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    Diagonal
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Diagonal
    
-    
    
-
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    Arguments
    
-    
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    input
    (Tensor) the input tensor. Must be at least 2-dimensional.
    offset
    (int, optional) which diagonal to consider. Default: 0        (main diagonal).
    dim1
    (int, optional) first dimension with respect to which to        take diagonal. Default: 0.
    dim2
    (int, optional) second dimension with respect to which to        take diagonal. Default: 1.
    
-
-    
    diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor 
    
-
-    
-
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-
    Returns a partial view of input with the its diagonal elements
-with respect to dim1 and dim2 appended as a dimension
-at the end of the shape.
    
-
    The argument offset controls which diagonal to consider:
      
-
    • If offset = 0, it is the main diagonal.
      • 
-
    • If offset > 0, it is above the main diagonal.
      • 
-
    • If offset < 0, it is below the main diagonal.
      • 
-
    
-
-
    Applying torch_diag_embed to the output of this function with
-the same arguments yields a diagonal matrix with the diagonal entries
-of the input. However, torch_diag_embed has different default
-dimensions, so those need to be explicitly specified.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-a
    #> torch_tensor 
-#> -0.4461  1.6781  0.7912
-#> -2.0005 -0.9287 -0.1604
-#>  0.6617 -0.6939  1.5567
-#> [ CPUFloatType{3,3} ]
    torch_diagonal(a, offset = 0)
    #> torch_tensor 
-#> -0.4461
-#> -0.9287
-#>  1.5567
-#> [ CPUFloatType{3} ]
    torch_diagonal(a, offset = 1)
    #> torch_tensor 
-#>  1.6781
-#> -0.1604
-#> [ CPUFloatType{2} ]
    x = torch_randn(c(2, 5, 4, 2))
-torch_diagonal(x, offset=-1, dim1=1, dim2=2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.3670
-#>  -1.7063
-#> 
-#> (2,.,.) = 
-#>   0.6795
-#>   1.1359
-#> 
-#> (3,.,.) = 
-#>  -0.5056
-#>  -0.1993
-#> 
-#> (4,.,.) = 
-#>   0.5929
-#>  -0.6632
-#> [ CPUFloatType{4,2,1} ]
    # }
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diff --git a/docs/reference/torch_digamma.html b/docs/reference/torch_digamma.html
deleted file mode 100644
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+++ /dev/null
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    Digamma
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Digamma
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-    
    input
    (Tensor) the tensor to compute the digamma function on
    
-
-    
    digamma(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Computes the logarithmic derivative of the gamma function on input.
    
-
    $$
-    \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_tensor(c(1, 0.5))
-torch_digamma(a)
    #> torch_tensor 
-#> -0.5772
-#> -1.9635
-#> [ CPUFloatType{2} ]
    # }
-
    
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diff --git a/docs/reference/torch_dist.html b/docs/reference/torch_dist.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Dist
    
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    (Tensor) the input tensor.
    other
    (Tensor) the Right-hand-side input tensor
    p
    (float, optional) the norm to be computed
    
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    dist(input, other, p=2) -> Tensor 
    
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-
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-broadcastable .
    
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    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(4))
-x
    #> torch_tensor 
-#>  0.3528
-#> -0.2518
-#> -0.8406
-#> -0.3756
-#> [ CPUFloatType{4} ]
    y = torch_randn(c(4))
-y
    #> torch_tensor 
-#>  0.5376
-#> -1.6162
-#> -0.8764
-#>  0.2081
-#> [ CPUFloatType{4} ]
    torch_dist(x, y, 3.5)
    #> torch_tensor 
-#> 1.38438
-#> [ CPUFloatType{} ]
    torch_dist(x, y, 3)
    #> torch_tensor 
-#> 1.40023
-#> [ CPUFloatType{} ]
    torch_dist(x, y, 0)
    #> torch_tensor 
-#> 4
-#> [ CPUFloatType{} ]
    torch_dist(x, y, 1)
    #> torch_tensor 
-#> 2.16879
-#> [ CPUFloatType{} ]
    # }
-
    
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diff --git a/docs/reference/torch_div.html b/docs/reference/torch_div.html
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    Div
    
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    (Tensor) the input tensor.
    other
    (Number) the number to be divided to each element of input
    
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-returns a new resulting tensor.
    
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-
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-other. The resulting tensor is returned.
    
-
    $$
-    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
-$$
-The shapes of input and other must be broadcastable
-. If the torch_dtype of input and
-other differ, the torch_dtype of the result tensor is determined
-following rules described in the type promotion documentation
-. If out is specified, the result must be
-castable  to the torch_dtype of the
-specified output tensor. Integral division by zero leads to undefined behavior.
    
-    
    Warning
    
-
-    
-
-
    Integer division using div is deprecated, and in a future release div will
-perform true division like torch_true_divide.
-Use torch_floor_divide (// in Python) to perform integer division,
-instead.
    
-
    $$
-    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
-$$
-If the torch_dtype of input and other differ, the
-torch_dtype of the result tensor is determined following rules
-described in the type promotion documentation . If
-out is specified, the result must be castable
-to the torch_dtype of the specified output tensor. Integral division
-by zero leads to undefined behavior.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(5))
-a
    #> torch_tensor 
-#> -1.2469
-#> -0.8301
-#>  0.6777
-#>  0.4991
-#>  2.3110
-#> [ CPUFloatType{5} ]
    torch_div(a, 0.5)
    #> torch_tensor 
-#> -2.4938
-#> -1.6601
-#>  1.3554
-#>  0.9983
-#>  4.6219
-#> [ CPUFloatType{5} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#> -0.1627  0.0346  0.2195  0.4264
-#> -1.3478 -2.4998 -1.5522  0.7143
-#>  0.2875 -1.3762  0.8071 -0.8691
-#>  0.7893 -1.7013 -0.2038 -0.3908
-#> [ CPUFloatType{4,4} ]
    b = torch_randn(c(4))
-b
    #> torch_tensor 
-#> -0.2924
-#>  1.8341
-#>  0.0283
-#> -0.6063
-#> [ CPUFloatType{4} ]
    torch_div(a, b)
    #> torch_tensor 
-#>   0.5566   0.0188   7.7430  -0.7033
-#>   4.6100  -1.3630 -54.7623  -1.1781
-#>  -0.9833  -0.7503  28.4751   1.4334
-#>  -2.6995  -0.9276  -7.1916   0.6445
-#> [ CPUFloatType{4,4} ]
    # }
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diff --git a/docs/reference/torch_dot.html b/docs/reference/torch_dot.html
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    (Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed
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    (bool) True to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed
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    (tuple, optional) the output tensors
    
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    (string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after -> and gives the indices for the output.           If the -> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses ... represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.
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-    
    # \dontrun{
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-x = torch_randn(c(5))
-y = torch_randn(c(4))
-torch_einsum('i,j->ij', list(x, y))  # outer product
    #> torch_tensor 
-#>  0.6481 -0.3038  0.7547  0.3279
-#> -0.7964  0.3732 -0.9273 -0.4029
-#>  0.2729 -0.1279  0.3178  0.1381
-#> -0.4882  0.2288 -0.5684 -0.2470
-#>  2.6219 -1.2288  3.0530  1.3266
-#> [ CPUFloatType{5,4} ]
    A = torch_randn(c(3,5,4))
-l = torch_randn(c(2,5))
-r = torch_randn(c(2,4))
-torch_einsum('bn,anm,bm->ba', list(l, A, r)) # compare torch_nn$functional$bilinear
    #> torch_tensor 
-#>  4.7791 -6.3600  2.0599
-#>  1.2294  0.6515 -3.1033
-#> [ CPUFloatType{2,3} ]
    As = torch_randn(c(3,2,5))
-Bs = torch_randn(c(3,5,4))
-torch_einsum('bij,bjk->bik', list(As, Bs)) # batch matrix multiplication
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.7724  0.1715  0.3778  0.5154
-#>  -0.7152  3.1174  0.5206 -3.0151
-#> 
-#> (2,.,.) = 
-#>  -2.1745  4.5514  0.2279  2.8187
-#>  -0.4776  1.4331 -1.1076  0.3273
-#> 
-#> (3,.,.) = 
-#>   1.0775  0.4867 -2.7086 -0.8871
-#>  -1.4834 -2.0710 -1.2663  0.2878
-#> [ CPUFloatType{3,2,4} ]
    A = torch_randn(c(3, 3))
-torch_einsum('ii->i', list(A)) # diagonal
    #> torch_tensor 
-#>  0.7209
-#>  0.7370
-#>  0.0642
-#> [ CPUFloatType{3} ]
    A = torch_randn(c(4, 3, 3))
-torch_einsum('...ii->...i', list(A)) # batch diagonal
    #> torch_tensor 
-#> -1.1323  2.2000 -0.2178
-#>  1.1550 -0.8415 -1.0067
-#>  2.1543 -0.0441 -1.3181
-#> -0.0296 -2.3285  0.7952
-#> [ CPUFloatType{4,3} ]
    A = torch_randn(c(2, 3, 4, 5))
-torch_einsum('...ij->...ji', list(A))$shape # batch permute
    #> [1] 2 3 5 4
    # }
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    (int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
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    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    pin_memory
    (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: False.
    memory_format
    (torch.memory_format, optional) the desired memory format of        returned Tensor. Default: torch_contiguous_format.
    
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    Examples
    
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    #> torch_tensor 
-#>  0.0000e+00  1.0842e-19 -2.0454e-24
-#>  8.5920e+09  8.4078e-45  4.9045e-44
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diff --git a/docs/reference/torch_empty_like.html b/docs/reference/torch_empty_like.html
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    (Tensor) the size of input will determine size of the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
    layout
    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
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    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
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-torch_empty_like(input) is equivalent to
-torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
-
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    Examples
    
-    
    # \dontrun{
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-torch_empty(list(2,3), dtype = torch_int64())
    #> torch_tensor 
-#>  1.4057e+14  1.4057e+14  1.4057e+14
-#>  0.0000e+00  8.5899e+09  1.4057e+14
-#> [ CPULongType{2,3} ]
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diff --git a/docs/reference/torch_empty_strided.html b/docs/reference/torch_empty_strided.html
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    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
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    (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: False.
    
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-torch_empty(size).as_strided(size, stride).
    
-    
    Warning
    
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-location. As a result, in-place operations (especially ones that are
-vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.
    
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    Examples
    
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    # \dontrun{
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-a
    #> torch_tensor 
-#>  0.0000e+00 -4.2887e-24  2.2101e-10
-#>  1.0842e-19  2.0005e+00  4.5592e+30
-#> [ CPUFloatType{2,3} ]
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-The inverse error function is defined in the range \((-1, 1)\) as:
    
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    #> torch_tensor 
-#>  0.0000
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    # \dontrun{
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    #> torch_tensor 
-#>  1  0  0
-#>  0  1  0
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-Ignoring the batch dimensions, it computes the following expression:
    
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-        \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = signal_ndim is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).
    
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-by signal_ndim. input must be a tensor with last dimension
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-x = torch_randn(c(4, 3, 2))
-torch_fft(x, 2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   1.6091  1.1177
-#>   1.7387  0.5245
-#>   1.3022 -7.5020
-#> 
-#> (2,.,.) = 
-#>  -2.2896  1.5947
-#>   2.3894 -3.7256
-#>   2.4421 -6.0534
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-#> (3,.,.) = 
-#>  -2.6684  0.2216
-#>   4.6351  2.1387
-#>   1.0104  2.1655
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-#> (4,.,.) = 
-#>   0.6484  1.5756
-#>   3.6644  0.9869
-#>   0.8331  1.2714
-#> [ CPUFloatType{4,3,2} ]
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-torch_fft(x, 1)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.6751  1.1274
-#>   3.1069 -0.0189
-#>   1.3969 -2.5296
-#> 
-#> (2,.,.) = 
-#>   1.0646 -0.5105
-#>   0.4540 -0.7223
-#>   1.9041 -2.0146
-#> 
-#> (3,.,.) = 
-#>   0.1455 -0.4577
-#>   0.0800  1.3505
-#>  -0.2407 -0.1386
-#> 
-#> (4,.,.) = 
-#>   1.0741  0.9585
-#>  -1.9022 -0.0848
-#>  -1.7582 -2.8191
-#> [ CPUFloatType{4,3,2} ]
    # arbitrary number of batch dimensions, 2D FFT
-x = torch_randn(c(3, 3, 5, 5, 2))
-torch_fft(x, 2)
    #> torch_tensor 
-#> (1,1,1,.,.) = 
-#>  -5.4725 -1.0036
-#>  -0.4072 -9.9106
-#>  -6.2423 -6.4070
-#>  -1.9919 -0.2080
-#>  -1.5095 -4.7941
-#> 
-#> (2,1,1,.,.) = 
-#>   -1.3392   3.2072
-#>   -2.8398   7.8139
-#>    0.8047  -3.0656
-#>   10.5509  -0.0158
-#>    0.4441   1.4712
-#> 
-#> (3,1,1,.,.) = 
-#>   5.8889 -0.8239
-#>   0.6557 -4.5634
-#>   3.6018 -5.1202
-#>   6.7747 -1.8286
-#>  -6.4471  3.7626
-#> 
-#> (1,2,1,.,.) = 
-#>  -0.2606  1.6037
-#>   1.2933  2.7796
-#>  -1.2262 -8.3113
-#>  -2.9285 -0.8387
-#>  -8.8958  7.9346
-#> 
-#> (2,2,1,.,.) = 
-#>   -5.8665  -5.8427
-#>    3.5695  -0.4600
-#>   10.6353  -4.9917
-#>   -1.8417  -2.2605
-#>    4.0205   3.3620
-#> 
-#> (3,2,1,.,.) = 
-#>  -5.3450  5.1190
-#>  -7.1003  3.1183
-#>   2.1035 -0.7317
-#>  -1.7222  6.8806
-#>   1.1505  7.5300
-#> 
-#> (1,3,1,.,.) = 
-#>  -9.1081  7.4756
-#>   5.1849 -1.2153
-#>  -1.6891 -2.1615
-#>  -4.9926  0.1991
-#>   5.4721 -2.1878
-#> 
-#> (2,3,1,.,.) = 
-#>  -4.9459  0.0120
-#>  -5.7250 -0.8370
-#>   0.7157  1.3166
-#>  -9.2864  4.1198
-#>  -2.5485  6.5091
-#> 
-#> (3,3,1,.,.) = 
-#>   2.9661 -4.1025
-#>   5.0720  3.2858
-#>  -1.6322 -9.3722
-#>  -0.3661 -5.5639
-#>   6.5081 -2.4129
-#> 
-#> (1,1,2,.,.) = 
-#>   -2.3401   1.2643
-#>    5.3967  -7.4990
-#>    0.1779   3.4217
-#>    6.0804   6.1065
-#>   10.1270  -3.5843
-#> 
-#> (2,1,2,.,.) = 
-#>   -9.0572  -1.9909
-#>    4.2537  -0.0385
-#>    5.9323  -1.1033
-#>   -1.4235  11.0225
-#>    0.3750   3.9369
-#> 
-#> (3,1,2,.,.) = 
-#>   7.3848 -1.2410
-#>  -1.4446  1.3323
-#>  -1.4867  6.3887
-#>  -1.3989  4.3918
-#>  -1.6242  0.4001
-#> 
-#> (1,2,2,.,.) = 
-#>   2.2515 -11.7348
-#>  -2.9592 -0.4199
-#>   6.0865 -2.5877
-#>  -3.9066  0.8276
-#>  -6.4707 -0.1932
-#> 
-#> (2,2,2,.,.) = 
-#>   -3.5320 -15.9640
-#>   -3.2020  -4.1973
-#>    3.5907  -1.9048
-#>    4.8187  10.5827
-#>    2.0354   3.6088
-#> 
-#> (3,2,2,.,.) = 
-#>  -1.5620  2.4318
-#>  -4.4488 -3.6563
-#>   1.4412  6.2894
-#>   3.7142 -4.4966
-#>  -1.9539  0.9322
-#> 
-#> (1,3,2,.,.) = 
-#>   -2.5739  11.5350
-#>   -5.6638  -1.9311
-#>   -7.1538   1.8841
-#>    9.9282  -5.0425
-#>    4.0550 -13.0364
-#> 
-#> (2,3,2,.,.) = 
-#>  -4.4776 -3.1815
-#>   5.2017  7.8643
-#>  -1.9960 -3.8433
-#>   1.8730 -3.0428
-#>   0.4528  1.8324
-#> 
-#> (3,3,2,.,.) = 
-#>  -1.6393  3.0116
-#>  -7.4910  4.6567
-#>   3.9892 -1.2984
-#>   2.3925  1.7542
-#>  -0.4821  2.7867
-#> 
-#> (1,1,3,.,.) = 
-#>  -1.9416  6.9300
-#>  -5.9122  4.7269
-#>  -6.1427 -3.6378
-#>   4.8333 -7.6424
-#>  -0.8626 -7.3429
-#> 
-#> (2,1,3,.,.) = 
-#>   0.7169  7.3508
-#>  -2.2415  0.6318
-#>   8.5924 -3.5120
-#>  -1.8660 -2.9021
-#>   2.5242  6.2449
-#> 
-#> (3,1,3,.,.) = 
-#>   3.2523 -9.4747
-#>   6.7858  2.1141
-#>  -3.1627 -8.3128
-#>   8.1738  1.7333
-#>  -12.5794  7.6793
-#> 
-#> (1,2,3,.,.) = 
-#>   2.1388 -0.8053
-#>  -7.5468 -4.9134
-#>  -1.4956  0.7191
-#>   4.5485  1.3213
-#>   1.9775  3.2742
-#> 
-#> (2,2,3,.,.) = 
-#>    2.7705  -3.2113
-#>   -7.1385  10.5362
-#>    2.1267  -3.4305
-#>   -3.4051  -2.5411
-#>   -2.7076   1.7485
-#> 
-#> (3,2,3,.,.) = 
-#>    3.3394  10.9207
-#>   -0.5970  -0.5034
-#>    8.1799   3.0040
-#>   -0.9163  -3.5497
-#>   -3.1114  -5.1809
-#> 
-#> (1,3,3,.,.) = 
-#>  -4.1027 -2.0095
-#>   2.7722 -4.7394
-#>  -3.5039 -1.9563
-#>  -4.3085  8.7966
-#>   2.7449 -4.6416
-#> 
-#> (2,3,3,.,.) = 
-#>  -8.7689 -5.5072
-#>   0.0556 -7.0627
-#>   6.2366  2.7693
-#>  -0.9913  0.2007
-#>   2.4020  2.7007
-#> 
-#> (3,3,3,.,.) = 
-#>    0.1972  -1.1722
-#>    7.4460   3.4332
-#>   -4.7450 -11.9086
-#>   12.5079 -12.2708
-#>   -2.9972  -0.8924
-#> 
-#> (1,1,4,.,.) = 
-#>  -2.6641 -4.3296
-#>   3.0735  1.2215
-#>   0.2079 -4.6184
-#>  -2.3720 -1.2896
-#>  -3.9109 -9.9345
-#> 
-#> (2,1,4,.,.) = 
-#>  -5.0131 -2.2123
-#>   2.7328 -6.4654
-#>  -3.3791 -1.4795
-#>  -3.0048  4.2683
-#>   8.6069  3.8774
-#> 
-#> (3,1,4,.,.) = 
-#>   0.5565 -3.4152
-#>   3.1825 -1.2100
-#>  -5.6883 -4.5265
-#>  -2.6019  6.2445
-#>  -1.2380  0.0733
-#> 
-#> (1,2,4,.,.) = 
-#>  -0.0619 -1.4112
-#>   3.0482 -12.0317
-#>   2.3142 -5.9550
-#>   1.6332 -4.9998
-#>   3.3110  3.2442
-#> 
-#> (2,2,4,.,.) = 
-#>   5.7054 -4.5310
-#>   4.0814  0.5770
-#>  -7.4352  1.0886
-#>  -4.4217 -0.5822
-#>  -2.2274 -2.5406
-#> 
-#> (3,2,4,.,.) = 
-#>   -6.0243   1.6214
-#>    0.9687   2.5785
-#>   -9.2330  12.6507
-#>    0.1245  -3.6675
-#>    2.2133  -0.1646
-#> 
-#> (1,3,4,.,.) = 
-#>  -9.8819  5.0379
-#>  -15.9787 -0.0044
-#>  -3.3972 -4.1292
-#>  -0.2631  3.1780
-#>  -8.3382  2.3260
-#> 
-#> (2,3,4,.,.) = 
-#>   2.6493  5.7085
-#>  -0.8552 -6.9286
-#>   8.8915  1.1790
-#>  -8.0308 -2.3641
-#>  -5.5359 -0.2864
-#> 
-#> (3,3,4,.,.) = 
-#>   6.0509 -4.4619
-#>   2.5174  1.0450
-#>   3.2386 -2.9879
-#>  -4.2332 -11.7932
-#>   8.3415 -11.6524
-#> 
-#> (1,1,5,.,.) = 
-#>  -2.6344  8.4594
-#>  -9.3032 -0.0826
-#>  -0.7539  0.4355
-#>  -5.7352  6.4314
-#>   1.3710  5.0478
-#> 
-#> (2,1,5,.,.) = 
-#>   5.7266 -5.1236
-#>  -3.1017 -0.2088
-#>  -6.6610  5.8354
-#>   3.1587  1.7131
-#>   3.4059  0.6908
-#> 
-#> (3,1,5,.,.) = 
-#>   12.7228   0.6750
-#>   -4.4296   8.4994
-#>    2.5491   1.5131
-#>   -1.2545  -5.1292
-#>    8.7953   3.5627
-#> 
-#> (1,2,5,.,.) = 
-#>  -2.5561 -2.6800
-#>   3.0704  6.9316
-#>  -4.2936  0.3370
-#>  -3.2507  9.0391
-#>   7.8262 -2.8746
-#> 
-#> (2,2,5,.,.) = 
-#>  -4.7915 -2.9852
-#>   0.4093 -1.2396
-#>   1.8851  0.8607
-#>  -3.2552 -4.0731
-#>   7.0195 -3.8280
-#> 
-#> (3,2,5,.,.) = 
-#>  -6.9953 -12.4542
-#>   7.8834  1.2176
-#>   9.6941 -9.1636
-#>  -15.3205  2.1126
-#>   0.3537 -10.4525
-#> 
-#> (1,3,5,.,.) = 
-#>  -10.7714   0.7571
-#>   -0.7565  -0.4820
-#>    5.6134  -8.9826
-#>   -1.1373  -4.2921
-#>    4.1096  -1.6137
-#> 
-#> (2,3,5,.,.) = 
-#>   3.5382  6.4175
-#>  -2.8167 -1.5735
-#>   8.0241 -4.9140
-#>   1.6143  7.2625
-#>  -2.5749  4.8191
-#> 
-#> (3,3,5,.,.) = 
-#>   9.8500 -0.4376
-#>  -4.1255 -2.8707
-#>  -4.6457 -0.7456
-#>  -4.6735  2.8975
-#>   2.7895  9.0651
-#> [ CPUFloatType{3,3,5,5,2} ]
    
-# }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Examples
    
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    # \dontrun{
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-torch_flatten(t)
    #> torch_tensor 
-#>  1
-#>  2
-#> [ CPUFloatType{2} ]
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    #> torch_tensor 
-#>  1  2
-#> [ CPUFloatType{1,2} ]
    # }
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diff --git a/docs/reference/torch_flip.html b/docs/reference/torch_flip.html
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-x = torch_arange(0, 8)$view(c(2, 2, 2))
-x
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0  1
-#>   2  3
-#> 
-#> (2,.,.) = 
-#>   4  5
-#>   6  7
-#> [ CPUFloatType{2,2,2} ]
    torch_flip(x, c(1, 2))
    #> torch_tensor 
-#> (1,.,.) = 
-#>   6  7
-#>   4  5
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-#> (2,.,.) = 
-#>   2  3
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    # }
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diff --git a/docs/reference/torch_floor.html b/docs/reference/torch_floor.html
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.4440
-#>  0.1195
-#>  2.0850
-#>  0.6923
-#> [ CPUFloatType{4} ]
    torch_floor(a)
    #> torch_tensor 
-#>  0
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-    
    # \dontrun{
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-a = torch_tensor(c(4.0, 3.0))
-b = torch_tensor(c(2.0, 2.0))
-torch_floor_divide(a, b)
    #> torch_tensor 
-#>  2
-#>  1
-#> [ CPUFloatType{2} ]
    torch_floor_divide(a, 1.4)
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-#>  2
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-#> [ CPUFloatType{2} ]
    # }
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    # \dontrun{
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-torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
    #> torch_tensor 
-#> -1
-#> -0
-#> -1
-#>  1
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    torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
    #> torch_tensor 
-#>  1.0000
-#>  0.5000
-#>  0.0000
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    # }
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diff --git a/docs/reference/torch_frac.html b/docs/reference/torch_frac.html
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    Examples
    
-    
    # \dontrun{
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-torch_frac(torch_tensor(c(1, 2.5, -3.2)))
    #> torch_tensor 
-#>  0.0000
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    device
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    requires_grad
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-
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-    
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-dtype or out are not set.
-In a future PyTorch release, when dtype and out are not set
-a bool fill_value will return a tensor of torch.bool dtype,
-and an integral fill_value will return a tensor of torch.long dtype.
    
-
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    Examples
    
-    
    # \dontrun{
-
-torch_full(list(2, 3), 3.141592)
    #> torch_tensor 
-#>  3.1416  3.1416  3.1416
-#>  3.1416  3.1416  3.1416
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    requires_grad
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-
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diff --git a/docs/reference/torch_gather.html b/docs/reference/torch_gather.html
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-and dim = i, then index must be an \(n\)-dimensional tensor with
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-and out will have the same size as index.
    
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    # \dontrun{
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-torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64()))
    #> torch_tensor 
-#>  1  1
-#>  4  3
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diff --git a/docs/reference/torch_ge.html b/docs/reference/torch_ge.html
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    # \dontrun{
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-torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  1  1
-#>  0  1
-#> [ CPUBoolType{2,2} ]
    # }
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diff --git a/docs/reference/torch_generator.html b/docs/reference/torch_generator.html
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    # \dontrun{
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-generator <- torch_generator()
-generator$current_seed()
    #> Loading required package: bit64
    #> Loading required package: bit
    #> Attaching package bit
    #> package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)
    #> creators: bit bitwhich
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-#> Attaching package: ‘bit’
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    #> package:bit64 (c) 2011-2012 Jens Oehlschlaegel
    #> creators: integer64 seq :
    #> coercion: as.integer64 as.vector as.logical as.integer as.double as.character as.bin
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    #> combine: c rep cbind rbind as.data.frame
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    #> for more help type ?bit64
    #> 
-#> Attaching package: ‘bit64’
    #> The following object is masked from ‘package:bit’:
-#> 
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    #> The following objects are masked from ‘package:base’:
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    #> integer64
-#> [1] 67280421310721
    generator$set_current_seed(1234567L)
-generator$current_seed()
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-#> [1] 1234567
    
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diff --git a/docs/reference/torch_geqrf.html b/docs/reference/torch_geqrf.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Geqrf
    
-    
    
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    Arguments
    
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    (Tensor) the input matrix
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    (tuple, optional) the output tuple of (Tensor, Tensor)
    
-
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    geqrf(input, out=None) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    This is a low-level function for calling LAPACK directly. This function
-returns a namedtuple (a, tau) as defined in LAPACK documentation for geqrf_ .
    
-
    You'll generally want to use torch_qr instead.
    
-
    Computes a QR decomposition of input, but without constructing
-\(Q\) and \(R\) as explicit separate matrices.
    
-
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-which produces a sequence of 'elementary reflectors'.
    
-
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diff --git a/docs/reference/torch_ger.html b/docs/reference/torch_ger.html
deleted file mode 100644
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-
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    (Tensor) 1-D input vector
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    (Tensor) 1-D input vector
    out
    (Tensor, optional) optional output matrix
    
-
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    Note
    
-
-    
    This function does not broadcast .
    
-    
    ger(input, vec2, out=None) -> Tensor 
    
-
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-
-
-
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-If input is a vector of size \(n\) and vec2 is a vector of
-size \(m\), then out must be a matrix of size \((n \times m)\).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-v1 = torch_arange(1., 5.)
-v2 = torch_arange(1., 4.)
-torch_ger(v1, v2)
    #> torch_tensor 
-#>   1   2   3
-#>   2   4   6
-#>   3   6   9
-#>   4   8  12
-#> [ CPUFloatType{4,3} ]
    # }
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diff --git a/docs/reference/torch_gt.html b/docs/reference/torch_gt.html
deleted file mode 100644
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--- a/docs/reference/torch_gt.html
+++ /dev/null
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-
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Gt
    
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    input
    (Tensor) the tensor to compare
    other
    (Tensor or float) the tensor or value to compare
    out
    (Tensor, optional) the output tensor that must be a BoolTensor
    
-
-    
    gt(input, other, out=None) -> Tensor 
    
-
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-
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-
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-
    The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  0  1
-#>  0  0
-#> [ CPUBoolType{2,2} ]
    # }
-
    
-  
    
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diff --git a/docs/reference/torch_hamming_window.html b/docs/reference/torch_hamming_window.html
deleted file mode 100644
index 4c0a0128c5c059aa9bab996823d621d28e224808..0000000000000000000000000000000000000000
--- a/docs/reference/torch_hamming_window.html
+++ /dev/null
@@ -1,259 +0,0 @@
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-Hamming_window — torch_hamming_window • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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-    
    Hamming_window
    
-    
    
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    Arguments
    
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    window_length
    (int) the size of returned window
    periodic
    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    alpha
    (float, optional) The coefficient \(\alpha\) in the equation above
    beta
    (float, optional) The coefficient \(\beta\) in the equation above
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    Note
    
-
-    
-
    If `window_length` \eqn{=1}, the returned window contains a single value 1.
-
    
-
-
    This is a generalized version of `torch_hann_window`.
-
    
-
-    
    hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Hamming window function.
    
-
    $$
-    w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.
    
-
    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_hamming_window(L, periodic=True) equal to
-torch_hamming_window(L + 1, periodic=False)[:-1]).
    
-
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diff --git a/docs/reference/torch_hann_window.html b/docs/reference/torch_hann_window.html
deleted file mode 100644
index bba67df29516a0e266ba171b4fb594d9bdc0625b..0000000000000000000000000000000000000000
--- a/docs/reference/torch_hann_window.html
+++ /dev/null
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-Hann_window — torch_hann_window • torch
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    Hann_window
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Hann_window
    
-    
    
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    Arguments
    
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    window_length
    (int) the size of returned window
    periodic
    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    Note
    
-
-    
-
    If `window_length` \eqn{=1}, the returned window contains a single value 1.
-
    
-
-    
    hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Hann window function.
    
-
    $$
-    w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
-            \sin^2 \left( \frac{\pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.
    
-
    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_hann_window(L, periodic=True) equal to
-torch_hann_window(L + 1, periodic=False)[:-1]).
    
-
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diff --git a/docs/reference/torch_histc.html b/docs/reference/torch_histc.html
deleted file mode 100644
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@@ -1,239 +0,0 @@
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    Histc
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Histc
    
-    
    
-
-
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    Arguments
    
-    
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-      
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    input
    (Tensor) the input tensor.
    bins
    (int) number of histogram bins
    min
    (int) lower end of the range (inclusive)
    max
    (int) upper end of the range (inclusive)
    out
    (Tensor, optional) the output tensor.
    
-
-    
    histc(input, bins=100, min=0, max=0, out=None) -> Tensor 
    
-
-    
-
-
-
    Computes the histogram of a tensor.
    
-
    The elements are sorted into equal width bins between min and
-max. If min and max are both zero, the minimum and
-maximum values of the data are used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3)
    #> torch_tensor 
-#>  0
-#>  2
-#>  1
-#>  0
-#> [ CPUFloatType{4} ]
    # }
-
    
-  
    
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diff --git a/docs/reference/torch_ifft.html b/docs/reference/torch_ifft.html
deleted file mode 100644
index bd4e5d815fce4b92b04f1c6134b37c2e08fe28f9..0000000000000000000000000000000000000000
--- a/docs/reference/torch_ifft.html
+++ /dev/null
@@ -1,288 +0,0 @@
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Ifft
    
-    
    
-
-
-    
    Arguments
    
-    
-    
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-      
-      
-    
-    
-      
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-    
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-      
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-    
-    
    input
    (Tensor) the input tensor of at least signal_ndim + 1        dimensions
    signal_ndim
    (int) the number of dimensions in each signal.        signal_ndim can only be 1, 2 or 3
    normalized
    (bool, optional) controls whether to return normalized results.        Default: False
    
-
-    
    Note
    
-
-    
-
    For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-
    
-
-    
    ifft(input, signal_ndim, normalized=False) -> Tensor 
    
-
-    
-
-
-
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-
    This method computes the complex-to-complex inverse discrete Fourier
-transform. Ignoring the batch dimensions, it computes the following
-expression:
    
-
    $$
-    X[\omega_1, \dots, \omega_d] =
-        \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = signal_ndim is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).
    
-
    The argument specifications are almost identical with torch_fft.
-However, if normalized is set to True, this instead returns the
-results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary
-operator. Therefore, to invert a torch_fft, the normalized
-argument should be set identically for torch_fft.
    
-
    Returns the real and the imaginary parts together as one tensor of the same
-shape of input.
    
-
    The inverse of this function is torch_fft.
    
-    
    Warning
    
-
-    
-
-
    For CPU tensors, this method is currently only available with MKL. Use
-torch_backends.mkl.is_available to check if MKL is installed.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 3, 2))
-x
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.4097 -0.4074
-#>   0.4656  0.3306
-#>  -0.5886 -2.1190
-#> 
-#> (2,.,.) = 
-#>   0.3168 -1.2703
-#>  -0.7706 -0.4545
-#>   0.5461  1.3650
-#> 
-#> (3,.,.) = 
-#>  -0.0778 -0.2976
-#>  -0.1324  0.2705
-#>   1.3860 -0.0941
-#> [ CPUFloatType{3,3,2} ]
    y = torch_fft(x, 2)
-torch_ifft(y, 2)  # recover x
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.4097 -0.4074
-#>   0.4656  0.3306
-#>  -0.5886 -2.1190
-#> 
-#> (2,.,.) = 
-#>   0.3168 -1.2703
-#>  -0.7706 -0.4545
-#>   0.5461  1.3650
-#> 
-#> (3,.,.) = 
-#>  -0.0778 -0.2976
-#>  -0.1324  0.2705
-#>   1.3860 -0.0941
-#> [ CPUFloatType{3,3,2} ]
    # }
-
    
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diff --git a/docs/reference/torch_imag.html b/docs/reference/torch_imag.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Imag
    
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    Arguments
    
-    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
-    
    imag(input, out=None) -> Tensor 
    
-
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-
-
-
    Returns the imaginary part of the input tensor.
    
-    
    Warning
    
-
-    
-
-
    Not yet implemented.
    
-
    $$
-    \mbox{out}_{i} = imag(\mbox{input}_{i})
-$$
    
-
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    Examples
    
-    
    
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diff --git a/docs/reference/torch_index_select.html b/docs/reference/torch_index_select.html
deleted file mode 100644
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+++ /dev/null
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    Index_select
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Index_select
    
-    
    
-
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    Arguments
    
-    
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    input
    (Tensor) the input tensor.
    dim
    (int) the dimension in which we index
    index
    (LongTensor) the 1-D tensor containing the indices to index
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
    The returned tensor does not use the same storage as the original
-tensor.  If out has a different shape than expected, we
-silently change it to the correct shape, reallocating the underlying
-storage if necessary.
    
-    
    index_select(input, dim, index, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a new tensor which indexes the input tensor along dimension
-dim using the entries in index which is a LongTensor.
    
-
    The returned tensor has the same number of dimensions as the original tensor
-(input).  The dim\ th dimension has the same size as the length
-of index; other dimensions have the same size as in the original tensor.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 4))
-x
    #> torch_tensor 
-#> -0.6090  0.2050 -0.2528  0.6190
-#>  0.3118  1.3114 -1.0626 -1.2048
-#>  0.3125 -0.2527  0.4871 -0.3394
-#> [ CPUFloatType{3,4} ]
    indices = torch_tensor(c(1, 3), dtype = torch_int64())
-torch_index_select(x, 1, indices)
    #> torch_tensor 
-#> -0.6090  0.2050 -0.2528  0.6190
-#>  0.3125 -0.2527  0.4871 -0.3394
-#> [ CPUFloatType{2,4} ]
    torch_index_select(x, 2, indices)
    #> torch_tensor 
-#> -0.6090 -0.2528
-#>  0.3118 -1.0626
-#>  0.3125  0.4871
-#> [ CPUFloatType{3,2} ]
    # }
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diff --git a/docs/reference/torch_inverse.html b/docs/reference/torch_inverse.html
deleted file mode 100644
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--- a/docs/reference/torch_inverse.html
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-Inverse — torch_inverse • torch
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    Inverse
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Inverse
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor of size \((*, n, n)\) where * is zero or more                    batch dimensions
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
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-
    Irrespective of the original strides, the returned tensors will be
-transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
-
    
-
-    
    inverse(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Takes the inverse of the square matrix input. input can be batches
-of 2D square tensors, in which case this function would return a tensor composed of
-individual inverses.
    
-
-    
    Examples
    
-    
    
-  
    
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diff --git a/docs/reference/torch_irfft.html b/docs/reference/torch_irfft.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Irfft
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor of at least signal_ndim + 1        dimensions
    signal_ndim
    (int) the number of dimensions in each signal.        signal_ndim can only be 1, 2 or 3
    normalized
    (bool, optional) controls whether to return normalized results.        Default: False
    onesided
    (bool, optional) controls whether input was halfed to avoid        redundancy, e.g., by torch_rfft(). Default: True
    signal_sizes
    (list or torch.Size, optional) the size of the original        signal (without batch dimension). Default: None
    
-
-    
    Note
    
-
-    
-
    Due to the conjugate symmetry, `input` do not need to contain the full
-complex frequency values. Roughly half of the values will be sufficient, as
-is the case when `input` is given by [`~torch.rfft`] with
-``rfft(signal, onesided=True)``. In such case, set the `onesided`
-argument of this method to ``True``. Moreover, the original signal shape
-information can sometimes be lost, optionally set `signal_sizes` to be
-the size of the original signal (without the batch dimensions if in batched
-mode) to recover it with correct shape.
-
-Therefore, to invert an [torch_rfft()], the `normalized` and
-`onesided` arguments should be set identically for [torch_irfft()],
-and preferably a `signal_sizes` is given to avoid size mismatch. See the
-example below for a case of size mismatch.
-
-See [torch_rfft()] for details on conjugate symmetry.
-
    
-
-
    The inverse of this function is torch_rfft().
    
-
    For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-
    
-
-    
    irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor 
    
-
-    
-
-
-
    Complex-to-real Inverse Discrete Fourier Transform
    
-
    This method computes the complex-to-real inverse discrete Fourier transform.
-It is mathematically equivalent with torch_ifft with differences only in
-formats of the input and output.
    
-
    The argument specifications are almost identical with torch_ifft.
-Similar to torch_ifft, if normalized is set to True,
-this normalizes the result by multiplying it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary, where
-\(N_i\) is the size of signal dimension \(i\).
    
-    
    Warning
    
-
-    
-
-
    Generally speaking, input to this function should contain values
-following conjugate symmetry. Note that even if onesided is
-True, often symmetry on some part is still needed. When this
-requirement is not satisfied, the behavior of torch_irfft is
-undefined. Since torch_autograd.gradcheck estimates numerical
-Jacobian with point perturbations, torch_irfft will almost
-certainly fail the check.
    
-
-
    For CPU tensors, this method is currently only available with MKL. Use
-torch_backends.mkl.is_available to check if MKL is installed.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(4, 4))
-torch_rfft(x, 2, onesided=TRUE)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -2.6442  0.0000
-#>   3.2951 -1.0012
-#>  -10.0001  0.0000
-#> 
-#> (2,.,.) = 
-#>   1.3956 -4.9153
-#>   3.1204  7.3054
-#>  -1.9820  2.2872
-#> 
-#> (3,.,.) = 
-#>   3.3926  0.0000
-#>   5.2495  2.0603
-#>  -4.0185  0.0000
-#> 
-#> (4,.,.) = 
-#>   1.3956  4.9153
-#>   4.1712  1.2315
-#>  -1.9820 -2.2872
-#> [ CPUFloatType{4,3,2} ]
    x = torch_randn(c(4, 5))
-torch_rfft(x, 2, onesided=TRUE)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   6.1729  0.0000
-#>   2.7828 -0.8086
-#>   0.0001 -0.7514
-#> 
-#> (2,.,.) = 
-#>  -0.3550  1.3322
-#>  -1.3598  3.0191
-#>   1.5158 -3.2844
-#> 
-#> (3,.,.) = 
-#>   5.1938  0.0000
-#>  -2.5545  1.6405
-#>   7.3076  5.2401
-#> 
-#> (4,.,.) = 
-#>  -0.3550 -1.3322
-#>   0.9130 -2.5817
-#>  -0.4966 -2.0859
-#> [ CPUFloatType{4,3,2} ]
    y = torch_rfft(x, 2, onesided=TRUE)
-torch_irfft(y, 2, onesided=TRUE, signal_sizes=c(4,5))  # recover x
    #> torch_tensor 
-#>  1.3437 -0.2165  0.6494  0.9663 -0.0787
-#> -0.7215  1.0848 -0.3526 -0.1508 -0.2811
-#>  1.3002 -0.5011  1.6580 -0.1709  0.7330
-#>  0.4254  1.6956 -1.7164 -0.2147  0.7211
-#> [ CPUFloatType{4,5} ]
    # }
-
    
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diff --git a/docs/reference/torch_is_complex.html b/docs/reference/torch_is_complex.html
deleted file mode 100644
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+++ /dev/null
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-Is_complex — torch_is_complex • torch
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    Is_complex
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Is_complex
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the PyTorch tensor to test
    
-
-    
    is_complex(input) -> (bool) 
    
-
-    
-
-
-
    Returns True if the data type of input is a complex data type i.e.,
-one of torch_complex64, and torch.complex128.
    
-
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diff --git a/docs/reference/torch_is_floating_point.html b/docs/reference/torch_is_floating_point.html
deleted file mode 100644
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--- a/docs/reference/torch_is_floating_point.html
+++ /dev/null
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    Is_floating_point
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Is_floating_point
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the PyTorch tensor to test
    
-
-    
    is_floating_point(input) -> (bool) 
    
-
-    
-
-
-
    Returns True if the data type of input is a floating point data type i.e.,
-one of torch_float64, torch.float32 and torch.float16.
    
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diff --git a/docs/reference/torch_isfinite.html b/docs/reference/torch_isfinite.html
deleted file mode 100644
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+++ /dev/null
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-
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-Isfinite — torch_isfinite • torch
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    Isfinite
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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-    
    Isfinite
    
-    
    
-
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    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    (Tensor) A tensor to check
    
-
-    
    TEST 
    
-
-    
-
-
-
    Returns a new tensor with boolean elements representing if each element is Finite or not.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
    #> torch_tensor 
-#>  1
-#>  0
-#>  1
-#>  0
-#>  0
-#> [ CPUBoolType{5} ]
    # }
-
    
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diff --git a/docs/reference/torch_isinf.html b/docs/reference/torch_isinf.html
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    Isinf
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Isinf
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    (Tensor) A tensor to check
    
-
-    
    TEST 
    
-
-    
-
-
-
    Returns a new tensor with boolean elements representing if each element is +/-INF or not.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
    #> torch_tensor 
-#>  0
-#>  1
-#>  0
-#>  1
-#>  0
-#> [ CPUBoolType{5} ]
    # }
-
    
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-torch_isnan(torch_tensor(c(1, NaN, 2)))
    #> torch_tensor 
-#>  0
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-they are of size 1. Otherwise, dim is squeezed
-(see torch_squeeze), resulting in both the values and
-indices tensors having 1 fewer dimension than the input tensor.
    
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    Examples
    
-    
    # \dontrun{
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-x
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
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    torch_kthvalue(x, 4)
    #> [[1]]
-#> torch_tensor 
-#> 4
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 3
-#> [ CPULongType{} ]
-#> 
    x=torch_arange(1.,7.)$resize_(c(2,3))
-x
    #> torch_tensor 
-#>  1  2  3
-#>  4  5  6
-#> [ CPUFloatType{2,3} ]
    torch_kthvalue(x, 2, 1, TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  4  5  6
-#> [ CPUFloatType{1,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  1  1  1
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-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
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-#>  1  0
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    end
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-$$
-The shapes of start and end must be
-broadcastable . If weight is a tensor, then
-the shapes of weight, start, and end must be broadcastable .
    
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    Examples
    
-    
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-end = torch_empty(4)$fill_(10)
-start
    #> torch_tensor 
-#>  1
-#>  2
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    end
    #> torch_tensor 
-#>  10
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    torch_lerp(start, end, 0.5)
    #> torch_tensor 
-#>  5.5000
-#>  6.0000
-#>  6.5000
-#>  7.0000
-#> [ CPUFloatType{4} ]
    torch_lerp(start, end, torch_full_like(start, 0.5))
    #> torch_tensor 
-#>  5.5000
-#>  6.0000
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-#> [ CPUFloatType{4} ]
    # }
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    out
    (Tensor, optional) the output tensor.
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    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
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    #> torch_tensor 
-#>   3.0000
-#>   4.7500
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    # }
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    Returns a new tensor with the natural logarithm of the elements
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    Examples
    
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    # \dontrun{
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-a = torch_randn(c(5))
-a
    #> torch_tensor 
-#>  1.6609
-#> -1.5017
-#>  0.7439
-#> -0.6239
-#>  1.2395
-#> [ CPUFloatType{5} ]
    torch_log(a)
    #> torch_tensor 
-#>  0.5074
-#>     nan
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-of input.
    
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    Examples
    
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    # \dontrun{
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    #> torch_tensor 
-#>  0.6619
-#>  0.0908
-#>  0.8331
-#>  0.1240
-#>  0.1908
-#> [ CPUFloatType{5} ]
    torch_log10(a)
    #> torch_tensor 
-#> -0.1792
-#> -1.0419
-#> -0.0793
-#> -0.9067
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-#> [ CPUFloatType{5} ]
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-
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    # \dontrun{
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-a = torch_randn(c(5))
-a
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-#> -0.7431
-#> -0.9335
-#>  0.2461
-#>  0.9212
-#> -0.0972
-#> [ CPUFloatType{5} ]
    torch_log1p(a)
    #> torch_tensor 
-#> -1.3591
-#> -2.7110
-#>  0.2200
-#>  0.6530
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-#> [ CPUFloatType{5} ]
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-of input.
    
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-$$
    
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    Examples
    
-    
    # \dontrun{
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-a
    #> torch_tensor 
-#>  0.5313
-#>  0.0120
-#>  0.0760
-#>  0.6910
-#>  0.9385
-#> [ CPUFloatType{5} ]
    torch_log2(a)
    #> torch_tensor 
-#> -0.9125
-#> -6.3856
-#> -3.7179
-#> -0.5332
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-is not invertible. In this case, double backward through `logdet` will
-be unstable in when `input` doesn't have distinct singular values. See
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    Examples
    
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    # \dontrun{
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-A = torch_randn(c(3, 3))
-torch_det(A)
    #> torch_tensor 
-#> -0.779207
-#> [ CPUFloatType{} ]
    torch_logdet(A)
    #> torch_tensor 
-#> nan
-#> [ CPUFloatType{} ]
    A
    #> torch_tensor 
-#> -0.1953 -0.9165 -0.4366
-#>  0.0674  0.1296 -1.5398
-#> -0.7582 -1.0511 -0.3637
-#> [ CPUFloatType{3,3} ]
    A$det()
    #> torch_tensor 
-#> -0.779207
-#> [ CPUFloatType{} ]
    A$det()$log()
    #> torch_tensor 
-#> nan
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    # }
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-dtype. If the input tensor is not a bool tensor, zeros are treated as False and non-zeros are treated as True.
    
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    Examples
    
-    
    # \dontrun{
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-torch_logical_not(torch_tensor(c(TRUE, FALSE)))
    #> torch_tensor 
-#>  0
-#>  1
-#> [ CPUBoolType{2} ]
    torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8()))
    #> torch_tensor 
-#>  1
-#>  0
-#>  0
-#> [ CPUBoolType{3} ]
    torch_logical_not(torch_tensor(c(0., 1.5, -10.), dtype=torch_double()))
    #> torch_tensor 
-#>  1
-#>  0
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-#> [ CPUBoolType{3} ]
    # }
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-treated as True.
    
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    # \dontrun{
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-torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
    #> torch_tensor 
-#>  0
-#>  0
-#>  1
-#> [ CPUBoolType{3} ]
    a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
-b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
-torch_logical_xor(a, b)
    #> torch_tensor 
-#>  1
-#>  1
-#>  0
-#>  0
-#> [ CPUBoolType{4} ]
    torch_logical_xor(a$to(dtype=torch_double()), b$to(dtype=torch_double()))
    #> torch_tensor 
-#>  1
-#>  1
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    torch_logical_xor(a$to(dtype=torch_double()), b)
    #> torch_tensor 
-#>  1
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diff --git a/docs/reference/torch_logspace.html b/docs/reference/torch_logspace.html
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    (int) number of points to sample between start        and end. Default: 100.
    base
    (float) base of the logarithm function. Default: 10.0.
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-
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-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_logspace(start=-10, end=10, steps=5)
    #> torch_tensor 
-#>  1.0000e-10
-#>  1.0000e-05
-#>  1.0000e+00
-#>  1.0000e+05
-#>  1.0000e+10
-#> [ CPUFloatType{5} ]
    torch_logspace(start=0.1, end=1.0, steps=5)
    #> torch_tensor 
-#>  1.2589
-#>  2.1135
-#>  3.5481
-#>  5.9566
-#> 10.0000
-#> [ CPUFloatType{5} ]
    torch_logspace(start=0.1, end=1.0, steps=1)
    #> torch_tensor 
-#>  1.2589
-#> [ CPUFloatType{1} ]
    torch_logspace(start=2, end=2, steps=1, base=2)
    #> torch_tensor 
-#>  4
-#> [ CPUFloatType{1} ]
    # }
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diff --git a/docs/reference/torch_logsumexp.html b/docs/reference/torch_logsumexp.html
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    (int or tuple of ints) the dimension or dimensions to reduce.
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    (bool) whether the output tensor has dim retained or not.
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    (Tensor, optional) the output tensor.
    
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-tensor in the given dimension dim. The computation is numerically
-stabilized.
    
-
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-
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-        \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
-$$
    
-
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-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-torch_logsumexp(a, 1)
    #> torch_tensor 
-#>  1.9705
-#>  0.9976
-#>  1.5809
-#> [ CPUFloatType{3} ]
    # }
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diff --git a/docs/reference/torch_lstsq.html b/docs/reference/torch_lstsq.html
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    (Tensor) the matrix \(B\)
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    out
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-rank matrix \(A\) of size \((m \times n)\) and a matrix \(B\) of
-size \((m \times k)\).
    
-
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-
    $$
-   \begin{array}{ll}
-   \min_X & \|AX-B\|_2.
-   \end{array}
-$$
-If \(m < n\), torch_lstsq() solves the least-norm problem:
    
-
    $$
-   \begin{array}{llll}
-   \min_X & \|X\|_2 & \mbox{subject to} & AX = B.
-   \end{array}
-$$
-Returned tensor \(X\) has shape \((\mbox{max}(m, n) \times k)\). The first \(n\)
-rows of \(X\) contains the solution. If \(m \geq n\), the residual sum of squares
-for the solution in each column is given by the sum of squares of elements in the
-remaining \(m - n\) rows of that column.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-A = torch_tensor(rbind(
- c(1,1,1),
- c(2,3,4),
- c(3,5,2),
- c(4,2,5),
- c(5,4,3)
-))
-B = torch_tensor(rbind(
- c(-10, -3),
- c(12, 14),
- c(14, 12),
- c(16, 16),
- c(18, 16)
-))
-out = torch_lstsq(B, A)
-out[[1]]
    #> torch_tensor 
-#>   2.0000   1.0000
-#>   1.0000   1.0000
-#>   1.0000   2.0000
-#>  10.9635   4.8501
-#>   8.9332   5.2418
-#> [ CPUFloatType{5,2} ]
    # }
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diff --git a/docs/reference/torch_lt.html b/docs/reference/torch_lt.html
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    (Tensor or float) the tensor or value to compare
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-    
    # \dontrun{
-
-torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  0  0
-#>  1  0
-#> [ CPUBoolType{2,2} ]
    # }
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diff --git a/docs/reference/torch_lu.html b/docs/reference/torch_lu.html
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-
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-    
    Computes the LU factorization of a matrix or batches of matrices A. Returns a
-tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True.
    
-    
    
-
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    torch_lu(A, pivot = TRUE, get_infos = FALSE, out = NULL)
    
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    (Tensor) the tensor to factor of size (, m, n)(,m,n)
    pivot
    (bool, optional) – controls whether pivoting is done. Default: TRUE
    get_infos
    (bool, optional) – if set to True, returns an info IntTensor. Default: FALSE
    out
    (tuple, optional) – optional output tuple. If get_infos is True, then the elements
-in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
-elements in the tuple are Tensor, IntTensor. Default: NULL
    
-
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    Examples
    
-    
    # \dontrun{
-
-A = torch_randn(c(2, 3, 3))
-torch_lu(A)
    #> [[1]]
-#> torch_tensor 
-#> (1,.,.) = 
-#>   1.5274 -0.8578 -1.5278
-#>  -0.5446  0.3682 -0.1468
-#>   0.6892  0.4103  1.5956
-#> 
-#> (2,.,.) = 
-#>  -1.3121 -0.1896 -2.3469
-#>  -0.7969  1.0315 -0.9561
-#>   0.7149 -0.0635  1.4525
-#> [ CPUFloatType{2,3,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3  2  3
-#>  3  2  3
-#> [ CPUIntType{2,3} ]
-#> 
    
-# }
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diff --git a/docs/reference/torch_lu_solve.html b/docs/reference/torch_lu_solve.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    b
    (Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.
    LU_data
    (Tensor) the pivoted LU factorization of A from torch_lu of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.
    LU_pivots
    (IntTensor) the pivots of the LU factorization from torch_lu of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of LU_pivots must be equal to the batch dimensions of                           LU_data.
    out
    (Tensor, optional) the output tensor.
    
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-
-    
-
-
-
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-LU factorization of A from torch_lu.
    
-
-    
    Examples
    
-    
    # \dontrun{
-A = torch_randn(c(2, 3, 3))
-b = torch_randn(c(2, 3, 1))
-out = torch_lu(A)
-x = torch_lu_solve(b, out[[1]], out[[2]])
-torch_norm(torch_bmm(A, x) - b)
    #> torch_tensor 
-#> 7.02886e-08
-#> [ CPUFloatType{} ]
    # }
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diff --git a/docs/reference/torch_masked_select.html b/docs/reference/torch_masked_select.html
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    input
    (Tensor) the input tensor.
    mask
    (BoolTensor) the tensor containing the binary mask to index with
    out
    (Tensor, optional) the output tensor.
    
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-as the original tensor
    
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    masked_select(input, mask, out=None) -> Tensor 
    
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-the boolean mask mask which is a BoolTensor.
    
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-to match, but they must be broadcastable .
    
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-    
    # \dontrun{
-
-x = torch_randn(c(3, 4))
-x
    #> torch_tensor 
-#>  0.1475 -0.7929 -0.3681  1.0487
-#> -1.1304  1.8525 -0.8021  0.2346
-#>  1.8222  0.7533  0.4753  0.3604
-#> [ CPUFloatType{3,4} ]
    mask = x$ge(0.5)
-mask
    #> torch_tensor 
-#>  0  0  0  1
-#>  0  1  0  0
-#>  1  1  0  0
-#> [ CPUBoolType{3,4} ]
    torch_masked_select(x, mask)
    #> torch_tensor 
-#>  1.0487
-#>  1.8525
-#>  1.8222
-#>  0.7533
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_matmul.html b/docs/reference/torch_matmul.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Matmul
    
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    Arguments
    
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    input
    (Tensor) the first tensor to be multiplied
    other
    (Tensor) the second tensor to be multiplied
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
-
    The 1-dimensional dot product version of this function does not support an `out` parameter.
-
    
-
-    
    matmul(input, other, out=None) -> Tensor 
    
-
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-
-
-
    Matrix product of two tensors.
    
-
    The behavior depends on the dimensionality of the tensors as follows:
      
-
    • If both tensors are 1-dimensional, the dot product (scalar) is returned.
      • 
-
    • If both arguments are 2-dimensional, the matrix-matrix product is returned.
      • 
-
    • If the first argument is 1-dimensional and the second argument is 2-dimensional,
-a 1 is prepended to its dimension for the purpose of the matrix multiply.
-After the matrix multiply, the prepended dimension is removed.
      • 
-
    • If the first argument is 2-dimensional and the second argument is 1-dimensional,
-the matrix-vector product is returned.
      • 
-
    • If both arguments are at least 1-dimensional and at least one argument is
-N-dimensional (where N > 2), then a batched matrix multiply is returned.  If the first
-argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
-batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
-1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
-The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
-must be broadcastable).  For example, if input is a
-\((j \times 1 \times n \times m)\) tensor and other is a \((k \times m \times p)\)
-tensor, out will be an \((j \times k \times n \times p)\) tensor.
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-# vector x vector
-tensor1 = torch_randn(c(3))
-tensor2 = torch_randn(c(3))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> -2.01064
-#> [ CPUFloatType{} ]
    # matrix x vector
-tensor1 = torch_randn(c(3, 4))
-tensor2 = torch_randn(c(4))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#>  0.8498
-#> -0.1725
-#> -0.2413
-#> [ CPUFloatType{3} ]
    # batched matrix x broadcasted vector
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(4))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> -0.3051  0.8335  0.7817
-#> -1.7290 -0.1315 -1.0013
-#> -2.2799 -2.0186 -1.7212
-#> -2.0462  2.6530  2.2152
-#>  1.0726 -1.8564 -0.0805
-#>  2.5083  0.1464 -1.9015
-#>  0.1432  1.3745  0.8548
-#> -0.5524  0.7222  0.1316
-#> -1.5975  0.3861 -2.5685
-#> -3.1016  2.3700  0.1975
-#> [ CPUFloatType{10,3} ]
    # batched matrix x batched matrix
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(10, 4, 5))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   2.7383  3.7776  1.1984 -0.0462  5.9024
-#>   0.1247 -2.9988  0.8401 -1.2394 -2.9948
-#>   3.0933  3.5992  3.5134 -3.3567  6.0899
-#> 
-#> (2,.,.) = 
-#>  -0.9786 -0.3967 -0.3440  0.3824 -1.2635
-#>   0.4944  0.7149  0.5734 -1.1592  0.6260
-#>   1.3268  0.8352  1.9060 -3.7034  0.1600
-#> 
-#> (3,.,.) = 
-#>   0.4321  0.9564  1.6369 -0.1843 -0.3877
-#>  -1.4475 -2.3336 -2.1501 -1.6377  3.0016
-#>  -1.9856  0.2134  1.8436  1.6812  1.5892
-#> 
-#> (4,.,.) = 
-#>   0.7306 -0.1213  0.6505 -2.7818 -0.1171
-#>  -1.4915  3.2909  0.5003  2.5146  1.3735
-#>   0.0837 -0.1117  0.8951  3.6226  1.1665
-#> 
-#> (5,.,.) = 
-#>  -1.9671  0.1609  2.2518  0.7587  2.6455
-#>  -0.5965 -1.3687 -2.4585 -1.6623 -3.6542
-#>   0.0899  1.6690  0.2659 -2.6994  2.8543
-#> 
-#> (6,.,.) = 
-#>  -4.5552  0.7339 -2.3152 -5.9254 -1.6420
-#>  -0.8628  0.0829 -0.6564  0.9928 -0.0438
-#>  -4.1770  0.7927 -2.3129 -4.7196 -0.5457
-#> 
-#> (7,.,.) = 
-#>   1.3550 -0.9541 -1.7768  2.5931  1.6567
-#>   4.4144  1.4763  1.1692 -2.8514 -1.1695
-#>   4.2236  0.0365 -0.3523  1.0364  0.0855
-#> 
-#> (8,.,.) = 
-#>  -3.3315  1.4609 -1.3792 -2.9510 -0.7443
-#>   1.3569 -2.2547  0.7054 -2.1291  5.2801
-#>  -0.0102  1.3483 -0.3866  2.5474 -3.7250
-#> 
-#> (9,.,.) = 
-#>   0.8332  2.8945 -1.7215  1.0412  1.7159
-#>  -1.0112 -5.9806  1.3106  3.5058 -2.5003
-#>   0.3979  0.5793 -0.9296 -2.5669 -3.1548
-#> 
-#> (10,.,.) = 
-#>   1.3155 -0.3605  0.2988 -0.0092 -0.7717
-#>   0.6723  1.2307 -1.8691  0.1663 -1.7657
-#>  -1.2338  0.4575  0.0627  1.7217  2.0104
-#> [ CPUFloatType{10,3,5} ]
    # batched matrix x broadcasted matrix
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(4, 5))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -5.1244  2.2971 -1.6275  2.7803 -4.4830
-#>   1.8873 -3.6560  1.6412  0.0507  0.1216
-#>  -0.5433  1.9666 -1.4803 -0.5111  0.0638
-#> 
-#> (2,.,.) = 
-#>   0.0601 -4.0015  2.7077  1.7992 -1.7779
-#>  -1.6433  1.4184 -1.3365  0.5739 -1.9782
-#>  -3.4216  6.9532 -3.3050 -0.2836  0.0183
-#> 
-#> (3,.,.) = 
-#>  -1.8677 -0.3711  0.3040  1.6625 -2.7921
-#>   3.2992 -0.5994 -1.6079 -2.2177 -1.6740
-#>  -3.7891  4.2637  2.9312  2.2233  0.8338
-#> 
-#> (4,.,.) = 
-#>  -2.1277  3.2238  0.0312  0.5080  1.3242
-#>   2.2424 -0.8814  0.2856 -1.2700  1.1283
-#>  -1.0811  2.6808 -0.7240 -0.2231  1.0720
-#> 
-#> (5,.,.) = 
-#>  -3.4333  2.0806 -0.9037  1.4697 -0.1231
-#>   2.3334 -3.4847  1.1505 -0.3664 -0.2367
-#>   1.2297 -2.4053  2.6418  0.6376 -1.1939
-#> 
-#> (6,.,.) = 
-#>  -2.2066  0.7770  1.6664  1.7391 -0.3200
-#>  -2.7109 -1.6160 -1.9334  1.4483  0.3787
-#>  -1.2449 -0.9002 -1.0765  0.8913 -1.9713
-#> 
-#> (7,.,.) = 
-#>   0.2789 -1.0134 -0.5889  0.0808 -1.8249
-#>   0.6334 -0.8853  2.2413  0.6094 -1.5061
-#>  -0.1358  0.5410  0.6620  0.1202  0.3539
-#> 
-#> (8,.,.) = 
-#>   0.5331  0.8902  1.7452 -0.4616  3.8193
-#>   0.4330  2.0916 -0.6593 -0.9295  0.1894
-#>   0.8105 -0.3216  0.7608 -0.4534  2.0104
-#> 
-#> (9,.,.) = 
-#>   2.2947  1.8882  2.8289 -1.2676  2.0636
-#>  -0.6974 -2.0088 -1.8360  0.1831  1.3406
-#>   4.7011 -3.7311  0.0230 -1.9564 -1.7259
-#> 
-#> (10,.,.) = 
-#>   2.7739 -3.8386 -1.8082 -1.4422 -0.0495
-#>   1.3869 -3.8928  0.8670  0.1969  0.0855
-#>  -3.0783  1.1996 -2.8824  0.9550 -0.9141
-#> [ CPUFloatType{10,3,5} ]
    # }
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diff --git a/docs/reference/torch_matrix_power.html b/docs/reference/torch_matrix_power.html
deleted file mode 100644
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-Matrix_power — torch_matrix_power • torch
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    Matrix_power
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Matrix_power
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the input tensor.
    n
    (int) the power to raise the matrix to
    
-
-    
    matrix_power(input, n) -> Tensor 
    
-
-    
-
-
-
    Returns the matrix raised to the power n for square matrices.
-For batch of matrices, each individual matrix is raised to the power n.
    
-
    If n is negative, then the inverse of the matrix (if invertible) is
-raised to the power n.  For a batch of matrices, the batched inverse
-(if invertible) is raised to the power n. If n is 0, then an identity matrix
-is returned.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(2, 2, 2))
-a
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.0871  0.0197
-#>   0.3185  0.4297
-#> 
-#> (2,.,.) = 
-#>  -0.7818 -0.6402
-#>   0.2400 -0.4367
-#> [ CPUFloatType{2,2,2} ]
    torch_matrix_power(a, 3)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  0.01 *
-#>   0.4446  0.4644
-#>    7.5134  8.5264
-#> 
-#> (2,.,.) = 
-#>  -0.1705 -0.6335
-#>   0.2375  0.1711
-#> [ CPUFloatType{2,2,2} ]
    # }
-
    
-  
    
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diff --git a/docs/reference/torch_matrix_rank.html b/docs/reference/torch_matrix_rank.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Matrix_rank
    
-    
    
-
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    Arguments
    
-    
-    
-    
-      
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-    
-      
-      
-    
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    input
    (Tensor) the input 2-D tensor
    tol
    (float, optional) the tolerance value. Default: None
    symmetric
    (bool, optional) indicates whether input is symmetric.                               Default: False
    
-
-    
    matrix_rank(input, tol=None, symmetric=False) -> Tensor 
    
-
-    
-
-
-
    Returns the numerical rank of a 2-D tensor. The method to compute the
-matrix rank is done using SVD by default. If symmetric is True,
-then input is assumed to be symmetric, and the computation of the
-rank is done by obtaining the eigenvalues.
    
-
    tol is the threshold below which the singular values (or the eigenvalues
-when symmetric is True) are considered to be 0. If tol is not
-specified, tol is set to S.max() * max(S.size()) * eps where S is the
-singular values (or the eigenvalues when symmetric is True), and eps
-is the epsilon value for the datatype of input.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_eye(10)
-torch_matrix_rank(a)
    #> torch_tensor 
-#> 10
-#> [ CPULongType{} ]
    # }
-
    
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diff --git a/docs/reference/torch_max.html b/docs/reference/torch_max.html
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    keepdim
    (bool) whether the output tensor has dim retained or not. Default: False.
    out
    (tuple, optional) the result tuple of two output tensors (max, max_indices)
    other
    (Tensor) the second input tensor
    
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-follows the broadcasting rules .
    
-    
    max(input) -> Tensor 
    
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-
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-value of each row of the input tensor in the given dimension
-dim. And indices is the index location of each maximum value found
-(argmax).
    
-    
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-
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-
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-The exact implementation details are device-specific.
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-
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-as input except in the dimension dim where they are of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting
-in the output tensors having 1 fewer dimension than input.
    
-    
    max(input, other, out=None) -> Tensor 
    
-
-    
-
-
-
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-element of the tensor other and an element-wise maximum is taken.
    
-
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-but they must be broadcastable .
    
-
    $$
-    \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -1.7404  0.4095  0.0815
-#> [ CPUFloatType{1,3} ]
    torch_max(a)
    #> torch_tensor 
-#> 0.409483
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#> -0.4374  0.4921  0.5690 -0.5727
-#> -0.7344  1.4273  0.3648  1.0731
-#>  0.4967  0.2687  0.5694 -0.4233
-#>  0.2623  0.0037 -1.2246 -0.3742
-#> [ CPUFloatType{4,4} ]
    torch_max(a, dim = 1)
    #> [[1]]
-#> torch_tensor 
-#>  0.4967
-#>  1.4273
-#>  0.5694
-#>  1.0731
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3
-#>  2
-#>  3
-#>  2
-#> [ CPULongType{4} ]
-#> 
    
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.0640
-#> -0.2356
-#> -0.5395
-#>  1.8484
-#> [ CPUFloatType{4} ]
    b = torch_randn(c(4))
-b
    #> torch_tensor 
-#>  0.4095
-#> -0.6778
-#> -0.0908
-#> -0.2021
-#> [ CPUFloatType{4} ]
    torch_max(a, other = b)
    #> torch_tensor 
-#>  0.4095
-#> -0.2356
-#> -0.0908
-#>  1.8484
-#> [ CPUFloatType{4} ]
    # }
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-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.0395  1.7826  1.2161
-#> [ CPUFloatType{1,3} ]
    torch_mean(a)
    #> torch_tensor 
-#> 0.986383
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.0216 -1.8383 -0.8037 -1.2803
-#>  1.6988 -1.2344 -0.5559  0.7407
-#> -1.5668  0.8250 -0.0814  0.6922
-#> -0.7400 -0.0428  0.7179 -0.2121
-#> [ CPUFloatType{4,4} ]
    torch_mean(a, 1)
    #> torch_tensor 
-#> -0.1466
-#> -0.5726
-#> -0.1808
-#> -0.0149
-#> [ CPUFloatType{4} ]
    torch_mean(a, 1, TRUE)
    #> torch_tensor 
-#> -0.1466 -0.5726 -0.1808 -0.0149
-#> [ CPUFloatType{1,4} ]
    # }
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diff --git a/docs/reference/torch_median.html b/docs/reference/torch_median.html
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    (Tensor) the input tensor.
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-value of each row of the input tensor in the given dimension
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-
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-as input except in the dimension dim where they are of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in
-the outputs tensor having 1 fewer dimension than input.
    
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -1.1294  0.8996  0.0937
-#> [ CPUFloatType{1,3} ]
    torch_median(a)
    #> torch_tensor 
-#> 0.0937234
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 5))
-a
    #> torch_tensor 
-#>  0.2865 -0.8226  0.6805  0.3636 -0.6890
-#>  0.8853  0.3427  0.8220 -2.2562 -1.8976
-#> -1.3180  0.3580  1.1346 -0.5496 -0.2493
-#> -1.1359  0.0354 -0.3702 -0.0126  1.0450
-#> [ CPUFloatType{4,5} ]
    torch_median(a, 1)
    #> [[1]]
-#> torch_tensor 
-#> -1.1359
-#>  0.0354
-#>  0.6805
-#> -0.5496
-#> -0.6890
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3
-#>  3
-#>  0
-#>  2
-#>  0
-#> [ CPULongType{5} ]
-#> 
    # }
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-y = torch_tensor(c(4, 5, 6))
-out = torch_meshgrid(list(x, y))
-out
    #> [[1]]
-#> torch_tensor 
-#>  1  1  1
-#>  2  2  2
-#>  3  3  3
-#> [ CPUFloatType{3,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  4  5  6
-#>  4  5  6
-#>  4  5  6
-#> [ CPUFloatType{3,3} ]
-#> 
    # }
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diff --git a/docs/reference/torch_min.html b/docs/reference/torch_min.html
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-value of each row of the input tensor in the given dimension
-dim. And indices is the index location of each minimum value found
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-minimal value found, unless it is unique.
-The exact implementation details are device-specific.
-Do not expect the same result when run on CPU and GPU in general.
    
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-input except in the dimension dim where they are of size 1.
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-element of the tensor other and an element-wise minimum is taken.
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-but they must be broadcastable .
    
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-$$
    
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    # \dontrun{
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-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -1.0189  1.0439  1.3884
-#> [ CPUFloatType{1,3} ]
    torch_min(a)
    #> torch_tensor 
-#> -1.01891
-#> [ CPUFloatType{} ]
    
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-a
    #> torch_tensor 
-#>  0.9733  2.4571  1.7912 -1.4290
-#>  0.5607 -0.5847 -0.4779 -0.7823
-#> -0.7391  0.6672 -0.9647  0.1703
-#> -0.5473 -0.2047 -0.1148  1.4254
-#> [ CPUFloatType{4,4} ]
    torch_min(a, dim = 1)
    #> [[1]]
-#> torch_tensor 
-#> -0.7391
-#> -0.5847
-#> -0.9647
-#> -1.4290
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3
-#>  2
-#>  3
-#>  1
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-#> 
    
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-a
    #> torch_tensor 
-#>  0.2877
-#> -1.1317
-#> -1.0846
-#>  0.0735
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    b = torch_randn(c(4))
-b
    #> torch_tensor 
-#> -1.2118
-#> -0.7290
-#> -0.8948
-#>  0.5896
-#> [ CPUFloatType{4} ]
    torch_min(a, other = b)
    #> torch_tensor 
-#> -1.2118
-#> -1.1317
-#> -1.0846
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    # }
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    # \dontrun{
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-mat1 = torch_randn(c(2, 3))
-mat2 = torch_randn(c(3, 3))
-torch_mm(mat1, mat2)
    #> torch_tensor 
-#> -4.2557 -0.6099 -0.8782
-#> -3.0933  0.3112 -0.9391
-#> [ CPUFloatType{2,3} ]
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    #> torch_tensor 
-#>  18
-#>   0
-#>   7
-#>   4
-#>  19
-#> [ CPUFloatType{5} ]
    torch_mode(a, 1)
    #> [[1]]
-#> torch_tensor 
-#> 0
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 1
-#> [ CPULongType{} ]
-#> 
    # }
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diff --git a/docs/reference/torch_mul.html b/docs/reference/torch_mul.html
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    Mul
    
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    input
    NA
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    (Number) the number to be multiplied to each element of input
    out
    NA
    input
    (Tensor) the first multiplicand tensor
    other
    (Tensor) the second multiplicand tensor
    out
    (Tensor, optional) the output tensor.
    
-
-    
    mul(input, other, out=None) 
    
-
-    
-
-
-
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-other and returns a new resulting tensor.
    
-
    $$
-    \mbox{out}_i = \mbox{other} \times \mbox{input}_i
-$$
-If input is of type FloatTensor or DoubleTensor, other
-should be a real number, otherwise it should be an integer
    
-
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-
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-element of the Tensor other. The resulting tensor is returned.
    
-
    The shapes of input and other must be
-broadcastable .
    
-
    $$
-    \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3))
-a
    #> torch_tensor 
-#>  0.7353
-#>  0.3087
-#>  0.8232
-#> [ CPUFloatType{3} ]
    torch_mul(a, 100)
    #> torch_tensor 
-#>  73.5282
-#>  30.8688
-#>  82.3200
-#> [ CPUFloatType{3} ]
    
-
-a = torch_randn(c(4, 1))
-a
    #> torch_tensor 
-#>  0.1683
-#>  0.6845
-#>  1.4773
-#>  1.1179
-#> [ CPUFloatType{4,1} ]
    b = torch_randn(c(1, 4))
-b
    #> torch_tensor 
-#> -1.4203  0.6324 -0.8087 -0.5061
-#> [ CPUFloatType{1,4} ]
    torch_mul(a, b)
    #> torch_tensor 
-#> -0.2390  0.1064 -0.1361 -0.0852
-#> -0.9722  0.4329 -0.5535 -0.3464
-#> -2.0981  0.9343 -1.1946 -0.7476
-#> -1.5877  0.7070 -0.9040 -0.5657
-#> [ CPUFloatType{4,4} ]
    # }
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diff --git a/docs/reference/torch_multinomial.html b/docs/reference/torch_multinomial.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input tensor containing probabilities
    num_samples
    (int) number of samples to draw
    replacement
    (bool, optional) whether to draw with replacement or not
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
-
    The rows of `input` do not need to sum to one (in which case we use
-the values as weights), but must be non-negative, finite and have
-a non-zero sum.
-
    
-
-
    Indices are ordered from left to right according to when each was sampled
-(first samples are placed in first column).
    
-
    If input is a vector, out is a vector of size num_samples.
    
-
    If input is a matrix with m rows, out is an matrix of shape
-\((m \times \mbox{num\_samples})\).
    
-
    If replacement is True, samples are drawn with replacement.
    
-
    If not, they are drawn without replacement, which means that when a
-sample index is drawn for a row, it cannot be drawn again for that row.
    
-
    When drawn without replacement, `num_samples` must be lower than
-number of non-zero elements in `input` (or the min number of non-zero
-elements in each row of `input` if it is a matrix).
-
    
-
-    
    multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor 
    
-
-    
-
-
-
    Returns a tensor where each row contains num_samples indices sampled
-from the multinomial probability distribution located in the corresponding row
-of tensor input.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights
-torch_multinomial(weights, 2)
    #> torch_tensor 
-#>  1
-#>  2
-#> [ CPULongType{2} ]
    torch_multinomial(weights, 4, replacement=TRUE)
    #> torch_tensor 
-#>  1
-#>  2
-#>  1
-#>  2
-#> [ CPULongType{4} ]
    # }
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diff --git a/docs/reference/torch_mv.html b/docs/reference/torch_mv.html
deleted file mode 100644
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-Mv — torch_mv • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Mv
    
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    input
    (Tensor) matrix to be multiplied
    vec
    (Tensor) vector to be multiplied
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    (Tensor, optional) the output tensor.
    
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    Note
    
-
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    This function does not broadcast .
    
-    
    mv(input, vec, out=None) -> Tensor 
    
-
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    Performs a matrix-vector product of the matrix input and the vector
-vec.
    
-
    If input is a \((n \times m)\) tensor, vec is a 1-D tensor of
-size \(m\), out will be 1-D of size \(n\).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-mat = torch_randn(c(2, 3))
-vec = torch_randn(c(3))
-torch_mv(mat, vec)
    #> torch_tensor 
-#> -0.9277
-#>  1.8568
-#> [ CPUFloatType{2} ]
    # }
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diff --git a/docs/reference/torch_mvlgamma.html b/docs/reference/torch_mvlgamma.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the tensor to compute the multivariate log-gamma function
    p
    (int) the number of dimensions
    
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    mvlgamma(input, p) -> Tensor 
    
-
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-
    Computes the multivariate log-gamma function <https://en.wikipedia.org/wiki/Multivariate_gamma_function>_) with dimension
-\(p\) element-wise, given by
    
-
    $$
-    \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
-$$
-where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the Gamma function.
    
-
    All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_empty(c(2, 3))$uniform_(1, 2)
-a
    #> torch_tensor 
-#>  1.2019  1.8425  1.1256
-#>  1.9082  1.8734  1.5464
-#> [ CPUFloatType{2,3} ]
    torch_mvlgamma(a, 2)
    #> torch_tensor 
-#>  0.7450  0.3997  0.8720
-#>  0.4162  0.4065  0.4292
-#> [ CPUFloatType{2,3} ]
    # }
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diff --git a/docs/reference/torch_narrow.html b/docs/reference/torch_narrow.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Narrow
    
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    Arguments
    
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    (Tensor) the tensor to narrow
    dim
    (int) the dimension along which to narrow
    start
    (int) the starting dimension
    length
    (int) the distance to the ending dimension
    
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    narrow(input, dim, start, length) -> Tensor 
    
-
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-
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-dimension dim is input from start to start + length. The
-returned tensor and input tensor share the same underlying storage.
    
-
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    Examples
    
-    
    # \dontrun{
-
-x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE))
-torch_narrow(x, 1, torch_tensor(0L)$sum(dim = 1), 2)
    #> torch_tensor 
-#>  1  2  3
-#>  4  5  6
-#> [ CPUIntType{2,3} ]
    torch_narrow(x, 2, torch_tensor(1L)$sum(dim = 1), 2)
    #> torch_tensor 
-#>  2  3
-#>  5  6
-#>  8  9
-#> [ CPUIntType{3,2} ]
    # }
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diff --git a/docs/reference/torch_ne.html b/docs/reference/torch_ne.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Ne
    
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    input
    (Tensor) the tensor to compare
    other
    (Tensor or float) the tensor or value to compare
    out
    (Tensor, optional) the output tensor that must be a BoolTensor
    
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    ne(input, other, out=None) -> Tensor 
    
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    Computes \(input \neq other\) element-wise.
    
-
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-broadcastable  with the first argument.
    
-
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    Examples
    
-    
    # \dontrun{
-
-torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  0  1
-#>  1  0
-#> [ CPUBoolType{2,2} ]
    # }
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diff --git a/docs/reference/torch_neg.html b/docs/reference/torch_neg.html
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    Neg
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Neg
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
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    neg(input, out=None) -> Tensor 
    
-
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    Returns a new tensor with the negative of the elements of input.
    
-
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-    \mbox{out} = -1 \times \mbox{input}
-$$
    
-
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(5))
-a
    #> torch_tensor 
-#>  0.3160
-#> -0.4731
-#>  0.1641
-#>  0.6355
-#>  0.2480
-#> [ CPUFloatType{5} ]
    torch_neg(a)
    #> torch_tensor 
-#> -0.3160
-#>  0.4731
-#> -0.1641
-#> -0.6355
-#> -0.2480
-#> [ CPUFloatType{5} ]
    # }
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    (Tensor) the input tensor.
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    (LongTensor, optional) the output tensor containing indices
    
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    [`torch_nonzero(..., as_tuple=False) &lt;torch.nonzero&gt;`] (default) returns a
-2-D tensor where each row is the index for a nonzero value.
-
-[`torch_nonzero(..., as_tuple=True) &lt;torch.nonzero&gt;`] returns a tuple of 1-D
-index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]``
-gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor
-contains nonzero indices for a certain dimension.
-
-See below for more details on the two behaviors.
-
    
-
-    
    nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors 
    
-
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-
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-
    When as_tuple is False (default):
    
-
    Returns a tensor containing the indices of all non-zero elements of
-input.  Each row in the result contains the indices of a non-zero
-element in input. The result is sorted lexicographically, with
-the last index changing the fastest (C-style).
    
-
    If input has \(n\) dimensions, then the resulting indices tensor
-out is of size \((z \times n)\), where \(z\) is the total number of
-non-zero elements in the input tensor.
    
-
    When as_tuple is True:
    
-
    Returns a tuple of 1-D tensors, one for each dimension in input,
-each containing the indices (in that dimension) of all non-zero elements of
-input .
    
-
    If input has \(n\) dimensions, then the resulting tuple contains \(n\)
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-non-zero elements in the input tensor.
    
-
    As a special case, when input has zero dimensions and a nonzero scalar
-value, it is treated as a one-dimensional tensor with one element.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1)))
    #> torch_tensor 
-#>  0
-#>  1
-#>  2
-#>  4
-#> [ CPULongType{4,1} ]
    # }
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diff --git a/docs/reference/torch_norm.html b/docs/reference/torch_norm.html
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    input
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    (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: 'fro'        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        None   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is None  sum(abs(x)ord)(1./ord)        =====  ============================  ==========================
    dim
    (int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is None, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.
    keepdim
    (bool, optional) whether the output tensors have dim        retained or not. Ignored if dim = None and        out = None. Default: False
    out
    (Tensor, optional) the output tensor. Ignored if        dim = None and out = None.
    dtype
    (torch.dtype, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: None.
    
-
-    
    TEST 
    
-
-    
-
-
-
    Returns the matrix norm or vector norm of a given tensor.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_arange(0, 9, dtype = torch_float())
-b = a$reshape(list(3, 3))
-torch_norm(a)
    #> torch_tensor 
-#> 14.2829
-#> [ CPUFloatType{} ]
    torch_norm(b)
    #> torch_tensor 
-#> 14.2829
-#> [ CPUFloatType{} ]
    torch_norm(a, Inf)
    #> torch_tensor 
-#> 8
-#> [ CPUFloatType{} ]
    torch_norm(b, Inf)
    #> torch_tensor 
-#> 8
-#> [ CPUFloatType{} ]
    
-# }
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diff --git a/docs/reference/torch_normal.html b/docs/reference/torch_normal.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    mean
    (Tensor) the tensor of per-element means
    std
    (Tensor) the tensor of per-element standard deviations
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    out
    (Tensor, optional) the output tensor.
    size
    (int...) a sequence of integers defining the shape of the output tensor.
    
-
-    
    Note
    
-
-    
    When the shapes do not match, the shape of mean
-is used as the shape for the returned output tensor
    
-    
    normal(mean, std, *, generator=None, out=None) -> Tensor 
    
-
-    
-
-
-
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-whose mean and standard deviation are given.
    
-
    The mean is a tensor with the mean of
-each output element's normal distribution
    
-
    The std is a tensor with the standard deviation of
-each output element's normal distribution
    
-
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-total number of elements in each tensor need to be the same.
    
-    
    normal(mean=0.0, std, out=None) -> Tensor 
    
-
-    
-
-
-
    Similar to the function above, but the means are shared among all drawn
-elements.
    
-    
    normal(mean, std=1.0, out=None) -> Tensor 
    
-
-    
-
-
-
    Similar to the function above, but the standard-deviations are shared among
-all drawn elements.
    
-    
    normal(mean, std, size, *, out=None) -> Tensor 
    
-
-    
-
-
-
    Similar to the function above, but the means and standard deviations are shared
-among all drawn elements. The resulting tensor has size given by size.
    
-
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    Examples
    
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diff --git a/docs/reference/torch_ones.html b/docs/reference/torch_ones.html
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    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
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    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor filled with the scalar value 1, with the shape defined
-by the variable argument size.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_ones(c(2, 3))
    #> torch_tensor 
-#>  1  1  1
-#>  1  1  1
-#> [ CPUFloatType{2,3} ]
    torch_ones(c(5))
    #> torch_tensor 
-#>  1
-#>  1
-#>  1
-#>  1
-#>  1
-#> [ CPUFloatType{5} ]
    # }
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diff --git a/docs/reference/torch_ones_like.html b/docs/reference/torch_ones_like.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Ones_like
    
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    (Tensor) the size of input will determine size of the output tensor.
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    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
    layout
    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
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-
-    
-
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-torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
-    
    Warning
    
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-    
-
-
    As of 0.4, this function does not support an out keyword. As an alternative,
-the old torch_ones_like(input, out=output) is equivalent to
-torch_ones(input.size(), out=output).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-input = torch_empty(c(2, 3))
-torch_ones_like(input)
    #> torch_tensor 
-#>  1  1  1
-#>  1  1  1
-#> [ CPUFloatType{2,3} ]
    # }
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diff --git a/docs/reference/torch_orgqr.html b/docs/reference/torch_orgqr.html
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-
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-
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-tuple returned by torch_geqrf.
    
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diff --git a/docs/reference/torch_ormqr.html b/docs/reference/torch_ormqr.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the a from torch_geqrf.
    input2
    (Tensor) the tau from torch_geqrf.
    input3
    (Tensor) the matrix to be multiplied.
    
-
-    
    ormqr(input, input2, input3, left=True, transpose=False) -> Tensor 
    
-
-    
-
-
-
    Multiplies mat (given by input3) by the orthogonal Q matrix of the QR factorization
-formed by torch_geqrf that is represented by (a, tau) (given by (input, input2)).
    
-
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-See LAPACK documentation for ormqr_ for further details.
    
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diff --git a/docs/reference/torch_pdist.html b/docs/reference/torch_pdist.html
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    input
    NA input tensor of shape \(N \times M\).
    p
    NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).
    
-
-    
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-
-    
-
-
-
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-This is identical to the upper triangular portion, excluding the diagonal, of
-torch_norm(input[:, None] - input, dim=2, p=p). This function will be faster
-if the rows are contiguous.
    
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-\(\frac{1}{2} N (N - 1)\).
    
-
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-equivalent to scipy.spatial.distance.pdist(input, 'hamming') * M.
-When \(p = \infty\), the closest scipy function is
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    (float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15
    
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    This method is implemented using the Singular Value Decomposition.
-
    
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    The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
-Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
-However, this method is backprop-able due to the implementation by using SVD results, and
-could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
-See `~torch.svd` for more details.
-
    
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    pinverse(input, rcond=1e-15) -> Tensor 
    
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-Please look at Moore-Penrose inverse_ for more details
    
-
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    Examples
    
-    
    # \dontrun{
-
-input = torch_randn(c(3, 5))
-input
    #> torch_tensor 
-#>  0.0625  0.0470 -0.6356  1.0166 -0.2998
-#>  0.2736 -0.5027  2.6768  0.3714  0.5533
-#> -0.5951  1.2603  0.2886  0.6099 -1.3339
-#> [ CPUFloatType{3,5} ]
    torch_pinverse(input)
    #> torch_tensor 
-#>  0.1974  0.0598 -0.1754
-#> -0.2446 -0.0864  0.3409
-#> -0.1949  0.3106  0.1489
-#>  0.8721  0.2108 -0.0062
-#>  0.0375  0.0552 -0.3200
-#> [ CPUFloatType{5,3} ]
    # Batched pinverse example
-a = torch_randn(c(2,6,3))
-b = torch_pinverse(a)
-torch_matmul(b, a)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   1.0000e+00  5.2154e-08 -1.0431e-07
-#>   2.9802e-08  1.0000e+00  3.7253e-08
-#>   2.9802e-08 -6.7055e-08  1.0000e+00
-#> 
-#> (2,.,.) = 
-#>   1.0000e+00  2.3283e-08  1.9372e-07
-#>   2.9802e-08  1.0000e+00 -5.9605e-07
-#>   2.9802e-08  1.5087e-07  1.0000e+00
-#> [ CPUFloatType{2,3,3} ]
    # }
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    Pixel_shuffle
    
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    Arguments
    
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    (Tensor) the input tensor
    upscale_factor
    (int) factor to increase spatial resolution by
    
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    Rearranges elements in a tensor of shape 
    
-
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    math:(*, C \times r^2, H, W) to a :
    
-
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-tensor of shape \((*, C, H \times r, W \times r)\).
    
-
    See ~torch.nn.PixelShuffle for details.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-input = torch_randn(c(1, 9, 4, 4))
-output = nnf_pixel_shuffle(input, 3)
-print(output$size())
    #> [1]  1  1 12 12
    # }
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diff --git a/docs/reference/torch_poisson.html b/docs/reference/torch_poisson.html
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    input
    (Tensor) the input tensor containing the rates of the Poisson distribution
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    
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-sampled from a Poisson distribution with rate parameter given by the corresponding
-element in input i.e.,
    
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    Examples
    
-    
    # \dontrun{
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-rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5
-torch_poisson(rates)
    #> torch_tensor 
-#>  1  4  0  4
-#>  6  0  4  2
-#>  1  0  1  0
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-#> [ CPUFloatType{4,4} ]
    # }
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    n
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    input
    (Tensor) the input tensor.
    out
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-\(n \geq 0\) is called the order of the polygamma function.
    
-
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-    \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
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diff --git a/docs/reference/torch_pow.html b/docs/reference/torch_pow.html
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-
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-The returned tensor out is of the same shape as exponent
    
-
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-$$
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.6638
-#>  0.2351
-#> -0.1040
-#>  0.7775
-#> [ CPUFloatType{4} ]
    torch_pow(a, 2)
    #> torch_tensor 
-#>  0.4406
-#>  0.0553
-#>  0.0108
-#>  0.6045
-#> [ CPUFloatType{4} ]
    exp = torch_arange(1., 5.)
-a = torch_arange(1., 5.)
-a
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
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    exp
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
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    torch_pow(a, exp)
    #> torch_tensor 
-#>    1
-#>    4
-#>   27
-#>  256
-#> [ CPUFloatType{4} ]
    
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-exp = torch_arange(1., 5.)
-base = 2
-torch_pow(base, exp)
    #> torch_tensor 
-#>   2
-#>   4
-#>   8
-#>  16
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_prod.html b/docs/reference/torch_prod.html
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-Otherwise, dim is squeezed (see torch_squeeze), resulting in
-the output tensor having 1 fewer dimension than input.
    
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#>  0.0090  0.8878  1.0236
-#> [ CPUFloatType{1,3} ]
    torch_prod(a)
    #> torch_tensor 
-#> 0.00817587
-#> [ CPUFloatType{} ]
    
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-a = torch_randn(c(4, 2))
-a
    #> torch_tensor 
-#> -1.1330  0.8404
-#>  2.0557  0.2876
-#>  2.0148  1.2245
-#>  0.4052  0.2208
-#> [ CPUFloatType{4,2} ]
    torch_prod(a, 1)
    #> torch_tensor 
-#> -1.9012
-#>  0.0653
-#> [ CPUFloatType{2} ]
    # }
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-documentation  for more information on the type
-promotion logic.
    
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    Examples
    
-    
    # \dontrun{
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-torch_promote_types(torch_int32(), torch_float32())
    #> torch_Float
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    #> torch_Long
    # }
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diff --git a/docs/reference/torch_qr.html b/docs/reference/torch_qr.html
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    input
    (Tensor) the input tensor of size \((*, m, n)\) where * is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).
    some
    (bool, optional) Set to True for reduced QR decomposition and False for                complete QR decomposition.
    out
    (tuple, optional) tuple of Q and R tensors                satisfying input = torch.matmul(Q, R).                The dimensions of Q and R are \((*, m, k)\) and \((*, k, n)\)                respectively, where \(k = \min(m, n)\) if some: is True and                \(k = m\) otherwise.
    
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    Note
    
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    precision may be lost if the magnitudes of the elements of input
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    While it should always give you a valid decomposition, it may not
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-LAPACK implementation.
    
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    qr(input, some=True, out=None) -> (Tensor, Tensor) 
    
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    Computes the QR decomposition of a matrix or a batch of matrices input,
-and returns a namedtuple (Q, R) of tensors such that \(\mbox{input} = Q R\)
-with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
-\(R\) being an upper triangular matrix or batch of upper triangular matrices.
    
-
    If some is True, then this function returns the thin (reduced) QR factorization.
-Otherwise, if some is False, this function returns the complete QR factorization.
    
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    Examples
    
-    
    # \dontrun{
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-a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE))
-out = torch_qr(a)
-q = out[[1]]
-r = out[[2]]
-torch_mm(q, r)$round()
    #> torch_tensor 
-#>   12  -51    4
-#>    6  167  -68
-#>   -4   24  -41
-#> [ CPUFloatType{3,3} ]
    torch_mm(q$t(), q)$round()
    #> torch_tensor 
-#>  1  0  0
-#>  0  1 -0
-#>  0 -0  1
-#> [ CPUFloatType{3,3} ]
    # }
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diff --git a/docs/reference/torch_qscheme.html b/docs/reference/torch_qscheme.html
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    Creates the corresponding Scheme object
    
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    torch_per_channel_affine()
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diff --git a/docs/reference/torch_quantize_per_channel.html b/docs/reference/torch_quantize_per_channel.html
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-Quantize_per_channel — torch_quantize_per_channel • torch
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    input
    (Tensor) float tensor to quantize
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    (int) integer 1D tensor of offset to use, size should match input.size(axis)
    axis
    (int) dimension on which apply per-channel quantization
    dtype
    (torch.dtype) the desired data type of returned tensor.        Has to be one of the quantized dtypes: torch_quint8, torch.qint8, torch.qint32
    
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    Converts a float tensor to per-channel quantized tensor with given scales and zero points.
    
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    Examples
    
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    # \dontrun{
-x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE))
-torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),
-                           torch_tensor(c(10L, 0L)), 0, torch_quint8())
    #> torch_tensor 
-#> -1  0
-#>  1  2
-#> [ CPUFloatType{2,2} ]
    torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),
-                           torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr()
    #> torch_tensor 
-#>    0   10
-#>  100  200
-#> [ CPUByteType{2,2} ]
    # }
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diff --git a/docs/reference/torch_quantize_per_tensor.html b/docs/reference/torch_quantize_per_tensor.html
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-Quantize_per_tensor — torch_quantize_per_tensor • torch
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    (Tensor) float tensor to quantize
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    (float) scale to apply in quantization formula
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    (int) offset in integer value that maps to float zero
    dtype
    (torch.dtype) the desired data type of returned tensor.        Has to be one of the quantized dtypes: torch_quint8, torch.qint8, torch.qint32
    
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    Examples
    
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    # \dontrun{
-torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())
    #> torch_tensor 
-#> -1
-#>  0
-#>  1
-#>  2
-#> [ CPUFloatType{4} ]
    torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr()
    #> torch_tensor 
-#>   0
-#>  10
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-#> [ CPUByteType{4} ]
    # }
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diff --git a/docs/reference/torch_rand.html b/docs/reference/torch_rand.html
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    (int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
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    (Tensor, optional) the output tensor.
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    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
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    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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    Examples
    
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    # \dontrun{
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-torch_rand(4)
    #> torch_tensor 
-#>  0.8391
-#>  0.5766
-#>  0.5790
-#>  0.0523
-#> [ CPUFloatType{4} ]
    torch_rand(c(2, 3))
    #> torch_tensor 
-#>  0.9712  0.5669  0.1881
-#>  0.4962  0.3052  0.8577
-#> [ CPUFloatType{2,3} ]
    # }
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diff --git a/docs/reference/torch_rand_like.html b/docs/reference/torch_rand_like.html
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    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
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    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
    (torch.memory_format, optional) the desired memory format of        returned Tensor. Default: torch_preserve_format.
    
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-torch_rand_like(input) is equivalent to
-torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
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diff --git a/docs/reference/torch_randint.html b/docs/reference/torch_randint.html
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    (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
    high
    (int) One above the highest integer to be drawn from the distribution.
    size
    (tuple) a tuple defining the shape of the output tensor.
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
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    # \dontrun{
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    #> torch_tensor 
-#>  4
-#>  3
-#>  3
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    torch_randint(0, 10, size = list(2, 2))
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-#>  0  7
-#>  8  6
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    torch_randint(3, 10, list(2, 2))
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-#>  8  8
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diff --git a/docs/reference/torch_randint_like.html b/docs/reference/torch_randint_like.html
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    input
    (Tensor) the size of input will determine size of the output tensor.
    low
    (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
    high
    (int) One above the highest integer to be drawn from the distribution.
    dtype
    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
    layout
    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
    (torch.memory_format, optional) the desired memory format of        returned Tensor. Default: torch_preserve_format.
    
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-    
    randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, 
    
-
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-
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-
    Returns a tensor with the same shape as Tensor input filled with
-random integers generated uniformly between low (inclusive) and
-high (exclusive).
    
-
    .. note:
-With the global dtype default (torch_float32), this function returns
-a tensor with dtype torch_int64.
    
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diff --git a/docs/reference/torch_randn.html b/docs/reference/torch_randn.html
deleted file mode 100644
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    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor filled with random numbers from a normal distribution
-with mean 0 and variance 1 (also called the standard normal
-distribution).
    
-
    $$
-    \mbox{out}_{i} \sim \mathcal{N}(0, 1)
-$$
-The shape of the tensor is defined by the variable argument size.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_randn(c(4))
    #> torch_tensor 
-#> -0.5578
-#> -1.6968
-#> -0.0944
-#> -0.7900
-#> [ CPUFloatType{4} ]
    torch_randn(c(2, 3))
    #> torch_tensor 
-#> -2.1279  1.0919  2.2659
-#>  0.1722  0.1719 -1.1738
-#> [ CPUFloatType{2,3} ]
    # }
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diff --git a/docs/reference/torch_randn_like.html b/docs/reference/torch_randn_like.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the size of input will determine size of the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
    layout
    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
    (torch.memory_format, optional) the desired memory format of        returned Tensor. Default: torch_preserve_format.
    
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    randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor with the same size as input that is filled with
-random numbers from a normal distribution with mean 0 and variance 1.
-torch_randn_like(input) is equivalent to
-torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
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diff --git a/docs/reference/torch_randperm.html b/docs/reference/torch_randperm.html
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    layout
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    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-
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    Examples
    
-    
    # \dontrun{
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-torch_randperm(4)
    #> torch_tensor 
-#>  0
-#>  2
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-#> [ CPULongType{4} ]
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diff --git a/docs/reference/torch_range.html b/docs/reference/torch_range.html
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    (float) the starting value for the set of points. Default: 0.
    end
    (float) the ending value for the set of points
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    (float) the gap between each pair of adjacent points. Default: 1.
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    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). If dtype is not given, infer the data type from the other input        arguments. If any of start, end, or stop are floating-point, the        dtype is inferred to be the default dtype, see        ~torch.get_default_dtype. Otherwise, the dtype is inferred to        be torch.int64.
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
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    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-
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    Examples
    
-    
    # \dontrun{
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-torch_range(1, 4)
    #> Warning: This function is deprecated in favor of torch_arange.
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{3} ]
    torch_range(1, 4, 0.5)
    #> Warning: This function is deprecated in favor of torch_arange.
    #> torch_tensor 
-#>  1.0000
-#>  1.5000
-#>  2.0000
-#>  2.5000
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-#> [ CPUFloatType{6} ]
    # }
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diff --git a/docs/reference/torch_real.html b/docs/reference/torch_real.html
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diff --git a/docs/reference/torch_reciprocal.html b/docs/reference/torch_reciprocal.html
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-#> -0.6585
-#>  0.2569
-#>  1.4761
-#> -0.0839
-#> [ CPUFloatType{4} ]
    torch_reciprocal(a)
    #> torch_tensor 
-#> -1.5185
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diff --git a/docs/reference/torch_reduction.html b/docs/reference/torch_reduction.html
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-numbers. The remainder has the same sign as the divisor.
    
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-other must be broadcastable .
    
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    Examples
    
-    
    # \dontrun{
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-torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
    #> torch_tensor 
-#>  1
-#>  0
-#>  1
-#>  1
-#>  0
-#>  1
-#> [ CPUFloatType{6} ]
    torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
    #> torch_tensor 
-#>  1.0000
-#>  0.5000
-#>  0.0000
-#>  1.0000
-#>  0.5000
-#> [ CPUFloatType{5} ]
    # }
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diff --git a/docs/reference/torch_renorm.html b/docs/reference/torch_renorm.html
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    maxnorm
    (float) the maximum norm to keep each sub-tensor under
    out
    (Tensor, optional) the output tensor.
    
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-
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-
-
-
    Returns a tensor where each sub-tensor of input along dimension
-dim is normalized such that the p-norm of the sub-tensor is lower
-than the value maxnorm
    
-
-    
    Examples
    
-    
    # \dontrun{
-x = torch_ones(c(3, 3))
-x[2,]$fill_(2)
    #> torch_tensor 
-#>  2
-#>  2
-#>  2
-#> [ CPUFloatType{3} ]
    x[3,]$fill_(3)
    #> torch_tensor 
-#>  3
-#>  3
-#>  3
-#> [ CPUFloatType{3} ]
    x
    #> torch_tensor 
-#>  1  1  1
-#>  2  2  2
-#>  3  3  3
-#> [ CPUFloatType{3,3} ]
    torch_renorm(x, 1, 1, 5)
    #> torch_tensor 
-#>  1.0000  1.0000  1.0000
-#>  1.6667  1.6667  1.6667
-#>  1.6667  1.6667  1.6667
-#> [ CPUFloatType{3,3} ]
    # }
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diff --git a/docs/reference/torch_repeat_interleave.html b/docs/reference/torch_repeat_interleave.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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    Repeat_interleave
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    repeats
    (Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.
    dim
    (int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.
    
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    repeat_interleave(input, repeats, dim=None) -> Tensor 
    
-
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-
-
    Repeat elements of a tensor.
    
-    
    Warning
    
-
-    
-
    This is different from `torch_Tensor.repeat` but similar to ``numpy.repeat``.
-
    
-
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    repeat_interleave(repeats) -> Tensor 
    
-
-    
-
-
-
    If the repeats is tensor([n1, n2, n3, ...]), then the output will be
-tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...]) where 0 appears n1 times,
-1 appears n2 times, 2 appears n3 times, etc.
    
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    Examples
    
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diff --git a/docs/reference/torch_reshape.html b/docs/reference/torch_reshape.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Reshape
    
-    
    
-
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    Arguments
    
-    
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-      
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-      
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    input
    (Tensor) the tensor to be reshaped
    shape
    (tuple of ints) the new shape
    
-
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    reshape(input, shape) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor with the same data and number of elements as input,
-but with the specified shape. When possible, the returned tensor will be a view
-of input. Otherwise, it will be a copy. Contiguous inputs and inputs
-with compatible strides can be reshaped without copying, but you should not
-depend on the copying vs. viewing behavior.
    
-
    See torch_Tensor.view on when it is possible to return a view.
    
-
    A single dimension may be -1, in which case it's inferred from the remaining
-dimensions and the number of elements in input.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_arange(0, 4)
-torch_reshape(a, list(2, 2))
    #> torch_tensor 
-#>  0  1
-#>  2  3
-#> [ CPUFloatType{2,2} ]
    b = torch_tensor(matrix(c(0, 1, 2, 3), ncol = 2, byrow=TRUE))
-torch_reshape(b, list(-1))
    #> torch_tensor 
-#>  0
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_result_type.html b/docs/reference/torch_result_type.html
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    Result_type
    
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    Arguments
    
-    
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-      
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    tensor1
    (Tensor or Number) an input tensor or number
    tensor2
    (Tensor or Number) an input tensor or number
    
-
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-
-    
-
-
-
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-operation on the provided input tensors. See type promotion documentation
-for more information on the type promotion logic.
    
-
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    Examples
    
-    
    # \dontrun{
-
-torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
    #> torch_Float
    # torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))
-# }
-
    
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diff --git a/docs/reference/torch_rfft.html b/docs/reference/torch_rfft.html
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    (Tensor) the input tensor of at least signal_ndim dimensions
    signal_ndim
    (int) the number of dimensions in each signal.        signal_ndim can only be 1, 2 or 3
    normalized
    (bool, optional) controls whether to return normalized results.        Default: False
    onesided
    (bool, optional) controls whether to return half of results to        avoid redundancy. Default: True
    
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-
-    
-
    For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-
    
-
-    
    rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor 
    
-
-    
-
-
-
    Real-to-complex Discrete Fourier Transform
    
-
    This method computes the real-to-complex discrete Fourier transform. It is
-mathematically equivalent with torch_fft with differences only in
-formats of the input and output.
    
-
    This method supports 1D, 2D and 3D real-to-complex transforms, indicated
-by signal_ndim. input must be a tensor with at least
-signal_ndim dimensions with optionally arbitrary number of leading batch
-dimensions. If normalized is set to True, this normalizes the result
-by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
-unitary, where \(N_i\) is the size of signal dimension \(i\).
    
-
    The real-to-complex Fourier transform results follow conjugate symmetry:
    
-
    $$
-    X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
-$$
-where the index arithmetic is computed modulus the size of the corresponding
-dimension, \(\ ^*\) is the conjugate operator, and
-\(d\) = signal_ndim. onesided flag controls whether to avoid
-redundancy in the output results. If set to True (default), the output will
-not be full complex result of shape \((*, 2)\), where \(*\) is the shape
-of input, but instead the last dimension will be halfed as of size
-\(\lfloor \frac{N_d}{2} \rfloor + 1\).
    
-
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-    
    Warning
    
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-    
-
-
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-torch_backends.mkl.is_available to check if MKL is installed.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(5, 5))
-torch_rfft(x, 2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   4.9202  0.0000
-#>   2.9093  2.7036
-#>   1.8227 -4.8232
-#> 
-#> (2,.,.) = 
-#>  -2.0160  4.4730
-#>   1.8297 -3.0301
-#>   0.4639  0.6830
-#> 
-#> (3,.,.) = 
-#>  -6.8659  1.3907
-#>  -5.6917 -4.3527
-#>   2.7115  0.4562
-#> 
-#> (4,.,.) = 
-#>  -6.8659 -1.3907
-#>  -3.4394 -1.4155
-#>   4.7454 -1.6532
-#> 
-#> (5,.,.) = 
-#>  -2.0160 -4.4730
-#>  -1.0448  5.6595
-#>   5.7855 -3.2515
-#> [ CPUFloatType{5,3,2} ]
    torch_rfft(x, 2, onesided=FALSE)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   4.9202  0.0000
-#>   2.9093  2.7036
-#>   1.8227 -4.8232
-#>   1.8227  4.8232
-#>   2.9093 -2.7036
-#> 
-#> (2,.,.) = 
-#>  -2.0160  4.4730
-#>   1.8297 -3.0301
-#>   0.4639  0.6830
-#>   5.7855  3.2515
-#>  -1.0448 -5.6595
-#> 
-#> (3,.,.) = 
-#>  -6.8659  1.3907
-#>  -5.6917 -4.3527
-#>   2.7115  0.4562
-#>   4.7454  1.6532
-#>  -3.4394  1.4155
-#> 
-#> (4,.,.) = 
-#>  -6.8659 -1.3907
-#>  -3.4394 -1.4155
-#>   4.7454 -1.6532
-#>   2.7115 -0.4562
-#>  -5.6917  4.3527
-#> 
-#> (5,.,.) = 
-#>  -2.0160 -4.4730
-#>  -1.0448  5.6595
-#>   5.7855 -3.2515
-#>   0.4639 -0.6830
-#>   1.8297  3.0301
-#> [ CPUFloatType{5,5,2} ]
    # }
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diff --git a/docs/reference/torch_roll.html b/docs/reference/torch_roll.html
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    input
    (Tensor) the input tensor.
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    (int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value
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-
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-last position are re-introduced at the first position. If a dimension is not
-specified, the tensor will be flattened before rolling and then restored
-to the original shape.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2))
-x
    #> torch_tensor 
-#>  1  2
-#>  3  4
-#>  5  6
-#>  7  8
-#> [ CPUFloatType{4,2} ]
    torch_roll(x, 1, 1)
    #> torch_tensor 
-#>  7  8
-#>  1  2
-#>  3  4
-#>  5  6
-#> [ CPUFloatType{4,2} ]
    torch_roll(x, -1, 1)
    #> torch_tensor 
-#>  3  4
-#>  5  6
-#>  7  8
-#>  1  2
-#> [ CPUFloatType{4,2} ]
    torch_roll(x, shifts=list(2, 1), dims=list(1, 2))
    #> torch_tensor 
-#>  6  5
-#>  8  7
-#>  2  1
-#>  4  3
-#> [ CPUFloatType{4,2} ]
    # }
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-
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-Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_arange(0, 4)$view(c(2, 2))
-x
    #> torch_tensor 
-#>  0  1
-#>  2  3
-#> [ CPUFloatType{2,2} ]
    torch_rot90(x, 1, c(1, 2))
    #> torch_tensor 
-#>  1  3
-#>  0  2
-#> [ CPUFloatType{2,2} ]
    x = torch_arange(0, 8)$view(c(2, 2, 2))
-x
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0  1
-#>   2  3
-#> 
-#> (2,.,.) = 
-#>   4  5
-#>   6  7
-#> [ CPUFloatType{2,2,2} ]
    torch_rot90(x, 1, c(1, 2))
    #> torch_tensor 
-#> (1,.,.) = 
-#>   2  3
-#>   6  7
-#> 
-#> (2,.,.) = 
-#>   0  1
-#>   4  5
-#> [ CPUFloatType{2,2,2} ]
    # }
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-to the closest integer.
    
-
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.8911
-#> -0.2131
-#> -0.4903
-#> -0.0724
-#> [ CPUFloatType{4} ]
    torch_round(a)
    #> torch_tensor 
-#>  1
-#> -0
-#> -0
-#> -0
-#> [ CPUFloatType{4} ]
    # }
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-$$
    
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-    
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-a
    #> torch_tensor 
-#>  0.5826
-#> -0.3344
-#> -0.2779
-#> -1.1775
-#> [ CPUFloatType{4} ]
    torch_rsqrt(a)
    #> torch_tensor 
-#>  1.3101
-#>     nan
-#>     nan
-#>     nan
-#> [ CPUFloatType{4} ]
    # }
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-term storage.
    
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    path
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-    
    
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-    
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-#>  2.1600
-#> -1.3253
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    #> torch_tensor 
-#>  0.8966
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    Arguments
    
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    out
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-#>  0.7000
-#> -1.2000
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-#>  2.3000
-#> [ CPUFloatType{4} ]
    torch_sign(a)
    #> torch_tensor 
-#>  1
-#> -1
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-
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-    
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-a
    #> torch_tensor 
-#>  0.5115
-#>  0.7241
-#> -0.6876
-#> -0.3453
-#> [ CPUFloatType{4} ]
    torch_sin(a)
    #> torch_tensor 
-#>  0.4895
-#>  0.6625
-#> -0.6347
-#> -0.3384
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_sinh.html b/docs/reference/torch_sinh.html
deleted file mode 100644
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+++ /dev/null
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    Sinh
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Sinh
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
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-      
-      
-    
-    
    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
-    
    sinh(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a new tensor with the hyperbolic sine of the elements of
-input.
    
-
    $$
-    \mbox{out}_{i} = \sinh(\mbox{input}_{i})
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.7504
-#> -0.3593
-#> -0.6244
-#> -1.7192
-#> [ CPUFloatType{4} ]
    torch_sinh(a)
    #> torch_tensor 
-#>  0.8228
-#> -0.3671
-#> -0.6658
-#> -2.7003
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_slogdet.html b/docs/reference/torch_slogdet.html
deleted file mode 100644
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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-    
    Slogdet
    
-    
    
-
-
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    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the input tensor of size (*, n, n) where * is zero or more                batch dimensions.
    
-
-    
    Note
    
-
-    
-
    If ``input`` has zero determinant, this returns ``(0, -inf)``.
-
    
-
-
    Backward through `slogdet` internally uses SVD results when `input`
-is not invertible. In this case, double backward through `slogdet`
-will be unstable in when `input` doesn't have distinct singular values.
-See `~torch.svd` for details.
-
    
-
-    
    slogdet(input) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-A = torch_randn(c(3, 3))
-A
    #> torch_tensor 
-#>  0.0461 -1.3909  0.9825
-#>  0.5340  0.3877 -0.2309
-#>  0.3683  1.6290  0.3208
-#> [ CPUFloatType{3,3} ]
    torch_det(A)
    #> torch_tensor 
-#> 1.094
-#> [ CPUFloatType{} ]
    torch_logdet(A)
    #> torch_tensor 
-#> 0.0898445
-#> [ CPUFloatType{} ]
    torch_slogdet(A)
    #> [[1]]
-#> torch_tensor 
-#> 1
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 0.0898445
-#> [ CPUFloatType{} ]
-#> 
    # }
-
    
-  
    
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diff --git a/docs/reference/torch_solve.html b/docs/reference/torch_solve.html
deleted file mode 100644
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--- a/docs/reference/torch_solve.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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    Solve
    
-    
    
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    Arguments
    
-    
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    input
    (Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.
    A
    (Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.
    out
    ((Tensor, Tensor) optional output tuple.
    
-
-    
    Note
    
-
-    
-
    Irrespective of the original strides, the returned matrices
-`solution` and `LU` will be transposed, i.e. with strides like
-`B.contiguous().transpose(-1, -2).stride()` and
-`A.contiguous().transpose(-1, -2).stride()` respectively.
-
    
-
-    
    torch.solve(input, A, out=None) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    This function returns the solution to the system of linear
-equations represented by \(AX = B\) and the LU factorization of
-A, in order as a namedtuple solution, LU.
    
-
    LU contains L and U factors for LU factorization of A.
    
-
    torch_solve(B, A) can take in 2D inputs B, A or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs solution, LU.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-A = torch_tensor(rbind(c(6.80, -2.11,  5.66,  5.97,  8.23),
-                      c(-6.05, -3.30,  5.36, -4.44,  1.08),
-                      c(-0.45,  2.58, -2.70,  0.27,  9.04),
-                      c(8.32,  2.71,  4.35,  -7.17,  2.14),
-                      c(-9.67, -5.14, -7.26,  6.08, -6.87)))$t()
-B = torch_tensor(rbind(c(4.02,  6.19, -8.22, -7.57, -3.03),
-                      c(-1.56,  4.00, -8.67,  1.75,  2.86),
-                      c(9.81, -4.09, -4.57, -8.61,  8.99)))$t()
-out = torch_solve(B, A)
-X = out[[1]]
-LU = out[[2]]
-torch_dist(B, torch_mm(A, X))
    #> torch_tensor 
-#> 7.09771e-06
-#> [ CPUFloatType{} ]
    # Batched solver example
-A = torch_randn(c(2, 3, 1, 4, 4))
-B = torch_randn(c(2, 3, 1, 4, 6))
-out = torch_solve(B, A)
-X = out[[1]]
-LU = out[[2]]
-torch_dist(B, A$matmul(X))
    #> torch_tensor 
-#> 6.14486e-06
-#> [ CPUFloatType{} ]
    # }
-
    
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diff --git a/docs/reference/torch_sort.html b/docs/reference/torch_sort.html
deleted file mode 100644
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--- a/docs/reference/torch_sort.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    input
    (Tensor) the input tensor.
    dim
    (int, optional) the dimension to sort along
    descending
    (bool, optional) controls the sorting order (ascending or descending)
    out
    (tuple, optional) the output tuple of (Tensor, LongTensor) that can        be optionally given to be used as output buffers
    
-
-    
    sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor) 
    
-
-    
-
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-
    Sorts the elements of the input tensor along a given dimension
-in ascending order by value.
    
-
    If dim is not given, the last dimension of the input is chosen.
    
-
    If descending is True then the elements are sorted in descending
-order by value.
    
-
    A namedtuple of (values, indices) is returned, where the values are the
-sorted values and indices are the indices of the elements in the original
-input tensor.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 4))
-out = torch_sort(x)
-out
    #> [[1]]
-#> torch_tensor 
-#> -0.9253 -0.3520 -0.0946  1.1472
-#> -0.6827  0.1674  0.7136  1.4204
-#> -0.4754 -0.0864  0.5561  1.1917
-#> [ CPUFloatType{3,4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3  0  1  2
-#>  0  1  2  3
-#>  2  0  3  1
-#> [ CPULongType{3,4} ]
-#> 
    out = torch_sort(x, 1)
-out
    #> [[1]]
-#> torch_tensor 
-#> -0.6827 -0.0946 -0.4754 -0.9253
-#> -0.3520  0.1674  0.7136  0.5561
-#> -0.0864  1.1917  1.1472  1.4204
-#> [ CPUFloatType{3,4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  1  0  2  0
-#>  0  1  1  2
-#>  2  2  0  1
-#> [ CPULongType{3,4} ]
-#> 
    # }
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diff --git a/docs/reference/torch_sparse_coo_tensor.html b/docs/reference/torch_sparse_coo_tensor.html
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+++ /dev/null
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    Sparse_coo_tensor
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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-    
    Sparse_coo_tensor
    
-    
    
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    Arguments
    
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    indices
    (array_like) Initial data for the tensor. Can be a list, tuple,        NumPy ndarray, scalar, and other types. Will be cast to a torch_LongTensor        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.
    values
    (array_like) Initial values for the tensor. Can be a list, tuple,        NumPy ndarray, scalar, and other types.
    size
    (list, tuple, or torch.Size, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, infers data type from values.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
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-with the given values. A sparse tensor can be uncoalesced, in that case, there are duplicate
-coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
-torch_sparse_.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64())
-v = torch_tensor(c(3, 4, 5), dtype=torch_float32())
-torch_sparse_coo_tensor(i, v)
    #> torch_tensor 
-#> [ SparseCPUFloatType{}
-#> indices:
-#>  0  1  1
-#>  2  0  2
-#> [ CPULongType{2,3} ]
-#> values:
-#>  3
-#>  4
-#>  5
-#> [ CPUFloatType{3} ]
-#> size:
-#> [2, 3]
-#> ]
    torch_sparse_coo_tensor(i, v, c(2, 4))
    #> torch_tensor 
-#> [ SparseCPUFloatType{}
-#> indices:
-#>  0  1  1
-#>  2  0  2
-#> [ CPULongType{2,3} ]
-#> values:
-#>  3
-#>  4
-#>  5
-#> [ CPUFloatType{3} ]
-#> size:
-#> [2, 4]
-#> ]
    
-# create empty sparse tensors
-S = torch_sparse_coo_tensor(
-  torch_empty(c(1, 0), dtype = torch_int64()),
-  torch_tensor(numeric(), dtype = torch_float32()),
-  c(1)
-)
-S = torch_sparse_coo_tensor(
-  torch_empty(c(1, 0), dtype = torch_int64()),
-  torch_empty(c(0, 2)),
-  c(1, 2)
-)
-# }
    
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diff --git a/docs/reference/torch_split.html b/docs/reference/torch_split.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Split
    
-    
    
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    Arguments
    
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    tensor
    (Tensor) tensor to split.
    split_size_or_sections
    (int) size of a single chunk or        list of sizes for each chunk
    dim
    (int) dimension along which to split the tensor.
    
-
-    
    TEST 
    
-
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-
    Splits the tensor into chunks. Each chunk is a view of the original tensor.
    If `split_size_or_sections` is an integer type, then `tensor` will
-be split into equally sized chunks (if possible). Last chunk will be smaller if
-the tensor size along the given dimension `dim` is not divisible by
-`split_size`.
-
-If `split_size_or_sections` is a list, then `tensor` will be split
-into ``len(split_size_or_sections)`` chunks with sizes in `dim` according
-to `split_size_or_sections`.
-
    
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diff --git a/docs/reference/torch_sqrt.html b/docs/reference/torch_sqrt.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Sqrt
    
-    
    
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    Arguments
    
-    
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    out
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-
-    
-
-
-
    Returns a new tensor with the square-root of the elements of input.
    
-
    $$
-    \mbox{out}_{i} = \sqrt{\mbox{input}_{i}}
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.4471
-#>  1.6376
-#>  0.0918
-#>  0.6598
-#> [ CPUFloatType{4} ]
    torch_sqrt(a)
    #> torch_tensor 
-#>  0.6686
-#>  1.2797
-#>  0.3031
-#>  0.8123
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_square.html b/docs/reference/torch_square.html
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.3828
-#>  0.0233
-#> -1.1883
-#>  1.0369
-#> [ CPUFloatType{4} ]
    torch_square(a)
    #> torch_tensor 
-#>  0.1466
-#>  0.0005
-#>  1.4121
-#>  1.0752
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_squeeze.html b/docs/reference/torch_squeeze.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor.
    dim
    (int, optional) if given, the input will be squeezed only in           this dimension
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
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-so changing the contents of one will change the contents of the other.
    
-    
    squeeze(input, dim=None, out=None) -> Tensor 
    
-
-    
-
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-
    Returns a tensor with all the dimensions of input of size 1 removed.
    
-
    For example, if input is of shape:
-\((A \times 1 \times B \times C \times 1 \times D)\) then the out tensor
-will be of shape: \((A \times B \times C \times D)\).
    
-
    When dim is given, a squeeze operation is done only in the given
-dimension. If input is of shape: \((A \times 1 \times B)\),
-squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1)
-will squeeze the tensor to the shape \((A \times B)\).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_zeros(c(2, 1, 2, 1, 2))
-x
    #> torch_tensor 
-#> (1,1,1,.,.) = 
-#>   0  0
-#> 
-#> (2,1,1,.,.) = 
-#>   0  0
-#> 
-#> (1,1,2,.,.) = 
-#>   0  0
-#> 
-#> (2,1,2,.,.) = 
-#>   0  0
-#> [ CPUFloatType{2,1,2,1,2} ]
    y = torch_squeeze(x)
-y
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0  0
-#>   0  0
-#> 
-#> (2,.,.) = 
-#>   0  0
-#>   0  0
-#> [ CPUFloatType{2,2,2} ]
    y = torch_squeeze(x, 1)
-y
    #> torch_tensor 
-#> (1,1,1,.,.) = 
-#>   0  0
-#> 
-#> (2,1,1,.,.) = 
-#>   0  0
-#> 
-#> (1,1,2,.,.) = 
-#>   0  0
-#> 
-#> (2,1,2,.,.) = 
-#>   0  0
-#> [ CPUFloatType{2,1,2,1,2} ]
    y = torch_squeeze(x, 2)
-y
    #> torch_tensor 
-#> (1,1,.,.) = 
-#>   0  0
-#> 
-#> (2,1,.,.) = 
-#>   0  0
-#> 
-#> (1,2,.,.) = 
-#>   0  0
-#> 
-#> (2,2,.,.) = 
-#>   0  0
-#> [ CPUFloatType{2,2,1,2} ]
    # }
-
    
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diff --git a/docs/reference/torch_stack.html b/docs/reference/torch_stack.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Stack
    
-    
    
-
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    Arguments
    
-    
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    tensors
    (sequence of Tensors) sequence of tensors to concatenate
    dim
    (int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)
    out
    (Tensor, optional) the output tensor.
    
-
-    
    stack(tensors, dim=0, out=None) -> Tensor 
    
-
-    
-
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-
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-
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-
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diff --git a/docs/reference/torch_std.html b/docs/reference/torch_std.html
deleted file mode 100644
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    
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    (Tensor) the input tensor.
    unbiased
    (bool) whether to use the unbiased estimation or not
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    out
    (Tensor, optional) the output tensor.
    
-
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    std(input, unbiased=True) -> Tensor 
    
-
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-
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-
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-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns the standard-deviation of each row of the input tensor in the
-dimension dim. If dim is a list of dimensions,
-reduce over all of them.
    
-
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-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.3162  0.4255  0.1976
-#> [ CPUFloatType{1,3} ]
    torch_std(a)
    #> torch_tensor 
-#> 0.379947
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  1.2036 -2.0630 -2.1182 -0.6214
-#> -1.6360  0.5014 -0.1266 -1.7918
-#>  0.2972 -0.5018  1.3086  1.4842
-#>  0.6903 -0.5105  0.5911  0.7067
-#> [ CPUFloatType{4,4} ]
    torch_std(a, dim=1)
    #> torch_tensor 
-#>  1.2400
-#>  1.0589
-#>  1.4759
-#>  1.4476
-#> [ CPUFloatType{4} ]
    # }
-
    
-  
    
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diff --git a/docs/reference/torch_std_mean.html b/docs/reference/torch_std_mean.html
deleted file mode 100644
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Std_mean
    
-    
    
-
-
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    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
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-      
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-      
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    input
    (Tensor) the input tensor.
    unbiased
    (bool) whether to use the unbiased estimation or not
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    
-
-    
    std_mean(input, unbiased=True) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    Returns the standard-deviation and mean of all elements in the input tensor.
    
-
    If unbiased is False, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    std_mean(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    Returns the standard-deviation and mean of each row of the input tensor in the
-dimension dim. If dim is a list of dimensions,
-reduce over all of them.
    
-
    If keepdim is True, the output tensor is of the same size
-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.8958  2.0764  0.0589
-#> [ CPUFloatType{1,3} ]
    torch_std_mean(a)
    #> [[1]]
-#> torch_tensor 
-#> 1.51748
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 0.413156
-#> [ CPUFloatType{} ]
-#> 
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.2699 -0.5021 -0.4712  1.0471
-#> -2.2339  0.6823 -0.9388  0.5461
-#> -0.4050  0.9288  0.8127  1.5763
-#>  1.1732 -0.2562  0.5626  0.8124
-#> [ CPUFloatType{4,4} ]
    torch_std_mean(a, 1)
    #> [[1]]
-#> torch_tensor 
-#>  1.4429
-#>  0.6986
-#>  0.8327
-#>  0.4380
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> -0.2989
-#>  0.2132
-#> -0.0087
-#>  0.9955
-#> [ CPUFloatType{4} ]
-#> 
    # }
-
    
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor
    n_fft
    (int) size of Fourier transform
    hop_length
    (int, optional) the distance between neighboring sliding window        frames. Default: None (treated as equal to floor(n_fft / 4))
    win_length
    (int, optional) the size of window frame and STFT filter.        Default: None  (treated as equal to n_fft)
    window
    (Tensor, optional) the optional window function.        Default: None (treated as window of all \(1\) s)
    center
    (bool, optional) whether to pad input on both sides so        that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).        Default: True
    pad_mode
    (string, optional) controls the padding method used when        center is True. Default: "reflect"
    normalized
    (bool, optional) controls whether to return the normalized STFT results         Default: False
    onesided
    (bool, optional) controls whether to return half of results to        avoid redundancy Default: True
    
-
-    
    Short-time Fourier transform (STFT). 
    
-
-    
-
-
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    Ignoring the optional batch dimension, this method computes the following
-expression:
-
    
-
-
    $$
-        X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
-                            \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ %
-                            \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),
-$$
-where \(m\) is the index of the sliding window, and \(\omega\) is
-the frequency that \(0 \leq \omega < \mbox{n\_fft}\). When
-onesided is the default value True,
    * `input` must be either a 1-D time sequence or a 2-D batch of time
-  sequences.
-
-* If `hop_length` is ``None`` (default), it is treated as equal to
-  ``floor(n_fft / 4)``.
-
-* If `win_length` is ``None`` (default), it is treated as equal to
-  `n_fft`.
-
-* `window` can be a 1-D tensor of size `win_length`, e.g., from
-  `torch_hann_window`. If `window` is ``None`` (default), it is
-  treated as if having \eqn{1} everywhere in the window. If
-  \eqn{\mbox{win\_length} &lt; \mbox{n\_fft}}, `window` will be padded on
-  both sides to length `n_fft` before being applied.
-
-* If `center` is ``True`` (default), `input` will be padded on
-  both sides so that the \eqn{t}-th frame is centered at time
-  \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
-  begins at time  \eqn{t \times \mbox{hop\_length}}.
-
-* `pad_mode` determines the padding method used on `input` when
-  `center` is ``True``. See `torch_nn.functional.pad` for
-  all available options. Default is ``"reflect"``.
-
-* If `onesided` is ``True`` (default), only values for \eqn{\omega}
-  in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
-  are returned because the real-to-complex Fourier transform satisfies the
-  conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
-
-* If `normalized` is ``True`` (default is ``False``), the function
-  returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
-
-Returns the real and the imaginary parts together as one tensor of size
-\eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
-batch size of `input`, \eqn{N} is the number of frequencies where
-STFT is applied, \eqn{T} is the total number of frames used, and each pair
-in the last dimension represents a complex number as the real part and the
-imaginary part.
-
-.. warning::
-  This function changed signature at version 0.4.1. Calling with the
-  previous signature may cause error or return incorrect result.
-
    
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-  
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diff --git a/docs/reference/torch_sum.html b/docs/reference/torch_sum.html
deleted file mode 100644
index ad2eabd388b65f7f5ae22edf9e4b743a46f4ddf5..0000000000000000000000000000000000000000
--- a/docs/reference/torch_sum.html
+++ /dev/null
@@ -1,263 +0,0 @@
-
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-  
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-Sum — torch_sum • torch
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-    
    Sum
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Sum
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to dtype before the operation        is performed. This is useful for preventing data type overflows. Default: None.
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    
-
-    
    sum(input, dtype=None) -> Tensor 
    
-
-    
-
-
-
    Returns the sum of all elements in the input tensor.
    
-    
    sum(input, dim, keepdim=False, dtype=None) -> Tensor 
    
-
-    
-
-
-
    Returns the sum of each row of the input tensor in the given
-dimension dim. If dim is a list of dimensions,
-reduce over all of them.
    
-
    If keepdim is True, the output tensor is of the same size
-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.6977 -0.5155 -1.9107
-#> [ CPUFloatType{1,3} ]
    torch_sum(a)
    #> torch_tensor 
-#> -3.12391
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#> -0.4190  0.0463 -0.7716  0.4229
-#> -1.2665  0.4791 -0.5515 -1.0623
-#>  1.1148 -1.2247  0.0682  1.1490
-#> -0.7653 -1.3195  0.4248 -0.6928
-#> [ CPUFloatType{4,4} ]
    torch_sum(a, 1)
    #> torch_tensor 
-#> -1.3360
-#> -2.0188
-#> -0.8302
-#> -0.1831
-#> [ CPUFloatType{4} ]
    b = torch_arange(0, 4 * 5 * 6)$view(c(4, 5, 6))
-torch_sum(b, list(2, 1))
    #> torch_tensor 
-#>   435
-#>  1335
-#>  2235
-#>  3135
-#> [ CPUFloatType{4} ]
    # }
-
    
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diff --git a/docs/reference/torch_svd.html b/docs/reference/torch_svd.html
deleted file mode 100644
index e933a9b77244378267f9988755b1c2a4f3ec775c..0000000000000000000000000000000000000000
--- a/docs/reference/torch_svd.html
+++ /dev/null
@@ -1,274 +0,0 @@
-
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-Svd — torch_svd • torch
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-    
    
-    
    Svd
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Svd
    
-    
    
-
-
-    
    Arguments
    
-    
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-    
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    input
    (Tensor) the input tensor of size \((*, m, n)\) where * is zero or more                    batch dimensions consisting of \(m \times n\) matrices.
    some
    (bool, optional) controls the shape of returned U and V
    compute_uv
    (bool, optional) option whether to compute U and V or not
    out
    (tuple, optional) the output tuple of tensors
    
-
-    
    Note
    
-
-    
    The singular values are returned in descending order. If input is a batch of matrices,
-then the singular values of each matrix in the batch is returned in descending order.
    
-
    The implementation of SVD on CPU uses the LAPACK routine ?gesdd (a divide-and-conquer
-algorithm) instead of ?gesvd for speed. Analogously, the SVD on GPU uses the MAGMA routine
-gesdd as well.
    
-
    Irrespective of the original strides, the returned matrix U
-will be transposed, i.e. with strides U.contiguous().transpose(-2, -1).stride()
    
-
    Extra care needs to be taken when backward through U and V
-outputs. Such operation is really only stable when input is
-full rank with all distinct singular values. Otherwise, NaN can
-appear as the gradients are not properly defined. Also, notice that
-double backward will usually do an additional backward through U and
-V even if the original backward is only on S.
    
-
    When some = False, the gradients on U[..., :, min(m, n):]
-and V[..., :, min(m, n):] will be ignored in backward as those vectors
-can be arbitrary bases of the subspaces.
    
-
    When compute_uv = False, backward cannot be performed since U and V
-from the forward pass is required for the backward operation.
    
-    
    svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) 
    
-
-    
-
-
-
    This function returns a namedtuple (U, S, V) which is the singular value
-decomposition of a input real matrix or batches of real matrices input such that
-\(input = U \times diag(S) \times V^T\).
    
-
    If some is True (default), the method returns the reduced singular value decomposition
-i.e., if the last two dimensions of input are m and n, then the returned
-U and V matrices will contain only \(min(n, m)\) orthonormal columns.
    
-
    If compute_uv is False, the returned U and V matrices will be zero matrices
-of shape \((m \times m)\) and \((n \times n)\) respectively. some will be ignored here.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(5, 3))
-a
    #> torch_tensor 
-#> -0.7888 -0.7696  0.3986
-#> -0.7711  0.2121 -1.5684
-#>  0.3715 -0.0456 -1.0608
-#>  1.1539 -0.8844 -0.7384
-#> -1.3783 -0.2028  1.6554
-#> [ CPUFloatType{5,3} ]
    out = torch_svd(a)
-u = out[[1]]
-s = out[[2]]
-v = out[[3]]
-torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t()))
    #> torch_tensor 
-#> 8.51333e-07
-#> [ CPUFloatType{} ]
    a_big = torch_randn(c(7, 5, 3))
-out = torch_svd(a_big)
-u = out[[1]]
-s = out[[2]]
-v = out[[3]]
-torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1)))
    #> torch_tensor 
-#> 2.71036e-06
-#> [ CPUFloatType{} ]
    # }
-
    
-  
    
-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
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diff --git a/docs/reference/torch_symeig.html b/docs/reference/torch_symeig.html
deleted file mode 100644
index 8a6a067ed13ae7dfbd63a6b318137bb54bf77e16..0000000000000000000000000000000000000000
--- a/docs/reference/torch_symeig.html
+++ /dev/null
@@ -1,275 +0,0 @@
-
-
-
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-  
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-Symeig — torch_symeig • torch
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-    
    Symeig
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Symeig
    
-    
    
-
-
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    Arguments
    
-    
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    input
    (Tensor) the input tensor of size \((*, n, n)\) where * is zero or more                    batch dimensions consisting of symmetric matrices.
    eigenvectors
    (boolean, optional) controls whether eigenvectors have to be computed
    upper
    (boolean, optional) controls whether to consider upper-triangular or lower-triangular region
    out
    (tuple, optional) the output tuple of (Tensor, Tensor)
    
-
-    
    Note
    
-
-    
    The eigenvalues are returned in ascending order. If input is a batch of matrices,
-then the eigenvalues of each matrix in the batch is returned in ascending order.
    
-
    Irrespective of the original strides, the returned matrix V will
-be transposed, i.e. with strides V.contiguous().transpose(-1, -2).stride().
    
-
    Extra care needs to be taken when backward through outputs. Such
-operation is really only stable when all eigenvalues are distinct.
-Otherwise, NaN can appear as the gradients are not properly defined.
    
-    
    symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    This function returns eigenvalues and eigenvectors
-of a real symmetric matrix input or a batch of real symmetric matrices,
-represented by a namedtuple (eigenvalues, eigenvectors).
    
-
    This function calculates all eigenvalues (and vectors) of input
-such that \(\mbox{input} = V \mbox{diag}(e) V^T\).
    
-
    The boolean argument eigenvectors defines computation of
-both eigenvectors and eigenvalues or eigenvalues only.
    
-
    If it is False, only eigenvalues are computed. If it is True,
-both eigenvalues and eigenvectors are computed.
    
-
    Since the input matrix input is supposed to be symmetric,
-only the upper triangular portion is used by default.
    
-
    If upper is False, then lower triangular portion is used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(5, 5))
-a = a + a$t()  # To make a symmetric
-a
    #> torch_tensor 
-#>  2.1703 -0.5663 -0.5122 -0.2134 -0.0549
-#> -0.5663  0.9832 -0.9685  0.1017  0.9142
-#> -0.5122 -0.9685 -0.8703  0.7874 -0.6067
-#> -0.2134  0.1017  0.7874  2.8112 -0.1549
-#> -0.0549  0.9142 -0.6067 -0.1549 -0.4494
-#> [ CPUFloatType{5,5} ]
    o = torch_symeig(a, eigenvectors=TRUE)
-e = o[[1]]
-v = o[[2]]
-e
    #> torch_tensor 
-#> -1.5747
-#> -0.9141
-#>  1.6122
-#>  2.4168
-#>  3.1047
-#> [ CPUFloatType{5} ]
    v
    #> torch_tensor 
-#>  0.1632 -0.0736  0.4932  0.7860 -0.3270
-#>  0.3015 -0.4689  0.6486 -0.5181 -0.0109
-#>  0.8988 -0.0414 -0.3445  0.1244  0.2372
-#> -0.1524  0.0538  0.3046  0.2248  0.9114
-#>  0.2266  0.8776  0.3530 -0.2188 -0.0780
-#> [ CPUFloatType{5,5} ]
    a_big = torch_randn(c(5, 2, 2))
-a_big = a_big + a_big$transpose(-2, -1)  # To make a_big symmetric
-o = a_big$symeig(eigenvectors=TRUE)
-e = o[[1]]
-v = o[[2]]
-torch_allclose(torch_matmul(v, torch_matmul(e$diag_embed(), v$transpose(-2, -1))), a_big)
    #> [1] TRUE
    # }
-
    
-  
    
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diff --git a/docs/reference/torch_t.html b/docs/reference/torch_t.html
deleted file mode 100644
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--- a/docs/reference/torch_t.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input tensor.
    
-
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    t(input) -> Tensor 
    
-
-    
-
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-
    Expects input to be <= 2-D tensor and transposes dimensions 0
-and 1.
    
-
    0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
-is equivalent to transpose(input, 0, 1).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(2,3))
-x
    #> torch_tensor 
-#> -1.4813  0.4524 -0.0294
-#>  2.6447 -0.7806  0.5434
-#> [ CPUFloatType{2,3} ]
    torch_t(x)
    #> torch_tensor 
-#> -1.4813  2.6447
-#>  0.4524 -0.7806
-#> -0.0294  0.5434
-#> [ CPUFloatType{3,2} ]
    x = torch_randn(c(3))
-x
    #> torch_tensor 
-#> -0.0126
-#> -0.5420
-#>  0.5410
-#> [ CPUFloatType{3} ]
    torch_t(x)
    #> torch_tensor 
-#> -0.0126
-#> -0.5420
-#>  0.5410
-#> [ CPUFloatType{3} ]
    x = torch_randn(c(2, 3))
-x
    #> torch_tensor 
-#> -1.9746 -0.2671  0.6073
-#> -0.1863  0.6615  1.5133
-#> [ CPUFloatType{2,3} ]
    torch_t(x)
    #> torch_tensor 
-#> -1.9746 -0.1863
-#> -0.2671  0.6615
-#>  0.6073  1.5133
-#> [ CPUFloatType{3,2} ]
    # }
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diff --git a/docs/reference/torch_take.html b/docs/reference/torch_take.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Take
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    indices
    (LongTensor) the indices into tensor
    
-
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    take(input, index) -> Tensor 
    
-
-    
-
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-
    Returns a new tensor with the elements of input at the given indices.
-The input tensor is treated as if it were viewed as a 1-D tensor. The result
-takes the same shape as the indices.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
-torch_take(src, torch_tensor(c(0, 2, 5), dtype = torch_int64()))
    #> torch_tensor 
-#>  8
-#>  3
-#>  7
-#> [ CPUFloatType{3} ]
    # }
-
    
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diff --git a/docs/reference/torch_tan.html b/docs/reference/torch_tan.html
deleted file mode 100644
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    Examples
    
-    
    # \dontrun{
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-a
    #> torch_tensor 
-#> -1.1158
-#>  0.0583
-#>  0.2334
-#> -0.9159
-#> [ CPUFloatType{4} ]
    torch_tan(a)
    #> torch_tensor 
-#> -2.0440
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-
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-of input.
    
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-    \mbox{out}_{i} = \tanh(\mbox{input}_{i})
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    Examples
    
-    
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-#> -0.5525
-#> -0.3071
-#>  0.8392
-#> -0.6511
-#> [ CPUFloatType{4} ]
    torch_tanh(a)
    #> torch_tensor 
-#> -0.5024
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-    
    
-
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    torch_tensor(
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    pin_memory
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-
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-    
    # \dontrun{
-torch_tensor(c(1,2,3,4))
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#> [ CPUFloatType{4} ]
    torch_tensor(c(1,2,3,4), dtype = torch_int())
    #> torch_tensor 
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-of the elements in the original input tensor.
    
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-k elements are themselves sorted
    
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-    
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-x
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#>  5
-#> [ CPUFloatType{5} ]
    torch_topk(x, 3)
    #> [[1]]
-#> torch_tensor 
-#>  5
-#>  4
-#>  3
-#> [ CPUFloatType{3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  4
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-x
    #> torch_tensor 
-#>  1  2  3
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-x
    #> torch_tensor 
-#>  0.3114 -0.1716  0.4504
-#> -0.4723 -1.0927  2.1773
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    #> torch_tensor 
-#>  0.3114 -0.4723
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    # \dontrun{
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    #> torch_tensor 
-#>  0.0190  1.0024  1.9078
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    x = torch_tensor(matrix(c(1, 3, 4, 1, 2, 3), ncol = 3, byrow=TRUE))
-torch_trapz(y, x = x)
    #> torch_tensor 
-#>  2.4765
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))
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    (Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions
    upper
    (bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: True.
    transpose
    (bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: False.
    unitriangular
    (bool, optional) whether \(A\) is unit triangular.        If True, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: False.
    
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    triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) 
    
-
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-
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-and multiple right-hand sides \(b\).
    
-
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-with the default keyword arguments.
    
-
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-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs X
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-A = torch_randn(c(2, 2))$triu()
-A
    #> torch_tensor 
-#> -0.3460  0.1356
-#>  0.0000  1.5035
-#> [ CPUFloatType{2,2} ]
    b = torch_randn(c(2, 3))
-b
    #> torch_tensor 
-#> -0.4014 -0.1958  0.0379
-#> -1.3143 -0.0766 -0.3524
-#> [ CPUFloatType{2,3} ]
    torch_triangular_solve(b, A)
    #> [[1]]
-#> torch_tensor 
-#>  0.8174  0.5459 -0.2014
-#> -0.8742 -0.0509 -0.2344
-#> [ CPUFloatType{2,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> -0.3460  0.1356
-#>  0.0000  1.5035
-#> [ CPUFloatType{2,2} ]
-#> 
    # }
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diff --git a/docs/reference/torch_tril.html b/docs/reference/torch_tril.html
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    (Tensor) the input tensor.
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    (int, optional) the diagonal to consider
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    (Tensor, optional) the output tensor.
    
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-
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-input, the other elements of the result tensor out are set to 0.
    
-
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-below the diagonal.
    
-
    The argument diagonal controls which diagonal to consider. If
-diagonal = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-a
    #> torch_tensor 
-#> -0.6849  0.8749 -0.7531
-#> -0.5497 -1.0238  0.3323
-#> -0.0980  0.0908 -0.4867
-#> [ CPUFloatType{3,3} ]
    torch_tril(a)
    #> torch_tensor 
-#> -0.6849  0.0000  0.0000
-#> -0.5497 -1.0238  0.0000
-#> -0.0980  0.0908 -0.4867
-#> [ CPUFloatType{3,3} ]
    b = torch_randn(c(4, 6))
-b
    #> torch_tensor 
-#> -0.7116 -0.9359 -1.5487  1.6909  0.9290 -1.8224
-#>  2.1791 -0.8098  1.4367 -0.5204  1.0782 -0.4998
-#>  1.3149 -1.0202 -0.4302 -0.5773  0.0928 -1.0440
-#> -1.7950  0.6438 -0.7581  0.0569 -1.0737  1.3707
-#> [ CPUFloatType{4,6} ]
    torch_tril(b, diagonal=1)
    #> torch_tensor 
-#> -0.7116 -0.9359  0.0000  0.0000  0.0000  0.0000
-#>  2.1791 -0.8098  1.4367  0.0000  0.0000  0.0000
-#>  1.3149 -1.0202 -0.4302 -0.5773  0.0000  0.0000
-#> -1.7950  0.6438 -0.7581  0.0569 -1.0737  0.0000
-#> [ CPUFloatType{4,6} ]
    torch_tril(b, diagonal=-1)
    #> torch_tensor 
-#>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
-#>  2.1791  0.0000  0.0000  0.0000  0.0000  0.0000
-#>  1.3149 -1.0202  0.0000  0.0000  0.0000  0.0000
-#> -1.7950  0.6438 -0.7581  0.0000  0.0000  0.0000
-#> [ CPUFloatType{4,6} ]
    # }
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diff --git a/docs/reference/torch_tril_indices.html b/docs/reference/torch_tril_indices.html
deleted file mode 100644
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--- a/docs/reference/torch_tril_indices.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Tril_indices
    
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    Arguments
    
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    row
    (int) number of rows in the 2-D matrix.
    col
    (int) number of columns in the 2-D matrix.
    offset
    (int) diagonal offset from the main diagonal.        Default: if not provided, 0.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, torch_long.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    layout
    (torch.layout, optional) currently only support torch_strided.
    
-
-    
    Note
    
-
-    
-
    When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
-prevent overflow during calculation.
-
    
-
-    
    tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor 
    
-
-    
-
-
-
    Returns the indices of the lower triangular part of a row-by-
-col matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.
    
-
    The lower triangular part of the matrix is defined as the elements on and
-below the diagonal.
    
-
    The argument offset controls which diagonal to consider. If
-offset = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
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    Examples
    
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diff --git a/docs/reference/torch_triu.html b/docs/reference/torch_triu.html
deleted file mode 100644
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    input
    (Tensor) the input tensor.
    diagonal
    (int, optional) the diagonal to consider
    out
    (Tensor, optional) the output tensor.
    
-
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    triu(input, diagonal=0, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
-input, the other elements of the result tensor out are set to 0.
    
-
    The upper triangular part of the matrix is defined as the elements on and
-above the diagonal.
    
-
    The argument diagonal controls which diagonal to consider. If
-diagonal = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-a
    #> torch_tensor 
-#> -0.3956 -0.1868  0.0874
-#> -0.3459 -0.2626 -0.7964
-#>  0.5357 -0.4833 -0.2087
-#> [ CPUFloatType{3,3} ]
    torch_triu(a)
    #> torch_tensor 
-#> -0.3956 -0.1868  0.0874
-#>  0.0000 -0.2626 -0.7964
-#>  0.0000  0.0000 -0.2087
-#> [ CPUFloatType{3,3} ]
    torch_triu(a, diagonal=1)
    #> torch_tensor 
-#> 0.01 *
-#>  0.0000 -18.6760  8.7416
-#>   0.0000  0.0000 -79.6368
-#>   0.0000  0.0000  0.0000
-#> [ CPUFloatType{3,3} ]
    torch_triu(a, diagonal=-1)
    #> torch_tensor 
-#> -0.3956 -0.1868  0.0874
-#> -0.3459 -0.2626 -0.7964
-#>  0.0000 -0.4833 -0.2087
-#> [ CPUFloatType{3,3} ]
    b = torch_randn(c(4, 6))
-b
    #> torch_tensor 
-#> -0.1247  0.3568  1.5481  0.9310  0.2551 -1.8148
-#>  0.7493  0.8313 -0.6427  0.3658 -0.2912  0.3553
-#>  0.9661  2.0171  0.9854 -0.1047 -1.6832 -0.0952
-#>  0.0011  0.5442  0.5278 -0.5429  0.4507 -0.8038
-#> [ CPUFloatType{4,6} ]
    torch_triu(b, diagonal=1)
    #> torch_tensor 
-#>  0.0000  0.3568  1.5481  0.9310  0.2551 -1.8148
-#>  0.0000  0.0000 -0.6427  0.3658 -0.2912  0.3553
-#>  0.0000  0.0000  0.0000 -0.1047 -1.6832 -0.0952
-#>  0.0000  0.0000  0.0000  0.0000  0.4507 -0.8038
-#> [ CPUFloatType{4,6} ]
    torch_triu(b, diagonal=-1)
    #> torch_tensor 
-#> -0.1247  0.3568  1.5481  0.9310  0.2551 -1.8148
-#>  0.7493  0.8313 -0.6427  0.3658 -0.2912  0.3553
-#>  0.0000  2.0171  0.9854 -0.1047 -1.6832 -0.0952
-#>  0.0000  0.0000  0.5278 -0.5429  0.4507 -0.8038
-#> [ CPUFloatType{4,6} ]
    # }
-
    
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diff --git a/docs/reference/torch_triu_indices.html b/docs/reference/torch_triu_indices.html
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    row
    (int) number of rows in the 2-D matrix.
    col
    (int) number of columns in the 2-D matrix.
    offset
    (int) diagonal offset from the main diagonal.        Default: if not provided, 0.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, torch_long.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    layout
    (torch.layout, optional) currently only support torch_strided.
    
-
-    
    Note
    
-
-    
-
    When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
-prevent overflow during calculation.
-
    
-
-    
    triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor 
    
-
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-
-
-
    Returns the indices of the upper triangular part of a row by
-col matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.
    
-
    The upper triangular part of the matrix is defined as the elements on and
-above the diagonal.
    
-
    The argument offset controls which diagonal to consider. If
-offset = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
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diff --git a/docs/reference/torch_true_divide.html b/docs/reference/torch_true_divide.html
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    (Tensor) the dividend
    divisor
    (Tensor or Scalar) the divisor
    
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-
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-in floating point. Analogous to division in Python 3 and equivalent to
-torch_div except when both inputs have bool or integer scalar types,
-in which case they are cast to the default (floating) scalar type before the division.
    
-
    $$
-    \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
-$$
    
-
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    Examples
    
-    
    # \dontrun{
-
-dividend = torch_tensor(c(5, 3), dtype=torch_int())
-divisor = torch_tensor(c(3, 2), dtype=torch_int())
-torch_true_divide(dividend, divisor)
    #> torch_tensor 
-#>  1.6667
-#>  1.5000
-#> [ CPUFloatType{2} ]
    torch_true_divide(dividend, 2)
    #> torch_tensor 
-#>  2.5000
-#>  1.5000
-#> [ CPUFloatType{2} ]
    # }
-
    
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor, optional) the output tensor.
    
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    trunc(input, out=None) -> Tensor 
    
-
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    Returns a new tensor with the truncated integer values of
-the elements of input.
    
-
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.4632
-#> -1.3494
-#>  0.0517
-#> -1.1300
-#> [ CPUFloatType{4} ]
    torch_trunc(a)
    #> torch_tensor 
-#> -0
-#> -1
-#>  0
-#> -1
-#> [ CPUFloatType{4} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Examples
    
-    
    # \dontrun{
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-torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE)))
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUIntType{3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  4
-#>  5
-#>  6
-#> [ CPUIntType{3} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#>  7
-#>  8
-#>  9
-#> [ CPUIntType{3} ]
-#> 
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-    
    
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    (Tensor) the input tensor
    return_inverse
    (bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.
    return_counts
    (bool) Whether to also return the counts for each unique        element.
    dim
    (int) the dimension to apply unique. If None, the unique of the        flattened input is returned. default: None
    
-
-    
    TEST 
    
-
-    
-
-
-
    Eliminates all but the first element from every consecutive group of equivalent elements.
    .. note:: This function is different from [`torch_unique`] in the sense that this function
-    only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
-    in C++.
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2))
-output = torch_unique_consecutive(x)
-output
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  1
-#>  2
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
    torch_unique_consecutive(x, return_inverse=TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  1
-#>  2
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  0
-#>  0
-#>  1
-#>  1
-#>  2
-#>  3
-#>  3
-#>  4
-#> [ CPULongType{8} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
    torch_unique_consecutive(x, return_counts=TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  1
-#>  2
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#>  2
-#>  2
-#>  1
-#>  2
-#>  1
-#> [ CPULongType{5} ]
-#> 
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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-
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-    
    Unsqueeze
    
-    
    
-
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    Arguments
    
-    
-    
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    (Tensor) the input tensor.
    dim
    (int) the index at which to insert the singleton dimension
    
-
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    unsqueeze(input, dim) -> Tensor 
    
-
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-specified position.
    
-
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-
    A dim value within the range [-input.dim() - 1, input.dim() + 1)
-can be used. Negative dim will correspond to unsqueeze
-applied at dim = dim + input.dim() + 1.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_tensor(c(1, 2, 3, 4))
-torch_unsqueeze(x, 1)
    #> torch_tensor 
-#>  1  2  3  4
-#> [ CPUFloatType{1,4} ]
    torch_unsqueeze(x, 2)
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#> [ CPUFloatType{4,1} ]
    # }
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diff --git a/docs/reference/torch_var.html b/docs/reference/torch_var.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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-    
    
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    (Tensor) the input tensor.
    unbiased
    (bool) whether to use the unbiased estimation or not
    dim
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    keepdim
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-
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-biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor 
    
-
-    
-
-
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-dimension dim.
    
-
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-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#>  0.9097 -1.4605 -0.9481
-#> [ CPUFloatType{1,3} ]
    torch_var(a)
    #> torch_tensor 
-#> 1.55533
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.9322 -0.2661 -1.1364 -0.1596
-#> -0.8385  0.0366 -0.8830 -0.5310
-#>  1.6003  0.1409 -0.4186  2.4136
-#> -0.7193 -0.5766  0.0958 -0.3928
-#> [ CPUFloatType{4,4} ]
    torch_var(a, 1)
    #> torch_tensor 
-#>  1.4709
-#>  0.1046
-#>  0.2947
-#>  1.9483
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_var_mean.html b/docs/reference/torch_var_mean.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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-    
    
-
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-    
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-      
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    input
    (Tensor) the input tensor.
    unbiased
    (bool) whether to use the unbiased estimation or not
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    
-
-    
    var_mean(input, unbiased=True) -> (Tensor, Tensor) 
    
-
-    
-
-
-
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-
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-biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    var_mean(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor) 
    
-
-    
-
-
-
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-dimension dim.
    
-
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-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#>  1.4242 -0.2759  0.7106
-#> [ CPUFloatType{1,3} ]
    torch_var_mean(a)
    #> [[1]]
-#> torch_tensor 
-#> 0.728761
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 0.61961
-#> [ CPUFloatType{} ]
-#> 
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.0532 -0.3304 -0.5824  1.8389
-#> -2.0310 -0.6095 -0.1087  0.3034
-#> -1.3414  1.7987 -0.3098  0.8658
-#>  0.0799 -0.7031 -0.5875 -1.0066
-#> [ CPUFloatType{4,4} ]
    torch_var_mean(a, 1)
    #> [[1]]
-#> torch_tensor 
-#>  1.1034
-#>  1.4015
-#>  0.0538
-#>  1.4116
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> -0.8098
-#>  0.0389
-#> -0.3971
-#>  0.5004
-#> [ CPUFloatType{4} ]
-#> 
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-
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-    
    
-
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    condition
    (BoolTensor) When True (nonzero), yield x, otherwise yield y
    x
    (Tensor) values selected at indices where condition is True
    y
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-
-    
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-
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-
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-
    
-
-
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-
    
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-
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-
-
-
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-
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-
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-    \mbox{out}_i = \left\{ \begin{array}{ll}
-        \mbox{x}_i & \mbox{if } \mbox{condition}_i \\
-        \mbox{y}_i & \mbox{otherwise} \\
-    \end{array}
-    \right.
-$$
    
-    
    where(condition) -> tuple of LongTensor 
    
-
-    
-
-
-
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-torch_nonzero(condition, as_tuple=True).
    
-
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-    
    
-  
    
-  
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    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
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-by the variable argument size.
    
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    Examples
    
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    # \dontrun{
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-torch_zeros(c(2, 3))
    #> torch_tensor 
-#>  0  0  0
-#>  0  0  0
-#> [ CPUFloatType{2,3} ]
    torch_zeros(c(5))
    #> torch_tensor 
-#>  0
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-#>  0
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-#> [ CPUFloatType{5} ]
    # }
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diff --git a/docs/reference/torch_zeros_like.html b/docs/reference/torch_zeros_like.html
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    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
    (torch.memory_format, optional) the desired memory format of        returned Tensor. Default: torch_preserve_format.
    
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    Returns a tensor filled with the scalar value 0, with the same size as
-input. torch_zeros_like(input) is equivalent to
-torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
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    Warning
    
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-the old torch_zeros_like(input, out=output) is equivalent to
-torch_zeros(input.size(), out=output).
    
-
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    Examples
    
-    
    # \dontrun{
-
-input = torch_empty(c(2, 3))
-torch_zeros_like(input)
    #> torch_tensor 
-#>  0  0  0
-#>  0  0  0
-#> [ CPUFloatType{2,3} ]
    # }
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diff --git a/docs/reference/utils_dataset.html b/docs/reference/utils_dataset.html
deleted file mode 100644
index a4d271a0304d4410faaede1ff04560e49f4b23ff..0000000000000000000000000000000000000000
--- a/docs/reference/utils_dataset.html
+++ /dev/null
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-An abstract class representing a <code>Dataset</code>. — utils_dataset • torch
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    All datasets that represent a map from keys to data samples should subclass
-it. All subclasses should overwrite get_item, supporting fetching a
-data sample for a given key. Subclasses could also optionally overwrite
-lenght, which is expected to return the size of the dataset by many
-~torch.utils.data.Sampler implementations and the default options
-of ~torch.utils.data.DataLoader.
    
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-dataset with non-integral indices/keys, a custom sampler must be provided.
    
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diff --git a/docs/reference/utils_dataset_tensor.html b/docs/reference/utils_dataset_tensor.html
deleted file mode 100644
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diff --git a/docs/reference/vision_make_grid.html b/docs/reference/vision_make_grid.html
deleted file mode 100644
index e3c2b7b24495cdad5c10a3d5da6d7fd1ff917bf2..0000000000000000000000000000000000000000
--- a/docs/reference/vision_make_grid.html
+++ /dev/null
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-A simplified version of torchvision.utils.make_grid. — vision_make_grid • torch
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    A simplified version of torchvision.utils.make_grid.
    
-    Source: R/vision_utils.R
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-    
    Arranges a batch of (image) tensors in a grid, with optional padding between images.
-Expects a 4d mini-batch tensor of shape (B x C x H x W).
    
-    
    
-
-    
    vision_make_grid(
-  tensor,
-  scale = TRUE,
-  num_rows = 8,
-  padding = 2,
-  pad_value = 0
-)
    
-
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    tensor
    tensor to arrange in grid
    scale
    whether to normalize (min-max-scale) the input tensor
    num_rows
    number of rows making up the grid (default 8)
    padding
    amount of padding between batch images (default 2)
    pad_value
    pixel value to use for padding
    
-
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diff --git a/docs/reference/with_enable_grad.html b/docs/reference/with_enable_grad.html
deleted file mode 100644
index 77e29315cb94d7a50d45a9d6b0b3ba3fca331950..0000000000000000000000000000000000000000
--- a/docs/reference/with_enable_grad.html
+++ /dev/null
@@ -1,226 +0,0 @@
-
-
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-Enable grad — with_enable_grad • torch
-
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    Enable grad
    
-    Source: R/autograd.R
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-    
    
-
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-    
    Context-manager that enables gradient calculation.
-Enables gradient calculation, if it has been disabled via with_no_grad.
    
-    
    
-
-    
    with_enable_grad(code)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    code
    code to be executed with gradient recording.
    
-
-    
    Details
    
-
-    
    This context manager is thread local; it will not affect computation in
-other threads.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x <- torch_tensor(1, requires_grad=TRUE)
-with_no_grad({
-  with_enable_grad({
-    y = x * 2
-  })
-})
-y$backward()
-x$grad
    #> torch_tensor 
-#>  2
-#> [ CPUFloatType{1} ]
    
-# }
-
    
-  
    
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diff --git a/docs/reference/with_no_grad.html b/docs/reference/with_no_grad.html
deleted file mode 100644
index 18994141059f5aeadede0d9d766453f2cdbc10c5..0000000000000000000000000000000000000000
--- a/docs/reference/with_no_grad.html
+++ /dev/null
@@ -1,226 +0,0 @@
-
-
-
-  
-  
-
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-Temporarily modify gradient recording. — with_no_grad • torch
-
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    Temporarily modify gradient recording.
    
-    Source: R/autograd.R
-    
-    
    
-
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-    
    Temporarily modify gradient recording.
    
-    
    
-
-    
    with_no_grad(code)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    code
    code to be executed with no gradient recording.
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-x <- torch_tensor(runif(5), requires_grad = TRUE)
-with_no_grad({
-  x$sub_(torch_tensor(as.numeric(1:5)))
-})
    #> torch_tensor 
-#> -0.1943
-#> -1.1859
-#> -2.5961
-#> -3.7816
-#> -4.5816
-#> [ CPUFloatType{5} ]
    x
    #> torch_tensor 
-#> -0.1943
-#> -1.1859
-#> -2.5961
-#> -3.7816
-#> -4.5816
-#> [ CPUFloatType{5} ]
    x$grad
    #> torch_tensor 
-#> [ Tensor (undefined) ]
    
-# }
-
    
-  
    
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    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/lantern/headers/src/main.cpp b/lantern/headers/src/main.cpp
index 4a00dcb7b25e4216108922e70a848528a0d34eca..20a372a8830d4564c24b3f29ee826b0d8aa68b23 100644
--- a/lantern/headers/src/main.cpp
+++ b/lantern/headers/src/main.cpp
@@ -142,7 +142,11 @@ std::string buildCalls(std::string name, YAML::Node node, size_t start)
         std::string call = node[idx]["name"].as();
         if ((dtype.find("c10::optional") != std::string::npos) & (type != "std::vector"))
         {
-            call = "optional<" + addNamespace(type) + ">(" + call + ")";
+            if (type != "double")
+            {
+                call = "optional<" + addNamespace(type) + ">(" + call + ")";
+            }
+            
             type = "c10::optional<" + type + ">";
         }
 
diff --git a/lantern/include/lantern/lantern.h b/lantern/include/lantern/lantern.h
index 6f086249d3ebd31a1d2ae741a5b267d1447a200b..d1fa85f297c886a49d1a87e24b17d884cf2f93a1 100644
--- a/lantern/include/lantern/lantern.h
+++ b/lantern/include/lantern/lantern.h
@@ -468,6 +468,53 @@ extern "C"
   HOST_API void lantern_Tensor_index_put_tensor_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_tensor_(self, index, rhs); LANTERN_HOST_HANDLER}
   LANTERN_API void (LANTERN_PTR _lantern_Tensor_index_put_scalar_) (void* self, void* index, void* rhs);
   HOST_API void lantern_Tensor_index_put_scalar_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_scalar_(self, index, rhs); LANTERN_HOST_HANDLER}
+  LANTERN_API void (LANTERN_PTR _lantern_manual_seed) (int64_t seed);
+  HOST_API void lantern_manual_seed (int64_t seed) {_lantern_manual_seed(seed); LANTERN_HOST_HANDLER}
+
+  LANTERN_API void* (LANTERN_PTR _lantern_load_state_dict) (const char * path);
+  HOST_API void * lantern_load_state_dict (const char * path)
+  {
+    void * ret = _lantern_load_state_dict(path);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_keys) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_keys (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_keys(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_values) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_values (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_values(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void (LANTERN_PTR _lantern_IValue_delete) (void * x);
+  HOST_API void lantern_IValue_delete (void* x)
+  {
+    _lantern_get_state_dict_values(x);
+    LANTERN_HOST_HANDLER;
+  }
+
+  LANTERN_API void (LANTERN_PTR _lantern_vector_string_delete) (void * x);
+  HOST_API void lantern_vector_string_delete (void* x)
+  {
+    _lantern_vector_string_delete(x);
+    LANTERN_HOST_HANDLER;
+  }
+
+  LANTERN_API int64_t *(LANTERN_PTR _lantern_Tensor_data_ptr_int64_t)(void *self);
+  HOST_API int64_t* lantern_Tensor_data_ptr_int64_t (void* self)
+  {
+    int64_t* ret = _lantern_Tensor_data_ptr_int64_t(self);
+    LANTERN_HOST_HANDLER;
+    return ret;
+  }
+
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4175,6 +4222,13 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_const_char_delete);
   LOAD_SYMBOL(_lantern_Tensor_index_put_tensor_);
   LOAD_SYMBOL(_lantern_Tensor_index_put_scalar_);
+  LOAD_SYMBOL(_lantern_manual_seed);
+  LOAD_SYMBOL(_lantern_load_state_dict);
+  LOAD_SYMBOL(_lantern_get_state_dict_keys);
+  LOAD_SYMBOL(_lantern_get_state_dict_values);
+  LOAD_SYMBOL(_lantern_IValue_delete);
+  LOAD_SYMBOL(_lantern_vector_string_delete);
+  LOAD_SYMBOL(_lantern_Tensor_data_ptr_int64_t);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/lantern/src/Delete.cpp b/lantern/src/Delete.cpp
index 083afa58f9db976f5a971030488d4e4a3ea8b295..2cd53ab91ff973adcb932f2f451f09dd5043e1e6 100644
--- a/lantern/src/Delete.cpp
+++ b/lantern/src/Delete.cpp
@@ -180,4 +180,18 @@ void _lantern_const_char_delete (const char * x)
   LANTERN_FUNCTION_START
   delete []x;
   LANTERN_FUNCTION_END_VOID
+}
+
+void _lantern_IValue_delete (void * x)
+{
+  LANTERN_FUNCTION_START
+  lantern_delete>(x);
+  LANTERN_FUNCTION_END_VOID
+}
+
+void _lantern_vector_string_delete (void * x)
+{
+  LANTERN_FUNCTION_START
+  lantern_delete>(x);
+  LANTERN_FUNCTION_END_VOID
 }
\ No newline at end of file
diff --git a/lantern/src/Generator.cpp b/lantern/src/Generator.cpp
index 2bdac78c78d98a7c44dba9b89d52db94f34987c7..bac252ab2410d809e63f67de5c8b428d33172884 100644
--- a/lantern/src/Generator.cpp
+++ b/lantern/src/Generator.cpp
@@ -28,4 +28,9 @@ void _lantern_Generator_set_current_seed(void *generator, uint64_t seed)
   LANTERN_FUNCTION_START
   reinterpret_cast> *>(generator)->get()->set_current_seed(seed);
   LANTERN_FUNCTION_END_VOID
+}
+
+void _lantern_manual_seed (int64_t seed)
+{
+  torch::manual_seed(seed);
 }
\ No newline at end of file
diff --git a/lantern/src/Save.cpp b/lantern/src/Save.cpp
index 94728aba008a1a6e1f93ecbf991ad0eadefec98f..acb0b2952588fc9883d9f31d1d407d26963e3fcd 100644
--- a/lantern/src/Save.cpp
+++ b/lantern/src/Save.cpp
@@ -66,4 +66,45 @@ void _lantern_test_print (void * x)
     auto t = reinterpret_cast *>(x)->get();
     std::cout << t << std::endl;
     LANTERN_FUNCTION_END_VOID
-}
\ No newline at end of file
+}
+
+void* _lantern_load_state_dict (const char * path)
+{
+    LANTERN_FUNCTION_START
+    std::ifstream file(path, std::ios::binary);
+    std::vector data((std::istreambuf_iterator(file)), std::istreambuf_iterator());
+    torch::IValue ivalue = torch::pickle_load(data);   
+    return (void*) new LanternObject(ivalue);
+    LANTERN_FUNCTION_END
+}
+
+void* _lantern_get_state_dict_keys (void * ivalue)
+{
+    LANTERN_FUNCTION_START
+    auto iv = reinterpret_cast*>(ivalue)->get();
+    auto d = iv.toGenericDict();
+    auto keys = new std::vector;
+    for (auto i = d.begin(); i != d.end(); ++i)
+    {
+        std::string key = i->key().toString()->string();
+        keys->push_back(key);
+    }
+    return (void*) keys;
+    LANTERN_FUNCTION_END
+}
+
+void * _lantern_get_state_dict_values (void* ivalue)
+{
+    LANTERN_FUNCTION_START
+    auto iv = reinterpret_cast*>(ivalue)->get();
+    auto d = iv.toGenericDict();
+    auto values = new LanternObject>;
+    for (auto i = d.begin(); i != d.end(); ++i)
+    {
+        torch::Tensor value = i->value().toTensor();
+        values->get().push_back(value);
+    }
+    return (void*) values;
+    LANTERN_FUNCTION_END
+}
+
diff --git a/lantern/src/Scalar.cpp b/lantern/src/Scalar.cpp
index 94062f4f364848f7d5f9237e12b127f7b6e2c36c..ddd2fc077e63871353a5e2556edbc89aeea3a775 100644
--- a/lantern/src/Scalar.cpp
+++ b/lantern/src/Scalar.cpp
@@ -76,4 +76,5 @@ void *_lantern_Scalar_nullopt()
   LANTERN_FUNCTION_START
   return (void *)new LanternObject>(c10::nullopt);
   LANTERN_FUNCTION_END
-}
\ No newline at end of file
+}
+
diff --git a/lantern/src/Tensor.cpp b/lantern/src/Tensor.cpp
index 9a99adeeeb7f7f8d869ce8d9e852c1c2e934ca31..b5f3a323bc7acb7c9b7b83afe2fdbae68e4d2628 100644
--- a/lantern/src/Tensor.cpp
+++ b/lantern/src/Tensor.cpp
@@ -98,6 +98,14 @@ uint8_t *_lantern_Tensor_data_ptr_uint8_t(void *self)
   LANTERN_FUNCTION_END
 }
 
+int64_t *_lantern_Tensor_data_ptr_int64_t(void *self)
+{
+  LANTERN_FUNCTION_START
+  torch::Tensor x = reinterpret_cast *>(self)->get();
+  return x.data_ptr();
+  LANTERN_FUNCTION_END
+}
+
 int32_t *_lantern_Tensor_data_ptr_int32_t(void *self)
 {
   LANTERN_FUNCTION_START
diff --git a/lantern/src/lantern.cpp b/lantern/src/lantern.cpp
index fae964ee800e4f29e01f82199db7132ac3097108..1c07e44ab1118dff1c0197a9e66d798b171bca55 100644
--- a/lantern/src/lantern.cpp
+++ b/lantern/src/lantern.cpp
@@ -12891,7 +12891,7 @@ void* _lantern_upsample_linear1d_out_tensor_tensor_intarrayref_bool_double(void*
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_linear1d_out(
-        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12899,7 +12899,7 @@ void* _lantern_upsample_linear1d_tensor_intarrayref_bool_double(void* self, void
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_linear1d(
-        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12907,7 +12907,7 @@ void* _lantern_upsample_linear1d_backward_out_tensor_tensor_intarrayref_intarray
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_linear1d_backward_out(
-        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12915,7 +12915,7 @@ void* _lantern_upsample_linear1d_backward_tensor_intarrayref_intarrayref_bool_do
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_linear1d_backward(
-        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12923,7 +12923,7 @@ void* _lantern_upsample_bilinear2d_out_tensor_tensor_intarrayref_bool_double_dou
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bilinear2d_out(
-        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12931,7 +12931,7 @@ void* _lantern_upsample_bilinear2d_tensor_intarrayref_bool_double_double(void* s
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bilinear2d(
-        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12939,7 +12939,7 @@ void* _lantern_upsample_bilinear2d_backward_out_tensor_tensor_intarrayref_intarr
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bilinear2d_backward_out(
-        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12947,7 +12947,7 @@ void* _lantern_upsample_bilinear2d_backward_tensor_intarrayref_intarrayref_bool_
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bilinear2d_backward(
-        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12955,7 +12955,7 @@ void* _lantern_upsample_bicubic2d_out_tensor_tensor_intarrayref_bool_double_doub
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bicubic2d_out(
-        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12963,7 +12963,7 @@ void* _lantern_upsample_bicubic2d_tensor_intarrayref_bool_double_double(void* se
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bicubic2d(
-        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12971,7 +12971,7 @@ void* _lantern_upsample_bicubic2d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bicubic2d_backward_out(
-        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12979,7 +12979,7 @@ void* _lantern_upsample_bicubic2d_backward_tensor_intarrayref_intarrayref_bool_d
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_bicubic2d_backward(
-        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12987,7 +12987,7 @@ void* _lantern_upsample_trilinear3d_out_tensor_tensor_intarrayref_bool_double_do
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_trilinear3d_out(
-        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -12995,7 +12995,7 @@ void* _lantern_upsample_trilinear3d_tensor_intarrayref_bool_double_double_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_trilinear3d(
-        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13003,7 +13003,7 @@ void* _lantern_upsample_trilinear3d_backward_out_tensor_tensor_intarrayref_intar
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_trilinear3d_backward_out(
-        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13011,7 +13011,7 @@ void* _lantern_upsample_trilinear3d_backward_tensor_intarrayref_intarrayref_bool
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_trilinear3d_backward(
-        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject*)align_corners)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13019,7 +13019,7 @@ void* _lantern_upsample_nearest1d_out_tensor_tensor_intarrayref_double(void* out
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest1d_out(
-        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13027,7 +13027,7 @@ void* _lantern_upsample_nearest1d_tensor_intarrayref_double(void* self, void* ou
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest1d(
-        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13035,7 +13035,7 @@ void* _lantern_upsample_nearest1d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest1d_backward_out(
-        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13043,7 +13043,7 @@ void* _lantern_upsample_nearest1d_backward_tensor_intarrayref_intarrayref_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest1d_backward(
-        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)optional(scales))->get()));
+        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)scales)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13051,7 +13051,7 @@ void* _lantern_upsample_nearest2d_out_tensor_tensor_intarrayref_double_double(vo
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest2d_out(
-        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13059,7 +13059,7 @@ void* _lantern_upsample_nearest2d_tensor_intarrayref_double_double(void* self, v
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest2d(
-        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13067,7 +13067,7 @@ void* _lantern_upsample_nearest2d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest2d_backward_out(
-        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13075,7 +13075,7 @@ void* _lantern_upsample_nearest2d_backward_tensor_intarrayref_intarrayref_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest2d_backward(
-        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13083,7 +13083,7 @@ void* _lantern_upsample_nearest3d_out_tensor_tensor_intarrayref_double_double_do
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest3d_out(
-        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)out)->get(), ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13091,7 +13091,7 @@ void* _lantern_upsample_nearest3d_tensor_intarrayref_double_double_double(void*
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest3d(
-        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)self)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13099,7 +13099,7 @@ void* _lantern_upsample_nearest3d_backward_out_tensor_tensor_intarrayref_intarra
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest3d_backward_out(
-        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_input)->get(), ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
@@ -13107,7 +13107,7 @@ void* _lantern_upsample_nearest3d_backward_tensor_intarrayref_intarrayref_double
 {
   LANTERN_FUNCTION_START
     return (void *) new LanternObject(torch::upsample_nearest3d_backward(
-        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)optional(scales_d))->get(), ((LanternObject>*)optional(scales_h))->get(), ((LanternObject>*)optional(scales_w))->get()));
+        ((LanternObject*)grad_output)->get(), ((LanternObject>*)output_size)->get(), ((LanternObject>*)input_size)->get(), ((LanternObject>*)scales_d)->get(), ((LanternObject>*)scales_h)->get(), ((LanternObject>*)scales_w)->get()));
   LANTERN_FUNCTION_END
 }
 
diff --git a/lantern/src/utils.cpp b/lantern/src/utils.cpp
index d2351dabde0faa456655bc706cd2aca0b0c94b7c..8a8fac420ccae063711f68292ae711d771616dcf 100644
--- a/lantern/src/utils.cpp
+++ b/lantern/src/utils.cpp
@@ -50,7 +50,7 @@ void *_lantern_optional_double(double x, bool is_null)
   LANTERN_FUNCTION_START
   c10::optional out;
   if (is_null)
-    out = NULL;
+    out = c10::nullopt;
   else
     out = x;
 
diff --git a/man/autograd_backward.Rd b/man/autograd_backward.Rd
index c2950f180246583a34403d9b8a47eb7113ff7540..0146e3ac7492e179db0ac96895195d9cf14c7d17 100644
--- a/man/autograd_backward.Rd
+++ b/man/autograd_backward.Rd
@@ -42,7 +42,7 @@ This function accumulates gradients in the leaves - you might need to zero
 them before calling it.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_tensor(1, requires_grad = TRUE)
 y <- 2 * x
 
diff --git a/man/autograd_function.Rd b/man/autograd_function.Rd
index 35c74b5b35d5dd46c2fe4ab47e2b14bdc2095cfb..951cbd7f757b1619a4c5af11a94963b08def87e4 100644
--- a/man/autograd_function.Rd
+++ b/man/autograd_function.Rd
@@ -32,7 +32,7 @@ processed in the topological ordering, by calling \code{backward()} methods of e
 Function object, and passing returned gradients on to next Function's.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 exp2 <- autograd_function(
   forward = function(ctx, i) {
diff --git a/man/autograd_grad.Rd b/man/autograd_grad.Rd
index 6ae0e294b1562f05b90b69d7ea4c42b659102268..87f3f6c2f5ccb1645eec1f736a69712d2dd59aaa 100644
--- a/man/autograd_grad.Rd
+++ b/man/autograd_grad.Rd
@@ -46,7 +46,7 @@ the specified inputs. If it’s \code{FALSE}, then gradient w.r.t. all remaining
 will still be computed, and will be accumulated into their \code{.grad} attribute.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_tensor(0.5, requires_grad = TRUE)
 b <- torch_tensor(0.9, requires_grad = TRUE)
 x <- torch_tensor(runif(100))
diff --git a/man/dataset.Rd b/man/dataset.Rd
index 75430d621fbda10df4ecee59ce5c0b4fcc845cc0..99e8010dd65b9d2a3691787701871e68ec29aa66 100644
--- a/man/dataset.Rd
+++ b/man/dataset.Rd
@@ -4,7 +4,7 @@
 \alias{dataset}
 \title{An abstract class representing a \code{Dataset}.}
 \usage{
-dataset(name = NULL, inherit = Dataset, ...)
+dataset(name = NULL, inherit = Dataset, ..., parent_env = parent.frame())
 }
 \arguments{
 \item{name}{a name for the dataset. It it's also used as the class
@@ -14,6 +14,9 @@ for it.}
 new dataset.}
 
 \item{...}{public methods for the dataset class}
+
+\item{parent_env}{An environment to use as the parent of newly-created
+objects.}
 }
 \description{
 All datasets that represent a map from keys to data samples should subclass
diff --git a/man/default_dtype.Rd b/man/default_dtype.Rd
index a18596ee1dfc64990495aec4e38fc65b0057da10..38c1a284615184c0048320e4d12c4a6fefc7d77e 100644
--- a/man/default_dtype.Rd
+++ b/man/default_dtype.Rd
@@ -16,3 +16,4 @@ torch_get_default_dtype()
 \description{
 Gets and sets the default floating point dtype.
 }
+\concept{tensor-attributes}
diff --git a/docs/reference/figures/torch.png b/man/figures/torch-full.png
similarity index 100%
rename from docs/reference/figures/torch.png
rename to man/figures/torch-full.png
diff --git a/man/figures/torch.png b/man/figures/torch.png
index 61d24b86074b110f4cf3298f417c4148938c8f05..5979d02181f69b5a53de418c149a7542531b0169 100644
Binary files a/man/figures/torch.png and b/man/figures/torch.png differ
diff --git a/man/is_nn_buffer.Rd b/man/is_nn_buffer.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..6abd6bdc3646e7ee7ffdb95af3ae3746220a6a0a
--- /dev/null
+++ b/man/is_nn_buffer.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{is_nn_buffer}
+\alias{is_nn_buffer}
+\title{Checks if the object is a nn_buffer}
+\usage{
+is_nn_buffer(x)
+}
+\arguments{
+\item{x}{object to check}
+}
+\description{
+Checks if the object is a nn_buffer
+}
diff --git a/man/is_nn_module.Rd b/man/is_nn_module.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..76deffc4fe832888b24b7c6190fc0522746498f3
--- /dev/null
+++ b/man/is_nn_module.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{is_nn_module}
+\alias{is_nn_module}
+\title{Checks if the object is an nn_module}
+\usage{
+is_nn_module(x)
+}
+\arguments{
+\item{x}{object to check}
+}
+\description{
+Checks if the object is an nn_module
+}
diff --git a/man/is_nn_parameter.Rd b/man/is_nn_parameter.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..d60fc8d3bcee186dc9d8f91a91dbde61df842275
--- /dev/null
+++ b/man/is_nn_parameter.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{is_nn_parameter}
+\alias{is_nn_parameter}
+\title{Checks if an object is a nn_parameter}
+\usage{
+is_nn_parameter(x)
+}
+\arguments{
+\item{x}{the object to check}
+}
+\description{
+Checks if an object is a nn_parameter
+}
diff --git a/man/is_torch_device.Rd b/man/is_torch_device.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..bc4a64ccf3651ebbccadceba9e70856405650c14
--- /dev/null
+++ b/man/is_torch_device.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/device.R
+\name{is_torch_device}
+\alias{is_torch_device}
+\title{Checks if object is a device}
+\usage{
+is_torch_device(x)
+}
+\arguments{
+\item{x}{object to check}
+}
+\description{
+Checks if object is a device
+}
+\concept{tensor-attributes}
diff --git a/man/is_torch_dtype.Rd b/man/is_torch_dtype.Rd
index 42dd780d66845772bcaefe4ff1292210f32a2068..633bb12c4af10efe28440d8957b4b19a477e84f2 100644
--- a/man/is_torch_dtype.Rd
+++ b/man/is_torch_dtype.Rd
@@ -12,3 +12,4 @@ is_torch_dtype(x)
 \description{
 Check if object is a torch data type
 }
+\concept{tensor-attributes}
diff --git a/man/is_undefined_tensor.Rd b/man/is_undefined_tensor.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..d4be336202ac28e279dc0ae063b5af05e850775c
--- /dev/null
+++ b/man/is_undefined_tensor.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tensor.R
+\name{is_undefined_tensor}
+\alias{is_undefined_tensor}
+\title{Checks if a tensor is undefined}
+\usage{
+is_undefined_tensor(x)
+}
+\arguments{
+\item{x}{tensor to check}
+}
+\description{
+Checks if a tensor is undefined
+}
diff --git a/man/load_state_dict.Rd b/man/load_state_dict.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..c6574d7d4ee5d14d59b13adf7e0731e9a7a749f7
--- /dev/null
+++ b/man/load_state_dict.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/save.R
+\name{load_state_dict}
+\alias{load_state_dict}
+\title{Load a state dict file}
+\usage{
+load_state_dict(path)
+}
+\arguments{
+\item{path}{to the state dict file}
+}
+\value{
+a named list of tensors.
+}
+\description{
+This function should only be used to load models saved in python.
+For it to work correctly you need to use \code{torch.save} with the flag:
+\verb{_use_new_zipfile_serialization=True} and also remove all \code{nn.Parameter}
+classes from the tensors in the dict.
+}
+\details{
+The above might change with development of \href{https://github.com/pytorch/pytorch/issues/37213}{this}
+in pytorch's C++ api.
+}
+\concept{serialization}
diff --git a/man/nn_adaptive_avg_pool1d.Rd b/man/nn_adaptive_avg_pool1d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..55c5c22d3e0d1658afb95629d2e6252b7af3062e
--- /dev/null
+++ b/man/nn_adaptive_avg_pool1d.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_avg_pool1d}
+\alias{nn_adaptive_avg_pool1d}
+\title{Applies a 1D adaptive average pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_avg_pool1d(output_size)
+}
+\arguments{
+\item{output_size}{the target output size H}
+}
+\description{
+The output size is H, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5
+m = nn_adaptive_avg_pool1d(5)
+input <- torch_randn(1, 64, 8)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_avg_pool2d.Rd b/man/nn_adaptive_avg_pool2d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..46d95ed735f42b5ce03cc17945701297bbec9688
--- /dev/null
+++ b/man/nn_adaptive_avg_pool2d.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_avg_pool2d}
+\alias{nn_adaptive_avg_pool2d}
+\title{Applies a 2D adaptive average pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_avg_pool2d(output_size)
+}
+\arguments{
+\item{output_size}{the target output size of the image of the form H x W.
+Can be a tuple (H, W) or a single H for a square image H x H.
+H and W can be either a \code{int}, or \code{NULL} which means the size will
+be the same as that of the input.}
+}
+\description{
+The output is of size H x W, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7
+m <- nn_adaptive_avg_pool2d(c(5,7))
+input <- torch_randn(1, 64, 8, 9)
+output <- m(input)
+# target output size of 7x7 (square)
+m <- nn_adaptive_avg_pool2d(7)
+input <- torch_randn(1, 64, 10, 9)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_avg_pool3d.Rd b/man/nn_adaptive_avg_pool3d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..ce1796d216de5a13e344c3f6a2cfcb3080dbc8b2
--- /dev/null
+++ b/man/nn_adaptive_avg_pool3d.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_avg_pool3d}
+\alias{nn_adaptive_avg_pool3d}
+\title{Applies a 3D adaptive average pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_avg_pool3d(output_size)
+}
+\arguments{
+\item{output_size}{the target output size of the form D x H x W.
+Can be a tuple (D, H, W) or a single number D for a cube D x D x D.
+D, H and W can be either a \code{int}, or \code{None} which means the size will
+be the same as that of the input.}
+}
+\description{
+The output is of size D x H x W, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7x9
+m <- nn_adaptive_avg_pool3d(c(5,7,9))
+input <- torch_randn(1, 64, 8, 9, 10)
+output <- m(input)
+# target output size of 7x7x7 (cube)
+m <- nn_adaptive_avg_pool3d(7)
+input <- torch_randn(1, 64, 10, 9, 8)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_max_pool1d.Rd b/man/nn_adaptive_max_pool1d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..f0302911cd7ef63b412f8883999ca28abf8927bd
--- /dev/null
+++ b/man/nn_adaptive_max_pool1d.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_max_pool1d}
+\alias{nn_adaptive_max_pool1d}
+\title{Applies a 1D adaptive max pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_max_pool1d(output_size, return_indices = FALSE)
+}
+\arguments{
+\item{output_size}{the target output size H}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool1d]{nn_max_unpool1d()}}. Default: \code{FALSE}}
+}
+\description{
+The output size is H, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5
+m <- nn_adaptive_max_pool1d(5)
+input <- torch_randn(1, 64, 8)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_max_pool2d.Rd b/man/nn_adaptive_max_pool2d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..39ba4a7e7e11527fa61fd255bf5e7adbe84f639e
--- /dev/null
+++ b/man/nn_adaptive_max_pool2d.Rd
@@ -0,0 +1,34 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_max_pool2d}
+\alias{nn_adaptive_max_pool2d}
+\title{Applies a 2D adaptive max pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_max_pool2d(output_size, return_indices = FALSE)
+}
+\arguments{
+\item{output_size}{the target output size of the image of the form H x W.
+Can be a tuple \verb{(H, W)} or a single H for a square image H x H.
+H and W can be either a \code{int}, or \code{None} which means the size will
+be the same as that of the input.}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool2d]{nn_max_unpool2d()}}. Default: \code{FALSE}}
+}
+\description{
+The output is of size H x W, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7
+m <- nn_adaptive_max_pool2d(c(5,7))
+input <- torch_randn(1, 64, 8, 9)
+output <- m(input)
+# target output size of 7x7 (square)
+m <- nn_adaptive_max_pool2d(7)
+input <- torch_randn(1, 64, 10, 9)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_adaptive_max_pool3d.Rd b/man/nn_adaptive_max_pool3d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..4c2d7ebc0f0b2ad78a022252c62ee2f1b4cdb1de
--- /dev/null
+++ b/man/nn_adaptive_max_pool3d.Rd
@@ -0,0 +1,34 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_adaptive_max_pool3d}
+\alias{nn_adaptive_max_pool3d}
+\title{Applies a 3D adaptive max pooling over an input signal composed of several input planes.}
+\usage{
+nn_adaptive_max_pool3d(output_size, return_indices = FALSE)
+}
+\arguments{
+\item{output_size}{the target output size of the image of the form D x H x W.
+Can be a tuple (D, H, W) or a single D for a cube D x D x D.
+D, H and W can be either a \code{int}, or \code{None} which means the size will
+be the same as that of the input.}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool3d]{nn_max_unpool3d()}}. Default: \code{FALSE}}
+}
+\description{
+The output is of size D x H x W, for any input size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# target output size of 5x7x9
+m <- nn_adaptive_max_pool3d(c(5,7,9))
+input <- torch_randn(1, 64, 8, 9, 10)
+output <- m(input)
+# target output size of 7x7x7 (cube)
+m <- nn_adaptive_max_pool3d(7)
+input <- torch_randn(1, 64, 10, 9, 8)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_avg_pool1d.Rd b/man/nn_avg_pool1d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..de708603f6d97cf9accbe7c8af487916eb7e956c
--- /dev/null
+++ b/man/nn_avg_pool1d.Rd
@@ -0,0 +1,65 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_avg_pool1d}
+\alias{nn_avg_pool1d}
+\title{Applies a 1D average pooling over an input signal composed of several
+input planes.}
+\usage{
+nn_avg_pool1d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on both sides}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+
+\item{count_include_pad}{when TRUE, will include the zero-padding in the averaging calculation}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, L)},
+output \eqn{(N, C, L_{out})} and \code{kernel_size} \eqn{k}
+can be precisely described as:
+
+\deqn{
+  \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
+\mbox{input}(N_i, C_j, \mbox{stride} \times l + m)
+}
+}
+\details{
+If \code{padding} is non-zero, then the input is implicitly zero-padded on both sides
+for \code{padding} number of points.
+
+The parameters \code{kernel_size}, \code{stride}, \code{padding} can each be
+an \code{int} or a one-element tuple.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, L_{in})}
+\item Output: \eqn{(N, C, L_{out})}, where
+}
+
+\deqn{
+  L_{out} = \left\lfloor \frac{L_{in} +
+      2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool with window of size=3, stride=2
+m <- nn_avg_pool1d(3, stride=2)
+m(torch_randn(1, 1, 8))
+
+}
+}
diff --git a/man/nn_avg_pool2d.Rd b/man/nn_avg_pool2d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..cb0e4af1892a63e86793e99c1280381b9bba03a5
--- /dev/null
+++ b/man/nn_avg_pool2d.Rd
@@ -0,0 +1,79 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_avg_pool2d}
+\alias{nn_avg_pool2d}
+\title{Applies a 2D average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_avg_pool2d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on both sides}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+
+\item{count_include_pad}{when TRUE, will include the zero-padding in the averaging calculation}
+
+\item{divisor_override}{if specified, it will be used as divisor, otherwise \code{kernel_size} will be used}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, H, W)},
+output \eqn{(N, C, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kH, kW)}
+can be precisely described as:
+
+\deqn{
+  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
+input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
+}
+}
+\details{
+If \code{padding} is non-zero, then the input is implicitly zero-padded on both sides
+for \code{padding} number of points.
+
+The parameters \code{kernel_size}, \code{stride}, \code{padding} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the height and width dimension
+\item a \code{tuple} of two ints -- in which case, the first \code{int} is used for the height dimension,
+and the second \code{int} for the width dimension
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+}
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] -
+      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool of square window of size=3, stride=2
+m <- nn_avg_pool2d(3, stride=2)
+# pool of non-square window
+m <- nn_avg_pool2d(c(3, 2), stride=c(2, 1))
+input <- torch_randn(20, 16, 50, 32)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_avg_pool3d.Rd b/man/nn_avg_pool3d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..ddcd87358ceb6b2c3d71ed1c9c6bd54ca6661deb
--- /dev/null
+++ b/man/nn_avg_pool3d.Rd
@@ -0,0 +1,85 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_avg_pool3d}
+\alias{nn_avg_pool3d}
+\title{Applies a 3D average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_avg_pool3d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE,
+  divisor_override = NULL
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on all three sides}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+
+\item{count_include_pad}{when TRUE, will include the zero-padding in the averaging calculation}
+
+\item{divisor_override}{if specified, it will be used as divisor, otherwise \code{kernel_size} will be used}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)},
+output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kD, kH, kW)}
+can be precisely described as:
+
+\deqn{
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = & \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+& \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
+\end{array}
+}
+}
+\details{
+If \code{padding} is non-zero, then the input is implicitly zero-padded on all three sides
+for \code{padding} number of points.
+
+The parameters \code{kernel_size}, \code{stride} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the depth, height and width dimension
+\item a \code{tuple} of three ints -- in which case, the first \code{int} is used for the depth dimension,
+the second \code{int} for the height dimension and the third \code{int} for the width dimension
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+}
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] -
+      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+}
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] -
+      \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool of square window of size=3, stride=2
+m = nn_avg_pool3d(3, stride=2)
+# pool of non-square window
+m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
+input = torch_randn(20, 16, 50,44, 31)
+output = m(input)
+
+}
+}
diff --git a/man/nn_batch_norm1d.Rd b/man/nn_batch_norm1d.Rd
index 89afd7a95cb31d4735f9ee0edf04f9f4c2183434..a9fc56560de7460c3dd7736a9a70556b3a387e6a 100644
--- a/man/nn_batch_norm1d.Rd
+++ b/man/nn_batch_norm1d.Rd
@@ -77,7 +77,7 @@ on \verb{(N, L)} slices, it's common terminology to call this Temporal Batch Nor
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With Learnable Parameters
 m <- nn_batch_norm1d(100)
 # Without Learnable Parameters
diff --git a/man/nn_batch_norm2d.Rd b/man/nn_batch_norm2d.Rd
index 09dfcd2633aa65845982cfca47aa1570563d56e6..a307791f714abe58741d99a4098400e1e85dee9c 100644
--- a/man/nn_batch_norm2d.Rd
+++ b/man/nn_batch_norm2d.Rd
@@ -75,7 +75,7 @@ on \verb{(N, H, W)} slices, it's common terminology to call this Spatial Batch N
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With Learnable Parameters
 m <- nn_batch_norm2d(100)
 # Without Learnable Parameters
diff --git a/man/nn_bce_loss.Rd b/man/nn_bce_loss.Rd
index 11a5ab1e371dc3f3eae8e88f5fcb098aebb90193..836c025b9280a70800d68b64711270577b365d6a 100644
--- a/man/nn_bce_loss.Rd
+++ b/man/nn_bce_loss.Rd
@@ -70,7 +70,7 @@ shape as input.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_sigmoid()
 loss <- nn_bce_loss()
 input <- torch_randn(3, requires_grad=TRUE)
diff --git a/man/nn_bce_with_logits_loss.Rd b/man/nn_bce_with_logits_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..84674f756ca8521c7c313faf2dbd451757ef31ae
--- /dev/null
+++ b/man/nn_bce_with_logits_loss.Rd
@@ -0,0 +1,95 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_bce_with_logits_loss}
+\alias{nn_bce_with_logits_loss}
+\title{BCE with logits loss}
+\usage{
+nn_bce_with_logits_loss(weight = NULL, reduction = "mean", pos_weight = NULL)
+}
+\arguments{
+\item{weight}{(Tensor, optional): a manual rescaling weight given to the loss
+of each batch element. If given, has to be a Tensor of size \code{nbatch}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+
+\item{pos_weight}{(Tensor, optional): a weight of positive examples.
+Must be a vector with length equal to the number of classes.}
+}
+\description{
+This loss combines a \code{Sigmoid} layer and the \code{BCELoss} in one single
+class. This version is more numerically stable than using a plain \code{Sigmoid}
+followed by a \code{BCELoss} as, by combining the operations into one layer,
+we take advantage of the log-sum-exp trick for numerical stability.
+}
+\details{
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described as:
+
+\deqn{
+  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = - w_n \left[ y_n \cdot \log \sigma(x_n)
+                   + (1 - y_n) \cdot \log (1 - \sigma(x_n)) \right],
+}
+
+where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then
+
+\deqn{
+  \ell(x, y) = \begin{array}{ll}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+This is used for measuring the error of a reconstruction in for example
+an auto-encoder. Note that the targets \code{t[i]} should be numbers
+between 0 and 1.
+It's possible to trade off recall and precision by adding weights to positive examples.
+In the case of multi-label classification the loss can be described as:
+
+\deqn{
+\ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad
+l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log \sigma(x_{n,c})
++ (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c})) \right],
+}
+where \eqn{c} is the class number (\eqn{c > 1} for multi-label binary
+classification,
+
+\eqn{c = 1} for single-label binary classification),
+\eqn{n} is the number of the sample in the batch and
+\eqn{p_c} is the weight of the positive answer for the class \eqn{c}.
+\eqn{p_c > 1} increases the recall, \eqn{p_c < 1} increases the precision.
+For example, if a dataset contains 100 positive and 300 negative examples of a single class,
+then \code{pos_weight} for the class should be equal to \eqn{\frac{300}{100}=3}.
+The loss would act as if the dataset contains \eqn{3\times 100=300} positive examples.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N, *)}, same
+shape as input.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_bce_with_logits_loss()
+input <- torch_randn(3, requires_grad=TRUE)
+target <- torch_empty(3)$random_(1, 2)
+output <- loss(input, target)
+output$backward()
+
+target <- torch_ones(10, 64, dtype=torch_float32())  # 64 classes, batch size = 10
+output <- torch_full(c(10, 64), 1.5)  # A prediction (logit)
+pos_weight <- torch_ones(64)  # All weights are equal to 1
+criterion <- nn_bce_with_logits_loss(pos_weight=pos_weight)
+criterion(output, target)  # -log(sigmoid(1.5))
+
+}
+}
diff --git a/man/nn_bilinear.Rd b/man/nn_bilinear.Rd
index 319e724a8b98e306e13ea41b55d9c97b9763978c..38cd14c8834fa5e8e3e82c12e3420d809fc2e018 100644
--- a/man/nn_bilinear.Rd
+++ b/man/nn_bilinear.Rd
@@ -47,7 +47,7 @@ If \code{bias} is \code{TRUE}, the values are initialized from
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_bilinear(20, 30, 50)
 input1 <- torch_randn(128, 20)
 input2 <- torch_randn(128, 30)
diff --git a/man/nn_buffer.Rd b/man/nn_buffer.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..6eb25d9f36590df37bb6d10c84d1bca86d9009b7
--- /dev/null
+++ b/man/nn_buffer.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{nn_buffer}
+\alias{nn_buffer}
+\title{Creates a nn_buffer}
+\usage{
+nn_buffer(x, persistent = TRUE)
+}
+\arguments{
+\item{x}{the tensor that will be converted to nn_buffer}
+
+\item{persistent}{whether the buffer should be persistent or not.}
+}
+\description{
+Indicates that a tensor is a buffer in a nn_module
+}
diff --git a/man/nn_celu.Rd b/man/nn_celu.Rd
index f73023dc0a2e243816697c853572ec6e4a236358..1610da86e697d299bc38d16cba9832c87aa1b567 100644
--- a/man/nn_celu.Rd
+++ b/man/nn_celu.Rd
@@ -32,7 +32,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_celu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_conv1d.Rd b/man/nn_conv1d.Rd
index afdaf9599626519390a2f36ac3d1752b67c65e5b..c5da12ae30c91314832e6bd5e5c41a6593ed6de0 100644
--- a/man/nn_conv1d.Rd
+++ b/man/nn_conv1d.Rd
@@ -127,7 +127,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_conv1d(16, 33, 3, stride=2)
 input <- torch_randn(20, 16, 50)
 output <- m(input)
diff --git a/man/nn_conv2d.Rd b/man/nn_conv2d.Rd
index c547406d75ee78d8a13f7000dee3dba5973ab6ee..ba8e5f5d9a705693f04479794fc6109fcec9e615 100644
--- a/man/nn_conv2d.Rd
+++ b/man/nn_conv2d.Rd
@@ -143,7 +143,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # With square kernels and equal stride
 m <- nn_conv2d(16, 33, 3, stride = 2)
diff --git a/man/nn_conv3d.Rd b/man/nn_conv3d.Rd
index da36ea3f0fb1553548e761bbd056a31cef011e8e..61f8a0a6b4305e82052a38166ebcde8fb5df83cd 100644
--- a/man/nn_conv3d.Rd
+++ b/man/nn_conv3d.Rd
@@ -130,7 +130,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With square kernels and equal stride
 m <- nn_conv3d(16, 33, 3, stride=2)
 # non-square kernels and unequal stride and with padding
diff --git a/man/nn_conv_transpose1d.Rd b/man/nn_conv_transpose1d.Rd
index 779b68658ec2c22a8ac843973d665bc98eaa1578..9ec9486eb841d20f22a56157167755b1f0ca93aa 100644
--- a/man/nn_conv_transpose1d.Rd
+++ b/man/nn_conv_transpose1d.Rd
@@ -125,7 +125,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_conv_transpose1d(32, 16, 2)
 input <- torch_randn(10, 32, 2)
 output <- m(input)
diff --git a/man/nn_conv_transpose2d.Rd b/man/nn_conv_transpose2d.Rd
index e09b88c26238140e87d60ed7e811fcfe7830f066..6f0921c99b889c2c409ef6ea09ec8504cedf5ddb 100644
--- a/man/nn_conv_transpose2d.Rd
+++ b/man/nn_conv_transpose2d.Rd
@@ -135,7 +135,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # With square kernels and equal stride
 m <- nn_conv_transpose2d(16, 33, 3, stride=2)
 # non-square kernels and unequal stride and with padding
diff --git a/man/nn_conv_transpose3d.Rd b/man/nn_conv_transpose3d.Rd
index 1122159d8b1d914146533f55a8e365b24f502f4e..b00b749d467339d2deb1a484432ffe9c2895dfb9 100644
--- a/man/nn_conv_transpose3d.Rd
+++ b/man/nn_conv_transpose3d.Rd
@@ -144,7 +144,7 @@ sampled from \eqn{\mathcal{U}(-\sqrt{k}, \sqrt{k})} where
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 # With square kernels and equal stride
 m <- nn_conv_transpose3d(16, 33, 3, stride=2)
diff --git a/man/nn_cosine_embedding_loss.Rd b/man/nn_cosine_embedding_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..9444260d3b6315d592eed9dac3186ea3e567289a
--- /dev/null
+++ b/man/nn_cosine_embedding_loss.Rd
@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_cosine_embedding_loss}
+\alias{nn_cosine_embedding_loss}
+\title{Cosine embedding loss}
+\usage{
+nn_cosine_embedding_loss(margin = 0, reduction = "mean")
+}
+\arguments{
+\item{margin}{(float, optional): Should be a number from \eqn{-1} to \eqn{1},
+\eqn{0} to \eqn{0.5} is suggested. If \code{margin} is missing, the
+default value is \eqn{0}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the loss given input tensors
+\eqn{x_1}, \eqn{x_2} and a \code{Tensor} label \eqn{y} with values 1 or -1.
+This is used for measuring whether two inputs are similar or dissimilar,
+using the cosine distance, and is typically used for learning nonlinear
+embeddings or semi-supervised learning.
+The loss function for each sample is:
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) =
+  \begin{array}{ll}
+1 - \cos(x_1, x_2), & \mbox{if } y = 1 \\
+\max(0, \cos(x_1, x_2) - \mbox{margin}), & \mbox{if } y = -1
+\end{array}
+}
+}
diff --git a/man/nn_cross_entropy_loss.Rd b/man/nn_cross_entropy_loss.Rd
index 15bff80715b639156ba1a3667465e25a764d2730..3c16d33455599d292e43dc5b192b71e39226ebab 100644
--- a/man/nn_cross_entropy_loss.Rd
+++ b/man/nn_cross_entropy_loss.Rd
@@ -74,7 +74,7 @@ of K-dimensional loss.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 loss <- nn_cross_entropy_loss()
 input <- torch_randn(3, 5, requires_grad=TRUE)
 target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long())
diff --git a/man/nn_ctc_loss.Rd b/man/nn_ctc_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..26c0e40e91d044dee08fe1648822bb1288b66473
--- /dev/null
+++ b/man/nn_ctc_loss.Rd
@@ -0,0 +1,127 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_ctc_loss}
+\alias{nn_ctc_loss}
+\title{The Connectionist Temporal Classification loss.}
+\usage{
+nn_ctc_loss(blank = 0, reduction = "mean", zero_infinity = FALSE)
+}
+\arguments{
+\item{blank}{(int, optional): blank label. Default \eqn{0}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the output losses will be divided by the target lengths and
+then the mean over the batch is taken. Default: \code{'mean'}}
+
+\item{zero_infinity}{(bool, optional):
+Whether to zero infinite losses and the associated gradients.
+Default: \code{FALSE}
+Infinite losses mainly occur when the inputs are too short
+to be aligned to the targets.}
+}
+\description{
+Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
+probability of possible alignments of input to target, producing a loss value which is differentiable
+with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
+limits the length of the target sequence such that it must be \eqn{\leq} the input length.
+}
+\note{
+In order to use CuDNN, the following must be satisfied: \code{targets} must be
+in concatenated format, all \code{input_lengths} must be \code{T}.  \eqn{blank=0},
+\code{target_lengths} \eqn{\leq 256}, the integer arguments must be of
+The regular implementation uses the (more common in PyTorch) \code{torch_long} dtype.
+dtype \code{torch_int32}.
+
+In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting \code{torch.backends.cudnn.deterministic = TRUE}.
+}
+\section{Shape}{
+
+\itemize{
+\item Log_probs: Tensor of size \eqn{(T, N, C)},
+where \eqn{T = \mbox{input length}},
+\eqn{N = \mbox{batch size}}, and
+\eqn{C = \mbox{number of classes (including blank)}}.
+The logarithmized probabilities of the outputs (e.g. obtained with
+[nnf)log_softmax()]).
+\item Targets: Tensor of size \eqn{(N, S)} or
+\eqn{(\mbox{sum}(\mbox{target\_lengths}))},
+where \eqn{N = \mbox{batch size}} and
+\eqn{S = \mbox{max target length, if shape is } (N, S)}.
+It represent the target sequences. Each element in the target
+sequence is a class index. And the target index cannot be blank (default=0).
+In the \eqn{(N, S)} form, targets are padded to the
+length of the longest sequence, and stacked.
+In the \eqn{(\mbox{sum}(\mbox{target\_lengths}))} form,
+the targets are assumed to be un-padded and
+concatenated within 1 dimension.
+\item Input_lengths: Tuple or tensor of size \eqn{(N)},
+where \eqn{N = \mbox{batch size}}. It represent the lengths of the
+inputs (must each be \eqn{\leq T}). And the lengths are specified
+for each sequence to achieve masking under the assumption that sequences
+are padded to equal lengths.
+\item Target_lengths: Tuple or tensor of size \eqn{(N)},
+where \eqn{N = \mbox{batch size}}. It represent lengths of the targets.
+Lengths are specified for each sequence to achieve masking under the
+assumption that sequences are padded to equal lengths. If target shape is
+\eqn{(N,S)}, target_lengths are effectively the stop index
+\eqn{s_n} for each target sequence, such that \code{target_n = targets[n,0:s_n]} for
+each target in a batch. Lengths must each be \eqn{\leq S}
+If the targets are given as a 1d tensor that is the concatenation of individual
+targets, the target_lengths must add up to the total length of the tensor.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then
+\eqn{(N)}, where \eqn{N = \mbox{batch size}}.
+}
+
+[nnf)log_softmax()]: R:nnf)log_softmax()
+[n,0:s_n]: R:n,0:s_n
+}
+
+\examples{
+if (torch_is_installed()) {
+# Target are to be padded
+T <- 50      # Input sequence length
+C <- 20      # Number of classes (including blank)
+N <- 16      # Batch size
+S <- 30      # Target sequence length of longest target in batch (padding length)
+S_min <- 10  # Minimum target length, for demonstration purposes
+
+# Initialize random batch of input vectors, for *size = (T,N,C)
+input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+
+# Initialize random batch of targets (0 = blank, 1:C = classes)
+target <- torch_randint(low=1, high=C, size=c(N, S), dtype=torch_long())
+
+input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+target_lengths <- torch_randint(low=S_min, high=S, size=c(N), dtype=torch_long())
+ctc_loss <- nn_ctc_loss()
+loss <- ctc_loss(input, target, input_lengths, target_lengths)
+loss$backward()
+
+
+# Target are to be un-padded
+T <- 50      # Input sequence length
+C <- 20      # Number of classes (including blank)
+N <- 16      # Batch size
+
+# Initialize random batch of input vectors, for *size = (T,N,C)
+input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()
+input_lengths <- torch_full(size=c(N), fill_value=TRUE, dtype=torch_long())
+
+# Initialize random batch of targets (0 = blank, 1:C = classes)
+target_lengths <- torch_randint(low=1, high=T, size=c(N), dtype=torch_long())
+target <- torch_randint(low=1, high=C, size=as.integer(sum(target_lengths)), dtype=torch_long())
+ctc_loss <- nn_ctc_loss()
+loss <- ctc_loss(input, target, input_lengths, target_lengths)
+loss$backward()
+
+}
+}
+\references{
+A. Graves et al.: Connectionist Temporal Classification:
+Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
+https://www.cs.toronto.edu/~graves/icml_2006.pdf
+}
diff --git a/man/nn_dropout.Rd b/man/nn_dropout.Rd
index e56c82b1903363a79597db5ee001e426138396e8..2d6da349854e47807522b02b807dfd61a66ff0a8 100644
--- a/man/nn_dropout.Rd
+++ b/man/nn_dropout.Rd
@@ -35,7 +35,7 @@ identity function.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_dropout(p = 0.2)
 input <- torch_randn(20, 16)
 output <- m(input)
diff --git a/man/nn_dropout2d.Rd b/man/nn_dropout2d.Rd
index 500a0479f14193c6ae7e90debf7529205a420b50..166feebd0ea5c5e1812aedb3c9d6e274f12bcf40 100644
--- a/man/nn_dropout2d.Rd
+++ b/man/nn_dropout2d.Rd
@@ -23,7 +23,7 @@ probability \code{p} using samples from a Bernoulli distribution.
 Usually the input comes from \link{nn_conv2d} modules.
 
 As described in the paper
-\href{http://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
+\href{https://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
 if adjacent pixels within feature maps are strongly correlated
 (as is normally the case in early convolution layers) then i.i.d. dropout
 will not regularize the activations and will otherwise just result
@@ -40,7 +40,7 @@ feature maps and should be used instead.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_dropout2d(p = 0.2)
 input <- torch_randn(20, 16, 32, 32)
 output <- m(input)
diff --git a/man/nn_dropout3d.Rd b/man/nn_dropout3d.Rd
index 87f6dd168f81c13714ff618280adb339a64704f9..fd5203124adff68956c288f7d8e37058008d36fd 100644
--- a/man/nn_dropout3d.Rd
+++ b/man/nn_dropout3d.Rd
@@ -23,7 +23,7 @@ probability \code{p} using samples from a Bernoulli distribution.
 Usually the input comes from \link{nn_conv2d} modules.
 
 As described in the paper
-\href{http://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
+\href{https://arxiv.org/abs/1411.4280}{Efficient Object Localization Using Convolutional Networks} ,
 if adjacent pixels within feature maps are strongly correlated
 (as is normally the case in early convolution layers) then i.i.d. dropout
 will not regularize the activations and will otherwise just result
@@ -41,7 +41,7 @@ feature maps and should be used instead.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_dropout3d(p = 0.2)
 input <- torch_randn(20, 16, 4, 32, 32)
 output <- m(input)
diff --git a/man/nn_elu.Rd b/man/nn_elu.Rd
index 4804599a299ad99e0de28b20c0ef69a652185e3f..b7052433dcfc253f3653189de9c699a8240dde4a 100644
--- a/man/nn_elu.Rd
+++ b/man/nn_elu.Rd
@@ -29,7 +29,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_elu()
 input <- torch_randn(2)
 output <-  m(input)
diff --git a/man/nn_embedding.Rd b/man/nn_embedding.Rd
index e12961531e589336b690fb09dfae8c228fe82627..42c28dff0e09d1f2b3734ea5aa32e57ef28bcfc5 100644
--- a/man/nn_embedding.Rd
+++ b/man/nn_embedding.Rd
@@ -72,7 +72,7 @@ initialized from \eqn{\mathcal{N}(0, 1)}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # an Embedding module containing 10 tensors of size 3
 embedding <- nn_embedding(10, 3)
 # a batch of 2 samples of 4 indices each
diff --git a/man/nn_fractional_max_pool2d.Rd b/man/nn_fractional_max_pool2d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..47f35e0cba9c6df68424aa4001ea081df092eb08
--- /dev/null
+++ b/man/nn_fractional_max_pool2d.Rd
@@ -0,0 +1,40 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_fractional_max_pool2d}
+\alias{nn_fractional_max_pool2d}
+\title{Applies a 2D fractional max pooling over an input signal composed of several input planes.}
+\usage{
+nn_fractional_max_pool2d(
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k) or a tuple \verb{(kh, kw)}}
+
+\item{output_size}{the target output size of the image of the form \verb{oH x oW}.
+Can be a tuple \verb{(oH, oW)} or a single number oH for a square image \verb{oH x oH}}
+
+\item{output_ratio}{If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool2d]{nn_max_unpool2d()}}. Default: \code{FALSE}}
+}
+\description{
+Fractional MaxPooling is described in detail in the paper
+\href{https://arxiv.org/abs/1412.6071}{Fractional MaxPooling} by Ben Graham
+}
+\details{
+The max-pooling operation is applied in \eqn{kH \times kW} regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+
+}
+}
diff --git a/man/nn_fractional_max_pool3d.Rd b/man/nn_fractional_max_pool3d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..16d8771c4287685b45957e6cc124a15ca07226ac
--- /dev/null
+++ b/man/nn_fractional_max_pool3d.Rd
@@ -0,0 +1,46 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_fractional_max_pool3d}
+\alias{nn_fractional_max_pool3d}
+\title{Applies a 3D fractional max pooling over an input signal composed of several input planes.}
+\usage{
+nn_fractional_max_pool3d(
+  kernel_size,
+  output_size = NULL,
+  output_ratio = NULL,
+  return_indices = FALSE
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k x k) or a tuple \verb{(kt x kh x kw)}}
+
+\item{output_size}{the target output size of the image of the form \verb{oT x oH x oW}.
+Can be a tuple \verb{(oT, oH, oW)} or a single number oH for a square image \verb{oH x oH x oH}}
+
+\item{output_ratio}{If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)}
+
+\item{return_indices}{if \code{TRUE}, will return the indices along with the outputs.
+Useful to pass to \code{\link[=nn_max_unpool3d]{nn_max_unpool3d()}}. Default: \code{FALSE}}
+}
+\description{
+Fractional MaxPooling is described in detail in the paper
+\href{https://arxiv.org/abs/1412.6071}{Fractional MaxPooling} by Ben Graham
+}
+\details{
+The max-pooling operation is applied in \eqn{kTxkHxkW} regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.
+}
+\examples{
+if (torch_is_installed()) {
+# pool of cubic window of size=3, and target output size 13x12x11
+m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11))
+# pool of cubic window and target output size being half of input size
+m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5))
+input = torch_randn(20, 16, 50, 32, 16)
+output = m(input)
+
+}
+}
diff --git a/man/nn_gelu.Rd b/man/nn_gelu.Rd
index 704760939ccf5557443fe82e86e3007c0e6b436b..470be9acbca5d34ae738ef470a1c01968fb086ef 100644
--- a/man/nn_gelu.Rd
+++ b/man/nn_gelu.Rd
@@ -23,7 +23,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m = nn_gelu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_glu.Rd b/man/nn_glu.Rd
index 5e3d3bce5074f590d13c2ba8638297d3215f9680..4c778c50ad148737c913909f06dedf4ad6b05b06 100644
--- a/man/nn_glu.Rd
+++ b/man/nn_glu.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_glu()
 input <- torch_randn(4, 2)
 output <- m(input)
diff --git a/man/nn_hardshrink.Rd b/man/nn_hardshrink.Rd
index 3eb111e2e6fc2d280be3f488731170258a3e9efe..6e690070b51224bf1c71e1a6e5e4933c48fbf29b 100644
--- a/man/nn_hardshrink.Rd
+++ b/man/nn_hardshrink.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_hardshrink()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_hardsigmoid.Rd b/man/nn_hardsigmoid.Rd
index 6ad2cb5327f15c9ac971097291313ff44b72f293..d96648106f4cb4a195ea1690ba9b1922cdca3c41 100644
--- a/man/nn_hardsigmoid.Rd
+++ b/man/nn_hardsigmoid.Rd
@@ -29,7 +29,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_hardsigmoid()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_hardswish.Rd b/man/nn_hardswish.Rd
index 33ba738eecfd4916d775a4764a670f387da52a0d..e1dc183f25683717762489bca6ab1d15fbd68700 100644
--- a/man/nn_hardswish.Rd
+++ b/man/nn_hardswish.Rd
@@ -29,7 +29,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 m <- nn_hardswish()
 input <- torch_randn(2)
diff --git a/man/nn_hardtanh.Rd b/man/nn_hardtanh.Rd
index 0af0b740fef60f263ffcb7530303ca8493d02b47..0e3ed49b292b2ba2d76b2c8544e482b93a342e1b 100644
--- a/man/nn_hardtanh.Rd
+++ b/man/nn_hardtanh.Rd
@@ -40,7 +40,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_hardtanh(-2, 2)
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_hinge_embedding_loss.Rd b/man/nn_hinge_embedding_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..524e787b7db79ef9ba7a3936455181f59ea56cb6
--- /dev/null
+++ b/man/nn_hinge_embedding_loss.Rd
@@ -0,0 +1,56 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_hinge_embedding_loss}
+\alias{nn_hinge_embedding_loss}
+\title{Hinge embedding loss}
+\usage{
+nn_hinge_embedding_loss(margin = 1, reduction = "mean")
+}
+\arguments{
+\item{margin}{(float, optional): Has a default value of \code{1}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Measures the loss given an input tensor \eqn{x} and a labels tensor \eqn{y}
+(containing 1 or -1).
+}
+\details{
+This is usually used for measuring whether two inputs are similar or
+dissimilar, e.g. using the L1 pairwise distance as \eqn{x}, and is typically
+used for learning nonlinear embeddings or semi-supervised learning.
+The loss function for \eqn{n}-th sample in the mini-batch is
+
+\deqn{
+  l_n = \begin{array}{ll}
+x_n, & \mbox{if}\; y_n = 1,\\
+\max \{0, \Delta - x_n\}, & \mbox{if}\; y_n = -1,
+\end{array}
+}
+
+and the total loss functions is
+
+\deqn{
+  \ell(x, y) = \begin{array}{ll}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+where \eqn{L = \{l_1,\dots,l_N\}^\top}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(*)} where \eqn{*} means, any number of dimensions. The sum operation
+operates over all the elements.
+\item Target: \eqn{(*)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then same shape as the input
+}
+}
+
diff --git a/man/nn_identity.Rd b/man/nn_identity.Rd
index 3c830f2c3fe36d2c6d0041ee17c0b6346f59bd69..7ab40e318c84098e3042c4d5c1115040a488d348 100644
--- a/man/nn_identity.Rd
+++ b/man/nn_identity.Rd
@@ -13,7 +13,7 @@ nn_identity(...)
 A placeholder identity operator that is argument-insensitive.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE)
 input <- torch_randn(128, 20)
 output <- m(input)
diff --git a/man/nn_init_constant_.Rd b/man/nn_init_constant_.Rd
index 3892155b87aab89eb56828df4a320e595e464c06..85705b26f38c4f2a9a29fb5abfbd64f0c134b407 100644
--- a/man/nn_init_constant_.Rd
+++ b/man/nn_init_constant_.Rd
@@ -15,7 +15,7 @@ nn_init_constant_(tensor, val)
 Fills the input Tensor with the value \code{val}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_constant_(w, 0.3)
 
diff --git a/man/nn_init_dirac_.Rd b/man/nn_init_dirac_.Rd
index 7e0a6715c2e88b72a337ab519018eab208a294bf..14f70c443d4b24c82271da3ae1e4d46ba0deefad 100644
--- a/man/nn_init_dirac_.Rd
+++ b/man/nn_init_dirac_.Rd
@@ -18,7 +18,7 @@ layers, where as many input channels are preserved as possible. In case
 of groups>1, each group of channels preserves identity.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 w <- torch_empty(3, 16, 5, 5)
 nn_init_dirac_(w)
diff --git a/man/nn_init_eye_.Rd b/man/nn_init_eye_.Rd
index af1ea83b6b0388e1cda1902b6139ff733b081cb4..61c129055e586ffd21b73406b64a390177e886e6 100644
--- a/man/nn_init_eye_.Rd
+++ b/man/nn_init_eye_.Rd
@@ -15,7 +15,7 @@ Preserves the identity of the inputs in \code{Linear} layers, where as
 many inputs are preserved as possible.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_eye_(w)
 
diff --git a/man/nn_init_kaiming_normal_.Rd b/man/nn_init_kaiming_normal_.Rd
index 10aa3a5a4408237fd862241d1309f21b71cffa1b..ed62a0c9d3233caf84843d56ebd30681313db7e2 100644
--- a/man/nn_init_kaiming_normal_.Rd
+++ b/man/nn_init_kaiming_normal_.Rd
@@ -30,7 +30,7 @@ described in \verb{Delving deep into rectifiers: Surpassing human-level performa
 normal distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_kaiming_normal_(w, mode = "fan_in", nonlinearity = "leaky_relu")
 
diff --git a/man/nn_init_kaiming_uniform_.Rd b/man/nn_init_kaiming_uniform_.Rd
index f3290d52604fca988aac73589ca035ecfab6a2e7..c46b03987b376df7fcc238f2347a293d2261485c 100644
--- a/man/nn_init_kaiming_uniform_.Rd
+++ b/man/nn_init_kaiming_uniform_.Rd
@@ -30,7 +30,7 @@ described in \verb{Delving deep into rectifiers: Surpassing human-level performa
 uniform distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_kaiming_uniform_(w, mode = "fan_in", nonlinearity = "leaky_relu")
 
diff --git a/man/nn_init_normal_.Rd b/man/nn_init_normal_.Rd
index a7d8bafc029f2b18b246ede6c1767a67fa4cf6fa..d9628c3f69efd5c56e27de4b996d6076f6417d35 100644
--- a/man/nn_init_normal_.Rd
+++ b/man/nn_init_normal_.Rd
@@ -17,7 +17,7 @@ nn_init_normal_(tensor, mean = 0, std = 1)
 Fills the input Tensor with values drawn from the normal distribution
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_normal_(w)
 
diff --git a/man/nn_init_ones_.Rd b/man/nn_init_ones_.Rd
index b28e681e6d8101d2d1c32478b53c8ac9831eebb1..0015a321b7576533fc82ad62b2b04923ad2257a7 100644
--- a/man/nn_init_ones_.Rd
+++ b/man/nn_init_ones_.Rd
@@ -13,7 +13,7 @@ nn_init_ones_(tensor)
 Fills the input Tensor with the scalar value \code{1}
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_ones_(w)
 
diff --git a/man/nn_init_orthogonal_.Rd b/man/nn_init_orthogonal_.Rd
index f400418a87b760aba415e9da526fb8b0bb723734..629eb414f70b763dffe6628b3ef7648f890c2a5c 100644
--- a/man/nn_init_orthogonal_.Rd
+++ b/man/nn_init_orthogonal_.Rd
@@ -18,7 +18,7 @@ at least 2 dimensions, and for tensors with more than 2 dimensions the
 trailing dimensions are flattened.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3,5)
 nn_init_orthogonal_(w)
 
diff --git a/man/nn_init_sparse_.Rd b/man/nn_init_sparse_.Rd
index 143ac581765a64883dffcd56a5ea73e84408b01f..6cae148562e15478667db31c1a79ca7fa9ae0470 100644
--- a/man/nn_init_sparse_.Rd
+++ b/man/nn_init_sparse_.Rd
@@ -20,7 +20,7 @@ non-zero elements will be drawn from the normal distribution
 as described in \verb{Deep learning via Hessian-free optimization} - Martens, J. (2010).
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 w <- torch_empty(3, 5)
 nn_init_sparse_(w, sparsity = 0.1)
diff --git a/man/nn_init_trunc_normal_.Rd b/man/nn_init_trunc_normal_.Rd
index 0d88e1f6780a1eccf45757773b9858acda3b912f..2d59c6d46fee9c318797c20eb7a8dacd95609660 100644
--- a/man/nn_init_trunc_normal_.Rd
+++ b/man/nn_init_trunc_normal_.Rd
@@ -22,7 +22,7 @@ Fills the input Tensor with values drawn from a truncated
 normal distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_trunc_normal_(w)
 
diff --git a/man/nn_init_uniform_.Rd b/man/nn_init_uniform_.Rd
index 4bf1cd2ba86d28c4110b1f51b37cde60dd4f5a3d..557665fd5ea0baa9af5ebbf5638fd6bad5a510c2 100644
--- a/man/nn_init_uniform_.Rd
+++ b/man/nn_init_uniform_.Rd
@@ -17,7 +17,7 @@ nn_init_uniform_(tensor, a = 0, b = 1)
 Fills the input Tensor with values drawn from the uniform distribution
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_uniform_(w)
 
diff --git a/man/nn_init_xavier_normal_.Rd b/man/nn_init_xavier_normal_.Rd
index 93c2336daa6127d5cbbdd0c02542be47597c29e3..d6a282ae7f90c7e848d6cb2eb996aacc6d236a3f 100644
--- a/man/nn_init_xavier_normal_.Rd
+++ b/man/nn_init_xavier_normal_.Rd
@@ -17,7 +17,7 @@ described in \verb{Understanding the difficulty of training deep feedforward neu
 distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_xavier_normal_(w)
 
diff --git a/man/nn_init_xavier_uniform_.Rd b/man/nn_init_xavier_uniform_.Rd
index 522cfc337a26ee652b3bce50e9a21a62cf417000..d0904cba02a7f88932fa82cc8bfeced1c0d690e7 100644
--- a/man/nn_init_xavier_uniform_.Rd
+++ b/man/nn_init_xavier_uniform_.Rd
@@ -17,7 +17,7 @@ described in \verb{Understanding the difficulty of training deep feedforward neu
 distribution.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_xavier_uniform_(w)
 
diff --git a/man/nn_init_zeros_.Rd b/man/nn_init_zeros_.Rd
index 96ba10e4ef49fd377de5d854f827695242fc6745..39b48a4fce0d07adfbb5ac5b92aba5e8a4c4a036 100644
--- a/man/nn_init_zeros_.Rd
+++ b/man/nn_init_zeros_.Rd
@@ -13,7 +13,7 @@ nn_init_zeros_(tensor)
 Fills the input Tensor with the scalar value \code{0}
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 w <- torch_empty(3, 5)
 nn_init_zeros_(w)
 
diff --git a/man/nn_kl_div_loss.Rd b/man/nn_kl_div_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..8a01bceea1bc6446aa6031918dde07f3c4ce9fdc
--- /dev/null
+++ b/man/nn_kl_div_loss.Rd
@@ -0,0 +1,76 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_kl_div_loss}
+\alias{nn_kl_div_loss}
+\title{Kullback-Leibler divergence loss}
+\usage{
+nn_kl_div_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'batchmean'} | \code{'sum'} | \code{'mean'}.
+\code{'none'}: no reduction will be applied.
+\code{'batchmean'}: the sum of the output will be divided by batchsize.
+\code{'sum'}: the output will be summed.
+\code{'mean'}: the output will be divided by the number of elements in the output.
+Default: \code{'mean'}}
+}
+\description{
+The Kullback-Leibler divergence loss measure
+\href{https://en.wikipedia.org/wiki/Kullback-Leibler_divergence}{Kullback-Leibler divergence}
+is a useful distance measure for continuous distributions and is often
+useful when performing direct regression over the space of (discretely sampled)
+continuous output distributions.
+}
+\details{
+As with \code{\link[=nn_nll_loss]{nn_nll_loss()}}, the \code{input} given is expected to contain
+\emph{log-probabilities} and is not restricted to a 2D Tensor.
+
+The targets are interpreted as \emph{probabilities} by default, but could be considered
+as \emph{log-probabilities} with \code{log_target} set to \code{TRUE}.
+
+This criterion expects a \code{target} \code{Tensor} of the same size as the
+\code{input} \code{Tensor}.
+
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described
+as:
+
+\deqn{
+  l(x,y) = L = \{ l_1,\dots,l_N \}, \quad
+l_n = y_n \cdot \left( \log y_n - x_n \right)
+}
+
+where the index \eqn{N} spans all dimensions of \code{input} and \eqn{L} has the same
+shape as \code{input}. If \code{reduction} is not \code{'none'} (default \code{'mean'}), then:
+
+\deqn{
+  \ell(x, y) = \begin{array}{ll}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';} \\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+In default \code{reduction} mode \code{'mean'}, the losses are averaged for each minibatch
+over observations \strong{as well as} over dimensions. \code{'batchmean'} mode gives the
+correct KL divergence where losses are averaged over batch dimension only.
+\code{'mean'} mode's behavior will be changed to the same as \code{'batchmean'} in the next
+major release.
+}
+\note{
+\code{reduction} = \code{'mean'} doesn't return the true kl divergence value,
+please use \code{reduction} = \code{'batchmean'} which aligns with KL math
+definition.
+In the next major release, \code{'mean'} will be changed to be the same as
+\code{'batchmean'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar by default. If \code{reduction} is \code{'none'}, then \eqn{(N, *)},
+the same shape as the input
+}
+}
+
diff --git a/man/nn_l1_loss.Rd b/man/nn_l1_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..d9ecaf5038d39e473fbb425661c8e6debfc98ba2
--- /dev/null
+++ b/man/nn_l1_loss.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_l1_loss}
+\alias{nn_l1_loss}
+\title{L1 loss}
+\usage{
+nn_l1_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the mean absolute error (MAE) between each
+element in the input \eqn{x} and target \eqn{y}.
+}
+\details{
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described
+as:
+
+\deqn{
+\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = \left| x_n - y_n \right|,
+}
+
+where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then:
+
+\deqn{
+\ell(x, y) =
+\begin{array}{ll}
+\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  & \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+\eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
+of \eqn{n} elements each.
+
+The sum operation still operates over all the elements, and divides by \eqn{n}.
+The division by \eqn{n} can be avoided if one sets \code{reduction = 'sum'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then
+\eqn{(N, *)}, same shape as the input
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_l1_loss()
+input <- torch_randn(3, 5, requires_grad=TRUE)
+target <- torch_randn(3, 5)
+output <- loss(input, target)
+output$backward()
+
+}
+}
diff --git a/man/nn_leaky_relu.Rd b/man/nn_leaky_relu.Rd
index 2fe9923b0064cd7f74e86aa6d1f5b66a5c7d4106..d2cb6874e80046fa7623f0f018f884fb9c4c7f5b 100644
--- a/man/nn_leaky_relu.Rd
+++ b/man/nn_leaky_relu.Rd
@@ -39,7 +39,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_leaky_relu(0.1)
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_linear.Rd b/man/nn_linear.Rd
index 9230a0bd2d10b2e12d8b05207e75590509f339e2..182ea9f6750732e9fd86ef6a3bbab650d7cdbf03 100644
--- a/man/nn_linear.Rd
+++ b/man/nn_linear.Rd
@@ -42,7 +42,7 @@ If \code{bias} is \code{TRUE}, the values are initialized from
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_linear(20, 30)
 input <- torch_randn(128, 20)
 output <- m(input)
diff --git a/man/nn_log_sigmoid.Rd b/man/nn_log_sigmoid.Rd
index 2aee1877a37f4708bc4d1e3173fee8540426e592..5f7284b1c9e7e6352b05317a345e8097a676a532 100644
--- a/man/nn_log_sigmoid.Rd
+++ b/man/nn_log_sigmoid.Rd
@@ -22,7 +22,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_log_sigmoid()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_log_softmax.Rd b/man/nn_log_softmax.Rd
index 59b96ab806496be8522da96d70816e2d497f8f78..7f9a35a4f891e2ff5112f246c4314fac7f13a43d 100644
--- a/man/nn_log_softmax.Rd
+++ b/man/nn_log_softmax.Rd
@@ -32,7 +32,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_log_softmax(1)
 input <- torch_randn(2, 3)
 output <- m(input)
diff --git a/man/nn_lp_pool1d.Rd b/man/nn_lp_pool1d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..476ff18491edbb5f2edb4c2f997605b9c1c25941
--- /dev/null
+++ b/man/nn_lp_pool1d.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_lp_pool1d}
+\alias{nn_lp_pool1d}
+\title{Applies a 1D power-average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
+}
+\arguments{
+\item{norm_type}{if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)}
+
+\item{kernel_size}{a single int, the size of the window}
+
+\item{stride}{a single int, the stride of the window. Default value is \code{kernel_size}}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+}
+\description{
+On each window, the function computed is:
+
+\deqn{
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+}
+}
+\details{
+\itemize{
+\item At p = \eqn{\infty}, one gets Max Pooling
+\item At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)
+}
+}
+\note{
+If the sum to the power of \code{p} is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, L_{in})}
+\item Output: \eqn{(N, C, L_{out})}, where
+}
+
+\deqn{
+  L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+# power-2 pool of window of length 3, with stride 2.
+m <- nn_lp_pool1d(2, 3, stride=2)
+input <- torch_randn(20, 16, 50)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_lp_pool2d.Rd b/man/nn_lp_pool2d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..26ca7ebc0a4f6bc0fef2a13d5b4f9b8d80b108f0
--- /dev/null
+++ b/man/nn_lp_pool2d.Rd
@@ -0,0 +1,70 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_lp_pool2d}
+\alias{nn_lp_pool2d}
+\title{Applies a 2D power-average pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
+}
+\arguments{
+\item{norm_type}{if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)}
+
+\item{kernel_size}{the size of the window}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+}
+\description{
+On each window, the function computed is:
+
+\deqn{
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+}
+}
+\details{
+\itemize{
+\item At p = \eqn{\infty}, one gets Max Pooling
+\item At p = 1, one gets Sum Pooling (which is proportional to average pooling)
+}
+
+The parameters \code{kernel_size}, \code{stride} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the height and width dimension
+\item a \code{tuple} of two ints -- in which case, the first \code{int} is used for the height dimension,
+and the second \code{int} for the width dimension
+}
+}
+\note{
+If the sum to the power of \code{p} is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+}
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# power-2 pool of square window of size=3, stride=2
+m <- nn_lp_pool2d(2, 3, stride=2)
+# pool of non-square window of power 1.2
+m <- nn_lp_pool2d(1.2, c(3, 2), stride=c(2, 1))
+input <- torch_randn(20, 16, 50, 32)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_margin_ranking_loss.Rd b/man/nn_margin_ranking_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..5b8ec22a47c88004ac1091237c3f3ed9506f855d
--- /dev/null
+++ b/man/nn_margin_ranking_loss.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_margin_ranking_loss}
+\alias{nn_margin_ranking_loss}
+\title{Margin ranking loss}
+\usage{
+nn_margin_ranking_loss(margin = 0, reduction = "mean")
+}
+\arguments{
+\item{margin}{(float, optional): Has a default value of \eqn{0}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the loss given
+inputs \eqn{x1}, \eqn{x2}, two 1D mini-batch \code{Tensors},
+and a label 1D mini-batch tensor \eqn{y} (containing 1 or -1).
+If \eqn{y = 1} then it assumed the first input should be ranked higher
+(have a larger value) than the second input, and vice-versa for \eqn{y = -1}.
+}
+\details{
+The loss function for each pair of samples in the mini-batch is:
+
+\deqn{
+  \mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{margin})
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input1: \eqn{(N)} where \code{N} is the batch size.
+\item Input2: \eqn{(N)}, same shape as the Input1.
+\item Target: \eqn{(N)}, same shape as the inputs.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N)}.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_margin_ranking_loss()
+input1 <- torch_randn(3, requires_grad=TRUE)
+input2 <- torch_randn(3, requires_grad=TRUE)
+target <- torch_randn(3)$sign()
+output <- loss(input1, input2, target)
+output$backward()
+
+}
+}
diff --git a/man/nn_max_pool1d.Rd b/man/nn_max_pool1d.Rd
index d597f6c1f78bcf27dfb2e666ed17a825dbb754f5..3a50f9d28b22840c5597dd884bbf8827c812d92b 100644
--- a/man/nn_max_pool1d.Rd
+++ b/man/nn_max_pool1d.Rd
@@ -59,7 +59,7 @@ has a nice visualization of what \code{dilation} does.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # pool of size=3, stride=2
 m <- nn_max_pool1d(3, stride=2)
 input <- torch_randn(20, 16, 50)
diff --git a/man/nn_max_pool2d.Rd b/man/nn_max_pool2d.Rd
index 10acccfd1d77e3da81b1b397adafccd913582e7e..91bcedd01f85e2bb24b957d74a6bab0dc3487b81 100644
--- a/man/nn_max_pool2d.Rd
+++ b/man/nn_max_pool2d.Rd
@@ -37,7 +37,7 @@ output \eqn{(N, C, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kH, kW)}
 can be precisely described as:
 
 \deqn{
-  \begin{array}{ll}
+\begin{array}{ll}
 out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
 & \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
                \mbox{stride[1]} \times w + n)
@@ -74,7 +74,7 @@ and the second \code{int} for the width dimension
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 # pool of square window of size=3, stride=2
 m <- nn_max_pool2d(3, stride=2)
 # pool of non-square window
diff --git a/man/nn_max_pool3d.Rd b/man/nn_max_pool3d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..18397a2cca22f3675261d5945a7d486fe40ad675
--- /dev/null
+++ b/man/nn_max_pool3d.Rd
@@ -0,0 +1,86 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_pool3d}
+\alias{nn_max_pool3d}
+\title{Applies a 3D max pooling over an input signal composed of several input
+planes.}
+\usage{
+nn_max_pool3d(
+  kernel_size,
+  stride = NULL,
+  padding = 0,
+  dilation = 1,
+  return_indices = FALSE,
+  ceil_mode = FALSE
+)
+}
+\arguments{
+\item{kernel_size}{the size of the window to take a max over}
+
+\item{stride}{the stride of the window. Default value is \code{kernel_size}}
+
+\item{padding}{implicit zero padding to be added on all three sides}
+
+\item{dilation}{a parameter that controls the stride of elements in the window}
+
+\item{return_indices}{if \code{TRUE}, will return the max indices along with the outputs.
+Useful for \code{torch_nn.MaxUnpool3d} later}
+
+\item{ceil_mode}{when TRUE, will use \code{ceil} instead of \code{floor} to compute the output shape}
+}
+\description{
+In the simplest case, the output value of the layer with input size \eqn{(N, C, D, H, W)},
+output \eqn{(N, C, D_{out}, H_{out}, W_{out})} and \code{kernel_size} \eqn{(kD, kH, kW)}
+can be precisely described as:
+}
+\details{
+\deqn{
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+ & \mbox{input}(N_i, C_j, \mbox{stride[0]} \times d + k, \mbox{stride[1]} \times h + m, \mbox{stride[2]} \times w + n)
+\end{array}
+}
+
+If \code{padding} is non-zero, then the input is implicitly zero-padded on both sides
+for \code{padding} number of points. \code{dilation} controls the spacing between the kernel points.
+It is harder to describe, but this \code{link}_ has a nice visualization of what \code{dilation} does.
+The parameters \code{kernel_size}, \code{stride}, \code{padding}, \code{dilation} can either be:
+\itemize{
+\item a single \code{int} -- in which case the same value is used for the depth, height and width dimension
+\item a \code{tuple} of three ints -- in which case, the first \code{int} is used for the depth dimension,
+the second \code{int} for the height dimension and the third \code{int} for the width dimension
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+\deqn{
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times
+    (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
+}
+}
+
+\deqn{
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times
+    (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
+}
+
+\deqn{
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times
+    (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+# pool of square window of size=3, stride=2
+m <- nn_max_pool3d(3, stride=2)
+# pool of non-square window
+m <- nn_max_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
+input <- torch_randn(20, 16, 50,44, 31)
+output <- m(input)
+
+}
+}
diff --git a/man/nn_max_unpool1d.Rd b/man/nn_max_unpool1d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..e485b0e3de1f3dc593d793e9b4e33c5e6dd82498
--- /dev/null
+++ b/man/nn_max_unpool1d.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_unpool1d}
+\alias{nn_max_unpool1d}
+\title{Computes a partial inverse of \code{MaxPool1d}.}
+\usage{
+nn_max_unpool1d(kernel_size, stride = NULL, padding = 0)
+}
+\arguments{
+\item{kernel_size}{(int or tuple): Size of the max pooling window.}
+
+\item{stride}{(int or tuple): Stride of the max pooling window.
+It is set to \code{kernel_size} by default.}
+
+\item{padding}{(int or tuple): Padding that was added to the input}
+}
+\description{
+\code{MaxPool1d} is not fully invertible, since the non-maximal values are lost.
+\code{MaxUnpool1d} takes in as input the output of \code{MaxPool1d}
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.
+}
+\note{
+\code{MaxPool1d} can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument \code{output_size} in the forward call.
+See the Inputs and Example below.
+}
+\section{Inputs}{
+
+\itemize{
+\item \code{input}: the input Tensor to invert
+\item \code{indices}: the indices given out by \code{\link[=nn_max_pool1d]{nn_max_pool1d()}}
+\item \code{output_size} (optional): the targeted output size
+}
+}
+
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in})}
+\item Output: \eqn{(N, C, H_{out})}, where
+\deqn{
+  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0]
+}
+or as given by \code{output_size} in the call operator
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool1d(2, stride=2)
+
+input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
+out <- pool(input)
+unpool(out[[1]], out[[2]])
+
+# Example showcasing the use of output_size
+input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
+out <- pool(input)
+unpool(out[[1]], out[[2]], output_size=input$size())
+unpool(out[[1]], out[[2]])
+
+}
+}
diff --git a/man/nn_max_unpool2d.Rd b/man/nn_max_unpool2d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..a0d018ff636569f4a0cd937f6b3da5628f9d3359
--- /dev/null
+++ b/man/nn_max_unpool2d.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_unpool2d}
+\alias{nn_max_unpool2d}
+\title{Computes a partial inverse of \code{MaxPool2d}.}
+\usage{
+nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)
+}
+\arguments{
+\item{kernel_size}{(int or tuple): Size of the max pooling window.}
+
+\item{stride}{(int or tuple): Stride of the max pooling window.
+It is set to \code{kernel_size} by default.}
+
+\item{padding}{(int or tuple): Padding that was added to the input}
+}
+\description{
+\code{MaxPool2d} is not fully invertible, since the non-maximal values are lost.
+\code{MaxUnpool2d} takes in as input the output of \code{MaxPool2d}
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.
+}
+\note{
+\code{MaxPool2d} can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument \code{output_size} in the forward call.
+See the Inputs and Example below.
+}
+\section{Inputs}{
+
+\itemize{
+\item \code{input}: the input Tensor to invert
+\item \code{indices}: the indices given out by \code{\link[=nn_max_pool2d]{nn_max_pool2d()}}
+\item \code{output_size} (optional): the targeted output size
+}
+}
+
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, H_{out}, W_{out})}, where
+\deqn{
+  H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
+}
+\deqn{
+  W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
+}
+or as given by \code{output_size} in the call operator
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+
+pool <- nn_max_pool2d(2, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool2d(2, stride=2)
+input <- torch_randn(1,1,4,4)
+out <- pool(input)
+unpool(out[[1]], out[[2]])
+
+# specify a different output size than input size
+unpool(out[[1]], out[[2]], output_size=c(1, 1, 5, 5))
+
+}
+}
diff --git a/man/nn_max_unpool3d.Rd b/man/nn_max_unpool3d.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..5228bf168b4c4524599db5a259958dbdc35178e2
--- /dev/null
+++ b/man/nn_max_unpool3d.Rd
@@ -0,0 +1,70 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-pooling.R
+\name{nn_max_unpool3d}
+\alias{nn_max_unpool3d}
+\title{Computes a partial inverse of \code{MaxPool3d}.}
+\usage{
+nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)
+}
+\arguments{
+\item{kernel_size}{(int or tuple): Size of the max pooling window.}
+
+\item{stride}{(int or tuple): Stride of the max pooling window.
+It is set to \code{kernel_size} by default.}
+
+\item{padding}{(int or tuple): Padding that was added to the input}
+}
+\description{
+\code{MaxPool3d} is not fully invertible, since the non-maximal values are lost.
+\code{MaxUnpool3d} takes in as input the output of \code{MaxPool3d}
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.
+}
+\note{
+\code{MaxPool3d} can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument \code{output_size} in the forward call.
+See the Inputs section below.
+}
+\section{Inputs}{
+
+\itemize{
+\item \code{input}: the input Tensor to invert
+\item \code{indices}: the indices given out by \code{\link[=nn_max_pool3d]{nn_max_pool3d()}}
+\item \code{output_size} (optional): the targeted output size
+}
+}
+
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C, D_{in}, H_{in}, W_{in})}
+\item Output: \eqn{(N, C, D_{out}, H_{out}, W_{out})}, where
+}
+
+\deqn{
+  D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
+}
+\deqn{
+  H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
+}
+\deqn{
+  W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
+}
+
+or as given by \code{output_size} in the call operator
+}
+
+\examples{
+if (torch_is_installed()) {
+  
+# pool of square window of size=3, stride=2
+pool <- nn_max_pool3d(3, stride=2, return_indices=TRUE)
+unpool <- nn_max_unpool3d(3, stride=2)
+out <- pool(torch_randn(20, 16, 51, 33, 15))
+unpooled_output <- unpool(out[[1]], out[[2]])
+unpooled_output$size()
+
+}
+}
diff --git a/man/nn_module.Rd b/man/nn_module.Rd
index 3f7ac0fea461372e2a4c766e1097833e96cf997c..3fbf27d81cf9bdb90c983664eeed5bffe488fd9c 100644
--- a/man/nn_module.Rd
+++ b/man/nn_module.Rd
@@ -4,7 +4,12 @@
 \alias{nn_module}
 \title{Base class for all neural network modules.}
 \usage{
-nn_module(classname = NULL, inherit = nn_Module, ...)
+nn_module(
+  classname = NULL,
+  inherit = nn_Module,
+  ...,
+  parent_env = parent.frame()
+)
 }
 \arguments{
 \item{classname}{an optional name for the module}
@@ -12,6 +17,8 @@ nn_module(classname = NULL, inherit = nn_Module, ...)
 \item{inherit}{an optional module to inherit from}
 
 \item{...}{methods implementation}
+
+\item{parent_env}{passed to \code{\link[R6:R6Class]{R6::R6Class()}}.}
 }
 \description{
 Your models should also subclass this class.
@@ -21,7 +28,7 @@ Modules can also contain other Modules, allowing to nest them in a tree
 structure. You can assign the submodules as regular attributes.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 model <- nn_module(
  initialize = function() {
    self$conv1 <- nn_conv2d(1, 20, 5)
diff --git a/man/nn_module_list.Rd b/man/nn_module_list.Rd
index beaea6f097b2a8f2dda981e01f172625499123be..54fbdc1e379509313613c4c18e7b4aa78a6e39fd 100644
--- a/man/nn_module_list.Rd
+++ b/man/nn_module_list.Rd
@@ -15,7 +15,7 @@ modules it contains are properly registered, and will be visible by all
 \code{nn_module} methods.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 my_module <- nn_module(
  initialize = function() {
diff --git a/man/nn_mse_loss.Rd b/man/nn_mse_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..b033895b012a81f48c9a93b08eda28d3911b76ee
--- /dev/null
+++ b/man/nn_mse_loss.Rd
@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_mse_loss}
+\alias{nn_mse_loss}
+\title{MSE loss}
+\usage{
+nn_mse_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the mean squared error (squared L2 norm) between
+each element in the input \eqn{x} and target \eqn{y}.
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described
+as:
+}
+\details{
+\deqn{
+  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = \left( x_n - y_n \right)^2,
+}
+
+where \eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then:
+
+\deqn{
+  \ell(x, y) =
+  \begin{array}{ll}
+\mbox{mean}(L), &  \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  &  \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+\eqn{x} and \eqn{y} are tensors of arbitrary shapes with a total
+of \eqn{n} elements each.
+
+The mean operation still operates over all the elements, and divides by \eqn{n}.
+The division by \eqn{n} can be avoided if one sets \code{reduction = 'sum'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_mse_loss()
+input <- torch_randn(3, 5, requires_grad=TRUE)
+target <- torch_randn(3, 5)
+output <- loss(input, target)
+output$backward()
+
+}
+}
diff --git a/man/nn_multi_margin_loss.Rd b/man/nn_multi_margin_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..d3f8eec2cd85d465e90eddfd3fb13725b3f1e702
--- /dev/null
+++ b/man/nn_multi_margin_loss.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_multi_margin_loss}
+\alias{nn_multi_margin_loss}
+\title{Multi margin loss}
+\usage{
+nn_multi_margin_loss(p = 1, margin = 1, weight = NULL, reduction = "mean")
+}
+\arguments{
+\item{p}{(int, optional): Has a default value of \eqn{1}. \eqn{1} and \eqn{2}
+are the only supported values.}
+
+\item{margin}{(float, optional): Has a default value of \eqn{1}.}
+
+\item{weight}{(Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size \code{C}. Otherwise, it is
+treated as if having all ones.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a multi-class classification hinge
+loss (margin-based loss) between input \eqn{x} (a 2D mini-batch \code{Tensor}) and
+output \eqn{y} (which is a 1D tensor of target class indices,
+\eqn{0 \leq y \leq \mbox{x.size}(1)-1}):
+}
+\details{
+For each mini-batch sample, the loss in terms of the 1D input \eqn{x} and scalar
+output \eqn{y} is:
+\deqn{
+  \mbox{loss}(x, y) = \frac{\sum_i \max(0, \mbox{margin} - x[y] + x[i]))^p}{\mbox{x.size}(0)}
+}
+
+where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}}
+and \eqn{i \neq y}.
+
+Optionally, you can give non-equal weighting on the classes by passing
+a 1D \code{weight} tensor into the constructor.
+The loss function then becomes:
+
+\deqn{
+  \mbox{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\mbox{margin} - x[y] + x[i]))^p)}{\mbox{x.size}(0)}
+}
+}
diff --git a/man/nn_multihead_attention.Rd b/man/nn_multihead_attention.Rd
index bb4fb936408d5bf0ef0fad67fcb5e9d475745462..ba8441bc784706d9d1e56be21a1a322ffe53d9c8 100644
--- a/man/nn_multihead_attention.Rd
+++ b/man/nn_multihead_attention.Rd
@@ -80,7 +80,7 @@ L is the target sequence length, S is the source sequence length.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 multihead_attn = nn_multihead_attention(embed_dim, num_heads)
 out <- multihead_attn(query, key, value)
diff --git a/man/nn_multilabel_margin_loss.Rd b/man/nn_multilabel_margin_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..61d07b22252df9548b942c34c86178d020abeeab
--- /dev/null
+++ b/man/nn_multilabel_margin_loss.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_multilabel_margin_loss}
+\alias{nn_multilabel_margin_loss}
+\title{Multilabel margin loss}
+\usage{
+nn_multilabel_margin_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a multi-class multi-classification
+hinge loss (margin-based loss) between input \eqn{x} (a 2D mini-batch \code{Tensor})
+and output \eqn{y} (which is a 2D \code{Tensor} of target class indices).
+For each sample in the mini-batch:
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\mbox{x.size}(0)}
+}
+
+where \eqn{x \in \left\{0, \; \cdots , \; \mbox{x.size}(0) - 1\right\}}, \
+\eqn{y \in \left\{0, \; \cdots , \; \mbox{y.size}(0) - 1\right\}}, \
+\eqn{0 \leq y[j] \leq \mbox{x.size}(0)-1}, \
+and \eqn{i \neq y[j]} for all \eqn{i} and \eqn{j}.
+\eqn{y} and \eqn{x} must have the same size.
+
+The criterion only considers a contiguous block of non-negative targets that
+starts at the front.
+This allows for different samples to have variable amounts of target classes.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(C)} or \eqn{(N, C)} where \code{N} is the batch size and \code{C}
+is the number of classes.
+\item Target: \eqn{(C)} or \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N)}.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_multilabel_margin_loss()
+x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1,4))
+# for target y, only consider labels 4 and 1, not after label -1
+y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1,4))
+loss(x, y)
+
+}
+}
diff --git a/man/nn_multilabel_soft_margin_loss.Rd b/man/nn_multilabel_soft_margin_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..a55f16bfa6fb2ca31dd79cf8a557e4d722cf3e5c
--- /dev/null
+++ b/man/nn_multilabel_soft_margin_loss.Rd
@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_multilabel_soft_margin_loss}
+\alias{nn_multilabel_soft_margin_loss}
+\title{Multi label soft margin loss}
+\usage{
+nn_multilabel_soft_margin_loss(weight = NULL, reduction = "mean")
+}
+\arguments{
+\item{weight}{(Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size \code{C}. Otherwise, it is
+treated as if having all ones.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a multi-label one-versus-all
+loss based on max-entropy, between input \eqn{x} and target \eqn{y} of size
+\eqn{(N, C)}.
+}
+\details{
+For each sample in the minibatch:
+
+\deqn{
+  loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1})
++ (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right)
+}
+
+where \eqn{i \in \left\{0, \; \cdots , \; \mbox{x.nElement}() - 1\right\}},
+\eqn{y[i] \in \left\{0, \; 1\right\}}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C)} where \code{N} is the batch size and \code{C} is the number of classes.
+\item Target: \eqn{(N, C)}, label targets padded by -1 ensuring same shape as the input.
+\item Output: scalar. If \code{reduction} is \code{'none'}, then \eqn{(N)}.
+}
+}
+
diff --git a/man/nn_nll_loss.Rd b/man/nn_nll_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..c80dc851e169d985633255b0cfdc1544146da437
--- /dev/null
+++ b/man/nn_nll_loss.Rd
@@ -0,0 +1,117 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_nll_loss}
+\alias{nn_nll_loss}
+\title{Nll loss}
+\usage{
+nn_nll_loss(weight = NULL, ignore_index = -100, reduction = "mean")
+}
+\arguments{
+\item{weight}{(Tensor, optional): a manual rescaling weight given to each
+class. If given, it has to be a Tensor of size \code{C}. Otherwise, it is
+treated as if having all ones.}
+
+\item{ignore_index}{(int, optional): Specifies a target value that is ignored
+and does not contribute to the input gradient.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will
+be applied, \code{'mean'}: the weighted mean of the output is taken,
+\code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in
+the meantime, specifying either of those two args will override
+\code{reduction}. Default: \code{'mean'}}
+}
+\description{
+The negative log likelihood loss. It is useful to train a classification
+problem with \code{C} classes.
+}
+\details{
+If provided, the optional argument \code{weight} should be a 1D Tensor assigning
+weight to each of the classes. This is particularly useful when you have an
+unbalanced training set.
+
+The \code{input} given through a forward call is expected to contain
+log-probabilities of each class. \code{input} has to be a Tensor of size either
+\eqn{(minibatch, C)} or \eqn{(minibatch, C, d_1, d_2, ..., d_K)}
+with \eqn{K \geq 1} for the \code{K}-dimensional case (described later).
+
+Obtaining log-probabilities in a neural network is easily achieved by
+adding a  \code{LogSoftmax}  layer in the last layer of your network.
+
+You may use \code{CrossEntropyLoss} instead, if you prefer not to add an extra
+layer.
+
+The \code{target} that this loss expects should be a class index in the range \eqn{[0, C-1]}
+where \verb{C = number of classes}; if \code{ignore_index} is specified, this loss also accepts
+this class index (this index may not necessarily be in the class range).
+
+The unreduced (i.e. with \code{reduction} set to \code{'none'}) loss can be described as:
+
+\deqn{
+\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = - w_{y_n} x_{n,y_n}, \quad
+w_{c} = \mbox{weight}[c] \cdot \mbox{1}\{c \not= \mbox{ignore\_index}\},
+}
+
+where \eqn{x} is the input, \eqn{y} is the target, \eqn{w} is the weight, and
+\eqn{N} is the batch size. If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then
+
+\deqn{
+\ell(x, y) = \begin{array}{ll}
+\sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, &
+  \mbox{if reduction} = \mbox{'mean';}\\
+\sum_{n=1}^N l_n,  &
+  \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+}
+
+Can also be used for higher dimension inputs, such as 2D images, by providing
+an input of size \eqn{(minibatch, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1},
+where \eqn{K} is the number of dimensions, and a target of appropriate shape
+(see below). In the case of images, it computes NLL loss per-pixel.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, C)} where \verb{C = number of classes}, or
+\eqn{(N, C, d_1, d_2, ..., d_K)} with \eqn{K \geq 1}
+in the case of \code{K}-dimensional loss.
+\item Target: \eqn{(N)} where each value is \eqn{0 \leq \mbox{targets}[i] \leq C-1}, or
+\eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case of
+K-dimensional loss.
+\item Output: scalar.
+}
+
+If \code{reduction} is \code{'none'}, then the same size as the target: \eqn{(N)}, or
+\eqn{(N, d_1, d_2, ..., d_K)} with \eqn{K \geq 1} in the case
+of K-dimensional loss.
+}
+
+\examples{
+if (torch_is_installed()) {
+m <- nn_log_softmax(dim=2)
+loss <- nn_nll_loss()
+# input is of size N x C = 3 x 5
+input <- torch_randn(3, 5, requires_grad=TRUE)
+# each element in target has to have 0 <= value < C
+target <- torch_tensor(c(2, 1, 5), dtype = torch_long())
+output <- loss(m(input), target)
+output$backward()
+
+# 2D loss example (used, for example, with image inputs)
+N <- 5
+C <- 4
+loss <- nn_nll_loss()
+# input is of size N x C x height x width
+data <- torch_randn(N, 16, 10, 10)
+conv <- nn_conv2d(16, C, c(3, 3))
+m <- nn_log_softmax(dim=1)
+# each element in target has to have 0 <= value < C
+target <- torch_empty(N, 8, 8, dtype=torch_long())$random_(1, C)
+output <- loss(m(conv(data)), target)
+output$backward()
+
+}
+}
diff --git a/man/nn_pairwise_distance.Rd b/man/nn_pairwise_distance.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..fd5d150b36cb262e18c79dd12abf3156e31ea330
--- /dev/null
+++ b/man/nn_pairwise_distance.Rd
@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-distance.R
+\name{nn_pairwise_distance}
+\alias{nn_pairwise_distance}
+\title{Pairwise distance}
+\usage{
+nn_pairwise_distance(p = 2, eps = 1e-06, keepdim = FALSE)
+}
+\arguments{
+\item{p}{(real): the norm degree. Default: 2}
+
+\item{eps}{(float, optional): Small value to avoid division by zero.
+Default: 1e-6}
+
+\item{keepdim}{(bool, optional): Determines whether or not to keep the vector dimension.
+Default: FALSE}
+}
+\description{
+Computes the batchwise pairwise distance between vectors \eqn{v_1}, \eqn{v_2}
+using the p-norm:
+}
+\details{
+\deqn{
+ \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}.
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input1: \eqn{(N, D)} where \verb{D = vector dimension}
+\item Input2: \eqn{(N, D)}, same shape as the Input1
+\item Output: \eqn{(N)}. If \code{keepdim} is \code{TRUE}, then \eqn{(N, 1)}.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+pdist <- nn_pairwise_distance(p=2)
+input1 <- torch_randn(100, 128)
+input2 <- torch_randn(100, 128)
+output <- pdist(input1, input2)
+
+}
+}
diff --git a/man/nn_parameter.Rd b/man/nn_parameter.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..2f4cf54302241f406ef6ca5e957ce45843af6a7d
--- /dev/null
+++ b/man/nn_parameter.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn.R
+\name{nn_parameter}
+\alias{nn_parameter}
+\title{Creates an \code{nn_parameter}}
+\usage{
+nn_parameter(x, requires_grad = TRUE)
+}
+\arguments{
+\item{x}{the tensor that you want to indicate as parameter}
+
+\item{requires_grad}{whether this parameter should  have
+\code{requires_grad = TRUE}}
+}
+\description{
+Indicates to nn_module that \code{x} is a parameter
+}
diff --git a/man/nn_poisson_nll_loss.Rd b/man/nn_poisson_nll_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..7805cc7189e914b772cf338b305f5947512274f0
--- /dev/null
+++ b/man/nn_poisson_nll_loss.Rd
@@ -0,0 +1,68 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_poisson_nll_loss}
+\alias{nn_poisson_nll_loss}
+\title{Poisson NLL loss}
+\usage{
+nn_poisson_nll_loss(
+  log_input = TRUE,
+  full = FALSE,
+  eps = 1e-08,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{log_input}{(bool, optional): if \code{TRUE} the loss is computed as
+\eqn{\exp(\mbox{input}) - \mbox{target}*\mbox{input}}, if \code{FALSE} the loss is
+\eqn{\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})}.}
+
+\item{full}{(bool, optional): whether to compute full loss, i. e. to add the
+Stirling approximation term
+\eqn{\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})}.}
+
+\item{eps}{(float, optional): Small value to avoid evaluation of \eqn{\log(0)} when
+\code{log_input = FALSE}. Default: 1e-8}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Negative log likelihood loss with Poisson distribution of target.
+The loss can be described as:
+}
+\details{
+\deqn{
+\mbox{target} \sim \mathrm{Poisson}(\mbox{input})
+\mbox{loss}(\mbox{input}, \mbox{target}) = \mbox{input} - \mbox{target} * \log(\mbox{input})
++ \log(\mbox{target!})
+}
+
+The last term can be omitted or approximated with Stirling formula. The
+approximation is used for target values more than 1. For targets less or
+equal to 1 zeros are added to the loss.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar by default. If \code{reduction} is \code{'none'}, then \eqn{(N, *)},
+the same shape as the input
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+loss <- nn_poisson_nll_loss()
+log_input <- torch_randn(5, 2, requires_grad=TRUE)
+target <- torch_randn(5, 2)
+output <- loss(log_input, target)
+output$backward()
+
+}
+}
diff --git a/man/nn_prelu.Rd b/man/nn_prelu.Rd
index ec64cb3906b9739bdbfef07d026dc7b66db6bc36..10d88364369be8c12958fdae0d4f68cad94a0f76 100644
--- a/man/nn_prelu.Rd
+++ b/man/nn_prelu.Rd
@@ -56,7 +56,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_prelu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_relu.Rd b/man/nn_relu.Rd
index 33776705b2d5746af567b0f613989ae583fff57b..348d09aa774b2d16cb487c7c1308f782cfcdae42 100644
--- a/man/nn_relu.Rd
+++ b/man/nn_relu.Rd
@@ -23,7 +23,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_relu()
 input <- torch_randn(2)
 m(input)
diff --git a/man/nn_relu6.Rd b/man/nn_relu6.Rd
index 82baa25c84d4b7cedb3a055cf0157a9f095ae6cf..27c4dd41cd90be2a76a3ed116cf2e63dcd1eac50 100644
--- a/man/nn_relu6.Rd
+++ b/man/nn_relu6.Rd
@@ -27,7 +27,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_relu6()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_rnn.Rd b/man/nn_rnn.Rd
index d4f40de2e6d4f6c80d5be96275d447193aa849ca..f60d8f0437d69482605aaf35e8a6f36145e3fa31 100644
--- a/man/nn_rnn.Rd
+++ b/man/nn_rnn.Rd
@@ -131,7 +131,7 @@ where \eqn{k = \frac{1}{\mbox{hidden\_size}}}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 rnn <- nn_rnn(10, 20, 2)
 input <- torch_randn(5, 3, 10)
 h0 <- torch_randn(2, 3, 20)
diff --git a/man/nn_rrelu.Rd b/man/nn_rrelu.Rd
index a2c61f439af72b21d0af7885e9fe203150c8cfb3..4d3efd17406fa3aedd3883932ddb42052b1657c2 100644
--- a/man/nn_rrelu.Rd
+++ b/man/nn_rrelu.Rd
@@ -45,7 +45,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_rrelu(0.1, 0.3)
 input <- torch_randn(2)
 m(input)
diff --git a/man/nn_selu.Rd b/man/nn_selu.Rd
index 4446c0229d61ddf18fd48015988e471ff11ea605..c368e2d36194abfc53051d30aef0c38b1b5b09cf 100644
--- a/man/nn_selu.Rd
+++ b/man/nn_selu.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_selu()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_sequential.Rd b/man/nn_sequential.Rd
index 26cec8adfd080f93eedda29908c91d4dd4abfca3..bbc44c25a6874a5f139cd3b279651688e04e2522 100644
--- a/man/nn_sequential.Rd
+++ b/man/nn_sequential.Rd
@@ -17,7 +17,7 @@ Modules will be added to it in the order they are passed in the constructor.
 See examples.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 model <- nn_sequential(
   nn_conv2d(1, 20, 5),
diff --git a/man/nn_sigmoid.Rd b/man/nn_sigmoid.Rd
index 680d5078a66ec91329cabc3f2c62fb3421160f83..fa351fc1aa4aff40b0e4d05fe226dac9497b7b1c 100644
--- a/man/nn_sigmoid.Rd
+++ b/man/nn_sigmoid.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_sigmoid()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_smooth_l1_loss.Rd b/man/nn_smooth_l1_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..8b251c82673cdbb6242afcadc981bbcf6aca6235
--- /dev/null
+++ b/man/nn_smooth_l1_loss.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_smooth_l1_loss}
+\alias{nn_smooth_l1_loss}
+\title{Smooth L1 loss}
+\usage{
+nn_smooth_l1_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that uses a squared term if the absolute
+element-wise error falls below 1 and an L1 term otherwise.
+It is less sensitive to outliers than the \code{MSELoss} and in some cases
+prevents exploding gradients (e.g. see \verb{Fast R-CNN} paper by Ross Girshick).
+Also known as the Huber loss:
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i}
+}
+
+where \eqn{z_{i}} is given by:
+
+\deqn{
+  z_{i} =
+  \begin{array}{ll}
+0.5 (x_i - y_i)^2, & \mbox{if } |x_i - y_i| < 1 \\
+|x_i - y_i| - 0.5, & \mbox{otherwise }
+\end{array}
+}
+
+\eqn{x} and \eqn{y} arbitrary shapes with a total of \eqn{n} elements each
+the sum operation still operates over all the elements, and divides by \eqn{n}.
+The division by \eqn{n} can be avoided if sets \code{reduction = 'sum'}.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(N, *)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then
+\eqn{(N, *)}, same shape as the input
+}
+}
+
diff --git a/man/nn_soft_margin_loss.Rd b/man/nn_soft_margin_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..5ba642d8d2c94424e0996f598a44b9cc0845ce03
--- /dev/null
+++ b/man/nn_soft_margin_loss.Rd
@@ -0,0 +1,36 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_soft_margin_loss}
+\alias{nn_soft_margin_loss}
+\title{Soft margin loss}
+\usage{
+nn_soft_margin_loss(reduction = "mean")
+}
+\arguments{
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that optimizes a two-class classification
+logistic loss between input tensor \eqn{x} and target tensor \eqn{y}
+(containing 1 or -1).
+}
+\details{
+\deqn{
+  \mbox{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\mbox{x.nelement}()}
+}
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(*)} where \eqn{*} means, any number of additional
+dimensions
+\item Target: \eqn{(*)}, same shape as the input
+\item Output: scalar. If \code{reduction} is \code{'none'}, then same shape as the input
+}
+}
+
diff --git a/man/nn_softmax.Rd b/man/nn_softmax.Rd
index 6d76fa43f1724cfbce0338cc145cdc4c78d594bc..87dae5141ef5d7bdecb7e79229ecece79953476d 100644
--- a/man/nn_softmax.Rd
+++ b/man/nn_softmax.Rd
@@ -44,7 +44,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softmax(1)
 input <- torch_randn(2, 3)
 output <- m(input)
diff --git a/man/nn_softmax2d.Rd b/man/nn_softmax2d.Rd
index 3d1b44d96b30cfaf88d6d23a460c0de7c2503d28..87fa085fc1bd9ccd2b866605de612c60ed118f0e 100644
--- a/man/nn_softmax2d.Rd
+++ b/man/nn_softmax2d.Rd
@@ -24,7 +24,7 @@ apply \code{Softmax} to each location \eqn{(Channels, h_i, w_j)}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softmax2d()
 input <- torch_randn(2, 3, 12, 13)
 output <- m(input)
diff --git a/man/nn_softmin.Rd b/man/nn_softmin.Rd
index f92c29183c7c2e65a6b7b8e0181f59c6c0915ecb..368596455569dc2180a9d3a94525a23219288f11 100644
--- a/man/nn_softmin.Rd
+++ b/man/nn_softmin.Rd
@@ -35,7 +35,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softmin(dim = 1)
 input <- torch_randn(2, 2)
 output <- m(input)
diff --git a/man/nn_softplus.Rd b/man/nn_softplus.Rd
index 581943c25d4175f81312b2384f70b69ae6518fb0..5d7aca47573acff58381c8d54b7dd2864ac68074 100644
--- a/man/nn_softplus.Rd
+++ b/man/nn_softplus.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softplus()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_softshrink.Rd b/man/nn_softshrink.Rd
index 833dcb05e585c0e8a14f4630b34d1bc87d4c83ca..0d0a70eeb8035c08a6b826d46773ccd9fb74735e 100644
--- a/man/nn_softshrink.Rd
+++ b/man/nn_softshrink.Rd
@@ -33,7 +33,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softshrink()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_softsign.Rd b/man/nn_softsign.Rd
index 263d37f105d833eaf4f28dc6f62522cd5728eb25..d72dfbc99b55106263bbaeb613ff8e105c69282a 100644
--- a/man/nn_softsign.Rd
+++ b/man/nn_softsign.Rd
@@ -22,7 +22,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_softsign()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_tanh.Rd b/man/nn_tanh.Rd
index 9536cd0a4ead9486ce27556e844059345d8f2fb0..e27331f1db4f65f57b3636e0e8391f3e1f19e95c 100644
--- a/man/nn_tanh.Rd
+++ b/man/nn_tanh.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_tanh()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_tanhshrink.Rd b/man/nn_tanhshrink.Rd
index d7e7b7d5b39abb939fceaecf09e5fb9e10b30bc4..28e5c1de6e2307a51dd151cc795d85b8366b094a 100644
--- a/man/nn_tanhshrink.Rd
+++ b/man/nn_tanhshrink.Rd
@@ -24,7 +24,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_tanhshrink()
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_threshold.Rd b/man/nn_threshold.Rd
index 74a42592516789228b323e758cdbb8e68961f730..dfc746d1a96e891ebd9f58d32bddac9aa6653e25 100644
--- a/man/nn_threshold.Rd
+++ b/man/nn_threshold.Rd
@@ -37,7 +37,7 @@ dimensions
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 m <- nn_threshold(0.1, 20)
 input <- torch_randn(2)
 output <- m(input)
diff --git a/man/nn_triplet_margin_loss.Rd b/man/nn_triplet_margin_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..980328e444c2239456f17a7070fe96ce8b048381
--- /dev/null
+++ b/man/nn_triplet_margin_loss.Rd
@@ -0,0 +1,79 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_triplet_margin_loss}
+\alias{nn_triplet_margin_loss}
+\title{Triplet margin loss}
+\usage{
+nn_triplet_margin_loss(
+  margin = 1,
+  p = 2,
+  eps = 1e-06,
+  swap = FALSE,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{margin}{(float, optional): Default: \eqn{1}.}
+
+\item{p}{(int, optional): The norm degree for pairwise distance. Default: \eqn{2}.}
+
+\item{eps}{constant to avoid NaN's}
+
+\item{swap}{(bool, optional): The distance swap is described in detail in the paper
+\href{http://www.bmva.org/bmvc/2016/papers/paper119/index.html}{Learning shallow convolutional feature descriptors with triplet losses} by
+V. Balntas, E. Riba et al. Default: \code{FALSE}.}
+
+\item{reduction}{(string, optional): Specifies the reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Note: \code{size_average}
+and \code{reduce} are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override \code{reduction}. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the triplet loss given an input
+tensors \eqn{x1}, \eqn{x2}, \eqn{x3} and a margin with a value greater than \eqn{0}.
+This is used for measuring a relative similarity between samples. A triplet
+is composed by \code{a}, \code{p} and \code{n} (i.e., \code{anchor}, \verb{positive examples} and \verb{negative examples} respectively). The shapes of all input tensors should be
+\eqn{(N, D)}.
+}
+\details{
+The distance swap is described in detail in the paper
+\href{http://www.bmva.org/bmvc/2016/papers/paper119/index.html}{Learning shallow convolutional feature descriptors with triplet losses} by
+V. Balntas, E. Riba et al.
+
+The loss function for each sample in the mini-batch is:
+
+\deqn{
+  L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+}
+
+where
+
+\deqn{
+  d(x_i, y_i) = | {\bf x}_i - {\bf y}_i |_p
+}
+
+See also \code{\link[=nn_triplet_margin_with_distance_loss]{nn_triplet_margin_with_distance_loss()}}, which computes the
+triplet margin loss for input tensors using a custom distance function.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, D)} where \eqn{D} is the vector dimension.
+\item Output: A Tensor of shape \eqn{(N)} if \code{reduction} is \code{'none'}, or a scalar
+otherwise.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2)
+anchor <- torch_randn(100, 128, requires_grad=TRUE)
+positive <- torch_randn(100, 128, requires_grad=TRUE)
+negative <- torch_randn(100, 128, requires_grad=TRUE)
+output <- triplet_loss(anchor, positive, negative)
+output$backward()
+
+}
+}
diff --git a/man/nn_triplet_margin_with_distance_loss.Rd b/man/nn_triplet_margin_with_distance_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..ffa9757c553f755bfc021a96d23c10dd04ba386d
--- /dev/null
+++ b/man/nn_triplet_margin_with_distance_loss.Rd
@@ -0,0 +1,119 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nn-loss.R
+\name{nn_triplet_margin_with_distance_loss}
+\alias{nn_triplet_margin_with_distance_loss}
+\title{Triplet margin with distance loss}
+\usage{
+nn_triplet_margin_with_distance_loss(
+  distance_function = NULL,
+  margin = 1,
+  swap = FALSE,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{distance_function}{(callable, optional): A nonnegative, real-valued function that
+quantifies the closeness of two tensors. If not specified,
+\code{\link[=nn_pairwise_distance]{nn_pairwise_distance()}} will be used.  Default: \code{None}}
+
+\item{margin}{(float, optional): A non-negative margin representing the minimum difference
+between the positive and negative distances required for the loss to be 0. Larger
+margins penalize cases where the negative examples are not distant enough from the
+anchors, relative to the positives. Default: \eqn{1}.}
+
+\item{swap}{(bool, optional): Whether to use the distance swap described in the paper
+\href{http://www.bmva.org/bmvc/2016/papers/paper119/index.html}{Learning shallow convolutional feature descriptors with triplet losses} by
+V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the
+negative example than the anchor is, swaps the positive example and the anchor in
+the loss computation. Default: \code{FALSE}.}
+
+\item{reduction}{(string, optional): Specifies the (optional) reduction to apply to the output:
+\code{'none'} | \code{'mean'} | \code{'sum'}. \code{'none'}: no reduction will be applied,
+\code{'mean'}: the sum of the output will be divided by the number of
+elements in the output, \code{'sum'}: the output will be summed. Default: \code{'mean'}}
+}
+\description{
+Creates a criterion that measures the triplet loss given input
+tensors \eqn{a}, \eqn{p}, and \eqn{n} (representing anchor,
+positive, and negative examples, respectively), and a nonnegative,
+real-valued function ("distance function") used to compute the relationship
+between the anchor and positive example ("positive distance") and the
+anchor and negative example ("negative distance").
+}
+\details{
+The unreduced loss (i.e., with \code{reduction} set to \code{'none'})
+can be described as:
+
+\deqn{
+  \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
+}
+
+where \eqn{N} is the batch size; \eqn{d} is a nonnegative, real-valued function
+quantifying the closeness of two tensors, referred to as the \code{distance_function};
+and \eqn{margin} is a non-negative margin representing the minimum difference
+between the positive and negative distances that is required for the loss to
+be 0.  The input tensors have \eqn{N} elements each and can be of any shape
+that the distance function can handle.
+If \code{reduction} is not \code{'none'}
+(default \code{'mean'}), then:
+
+\deqn{
+\ell(x, y) =
+\begin{array}{ll}
+\mbox{mean}(L), &  \mbox{if reduction} = \mbox{`mean';}\\
+            \mbox{sum}(L),  &  \mbox{if reduction} = \mbox{`sum'.}
+\end{array}
+}
+
+See also \code{\link[=nn_triplet_margin_loss]{nn_triplet_margin_loss()}}, which computes the triplet
+loss for input tensors using the \eqn{l_p} distance as the distance function.
+}
+\section{Shape}{
+
+\itemize{
+\item Input: \eqn{(N, *)} where \eqn{*} represents any number of additional dimensions
+as supported by the distance function.
+\item Output: A Tensor of shape \eqn{(N)} if \code{reduction} is \code{'none'}, or a scalar
+otherwise.
+}
+}
+
+\examples{
+if (torch_is_installed()) {
+# Initialize embeddings
+embedding <- nn_embedding(1000, 128)
+anchor_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+positive_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+negative_ids <- torch_randint(0, 1000, 1, dtype = torch_long())
+anchor <- embedding(anchor_ids)
+positive <- embedding(positive_ids)
+negative <- embedding(negative_ids)
+
+# Built-in Distance Function
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function=nn_pairwise_distance()
+)
+output <- triplet_loss(anchor, positive, negative)
+
+# Custom Distance Function
+l_infinity <- function(x1, x2) {
+  torch_max(torch_abs(x1 - x2), dim = 1)[[1]]
+}
+
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function=l_infinity, margin=1.5
+)
+output <- triplet_loss(anchor, positive, negative)
+
+# Custom Distance Function (Lambda)
+triplet_loss <- nn_triplet_margin_with_distance_loss(
+  distance_function = function(x, y) {
+    1 - nnf_cosine_similarity(x, y)
+  }
+)
+
+output <- triplet_loss(anchor, positive, negative)
+
+}
+}
diff --git a/man/nn_utils_rnn_pack_sequence.Rd b/man/nn_utils_rnn_pack_sequence.Rd
index 1ac69baa42538c8c9892f4ba98c0e39fbe81f5b1..7eb3bd0275c44e523b6e009f72c51979e2b76a71 100644
--- a/man/nn_utils_rnn_pack_sequence.Rd
+++ b/man/nn_utils_rnn_pack_sequence.Rd
@@ -27,7 +27,7 @@ is \code{TRUE}, the sequences should be sorted in the order of decreasing length
 \code{enforce_sorted = TRUE} is only necessary for ONNX export.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_tensor(c(1,2,3), dtype = torch_long())
 y <- torch_tensor(c(4, 5), dtype = torch_long())
 z <- torch_tensor(c(6), dtype = torch_long())
diff --git a/man/nn_utils_rnn_pad_packed_sequence.Rd b/man/nn_utils_rnn_pad_packed_sequence.Rd
index 2da99d8e78fc66b07ae7353f8cefd5a3f364ab15..89004084e55898e5f028f391b1c8f064c5e2d105 100644
--- a/man/nn_utils_rnn_pad_packed_sequence.Rd
+++ b/man/nn_utils_rnn_pad_packed_sequence.Rd
@@ -45,7 +45,7 @@ the data will be transposed into \verb{B x T x *} format.
 \code{nn_module} wrapped in \code{~torch.nn.DataParallel}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 seq <- torch_tensor(rbind(c(1,2,0), c(3,0,0), c(4,5,6)))        
 lens <- c(2,1,3)
 packed <- nn_utils_rnn_pack_padded_sequence(seq, lens, batch_first = TRUE,
diff --git a/man/nn_utils_rnn_pad_sequence.Rd b/man/nn_utils_rnn_pad_sequence.Rd
index f82849b488488c3829132c0061aef56767bcbb56..45a8f703e44f3f79c238271c0eb4b0b18af74c5f 100644
--- a/man/nn_utils_rnn_pad_sequence.Rd
+++ b/man/nn_utils_rnn_pad_sequence.Rd
@@ -36,7 +36,7 @@ where \code{T} is the length of the longest sequence. This function assumes
 trailing dimensions and type of all the Tensors in sequences are same.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 a <- torch_ones(25, 300)
 b <- torch_ones(22, 300)
 c <- torch_ones(15, 300)
diff --git a/man/nnf_elu.Rd b/man/nnf_elu.Rd
index 770156a1ccc004978af21dca2ac52a26178d3daf..fb3b47474d284c7fd0eda02a1dad665b6aa35c68 100644
--- a/man/nnf_elu.Rd
+++ b/man/nnf_elu.Rd
@@ -22,7 +22,7 @@ Applies element-wise,
 \deqn{ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_randn(2, 2)
 y <- nnf_elu(x, alpha = 1)
 nnf_elu_(x, alpha = 1)
diff --git a/man/nnf_selu.Rd b/man/nnf_selu.Rd
index 0e344eccca5114436f19736e8eec9a09a93b469f..89f5993bb96cfc48b2c2b700efc6613fadc2b1c4 100644
--- a/man/nnf_selu.Rd
+++ b/man/nnf_selu.Rd
@@ -22,7 +22,7 @@ with \eqn{\alpha=1.6732632423543772848170429916717} and
 \eqn{scale=1.0507009873554804934193349852946}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_randn(2, 2)
 y <- nnf_selu(x)
 nnf_selu_(x)
diff --git a/man/nnf_sigmoid.Rd b/man/nnf_sigmoid.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..ddd654e5650fd4d0840df8ec19dd60200da7a7b7
--- /dev/null
+++ b/man/nnf_sigmoid.Rd
@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nnf-activation.R
+\name{nnf_sigmoid}
+\alias{nnf_sigmoid}
+\title{Sigmoid}
+\usage{
+nnf_sigmoid(input)
+}
+\arguments{
+\item{input}{(N,*) tensor, where * means, any number of additional
+dimensions}
+}
+\description{
+Applies element-wise \eqn{Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}}
+}
diff --git a/man/nnf_triplet_margin_with_distance_loss.Rd b/man/nnf_triplet_margin_with_distance_loss.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..66b41e5e0094021ce371b5ecc9d700a95966e2e9
--- /dev/null
+++ b/man/nnf_triplet_margin_with_distance_loss.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nnf-loss.R
+\name{nnf_triplet_margin_with_distance_loss}
+\alias{nnf_triplet_margin_with_distance_loss}
+\title{Triplet margin with distance loss}
+\usage{
+nnf_triplet_margin_with_distance_loss(
+  anchor,
+  positive,
+  negative,
+  distance_function = NULL,
+  margin = 1,
+  swap = FALSE,
+  reduction = "mean"
+)
+}
+\arguments{
+\item{anchor}{the anchor input tensor}
+
+\item{positive}{the positive input tensor}
+
+\item{negative}{the negative input tensor}
+
+\item{distance_function}{(callable, optional): A nonnegative, real-valued function that
+quantifies the closeness of two tensors. If not specified,
+\code{\link[=nn_pairwise_distance]{nn_pairwise_distance()}} will be used.  Default: \code{None}}
+
+\item{margin}{Default: 1.}
+
+\item{swap}{The distance swap is described in detail in the paper Learning shallow
+convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
+Default: \code{FALSE}.}
+
+\item{reduction}{(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'}
+}
+\description{
+See \code{\link[=nn_triplet_margin_with_distance_loss]{nn_triplet_margin_with_distance_loss()}}
+}
diff --git a/man/optim_adam.Rd b/man/optim_adam.Rd
index 6948c81aa002b08da6184153882cd4d0031d84d9..6dac2bf348f3f48fa8c542739717137001dbe76c 100644
--- a/man/optim_adam.Rd
+++ b/man/optim_adam.Rd
@@ -35,7 +35,7 @@ algorithm from the paper \href{https://openreview.net/forum?id=ryQu7f-RZ}{On the
 It has been proposed in \href{https://arxiv.org/abs/1412.6980}{Adam: A Method for Stochastic Optimization}.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 optimizer <- optim_adam(model$parameters(), lr=0.1)
 optimizer$zero_grad()
diff --git a/man/optim_sgd.Rd b/man/optim_sgd.Rd
index 5bde16fac3aca3bb7adaaab8beb30fbee6a4c781..71adb63e0ef0b1bf0241c6c6666c1b84cee72d95 100644
--- a/man/optim_sgd.Rd
+++ b/man/optim_sgd.Rd
@@ -62,7 +62,7 @@ The Nesterov version is analogously modified.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 optimizer <- optim_sgd(model$parameters(), lr=0.1, momentum=0.9)
 optimizer$zero_grad()
diff --git a/man/pipe.Rd b/man/pipe.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..0eec752616534f156cac398f07bef8cc3f527935
--- /dev/null
+++ b/man/pipe.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-pipe.R
+\name{\%>\%}
+\alias{\%>\%}
+\title{Pipe operator}
+\usage{
+lhs \%>\% rhs
+}
+\description{
+See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+}
+\keyword{internal}
diff --git a/man/torch_abs.Rd b/man/torch_abs.Rd
index 97eb63d8bbee6f29692f3d15a58e491279d65574..4bb5b383fa917bc6c3e7d6d4c08273ce12f68d4d 100644
--- a/man/torch_abs.Rd
+++ b/man/torch_abs.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_abs}
 \alias{torch_abs}
 \title{Abs}
+\usage{
+torch_abs(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Abs
 }
-\section{abs(input, out=None) -> Tensor }{
+\section{abs(input) -> Tensor }{
 
 
 Computes the element-wise absolute value of the given \code{input} tensor.
@@ -23,7 +24,7 @@ Computes the element-wise absolute value of the given \code{input} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_abs(torch_tensor(c(-1, -2, 3)))
 }
diff --git a/man/torch_acos.Rd b/man/torch_acos.Rd
index aeea12bb6075ee17cb0bb9207955651ba1d239db..e2f5873f45fc677f20f21bf2efdc0b1b1bb0f3e3 100644
--- a/man/torch_acos.Rd
+++ b/man/torch_acos.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_acos}
 \alias{torch_acos}
 \title{Acos}
+\usage{
+torch_acos(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Acos
 }
-\section{acos(input, out=None) -> Tensor }{
+\section{acos(input) -> Tensor }{
 
 
 Returns a new tensor with the arccosine  of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the arccosine  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_adaptive_avg_pool1d.Rd b/man/torch_adaptive_avg_pool1d.Rd
index 024801cd4e08167fc635a3dcf8807a794f885299..a602da3efd329eff489a31643be93b9d27a53a9a 100644
--- a/man/torch_adaptive_avg_pool1d.Rd
+++ b/man/torch_adaptive_avg_pool1d.Rd
@@ -1,11 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_adaptive_avg_pool1d}
 \alias{torch_adaptive_avg_pool1d}
 \title{Adaptive_avg_pool1d}
+\usage{
+torch_adaptive_avg_pool1d(self, output_size)
+}
 \arguments{
-\item{output_size}{NA the target output size (single integer)}
+\item{self}{the input tensor}
+
+\item{output_size}{the target output size (single integer)}
 }
 \description{
 Adaptive_avg_pool1d
@@ -16,6 +21,6 @@ Adaptive_avg_pool1d
 Applies a 1D adaptive average pooling over an input signal composed of
 several input planes.
 
-See \code{~torch.nn.AdaptiveAvgPool1d} for details and output shape.
+See \code{\link[=nn_adaptive_avg_pool1d]{nn_adaptive_avg_pool1d()}} for details and output shape.
 }
 
diff --git a/man/torch_add.Rd b/man/torch_add.Rd
index 00b0b5748b1763e37883bfa79aff1eb34b8bb688..577bda478e023a4cfe4b9aaed742a670e3e05e27 100644
--- a/man/torch_add.Rd
+++ b/man/torch_add.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_add}
 \alias{torch_add}
 \title{Add}
+\usage{
+torch_add(self, other, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{value}{(Number) the number to be added to each element of \code{input}}
+\item{self}{(Tensor) the input tensor.}
 
-\item{other}{(Tensor) the second input tensor}
+\item{other}{(Tensor/Number) the second input tensor/number.}
 
 \item{alpha}{(Number) the scalar multiplier for \code{other}}
 }
 \description{
 Add
 }
-\section{add(input, other, out=None) }{
+\section{add(input, other, out=NULL) }{
 
 
 Adds the scalar \code{other} to each element of the input \code{input}
@@ -29,7 +30,7 @@ If \code{input} is of type FloatTensor or DoubleTensor, \code{other} must be
 a real number, otherwise it should be an integer.
 }
 
-\section{add(input, other, *, alpha=1, out=None) }{
+\section{add(input, other, *, alpha=1, out=NULL) }{
 
 
 Each element of the tensor \code{other} is multiplied by the scalar
@@ -47,7 +48,7 @@ a real number, otherwise it should be an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_addbmm.Rd b/man/torch_addbmm.Rd
index 904909b622c1ad7e1028618b3004845c815c5a24..a344a17c754fb63bdb4fcccbe2a2ffb76c5a1677 100644
--- a/man/torch_addbmm.Rd
+++ b/man/torch_addbmm.Rd
@@ -1,26 +1,27 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addbmm}
 \alias{torch_addbmm}
 \title{Addbmm}
+\usage{
+torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
+}
 \arguments{
+\item{self}{(Tensor) matrix to be added}
+
 \item{batch1}{(Tensor) the first batch of matrices to be multiplied}
 
 \item{batch2}{(Tensor) the second batch of matrices to be multiplied}
 
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
-\item{input}{(Tensor) matrix to be added}
-
 \item{alpha}{(Number, optional) multiplier for \code{batch1 @ batch2} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addbmm
 }
-\section{addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a batch matrix-matrix product of matrices stored
@@ -45,7 +46,7 @@ must be real numbers, otherwise they should be integers.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(3, 5))
 batch1 = torch_randn(c(10, 3, 4))
diff --git a/man/torch_addcdiv.Rd b/man/torch_addcdiv.Rd
index 0db51953a62d32602b2e2edd9ff101fc4224e31f..cf93b9124e56e06745e54bd1aa2f937e58028211 100644
--- a/man/torch_addcdiv.Rd
+++ b/man/torch_addcdiv.Rd
@@ -1,24 +1,25 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addcdiv}
 \alias{torch_addcdiv}
 \title{Addcdiv}
+\usage{
+torch_addcdiv(self, tensor1, tensor2, value = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be added}
+\item{self}{(Tensor) the tensor to be added}
 
 \item{tensor1}{(Tensor) the numerator tensor}
 
 \item{tensor2}{(Tensor) the denominator tensor}
 
 \item{value}{(Number, optional) multiplier for \eqn{\mbox{tensor1} / \mbox{tensor2}}}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addcdiv
 }
-\section{addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor }{
+\section{addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor }{
 
 
 Performs the element-wise division of \code{tensor1} by \code{tensor2},
@@ -50,7 +51,7 @@ a real number, otherwise an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_randn(c(1, 3))
 t1 = torch_randn(c(3, 1))
diff --git a/man/torch_addcmul.Rd b/man/torch_addcmul.Rd
index 490b56da9c57a865b5334870be13090b8bf5c804..b9a1170aea36f0a48fe1dd4472eb4a247b2f50cc 100644
--- a/man/torch_addcmul.Rd
+++ b/man/torch_addcmul.Rd
@@ -1,24 +1,25 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addcmul}
 \alias{torch_addcmul}
 \title{Addcmul}
+\usage{
+torch_addcmul(self, tensor1, tensor2, value = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be added}
+\item{self}{(Tensor) the tensor to be added}
 
 \item{tensor1}{(Tensor) the tensor to be multiplied}
 
 \item{tensor2}{(Tensor) the tensor to be multiplied}
 
 \item{value}{(Number, optional) multiplier for \eqn{tensor1 .* tensor2}}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addcmul
 }
-\section{addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor }{
+\section{addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor }{
 
 
 Performs the element-wise multiplication of \code{tensor1}
@@ -36,7 +37,7 @@ a real number, otherwise an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_randn(c(1, 3))
 t1 = torch_randn(c(3, 1))
diff --git a/man/torch_addmm.Rd b/man/torch_addmm.Rd
index b7b791b1469306c71ebb91f6588e05887d09e11d..d025e5e1bb7625cdb838d07bffb45f7560e37ece 100644
--- a/man/torch_addmm.Rd
+++ b/man/torch_addmm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addmm}
 \alias{torch_addmm}
 \title{Addmm}
+\usage{
+torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) matrix to be added}
+\item{self}{(Tensor) matrix to be added}
 
 \item{mat1}{(Tensor) the first matrix to be multiplied}
 
@@ -14,13 +17,11 @@
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{mat1 @ mat2} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addmm
 }
-\section{addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a matrix multiplication of the matrices \code{mat1} and \code{mat2}.
@@ -42,7 +43,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(2, 3))
 mat1 = torch_randn(c(2, 3))
diff --git a/man/torch_addmv.Rd b/man/torch_addmv.Rd
index eaebe9e06b7ee0dd2be81f8b4b2bddcdb26148f1..cb39401018ec2f6e700d2c9bbf70ec1c169fe59f 100644
--- a/man/torch_addmv.Rd
+++ b/man/torch_addmv.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addmv}
 \alias{torch_addmv}
 \title{Addmv}
+\usage{
+torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) vector to be added}
+\item{self}{(Tensor) vector to be added}
 
 \item{mat}{(Tensor) matrix to be multiplied}
 
@@ -14,13 +17,11 @@
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{mat @ vec} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addmv
 }
-\section{addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a matrix-vector product of the matrix \code{mat} and
@@ -43,7 +44,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(2))
 mat = torch_randn(c(2, 3))
diff --git a/man/torch_addr.Rd b/man/torch_addr.Rd
index 056a92cc6ae70123a15313d3e83b553324c1eac6..91329566a6335f3837b5bca10716b397e1c964d2 100644
--- a/man/torch_addr.Rd
+++ b/man/torch_addr.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_addr}
 \alias{torch_addr}
 \title{Addr}
+\usage{
+torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) matrix to be added}
+\item{self}{(Tensor) matrix to be added}
 
 \item{vec1}{(Tensor) the first vector of the outer product}
 
@@ -14,13 +17,11 @@
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{\mbox{vec1} \otimes \mbox{vec2}} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Addr
 }
-\section{addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs the outer-product of vectors \code{vec1} and \code{vec2}
@@ -44,7 +45,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 vec1 = torch_arange(1., 4.)
 vec2 = torch_arange(1., 3.)
diff --git a/man/torch_allclose.Rd b/man/torch_allclose.Rd
index af72fe5585fd89a2820b9780bca945e458574095..108f0f4ffc807e3648c71c76d3e317bc863e83fd 100644
--- a/man/torch_allclose.Rd
+++ b/man/torch_allclose.Rd
@@ -1,19 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_allclose}
 \alias{torch_allclose}
 \title{Allclose}
+\usage{
+torch_allclose(self, other, rtol = 1e-05, atol = 0, equal_nan = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) first tensor to compare}
+\item{self}{(Tensor) first tensor to compare}
 
 \item{other}{(Tensor) second tensor to compare}
 
-\item{atol}{(float, optional) absolute tolerance. Default: 1e-08}
-
 \item{rtol}{(float, optional) relative tolerance. Default: 1e-05}
 
-\item{equal_nan}{(bool, optional) if \code{True}, then two \code{NaN} s will be compared as equal. Default: \code{False}}
+\item{atol}{(float, optional) absolute tolerance. Default: 1e-08}
+
+\item{equal_nan}{(bool, optional) if \code{TRUE}, then two \code{NaN} s will be compared as equal. Default: \code{FALSE}}
 }
 \description{
 Allclose
@@ -31,7 +34,7 @@ elementwise, for all elements of \code{input} and \code{other}. The behaviour of
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08)))
 torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09)))
diff --git a/man/torch_angle.Rd b/man/torch_angle.Rd
index b49ab8e3f327f9f2d03e5bc9a198a6b8e6e903f0..ecaa37e05a4a83afa47665212c24a5c637a88219 100644
--- a/man/torch_angle.Rd
+++ b/man/torch_angle.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_angle}
 \alias{torch_angle}
 \title{Angle}
+\usage{
+torch_angle(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Angle
 }
-\section{angle(input, out=None) -> Tensor }{
+\section{angle(input) -> Tensor }{
 
 
 Computes the element-wise angle (in radians) of the given \code{input} tensor.
@@ -23,7 +24,7 @@ Computes the element-wise angle (in radians) of the given \code{input} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_angle(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))*180/3.14159
 }
diff --git a/man/torch_arange.Rd b/man/torch_arange.Rd
index 1599a99b052325e31e9fef3aa7b6b43e414c0c91..47d0782eaf9eebbab961467626ba10640a61d4b0 100644
--- a/man/torch_arange.Rd
+++ b/man/torch_arange.Rd
@@ -1,9 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_arange}
 \alias{torch_arange}
 \title{Arange}
+\usage{
+torch_arange(
+  start,
+  end,
+  step = 1,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(Number) the starting value for the set of points. Default: \code{0}.}
 
@@ -11,20 +22,18 @@
 
 \item{step}{(Number) the gap between each pair of adjacent points. Default: \code{1}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Arange
 }
-\section{arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a 1-D tensor of size \eqn{\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil}
@@ -41,7 +50,7 @@ in such cases.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_arange(start = 0, end = 5)
 torch_arange(1, 4)
diff --git a/man/torch_argmax.Rd b/man/torch_argmax.Rd
index d033d73087ad70ff7b8b3d5ebaa01aa96ea6c0be..ba3df76609a69f201a2b5f52745d36ed7e329848 100644
--- a/man/torch_argmax.Rd
+++ b/man/torch_argmax.Rd
@@ -1,15 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_argmax}
 \alias{torch_argmax}
 \title{Argmax}
+\usage{
+torch_argmax(self, dim = NULL, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
-\item{dim}{(int) the dimension to reduce. If \code{None}, the argmax of the flattened input is returned.}
+\item{dim}{(int) the dimension to reduce. If \code{NULL}, the argmax of the flattened input is returned.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=None}.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=NULL}.}
 }
 \description{
 Argmax
@@ -33,7 +36,7 @@ documentation for the exact semantics of this method.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 a = torch_randn(c(4, 4))
diff --git a/man/torch_argmin.Rd b/man/torch_argmin.Rd
index 66c956b6c1408a56c82f13bee9edf6dee3293bee..34f621181f0c6eb84fdfe634ef655d6ab491d17f 100644
--- a/man/torch_argmin.Rd
+++ b/man/torch_argmin.Rd
@@ -1,15 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_argmin}
 \alias{torch_argmin}
 \title{Argmin}
+\usage{
+torch_argmin(self, dim = NULL, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
-\item{dim}{(int) the dimension to reduce. If \code{None}, the argmin of the flattened input is returned.}
+\item{dim}{(int) the dimension to reduce. If \code{NULL}, the argmin of the flattened input is returned.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=None}.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Ignored if \code{dim=NULL}.}
 }
 \description{
 Argmin
@@ -23,7 +26,7 @@ This is the second value returned by \code{torch_min}. See its
 documentation for the exact semantics of this method.
 }
 
-\section{argmin(input, dim, keepdim=False, out=None) -> LongTensor }{
+\section{argmin(input, dim, keepdim=False, out=NULL) -> LongTensor }{
 
 
 Returns the indices of the minimum values of a tensor across a dimension.
@@ -33,7 +36,7 @@ documentation for the exact semantics of this method.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4, 4))
 a
diff --git a/man/torch_argsort.Rd b/man/torch_argsort.Rd
index 494bfc513b2bb0c28abca253a01f6262e0505acb..a8a3463a1a9611b21b35b79717cc8f3751c964db 100644
--- a/man/torch_argsort.Rd
+++ b/man/torch_argsort.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_argsort}
 \alias{torch_argsort}
 \title{Argsort}
+\usage{
+torch_argsort(self, dim = -1L, descending = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int, optional) the dimension to sort along}
 
@@ -25,7 +28,7 @@ for the exact semantics of this method.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4, 4))
 a
diff --git a/man/torch_as_strided.Rd b/man/torch_as_strided.Rd
index 91ec954a052c103215e9328941ffcd5f165251b6..703a5010f429a88dc0e8a96a39a44132b8866555 100644
--- a/man/torch_as_strided.Rd
+++ b/man/torch_as_strided.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_as_strided}
 \alias{torch_as_strided}
 \title{As_strided}
+\usage{
+torch_as_strided(self, size, stride, storage_offset = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{size}{(tuple or ints) the shape of the output tensor}
 
@@ -36,7 +39,7 @@ advisable to use.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 3))
 x
diff --git a/man/torch_asin.Rd b/man/torch_asin.Rd
index d7f9d9440333113fa5792d353b1721108d2d9c33..5e087de01ed0d7aed1bde46e1f788dee1f782293 100644
--- a/man/torch_asin.Rd
+++ b/man/torch_asin.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_asin}
 \alias{torch_asin}
 \title{Asin}
+\usage{
+torch_asin(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Asin
 }
-\section{asin(input, out=None) -> Tensor }{
+\section{asin(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the arcsine  of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the arcsine  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_atan.Rd b/man/torch_atan.Rd
index f803b4e94d5125a4144942b6afd03d31d5b6261f..016b42ed4b67448a6486cca844a150742ccdcdb2 100644
--- a/man/torch_atan.Rd
+++ b/man/torch_atan.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_atan}
 \alias{torch_atan}
 \title{Atan}
+\usage{
+torch_atan(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Atan
 }
-\section{atan(input, out=None) -> Tensor }{
+\section{atan(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the arctangent  of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the arctangent  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_atan2.Rd b/man/torch_atan2.Rd
index c64301f307a01eb83ae7915405abbdbaedd7282b..5dcfb72ae986666c536532dfb9062f746e223eab 100644
--- a/man/torch_atan2.Rd
+++ b/man/torch_atan2.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_atan2}
 \alias{torch_atan2}
 \title{Atan2}
+\usage{
+torch_atan2(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the first input tensor}
+\item{self}{(Tensor) the first input tensor}
 
 \item{other}{(Tensor) the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Atan2
 }
-\section{atan2(input, other, out=None) -> Tensor }{
+\section{atan2(input, other, out=NULL) -> Tensor }{
 
 
 Element-wise arctangent of \eqn{\mbox{input}_{i} / \mbox{other}_{i}}
@@ -29,7 +30,7 @@ broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_avg_pool1d.Rd b/man/torch_avg_pool1d.Rd
index bc242b13a3aa73e8e784edc7b92f011b665f5933..ba19f2e5144b02755c0b33eb74831795adf2e3c1 100644
--- a/man/torch_avg_pool1d.Rd
+++ b/man/torch_avg_pool1d.Rd
@@ -1,31 +1,41 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_avg_pool1d}
 \alias{torch_avg_pool1d}
 \title{Avg_pool1d}
+\usage{
+torch_avg_pool1d(
+  self,
+  kernel_size,
+  stride = list(),
+  padding = 0L,
+  ceil_mode = FALSE,
+  count_include_pad = TRUE
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
+\item{self}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 
-\item{kernel_size}{NA the size of the window. Can be a single number or a      tuple \verb{(kW,)}}
+\item{kernel_size}{the size of the window. Can be a single number or a tuple \verb{(kW,)}}
 
-\item{stride}{NA the stride of the window. Can be a single number or a tuple      \verb{(sW,)}. Default: \code{kernel_size}}
+\item{stride}{the stride of the window. Can be a single number or a tuple \verb{(sW,)}. Default: \code{kernel_size}}
 
-\item{padding}{NA implicit zero paddings on both sides of the input. Can be a      single number or a tuple \verb{(padW,)}. Default: 0}
+\item{padding}{implicit zero paddings on both sides of the input. Can be a single number or a tuple \verb{(padW,)}. Default: 0}
 
-\item{ceil_mode}{NA when True, will use \code{ceil} instead of \code{floor} to compute the        output shape. Default: \code{False}}
+\item{ceil_mode}{when \code{TRUE}, will use \code{ceil} instead of \code{floor} to compute the output shape. Default: \code{FALSE}}
 
-\item{count_include_pad}{NA when True, will include the zero-padding in the        averaging calculation. Default: \code{True}}
+\item{count_include_pad}{when \code{TRUE}, will include the zero-padding in the averaging calculation. Default: \code{TRUE}}
 }
 \description{
 Avg_pool1d
 }
-\section{avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor }{
+\section{avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor }{
 
 
 Applies a 1D average pooling over an input signal composed of several
 input planes.
 
-See \code{~torch.nn.AvgPool1d} for details and output shape.
+See \code{\link[=nn_avg_pool1d]{nn_avg_pool1d()}} for details and output shape.
 }
 
diff --git a/man/torch_baddbmm.Rd b/man/torch_baddbmm.Rd
index 9af53cd6f8b4e6424a65ce84e6603857b99074ce..7d16fda5f737003aa440fa5b6e1b02967a85fe4b 100644
--- a/man/torch_baddbmm.Rd
+++ b/man/torch_baddbmm.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_baddbmm}
 \alias{torch_baddbmm}
 \title{Baddbmm}
+\usage{
+torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be added}
+\item{self}{(Tensor) the tensor to be added}
 
 \item{batch1}{(Tensor) the first batch of matrices to be multiplied}
 
@@ -14,13 +17,11 @@
 \item{beta}{(Number, optional) multiplier for \code{input} (\eqn{\beta})}
 
 \item{alpha}{(Number, optional) multiplier for \eqn{\mbox{batch1} \mathbin{@} \mbox{batch2}} (\eqn{\alpha})}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Baddbmm
 }
-\section{baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor }{
+\section{baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor }{
 
 
 Performs a batch matrix-matrix product of matrices in \code{batch1}
@@ -45,7 +46,7 @@ For inputs of type \code{FloatTensor} or \code{DoubleTensor}, arguments \code{be
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 M = torch_randn(c(10, 3, 5))
 batch1 = torch_randn(c(10, 3, 4))
diff --git a/man/torch_bartlett_window.Rd b/man/torch_bartlett_window.Rd
index 7e22d7eb847d48f5deb1e05eae3038e957bddcad..fceba8e27d5bfb98b23c3edb9eb16048af84a77a 100644
--- a/man/torch_bartlett_window.Rd
+++ b/man/torch_bartlett_window.Rd
@@ -1,21 +1,31 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_bartlett_window}
 \alias{torch_bartlett_window}
 \title{Bartlett_window}
+\usage{
+torch_bartlett_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Bartlett_window
@@ -24,7 +34,7 @@ Bartlett_window
 \preformatted{If `window_length` \eqn{=1}, the returned window contains a single value 1.
 }
 }
-\section{bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Bartlett window function.
@@ -44,7 +54,7 @@ window trims off the last duplicate value from the symmetric window and is
 ready to be used as a periodic window with functions like
 \code{torch_stft}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_bartlett_window(L, periodic=True)} equal to
+\code{torch_bartlett_window(L, periodic=TRUE)} equal to
 \verb{torch_bartlett_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_bernoulli.Rd b/man/torch_bernoulli.Rd
index 27d2bd185d42146c5834387280924e41c620ba99..6ad61490d1b2c8bd5209fa65b1220f37868697bb 100644
--- a/man/torch_bernoulli.Rd
+++ b/man/torch_bernoulli.Rd
@@ -1,20 +1,25 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bernoulli}
 \alias{torch_bernoulli}
 \title{Bernoulli}
+\usage{
+torch_bernoulli(self, p, generator = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of probability values for the Bernoulli distribution}
+\item{self}{(Tensor) the input tensor of probability values for the Bernoulli
+distribution}
 
-\item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
+\item{p}{(Number) a probability value. If \code{p} is passed than it's used instead of
+the values in \code{self} tensor.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 }
 \description{
 Bernoulli
 }
-\section{bernoulli(input, *, generator=None, out=None) -> Tensor }{
+\section{bernoulli(input, *, generator=NULL, out=NULL) -> Tensor }{
 
 
 Draws binary random numbers (0 or 1) from a Bernoulli distribution.
@@ -39,7 +44,7 @@ point \code{dtype}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1)
 a
diff --git a/man/torch_bincount.Rd b/man/torch_bincount.Rd
index 61577dd0b74f8ba92edcd54e4e0ccfda5f292195..3a1bcdafc15fe0dfad66012ec04b9572670eabca 100644
--- a/man/torch_bincount.Rd
+++ b/man/torch_bincount.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bincount}
 \alias{torch_bincount}
 \title{Bincount}
+\usage{
+torch_bincount(self, weights = list(), minlength = 0L)
+}
 \arguments{
-\item{input}{(Tensor) 1-d int tensor}
+\item{self}{(Tensor) 1-d int tensor}
 
 \item{weights}{(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.}
 
@@ -14,7 +17,7 @@
 \description{
 Bincount
 }
-\section{bincount(input, weights=None, minlength=0) -> Tensor }{
+\section{bincount(input, weights=NULL, minlength=0) -> Tensor }{
 
 
 Count the frequency of each value in an array of non-negative ints.
@@ -31,7 +34,7 @@ tensor of size 0. If \code{minlength} is specified, the number of bins is at lea
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randint(0, 8, list(5), dtype=torch_int64())
 weights = torch_linspace(0, 1, steps=5)
diff --git a/man/torch_bitwise_and.Rd b/man/torch_bitwise_and.Rd
index 6ad1f5854987fe54d518a89161d3df8d6621b333..a68be3e311ac539b46e35d144b36db608dc8ad26 100644
--- a/man/torch_bitwise_and.Rd
+++ b/man/torch_bitwise_and.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_and}
 \alias{torch_bitwise_and}
 \title{Bitwise_and}
+\usage{
+torch_bitwise_and(self, other)
+}
 \arguments{
-\item{input}{NA the first input tensor}
+\item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bitwise_and
 }
-\section{bitwise_and(input, other, out=None) -> Tensor }{
+\section{bitwise_and(input, other, out=NULL) -> Tensor }{
 
 
 Computes the bitwise AND of \code{input} and \code{other}. The input tensor must be of
diff --git a/man/torch_bitwise_not.Rd b/man/torch_bitwise_not.Rd
index 63e275584fd15a9f58fb4fbd0d4e895804e16491..62d2fbb0a0cf7c946bbf77e9b336cc4fb021809f 100644
--- a/man/torch_bitwise_not.Rd
+++ b/man/torch_bitwise_not.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_not}
 \alias{torch_bitwise_not}
 \title{Bitwise_not}
+\usage{
+torch_bitwise_not(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Bitwise_not
 }
-\section{bitwise_not(input, out=None) -> Tensor }{
+\section{bitwise_not(input, out=NULL) -> Tensor }{
 
 
 Computes the bitwise NOT of the given input tensor. The input tensor must be of
diff --git a/man/torch_bitwise_or.Rd b/man/torch_bitwise_or.Rd
index 08886e511f2e5ca2ce2047b71daab71647038fc9..0b8a491212ea2b1a527f737ec1dc95889ab66de2 100644
--- a/man/torch_bitwise_or.Rd
+++ b/man/torch_bitwise_or.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_or}
 \alias{torch_bitwise_or}
 \title{Bitwise_or}
+\usage{
+torch_bitwise_or(self, other)
+}
 \arguments{
-\item{input}{NA the first input tensor}
+\item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bitwise_or
 }
-\section{bitwise_or(input, other, out=None) -> Tensor }{
+\section{bitwise_or(input, other, out=NULL) -> Tensor }{
 
 
 Computes the bitwise OR of \code{input} and \code{other}. The input tensor must be of
diff --git a/man/torch_bitwise_xor.Rd b/man/torch_bitwise_xor.Rd
index 3cb50dc4c9cff3651753912f798b38181728339a..dc675020a669f2afee9b5b87360d52cab36b5498 100644
--- a/man/torch_bitwise_xor.Rd
+++ b/man/torch_bitwise_xor.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bitwise_xor}
 \alias{torch_bitwise_xor}
 \title{Bitwise_xor}
+\usage{
+torch_bitwise_xor(self, other)
+}
 \arguments{
-\item{input}{NA the first input tensor}
+\item{self}{NA the first input tensor}
 
 \item{other}{NA the second input tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bitwise_xor
 }
-\section{bitwise_xor(input, other, out=None) -> Tensor }{
+\section{bitwise_xor(input, other, out=NULL) -> Tensor }{
 
 
 Computes the bitwise XOR of \code{input} and \code{other}. The input tensor must be of
diff --git a/man/torch_blackman_window.Rd b/man/torch_blackman_window.Rd
index e06037d7791cc6c918e0bf784e363cd479eb5022..ccaf3851e87d672c9889729510b9e34c2dbcee41 100644
--- a/man/torch_blackman_window.Rd
+++ b/man/torch_blackman_window.Rd
@@ -1,21 +1,31 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_blackman_window}
 \alias{torch_blackman_window}
 \title{Blackman_window}
+\usage{
+torch_blackman_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Blackman_window
@@ -24,7 +34,7 @@ Blackman_window
 \preformatted{If `window_length` \eqn{=1}, the returned window contains a single value 1.
 }
 }
-\section{blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Blackman window function.
@@ -40,7 +50,7 @@ window trims off the last duplicate value from the symmetric window and is
 ready to be used as a periodic window with functions like
 \code{torch_stft}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_blackman_window(L, periodic=True)} equal to
+\code{torch_blackman_window(L, periodic=TRUE)} equal to
 \verb{torch_blackman_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_bmm.Rd b/man/torch_bmm.Rd
index ea324ab85984b55de65d3d1b625b0aa2bc0837db..4fd68eb317a92a5a7d6757d73c11293fa72bece3 100644
--- a/man/torch_bmm.Rd
+++ b/man/torch_bmm.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_bmm}
 \alias{torch_bmm}
 \title{Bmm}
+\usage{
+torch_bmm(self, mat2)
+}
 \arguments{
-\item{input}{(Tensor) the first batch of matrices to be multiplied}
+\item{self}{(Tensor) the first batch of matrices to be multiplied}
 
 \item{mat2}{(Tensor) the second batch of matrices to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Bmm
@@ -18,7 +19,7 @@ Bmm
 This function does not broadcast .
 For broadcasting matrix products, see \code{\link{torch_matmul}}.
 }
-\section{bmm(input, mat2, out=None) -> Tensor }{
+\section{bmm(input, mat2, out=NULL) -> Tensor }{
 
 
 Performs a batch matrix-matrix product of matrices stored in \code{input}
@@ -37,7 +38,7 @@ If \code{input} is a \eqn{(b \times n \times m)} tensor, \code{mat2} is a
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randn(c(10, 3, 4))
 mat2 = torch_randn(c(10, 4, 5))
diff --git a/man/torch_broadcast_tensors.Rd b/man/torch_broadcast_tensors.Rd
index 33c2f9be9863f4036d9daf5d327c296071b75756..21ef4485c22f02f45861162e6c8d0578067dd97a 100644
--- a/man/torch_broadcast_tensors.Rd
+++ b/man/torch_broadcast_tensors.Rd
@@ -1,23 +1,26 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_broadcast_tensors}
 \alias{torch_broadcast_tensors}
 \title{Broadcast_tensors}
+\usage{
+torch_broadcast_tensors(tensors)
+}
 \arguments{
-\item{*tensors}{NA any number of tensors of the same type}
+\item{tensors}{a list containing any number of tensors of the same type}
 }
 \description{
 Broadcast_tensors
 }
-\section{broadcast_tensors(*tensors) -> List of Tensors }{
+\section{broadcast_tensors(tensors) -> List of Tensors }{
 
 
 Broadcasts the given tensors according to broadcasting-semantics.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(0, 3)$view(c(1, 3))
 y = torch_arange(0, 2)$view(c(2, 1))
diff --git a/man/torch_can_cast.Rd b/man/torch_can_cast.Rd
index 940b8a881529098bf75c2bb53bdb084900ce47e2..deb6e08a61e728ebca26df8eaca84af635e4aab8 100644
--- a/man/torch_can_cast.Rd
+++ b/man/torch_can_cast.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_can_cast}
 \alias{torch_can_cast}
 \title{Can_cast}
+\usage{
+torch_can_cast(from, to)
+}
 \arguments{
 \item{from}{(dtype) The original \code{torch_dtype}.}
 
@@ -20,7 +23,7 @@ described in the type promotion documentation .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_can_cast(torch_double(), torch_float())
 torch_can_cast(torch_float(), torch_int())
diff --git a/man/torch_cartesian_prod.Rd b/man/torch_cartesian_prod.Rd
index 6b22cabc139a213e9f00a2a3e230d90b54b4b8a5..a621dbdb54e760b7a10f56d6db560a19acd2c36a 100644
--- a/man/torch_cartesian_prod.Rd
+++ b/man/torch_cartesian_prod.Rd
@@ -1,24 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cartesian_prod}
 \alias{torch_cartesian_prod}
 \title{Cartesian_prod}
+\usage{
+torch_cartesian_prod(tensors)
+}
 \arguments{
-\item{*tensors}{NA any number of 1 dimensional tensors.}
+\item{tensors}{a list containing any number of 1 dimensional tensors.}
 }
 \description{
-Cartesian_prod
+Do cartesian product of the given sequence of tensors.
 }
-\section{TEST }{
-
-
-Do cartesian product of the given sequence of tensors. The behavior is similar to
-python's \code{itertools.product}.
-}
-
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = c(1, 2, 3)
 b = c(4, 5)
diff --git a/man/torch_cat.Rd b/man/torch_cat.Rd
index 0b1d5eed864098718ba46de244e55f245594472b..1b20e65b01542e31fa93044b11b11904f73028fb 100644
--- a/man/torch_cat.Rd
+++ b/man/torch_cat.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cat}
 \alias{torch_cat}
 \title{Cat}
+\usage{
+torch_cat(tensors, dim = 1L)
+}
 \arguments{
 \item{tensors}{(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.}
 
 \item{dim}{(int, optional) the dimension over which the tensors are concatenated}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Cat
 }
-\section{cat(tensors, dim=0, out=None) -> Tensor }{
+\section{cat(tensors, dim=0, out=NULL) -> Tensor }{
 
 
 Concatenates the given sequence of \code{seq} tensors in the given dimension.
@@ -28,7 +29,7 @@ and \code{\link{torch_chunk}}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(2, 3))
 x
diff --git a/man/torch_cdist.Rd b/man/torch_cdist.Rd
index a27c82a1435fffb6094592df0422c95ca01ccd19..c825fe6e289f1d8f847e66da170d0fc4763e84ef 100644
--- a/man/torch_cdist.Rd
+++ b/man/torch_cdist.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cdist}
 \alias{torch_cdist}
 \title{Cdist}
+\usage{
+torch_cdist(x1, x2, p = 2L, compute_mode = NULL)
+}
 \arguments{
 \item{x1}{(Tensor) input tensor of shape \eqn{B \times P \times M}.}
 
diff --git a/man/torch_ceil.Rd b/man/torch_ceil.Rd
index 5199c98a10ba41d8a362c54e35f6ee4012407f9c..e87a781633aa03711b79795249dea4b4b1bd0b3a 100644
--- a/man/torch_ceil.Rd
+++ b/man/torch_ceil.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ceil}
 \alias{torch_ceil}
 \title{Ceil}
+\usage{
+torch_ceil(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Ceil
 }
-\section{ceil(input, out=None) -> Tensor }{
+\section{ceil(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the ceil of the elements of \code{input},
@@ -24,7 +25,7 @@ the smallest integer greater than or equal to each element.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_celu.Rd b/man/torch_celu.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..7898283fb497ec1ae00a09d76ab4a77bd80a1eab
--- /dev/null
+++ b/man/torch_celu.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen-namespace-docs.R, R/gen-namespace.R
+\name{torch_celu}
+\alias{torch_celu}
+\title{Celu}
+\usage{
+torch_celu(self, alpha = 1)
+}
+\arguments{
+\item{self}{the input tensor}
+
+\item{alpha}{the alpha value for the CELU formulation. Default: 1.0}
+}
+\description{
+Celu
+}
+\section{celu(input, alpha=1.) -> Tensor }{
+
+
+See \code{\link[=nnf_celu]{nnf_celu()}} for more info.
+}
+
diff --git a/man/torch_celu_.Rd b/man/torch_celu_.Rd
index 2fc6f929d17d8fe4d6ed108a341fe661b784fb2b..a779371652622fe9d46e6e8e024a9896c98928f1 100644
--- a/man/torch_celu_.Rd
+++ b/man/torch_celu_.Rd
@@ -1,15 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_celu_}
 \alias{torch_celu_}
 \title{Celu_}
+\usage{
+torch_celu_(self, alpha = 1)
+}
+\arguments{
+\item{self}{the input tensor}
+
+\item{alpha}{the alpha value for the CELU formulation. Default: 1.0}
+}
 \description{
 Celu_
 }
 \section{celu_(input, alpha=1.) -> Tensor }{
 
 
-In-place version of \code{torch_celu}.
+In-place version of \code{\link[=torch_celu]{torch_celu()}}.
 }
 
diff --git a/man/torch_chain_matmul.Rd b/man/torch_chain_matmul.Rd
index 5c644f5e69c0d60dde39d5c30842be9f72178613..55e57f0742ef3c66d54c8b6993d44130ec617257 100644
--- a/man/torch_chain_matmul.Rd
+++ b/man/torch_chain_matmul.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_chain_matmul}
 \alias{torch_chain_matmul}
 \title{Chain_matmul}
+\usage{
+torch_chain_matmul(matrices)
+}
 \arguments{
 \item{matrices}{(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.}
 }
@@ -21,7 +24,7 @@ If \eqn{N} is 1, then this is a no-op - the original matrix is returned as is.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 4))
 b = torch_randn(c(4, 5))
diff --git a/man/torch_cholesky.Rd b/man/torch_cholesky.Rd
index 1d89e5c50b224b6ceff83045ec68249ea43b1afe..a9381a8f5c27741aa119bd6b7101ec4bab861ee6 100644
--- a/man/torch_cholesky.Rd
+++ b/man/torch_cholesky.Rd
@@ -1,46 +1,49 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cholesky}
 \alias{torch_cholesky}
 \title{Cholesky}
+\usage{
+torch_cholesky(self, upper = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where \code{*} is zero or more                batch dimensions consisting of symmetric positive-definite matrices.}
-
-\item{upper}{(bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: \code{False}}
+\item{self}{(Tensor) the input tensor \eqn{A} of size \eqn{(*, n, n)} where \code{*} is zero or more
+batch dimensions consisting of symmetric positive-definite matrices.}
 
-\item{out}{(Tensor, optional) the output matrix}
+\item{upper}{(bool, optional) flag that indicates whether to return a
+upper or lower triangular matrix. Default: \code{FALSE}}
 }
 \description{
 Cholesky
 }
-\section{cholesky(input, upper=False, out=None) -> Tensor }{
+\section{cholesky(input, upper=False, out=NULL) -> Tensor }{
 
 
 Computes the Cholesky decomposition of a symmetric positive-definite
 matrix \eqn{A} or for batches of symmetric positive-definite matrices.
 
-If \code{upper} is \code{True}, the returned matrix \code{U} is upper-triangular, and
+If \code{upper} is \code{TRUE}, the returned matrix \code{U} is upper-triangular, and
 the decomposition has the form:
 
 \deqn{
   A = U^TU
 }
-If \code{upper} is \code{False}, the returned matrix \code{L} is lower-triangular, and
+If \code{upper} is \code{FALSE}, the returned matrix \code{L} is lower-triangular, and
 the decomposition has the form:
 
 \deqn{
     A = LL^T
 }
-If \code{upper} is \code{True}, and \eqn{A} is a batch of symmetric positive-definite
+If \code{upper} is \code{TRUE}, and \eqn{A} is a batch of symmetric positive-definite
 matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
-of each of the individual matrices. Similarly, when \code{upper} is \code{False}, the returned
+of each of the individual matrices. Similarly, when \code{upper} is \code{FALSE}, the returned
 tensor will be composed of lower-triangular Cholesky factors of each of the individual
 matrices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a = torch_mm(a, a$t()) # make symmetric positive-definite
diff --git a/man/torch_cholesky_inverse.Rd b/man/torch_cholesky_inverse.Rd
index 5bd3748feba239c195f93518f0b255f7b4fb6090..5d34bccc2bad22bb236aa6cb46a93657e703cf24 100644
--- a/man/torch_cholesky_inverse.Rd
+++ b/man/torch_cholesky_inverse.Rd
@@ -1,33 +1,34 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cholesky_inverse}
 \alias{torch_cholesky_inverse}
 \title{Cholesky_inverse}
+\usage{
+torch_cholesky_inverse(self, upper = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor}
+\item{self}{(Tensor) the input 2-D tensor \eqn{u}, a upper or lower triangular           Cholesky factor}
 
 \item{upper}{(bool, optional) whether to return a lower (default) or upper triangular matrix}
-
-\item{out}{(Tensor, optional) the output tensor for \code{inv}}
 }
 \description{
 Cholesky_inverse
 }
-\section{cholesky_inverse(input, upper=False, out=None) -> Tensor }{
+\section{cholesky_inverse(input, upper=False, out=NULL) -> Tensor }{
 
 
 Computes the inverse of a symmetric positive-definite matrix \eqn{A} using its
 Cholesky factor \eqn{u}: returns matrix \code{inv}. The inverse is computed using
 LAPACK routines \code{dpotri} and \code{spotri} (and the corresponding MAGMA routines).
 
-If \code{upper} is \code{False}, \eqn{u} is lower triangular
+If \code{upper} is \code{FALSE}, \eqn{u} is lower triangular
 such that the returned tensor is
 
 \deqn{
     inv = (uu^{{T}})^{{-1}}
 }
-If \code{upper} is \code{True} or not provided, \eqn{u} is upper
+If \code{upper} is \code{TRUE} or not provided, \eqn{u} is upper
 triangular such that the returned tensor is
 
 \deqn{
@@ -36,7 +37,7 @@ triangular such that the returned tensor is
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 a = torch_randn(c(3, 3))
diff --git a/man/torch_cholesky_solve.Rd b/man/torch_cholesky_solve.Rd
index 266b66247d6bb6209fb6346a55459aec9ff7ef43..7d2ba2282afd1e039f9c99055ceaf2321ae7b090 100644
--- a/man/torch_cholesky_solve.Rd
+++ b/man/torch_cholesky_solve.Rd
@@ -1,34 +1,35 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cholesky_solve}
 \alias{torch_cholesky_solve}
 \title{Cholesky_solve}
+\usage{
+torch_cholesky_solve(self, input2, upper = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions}
+\item{self}{(Tensor) input matrix \eqn{b} of size \eqn{(*, m, k)},                where \eqn{*} is zero or more batch dimensions}
 
 \item{input2}{(Tensor) input matrix \eqn{u} of size \eqn{(*, m, m)},                where \eqn{*} is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor}
 
-\item{upper}{(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: \code{False}.}
-
-\item{out}{(Tensor, optional) the output tensor for \code{c}}
+\item{upper}{(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: \code{FALSE}.}
 }
 \description{
 Cholesky_solve
 }
-\section{cholesky_solve(input, input2, upper=False, out=None) -> Tensor }{
+\section{cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor }{
 
 
 Solves a linear system of equations with a positive semidefinite
 matrix to be inverted given its Cholesky factor matrix \eqn{u}.
 
-If \code{upper} is \code{False}, \eqn{u} is and lower triangular and \code{c} is
+If \code{upper} is \code{FALSE}, \eqn{u} is and lower triangular and \code{c} is
 returned such that:
 
 \deqn{
     c = (u u^T)^{{-1}} b
 }
-If \code{upper} is \code{True} or not provided, \eqn{u} is upper triangular
+If \code{upper} is \code{TRUE} or not provided, \eqn{u} is upper triangular
 and \code{c} is returned such that:
 
 \deqn{
@@ -40,7 +41,7 @@ batched outputs \code{c}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a = torch_mm(a, a$t()) # make symmetric positive definite
diff --git a/man/torch_chunk.Rd b/man/torch_chunk.Rd
index 7776f4cd7eaa5c2623d519a14e329d1c76297056..7950c02aea7a0b7c257970277257ba2e9fbc65ac 100644
--- a/man/torch_chunk.Rd
+++ b/man/torch_chunk.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_chunk}
 \alias{torch_chunk}
 \title{Chunk}
+\usage{
+torch_chunk(self, chunks, dim = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to split}
+\item{self}{(Tensor) the tensor to split}
 
 \item{chunks}{(int) number of chunks to return}
 
diff --git a/man/torch_clamp.Rd b/man/torch_clamp.Rd
index d28c41bc222e7d449fd627564f07576d39f1ee03..12284d21bacb4db01c6479c6bd40056407b3d926 100644
--- a/man/torch_clamp.Rd
+++ b/man/torch_clamp.Rd
@@ -1,24 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_clamp}
 \alias{torch_clamp}
 \title{Clamp}
+\usage{
+torch_clamp(self, min = NULL, max = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{min}{(Number) lower-bound of the range to be clamped to}
 
 \item{max}{(Number) upper-bound of the range to be clamped to}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{value}{(Number) minimal value of each element in the output}
 }
 \description{
 Clamp
 }
-\section{clamp(input, min, max, out=None) -> Tensor }{
+\section{clamp(input, min, max, out=NULL) -> Tensor }{
 
 
 Clamp all elements in \code{input} into the range \code{[} \code{min}, \code{max} \verb{]} and return
@@ -36,7 +35,7 @@ If \code{input} is of type \code{FloatTensor} or \code{DoubleTensor}, args \code
 and \code{max} must be real numbers, otherwise they should be integers.
 }
 
-\section{clamp(input, *, min, out=None) -> Tensor }{
+\section{clamp(input, *, min, out=NULL) -> Tensor }{
 
 
 Clamps all elements in \code{input} to be larger or equal \code{min}.
@@ -45,7 +44,7 @@ If \code{input} is of type \code{FloatTensor} or \code{DoubleTensor}, \code{valu
 should be a real number, otherwise it should be an integer.
 }
 
-\section{clamp(input, *, max, out=None) -> Tensor }{
+\section{clamp(input, *, max, out=NULL) -> Tensor }{
 
 
 Clamps all elements in \code{input} to be smaller or equal \code{max}.
@@ -55,7 +54,7 @@ should be a real number, otherwise it should be an integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_combinations.Rd b/man/torch_combinations.Rd
index ed1431d31af833fa1554a9b3c94c6cf88d6ef464..90315cd17fee2b90dd85361c0be74a81a270887f 100644
--- a/man/torch_combinations.Rd
+++ b/man/torch_combinations.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_combinations}
 \alias{torch_combinations}
 \title{Combinations}
+\usage{
+torch_combinations(self, r = 2L, with_replacement = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) 1D vector.}
+\item{self}{(Tensor) 1D vector.}
 
 \item{r}{(int, optional) number of elements to combine}
 
@@ -19,11 +22,11 @@ Combinations
 
 Compute combinations of length \eqn{r} of the given tensor. The behavior is similar to
 python's \code{itertools.combinations} when \code{with_replacement} is set to \code{False}, and
-\code{itertools.combinations_with_replacement} when \code{with_replacement} is set to \code{True}.
+\code{itertools.combinations_with_replacement} when \code{with_replacement} is set to \code{TRUE}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = c(1, 2, 3)
 tensor_a = torch_tensor(a)
diff --git a/man/torch_conj.Rd b/man/torch_conj.Rd
index 66329edc680dcb697aee624da055545a6e363744..96825cfd75a17c7a8b2cc5537028ab14a328c13f 100644
--- a/man/torch_conj.Rd
+++ b/man/torch_conj.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conj}
 \alias{torch_conj}
 \title{Conj}
+\usage{
+torch_conj(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Conj
 }
-\section{conj(input, out=None) -> Tensor }{
+\section{conj(input) -> Tensor }{
 
 
 Computes the element-wise conjugate of the given \code{input} tensor.
@@ -23,7 +24,7 @@ Computes the element-wise conjugate of the given \code{input} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_conj(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
 }
diff --git a/man/torch_conv1d.Rd b/man/torch_conv1d.Rd
index 47624ae4e01577692747f8e67695358ba6dd24ac..f4465a33e3930344c8d98a4245665088200727fd 100644
--- a/man/torch_conv1d.Rd
+++ b/man/torch_conv1d.Rd
@@ -1,40 +1,49 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv1d}
 \alias{torch_conv1d}
 \title{Conv1d}
+\usage{
+torch_conv1d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 
-\item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}}
+\item{weight}{filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: \code{None}}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: \code{NULL}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or      a one-element tuple \verb{(sW,)}. Default: 1}
+\item{stride}{the stride of the convolving kernel. Can be a single number or      a one-element tuple \verb{(sW,)}. Default: 1}
 
-\item{padding}{NA implicit paddings on both sides of the input. Can be a      single number or a one-element tuple \verb{(padW,)}. Default: 0}
+\item{padding}{implicit paddings on both sides of the input. Can be a      single number or a one-element tuple \verb{(padW,)}. Default: 0}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a one-element tuple \verb{(dW,)}. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a one-element tuple \verb{(dW,)}. Default: 1}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
 }
 \description{
 Conv1d
 }
-\section{conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
+\section{conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
 
 
 Applies a 1D convolution over an input signal composed of several input
 planes.
 
-See \code{~torch.nn.Conv1d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv1d]{nn_conv1d()}} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 filters = torch_randn(c(33, 16, 3))
 inputs = torch_randn(c(20, 16, 50))
diff --git a/man/torch_conv2d.Rd b/man/torch_conv2d.Rd
index f2f10add19ded7c20febb0d0ac6f61cd2e3c1aa8..21c7ebd1f297b832c60aaefee13b696d817f112c 100644
--- a/man/torch_conv2d.Rd
+++ b/man/torch_conv2d.Rd
@@ -1,40 +1,49 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv2d}
 \alias{torch_conv2d}
 \title{Conv2d}
+\usage{
+torch_conv2d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
 
-\item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}}
+\item{weight}{filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)}}
 
-\item{bias}{NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: \code{None}}
+\item{bias}{optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: \code{NULL}}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
 
-\item{padding}{NA implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padH, padW)}. Default: 0}
+\item{padding}{implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padH, padW)}. Default: 0}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 }
 \description{
 Conv2d
 }
-\section{conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
+\section{conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
 
 
 Applies a 2D convolution over an input image composed of several input
 planes.
 
-See \code{~torch.nn.Conv2d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv2d]{nn_conv2d()}} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # With square kernels and equal stride
 filters = torch_randn(c(8,4,3,3))
diff --git a/man/torch_conv3d.Rd b/man/torch_conv3d.Rd
index a85f099490915ea59f9267f78d4ecdae0c1fdfd4..fcc3f8c4ee30c6551f6400a2989afc41ae32ccc6 100644
--- a/man/torch_conv3d.Rd
+++ b/man/torch_conv3d.Rd
@@ -1,40 +1,49 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv3d}
 \alias{torch_conv3d}
 \title{Conv3d}
+\usage{
+torch_conv3d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  dilation = 1L,
+  groups = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
 
-\item{weight}{NA filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}}
+\item{weight}{filters of shape \eqn{(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)}}
 
-\item{bias}{NA optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{bias}{optional bias tensor of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
 
-\item{padding}{NA implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padT, padH, padW)}. Default: 0}
+\item{padding}{implicit paddings on both sides of the input. Can be a      single number or a tuple \verb{(padT, padH, padW)}. Default: 0}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by      the number of groups. Default: 1}
 }
 \description{
 Conv3d
 }
-\section{conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
+\section{conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor }{
 
 
 Applies a 3D convolution over an input image composed of several input
 planes.
 
-See \code{~torch.nn.Conv3d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv3d]{nn_conv3d()}} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # filters = torch_randn(c(33, 16, 3, 3, 3))
 # inputs = torch_randn(c(20, 16, 50, 10, 20))
diff --git a/man/torch_conv_tbc.Rd b/man/torch_conv_tbc.Rd
index 2508aa6216374c959ef0c6d0be48682d50440b3f..b4eda21f2dfdac262e819b0d1a13d42233a04acd 100644
--- a/man/torch_conv_tbc.Rd
+++ b/man/torch_conv_tbc.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_tbc}
 \alias{torch_conv_tbc}
 \title{Conv_tbc}
+\usage{
+torch_conv_tbc(self, weight, bias, pad = 0L)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})}}
+\item{self}{NA input tensor of shape \eqn{(\mbox{sequence length} \times batch \times \mbox{in\_channels})}}
 
 \item{weight}{NA filter of shape (\eqn{\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}})}
 
diff --git a/man/torch_conv_transpose1d.Rd b/man/torch_conv_transpose1d.Rd
index 85c97bef8f57c75d0b450f5df2d98902acdb6f27..ffdd5f23ad7d56f4689ecd4715aaa349f48f9ff3 100644
--- a/man/torch_conv_transpose1d.Rd
+++ b/man/torch_conv_transpose1d.Rd
@@ -1,42 +1,52 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_transpose1d}
 \alias{torch_conv_transpose1d}
 \title{Conv_transpose1d}
+\usage{
+torch_conv_transpose1d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iW)}}
 
-\item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}}
+\item{weight}{filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sW,)}. Default: 1}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sW,)}. Default: 1}
 
-\item{padding}{NA \code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padW,)}. Default: 0}
+\item{padding}{\code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padW,)}. Default: 0}
 
-\item{output_padding}{NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple \code{(out_padW)}. Default: 0}
+\item{output_padding}{additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple \code{(out_padW)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dW,)}. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dW,)}. Default: 1}
 }
 \description{
 Conv_transpose1d
 }
-\section{conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
+\section{conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
 
 
 Applies a 1D transposed convolution operator over an input signal
 composed of several input planes, sometimes also called "deconvolution".
 
-See \code{~torch.nn.ConvTranspose1d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv_transpose1d]{nn_conv_transpose1d()}}  for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 inputs = torch_randn(c(20, 16, 50))
 weights = torch_randn(c(16, 33, 5))
diff --git a/man/torch_conv_transpose2d.Rd b/man/torch_conv_transpose2d.Rd
index c12b7937798e00a05cb9b8c3ba9dc8aa6448f260..beb9e6a986286b952e961f0830b5f79d8a8ec980 100644
--- a/man/torch_conv_transpose2d.Rd
+++ b/man/torch_conv_transpose2d.Rd
@@ -1,42 +1,52 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_transpose2d}
 \alias{torch_conv_transpose2d}
 \title{Conv_transpose2d}
+\usage{
+torch_conv_transpose2d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)}}
 
-\item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}}
+\item{weight}{filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sH, sW)}. Default: 1}
 
-\item{padding}{NA \code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padH, padW)}. Default: 0}
+\item{padding}{\code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padH, padW)}. Default: 0}
 
-\item{output_padding}{NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple \verb{(out_padH, out_padW)}.      Default: 0}
+\item{output_padding}{additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple \verb{(out_padH, out_padW)}.      Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dH, dW)}. Default: 1}
 }
 \description{
 Conv_transpose2d
 }
-\section{conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
+\section{conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
 
 
 Applies a 2D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution".
 
-See \code{~torch.nn.ConvTranspose2d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv_transpose2d]{nn_conv_transpose2d()}} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # With square kernels and equal stride
 inputs = torch_randn(c(1, 4, 5, 5))
diff --git a/man/torch_conv_transpose3d.Rd b/man/torch_conv_transpose3d.Rd
index e7d5682afc0775b068d87d9b41d8b5cdd61d4177..cde9c094c7d4b504e2ed4886e2a3109ba6830c3d 100644
--- a/man/torch_conv_transpose3d.Rd
+++ b/man/torch_conv_transpose3d.Rd
@@ -1,42 +1,52 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_conv_transpose3d}
 \alias{torch_conv_transpose3d}
 \title{Conv_transpose3d}
+\usage{
+torch_conv_transpose3d(
+  input,
+  weight,
+  bias = list(),
+  stride = 1L,
+  padding = 0L,
+  output_padding = 0L,
+  groups = 1L,
+  dilation = 1L
+)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
+\item{input}{input tensor of shape \eqn{(\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)}}
 
-\item{weight}{NA filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}}
+\item{weight}{filters of shape \eqn{(\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)}}
 
-\item{bias}{NA optional bias of shape \eqn{(\mbox{out\_channels})}. Default: None}
+\item{bias}{optional bias of shape \eqn{(\mbox{out\_channels})}. Default: NULL}
 
-\item{stride}{NA the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
+\item{stride}{the stride of the convolving kernel. Can be a single number or a      tuple \verb{(sT, sH, sW)}. Default: 1}
 
-\item{padding}{NA \code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padT, padH, padW)}. Default: 0}
+\item{padding}{\code{dilation * (kernel_size - 1) - padding} zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      \verb{(padT, padH, padW)}. Default: 0}
 
-\item{output_padding}{NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      \verb{(out_padT, out_padH, out_padW)}. Default: 0}
+\item{output_padding}{additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      \verb{(out_padT, out_padH, out_padW)}. Default: 0}
 
-\item{groups}{NA split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
+\item{groups}{split input into groups, \eqn{\mbox{in\_channels}} should be divisible by the      number of groups. Default: 1}
 
-\item{dilation}{NA the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
+\item{dilation}{the spacing between kernel elements. Can be a single number or      a tuple \verb{(dT, dH, dW)}. Default: 1}
 }
 \description{
 Conv_transpose3d
 }
-\section{conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
+\section{conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor }{
 
 
 Applies a 3D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution"
 
-See \code{~torch.nn.ConvTranspose3d} for details and output shape.
-
-.. include:: cudnn_deterministic.rst
+See \code{\link[=nn_conv_transpose3d]{nn_conv_transpose3d()}} for details and output shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 inputs = torch_randn(c(20, 16, 50, 10, 20))
 weights = torch_randn(c(16, 33, 3, 3, 3))
diff --git a/man/torch_cos.Rd b/man/torch_cos.Rd
index bed5b82244127b0f81556c700af054460ee5dd49..feca69803cb1455d0024e65cc92e18041b7c7cd7 100644
--- a/man/torch_cos.Rd
+++ b/man/torch_cos.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cos}
 \alias{torch_cos}
 \title{Cos}
+\usage{
+torch_cos(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Cos
 }
-\section{cos(input, out=None) -> Tensor }{
+\section{cos(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the cosine  of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the cosine  of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_cosh.Rd b/man/torch_cosh.Rd
index 2c21e2d0c734ef54941ca9571c8219131394eedc..cfec6e0bc12d64636ace42c9f2e1bccd55283757 100644
--- a/man/torch_cosh.Rd
+++ b/man/torch_cosh.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cosh}
 \alias{torch_cosh}
 \title{Cosh}
+\usage{
+torch_cosh(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Cosh
 }
-\section{cosh(input, out=None) -> Tensor }{
+\section{cosh(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the hyperbolic cosine  of the elements of
@@ -24,7 +25,7 @@ Returns a new tensor with the hyperbolic cosine  of the elements of
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_cosine_similarity.Rd b/man/torch_cosine_similarity.Rd
index 11fd2d09601106c17ed2885d81a61f40d07ac8aa..60b7b421e4e49f16880e21fd1f3a23b13531fb02 100644
--- a/man/torch_cosine_similarity.Rd
+++ b/man/torch_cosine_similarity.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cosine_similarity}
 \alias{torch_cosine_similarity}
 \title{Cosine_similarity}
+\usage{
+torch_cosine_similarity(x1, x2, dim = 2L, eps = 0)
+}
 \arguments{
 \item{x1}{(Tensor) First input.}
 
@@ -27,7 +30,7 @@ Returns cosine similarity between x1 and x2, computed along dim.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input1 = torch_randn(c(100, 128))
 input2 = torch_randn(c(100, 128))
diff --git a/man/torch_cross.Rd b/man/torch_cross.Rd
index e0db09612a48cdbc66933f3848864b680fcd685f..f29ce62677afe56b3248814ee12468444a2670dd 100644
--- a/man/torch_cross.Rd
+++ b/man/torch_cross.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cross}
 \alias{torch_cross}
 \title{Cross}
+\usage{
+torch_cross(self, other, dim = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the second input tensor}
 
 \item{dim}{(int, optional) the dimension to take the cross-product in.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Cross
 }
-\section{cross(input, other, dim=-1, out=None) -> Tensor }{
+\section{cross(input, other, dim=-1, out=NULL) -> Tensor }{
 
 
 Returns the cross product of vectors in dimension \code{dim} of \code{input}
@@ -30,7 +31,7 @@ size 3.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4, 3))
 a
diff --git a/man/torch_cummax.Rd b/man/torch_cummax.Rd
index d509af4af3bf3e98dd2a9417dff7ae2b0327b7c3..07da9b8c1baeef193f3fe586f1eec2b488a35809 100644
--- a/man/torch_cummax.Rd
+++ b/man/torch_cummax.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cummax}
 \alias{torch_cummax}
 \title{Cummax}
+\usage{
+torch_cummax(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (values, indices)}
 }
 \description{
 Cummax
 }
-\section{cummax(input, dim, out=None) -> (Tensor, LongTensor) }{
+\section{cummax(input, dim) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the cumulative maximum of
@@ -27,7 +28,7 @@ location of each maximum value found in the dimension \code{dim}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_cummin.Rd b/man/torch_cummin.Rd
index 76b7ce05b347a24dac014bd3db3bd6e785a6f471..8950e566a43776ca773cd217b9a356a81e76878b 100644
--- a/man/torch_cummin.Rd
+++ b/man/torch_cummin.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cummin}
 \alias{torch_cummin}
 \title{Cummin}
+\usage{
+torch_cummin(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (values, indices)}
 }
 \description{
 Cummin
 }
-\section{cummin(input, dim, out=None) -> (Tensor, LongTensor) }{
+\section{cummin(input, dim) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the cumulative minimum of
@@ -27,7 +28,7 @@ location of each maximum value found in the dimension \code{dim}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_cumprod.Rd b/man/torch_cumprod.Rd
index 1fbedbccfa60ae19b942645749fa5a3535fe2a0a..0b30bcc7253687d3a1202a1e73c29abe3b10d32c 100644
--- a/man/torch_cumprod.Rd
+++ b/man/torch_cumprod.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cumprod}
 \alias{torch_cumprod}
 \title{Cumprod}
+\usage{
+torch_cumprod(self, dim, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Cumprod
 }
-\section{cumprod(input, dim, out=None, dtype=None) -> Tensor }{
+\section{cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor }{
 
 
 Returns the cumulative product of elements of \code{input} in the dimension
@@ -31,7 +32,7 @@ a vector of size N, with elements.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_cumsum.Rd b/man/torch_cumsum.Rd
index 87e3c1e9abae0368b427343925918f8f36240484..4caecb807d10620f7a9bf56172778227f2f7740c 100644
--- a/man/torch_cumsum.Rd
+++ b/man/torch_cumsum.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_cumsum}
 \alias{torch_cumsum}
 \title{Cumsum}
+\usage{
+torch_cumsum(self, dim, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to do the operation over}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Cumsum
 }
-\section{cumsum(input, dim, out=None, dtype=None) -> Tensor }{
+\section{cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor }{
 
 
 Returns the cumulative sum of elements of \code{input} in the dimension
@@ -31,7 +32,7 @@ a vector of size N, with elements.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(10))
 a
diff --git a/man/torch_det.Rd b/man/torch_det.Rd
index 8bdd81a6557a1a4f39d038af9a9bda79c1a73aaf..e248a5846b3dbe12194ed41831f5fc12a9a61183 100644
--- a/man/torch_det.Rd
+++ b/man/torch_det.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_det}
 \alias{torch_det}
 \title{Det}
+\usage{
+torch_det(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
+\item{self}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
 }
 \description{
 Det
@@ -24,7 +27,7 @@ Calculates determinant of a square matrix or batches of square matrices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(3, 3))
 torch_det(A)
diff --git a/man/torch_device.Rd b/man/torch_device.Rd
index d94312547de500366a9f7ea4b1fd1d949c72369f..e97293d2d60c6d070171a5a8de2334a579e24a9f 100644
--- a/man/torch_device.Rd
+++ b/man/torch_device.Rd
@@ -22,7 +22,7 @@ A \code{torch_device}  is an object representing the device on which a \code{tor
 is or will be allocated.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # Via string
 torch_device("cuda:1")
@@ -35,3 +35,4 @@ torch_device("cpu", 0)
 
 }
 }
+\concept{tensor-attributtes}
diff --git a/man/torch_diag.Rd b/man/torch_diag.Rd
index 0f5640c94390cb94e036213b4ec1ed94c2ff94e7..536398651c9269b63a67c88744d1e1ae27be5e8e 100644
--- a/man/torch_diag.Rd
+++ b/man/torch_diag.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diag}
 \alias{torch_diag}
 \title{Diag}
+\usage{
+torch_diag(self, diagonal = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Diag
 }
-\section{diag(input, diagonal=0, out=None) -> Tensor }{
+\section{diag(input, diagonal=0, out=NULL) -> Tensor }{
 
 \itemize{
 \item If \code{input} is a vector (1-D tensor), then returns a 2-D square tensor
diff --git a/man/torch_diag_embed.Rd b/man/torch_diag_embed.Rd
index 9378b6eb636decd82949bdd8b909add92e12118d..1a2eb842dacd46cea21a7b34c3af3fe8d478c0b2 100644
--- a/man/torch_diag_embed.Rd
+++ b/man/torch_diag_embed.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diag_embed}
 \alias{torch_diag_embed}
 \title{Diag_embed}
+\usage{
+torch_diag_embed(self, offset = 0L, dim1 = -2L, dim2 = -1L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor. Must be at least 1-dimensional.}
+\item{self}{(Tensor) the input tensor. Must be at least 1-dimensional.}
 
 \item{offset}{(int, optional) which diagonal to consider. Default: 0        (main diagonal).}
 
@@ -44,7 +47,7 @@ need to be explicitly specified.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(2, 3))
 torch_diag_embed(a)
diff --git a/man/torch_diagflat.Rd b/man/torch_diagflat.Rd
index 5a4ef9f1261afcc0b903f659b7c26a2268840b64..b38a3bf708812b6bd58f154b17d39ec55a6441bd 100644
--- a/man/torch_diagflat.Rd
+++ b/man/torch_diagflat.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diagflat}
 \alias{torch_diagflat}
 \title{Diagflat}
+\usage{
+torch_diagflat(self, offset = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{offset}{(int, optional) the diagonal to consider. Default: 0 (main        diagonal).}
 }
@@ -30,7 +33,7 @@ The argument \code{offset} controls which diagonal to consider:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3))
 a
diff --git a/man/torch_diagonal.Rd b/man/torch_diagonal.Rd
index cb0fc22123476304b2e9abd64eb4150b8374a8fd..16f35d401fe54b67ef501a94626a5260c4d0ffee 100644
--- a/man/torch_diagonal.Rd
+++ b/man/torch_diagonal.Rd
@@ -1,17 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_diagonal}
 \alias{torch_diagonal}
 \title{Diagonal}
+\usage{
+torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor. Must be at least 2-dimensional.}
+\item{self}{(Tensor) the input tensor. Must be at least 2-dimensional.}
 
-\item{offset}{(int, optional) which diagonal to consider. Default: 0        (main diagonal).}
+\item{outdim}{dimension name if \code{self} is a named tensor.}
 
 \item{dim1}{(int, optional) first dimension with respect to which to        take diagonal. Default: 0.}
 
 \item{dim2}{(int, optional) second dimension with respect to which to        take diagonal. Default: 1.}
+
+\item{offset}{(int, optional) which diagonal to consider. Default: 0        (main diagonal).}
 }
 \description{
 Diagonal
@@ -37,7 +42,7 @@ dimensions, so those need to be explicitly specified.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a
diff --git a/man/torch_digamma.Rd b/man/torch_digamma.Rd
index eec6b8305a62f1288a67635b13bde1d023586803..5fd0f37381378e798319798e12ad7acec7fbc967 100644
--- a/man/torch_digamma.Rd
+++ b/man/torch_digamma.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_digamma}
 \alias{torch_digamma}
 \title{Digamma}
+\usage{
+torch_digamma(self)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compute the digamma function on}
+\item{self}{(Tensor) the tensor to compute the digamma function on}
 }
 \description{
 Digamma
 }
-\section{digamma(input, out=None) -> Tensor }{
+\section{digamma(input, out=NULL) -> Tensor }{
 
 
 Computes the logarithmic derivative of the gamma function on \code{input}.
@@ -21,7 +24,7 @@ Computes the logarithmic derivative of the gamma function on \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(c(1, 0.5))
 torch_digamma(a)
diff --git a/man/torch_dist.Rd b/man/torch_dist.Rd
index f36a9a37318dd62d5160c7bb84237817acf255d7..cb2bd173da5e996778816df66bae215b71750b44 100644
--- a/man/torch_dist.Rd
+++ b/man/torch_dist.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_dist}
 \alias{torch_dist}
 \title{Dist}
+\usage{
+torch_dist(self, other, p = 2L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the Right-hand-side input tensor}
 
@@ -24,7 +27,7 @@ broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(4))
 x
diff --git a/man/torch_div.Rd b/man/torch_div.Rd
index 7ce99611aa1510fc0f9eff5f32088a59e9b5aa4d..b88ebc4c7df4614c98536e0ee32e3bc1d137a575 100644
--- a/man/torch_div.Rd
+++ b/man/torch_div.Rd
@@ -1,18 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_div}
 \alias{torch_div}
 \title{Div}
+\usage{
+torch_div(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Number) the number to be divided to each element of \code{input}}
 }
 \description{
 Div
 }
-\section{div(input, other, out=None) -> Tensor }{
+\section{div(input, other, out=NULL) -> Tensor }{
 
 
 Divides each element of the input \code{input} with the scalar \code{other} and
@@ -38,8 +41,8 @@ specified output tensor. Integral division by zero leads to undefined behavior.
 \section{Warning}{
 
 Integer division using div is deprecated, and in a future release div will
-perform true division like \code{\link{torch_true_divide}}.
-Use \code{\link{torch_floor_divide}} (// in Python) to perform integer division,
+perform true division like \code{\link[=torch_true_divide]{torch_true_divide()}}.
+Use \code{\link[=torch_floor_divide]{torch_floor_divide()}} to perform integer division,
 instead.
 
 \deqn{
@@ -54,7 +57,7 @@ by zero leads to undefined behavior.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_dot.Rd b/man/torch_dot.Rd
index 398efde8659961557c7abf401901ccdf25983ce0..edcf24ea65fb8a88cfb6f43c7727dcb6c75d7ea1 100644
--- a/man/torch_dot.Rd
+++ b/man/torch_dot.Rd
@@ -1,9 +1,17 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_dot}
 \alias{torch_dot}
 \title{Dot}
+\usage{
+torch_dot(self, tensor)
+}
+\arguments{
+\item{self}{the input tensor}
+
+\item{tensor}{the other input tensor}
+}
 \description{
 Dot
 }
@@ -17,7 +25,7 @@ Computes the dot product (inner product) of two tensors.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_dot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1)))
 }
diff --git a/man/torch_dtype.Rd b/man/torch_dtype.Rd
index 0267873dd20af36b02198ea93cb6e22365abda61..3ea81c1ede2fda610ad202ddc5dae0e51f78cd04 100644
--- a/man/torch_dtype.Rd
+++ b/man/torch_dtype.Rd
@@ -61,3 +61,4 @@ torch_qint32()
 \description{
 Returns the correspondent data type.
 }
+\concept{tensor-attributes}
diff --git a/man/torch_eig.Rd b/man/torch_eig.Rd
index abd282e4a45c730acbedbc7f509fdeed9dd0126f..ed48c72c330af133c4df60ec968c43d1c9dda321 100644
--- a/man/torch_eig.Rd
+++ b/man/torch_eig.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_eig}
 \alias{torch_eig}
 \title{Eig}
+\usage{
+torch_eig(self, eigenvectors = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed}
-
-\item{eigenvectors}{(bool) \code{True} to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed}
+\item{self}{(Tensor) the square matrix of shape \eqn{(n \times n)} for which the eigenvalues and eigenvectors        will be computed}
 
-\item{out}{(tuple, optional) the output tensors}
+\item{eigenvectors}{(bool) \code{TRUE} to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed}
 }
 \description{
 Eig
@@ -19,7 +20,7 @@ Eig
 for [`torch_symeig`]
 }
 }
-\section{eig(input, eigenvectors=False, out=None) -> (Tensor, Tensor) }{
+\section{eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor) }{
 
 
 Computes the eigenvalues and eigenvectors of a real square matrix.
diff --git a/man/torch_einsum.Rd b/man/torch_einsum.Rd
index bdb195f356f8228d294431c19a5c62889654e02c..4658a5d618e9a46a765e0e17121f70f616bef073 100644
--- a/man/torch_einsum.Rd
+++ b/man/torch_einsum.Rd
@@ -1,13 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_einsum}
 \alias{torch_einsum}
 \title{Einsum}
+\usage{
+torch_einsum(equation, tensors)
+}
 \arguments{
 \item{equation}{(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after \verb{->} and gives the indices for the output.           If the \verb{->} and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses \code{...} represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.}
 
-\item{operands}{(Tensor) The operands to compute the Einstein sum of.}
+\item{tensors}{(Tensor) The operands to compute the Einstein sum of.}
 }
 \description{
 Einsum
@@ -20,7 +23,9 @@ Einstein summation convention.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
+
+if (FALSE) {
 
 x = torch_randn(c(5))
 y = torch_randn(c(4))
@@ -38,5 +43,7 @@ A = torch_randn(c(4, 3, 3))
 torch_einsum('...ii->...i', list(A)) # batch diagonal
 A = torch_randn(c(2, 3, 4, 5))
 torch_einsum('...ij->...ji', list(A))$shape # batch permute
+
+}
 }
 }
diff --git a/man/torch_empty.Rd b/man/torch_empty.Rd
index 8412dbd9205ab617cdd4470bf13c950e4dff7216..f465eafaadc5fef31b3531465216fffdf3f01caf 100644
--- a/man/torch_empty.Rd
+++ b/man/torch_empty.Rd
@@ -1,30 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_empty}
 \alias{torch_empty}
 \title{Empty}
+\usage{
+torch_empty(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
+\item{...}{a sequence of integers defining the shape of the output tensor.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{names}{optional character vector naming each dimension.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
-
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
-
-\item{pin_memory}{(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_contiguous_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Empty
 }
-\section{empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor }{
+\section{empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor }{
 
 
 Returns a tensor filled with uninitialized data. The shape of the tensor is
@@ -32,7 +38,7 @@ defined by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_empty(c(2, 3))
 }
diff --git a/man/torch_empty_like.Rd b/man/torch_empty_like.Rd
index 80e8bb107fca977042e92578981a87028118b4cd..dcc40bac0332c0097f297a102a0a08360d47921a 100644
--- a/man/torch_empty_like.Rd
+++ b/man/torch_empty_like.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_empty_like}
 \alias{torch_empty_like}
 \title{Empty_like}
+\usage{
+torch_empty_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Empty_like
 }
-\section{empty_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns an uninitialized tensor with the same size as \code{input}.
@@ -29,7 +39,7 @@ Returns an uninitialized tensor with the same size as \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_empty(list(2,3), dtype = torch_int64())
 }
diff --git a/man/torch_empty_strided.Rd b/man/torch_empty_strided.Rd
index 983d79fb2b6513086fafb9fe68b448123db96017..8e2ee213dee594fc377aa4bcafe4bd71841e60b0 100644
--- a/man/torch_empty_strided.Rd
+++ b/man/torch_empty_strided.Rd
@@ -1,28 +1,39 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_empty_strided}
 \alias{torch_empty_strided}
 \title{Empty_strided}
+\usage{
+torch_empty_strided(
+  size,
+  stride,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  pin_memory = FALSE
+)
+}
 \arguments{
 \item{size}{(tuple of ints) the shape of the output tensor}
 
 \item{stride}{(tuple of ints) the strides of the output tensor}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{pin_memory}{(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: \code{False}.}
+\item{pin_memory}{(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: \code{FALSE}.}
 }
 \description{
 Empty_strided
 }
-\section{empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor }{
+\section{empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor }{
 
 
 Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
@@ -40,7 +51,7 @@ the tensors, please clone them first.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_empty_strided(list(2, 3), list(1, 2))
 a
diff --git a/man/torch_eq.Rd b/man/torch_eq.Rd
index d187438c37d7e1b8a9019474362c877f9cbbc568..16aa5fbb8c02e1096eb868ad92441b01a240f58d 100644
--- a/man/torch_eq.Rd
+++ b/man/torch_eq.Rd
@@ -1,20 +1,22 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_eq}
 \alias{torch_eq}
 \title{Eq}
+\usage{
+torch_eq(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
-
-\item{other}{(Tensor or float) the tensor or value to compare}
+\item{self}{(Tensor) the tensor to compare}
 
-\item{out}{(Tensor, optional) the output tensor. Must be a \code{ByteTensor}}
+\item{other}{(Tensor or float) the tensor or value to compare
+Must be a \code{ByteTensor}}
 }
 \description{
 Eq
 }
-\section{eq(input, other, out=None) -> Tensor }{
+\section{eq(input, other, out=NULL) -> Tensor }{
 
 
 Computes element-wise equality
@@ -24,7 +26,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_eq(torch_tensor(c(1,2,3,4)), torch_tensor(c(1, 3, 2, 4)))
 }
diff --git a/man/torch_equal.Rd b/man/torch_equal.Rd
index c740641d4091fdabaa7fdd0d4d553a83cda2677c..c24a687734357630597f4072ffb3d152e0178d06 100644
--- a/man/torch_equal.Rd
+++ b/man/torch_equal.Rd
@@ -1,20 +1,28 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_equal}
 \alias{torch_equal}
 \title{Equal}
+\usage{
+torch_equal(self, other)
+}
+\arguments{
+\item{self}{the input tensor}
+
+\item{other}{the other input tensor}
+}
 \description{
 Equal
 }
 \section{equal(input, other) -> bool }{
 
 
-\code{True} if two tensors have the same size and elements, \code{False} otherwise.
+\code{TRUE} if two tensors have the same size and elements, \code{FALSE} otherwise.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_equal(torch_tensor(c(1, 2)), torch_tensor(c(1, 2)))
 }
diff --git a/man/torch_erf.Rd b/man/torch_erf.Rd
index b7d2866301805359ea33b546087346bfa15bb844..093bff668f8f29920da5befa3aeb92d23103d446 100644
--- a/man/torch_erf.Rd
+++ b/man/torch_erf.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_erf}
 \alias{torch_erf}
 \title{Erf}
+\usage{
+torch_erf(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Erf
 }
-\section{erf(input, out=None) -> Tensor }{
+\section{erf(input, out=NULL) -> Tensor }{
 
 
 Computes the error function of each element. The error function is defined as follows:
@@ -23,7 +24,7 @@ Computes the error function of each element. The error function is defined as fo
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_erf(torch_tensor(c(0, -1., 10.)))
 }
diff --git a/man/torch_erfc.Rd b/man/torch_erfc.Rd
index 59108819d5d022d84c492dd25b3341d560c99756..eaf8fe8f3c6ab4c7a2241baed2d6d99eec2e14f9 100644
--- a/man/torch_erfc.Rd
+++ b/man/torch_erfc.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_erfc}
 \alias{torch_erfc}
 \title{Erfc}
+\usage{
+torch_erfc(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Erfc
 }
-\section{erfc(input, out=None) -> Tensor }{
+\section{erfc(input, out=NULL) -> Tensor }{
 
 
 Computes the complementary error function of each element of \code{input}.
@@ -24,7 +25,7 @@ The complementary error function is defined as follows:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_erfc(torch_tensor(c(0, -1., 10.)))
 }
diff --git a/man/torch_erfinv.Rd b/man/torch_erfinv.Rd
index 13aefc48938171df549e650716afa9c1c86b8d85..b2a1703cc20cc8e20e3d21b495c97f7bcdc12327 100644
--- a/man/torch_erfinv.Rd
+++ b/man/torch_erfinv.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_erfinv}
 \alias{torch_erfinv}
 \title{Erfinv}
+\usage{
+torch_erfinv(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Erfinv
 }
-\section{erfinv(input, out=None) -> Tensor }{
+\section{erfinv(input, out=NULL) -> Tensor }{
 
 
 Computes the inverse error function of each element of \code{input}.
@@ -24,7 +25,7 @@ The inverse error function is defined in the range \eqn{(-1, 1)} as:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_erfinv(torch_tensor(c(0, 0.5, -1.)))
 }
diff --git a/man/torch_exp.Rd b/man/torch_exp.Rd
index bd6da89d4d40c2968d1a6916bba51105f130f504..e605f9fcef6ca2575bf01b5bea71f660a8db5565 100644
--- a/man/torch_exp.Rd
+++ b/man/torch_exp.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_exp}
 \alias{torch_exp}
 \title{Exp}
+\usage{
+torch_exp(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Exp
 }
-\section{exp(input, out=None) -> Tensor }{
+\section{exp(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the exponential of the elements
@@ -24,7 +25,7 @@ of the input tensor \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_exp(torch_tensor(c(0, log(2))))
 }
diff --git a/man/torch_expm1.Rd b/man/torch_expm1.Rd
index ee95ba0717cbd9e09c4d8149499769914b2cdb3c..15b7a3dc4ca4cd2811b34525552974012c81f541 100644
--- a/man/torch_expm1.Rd
+++ b/man/torch_expm1.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_expm1}
 \alias{torch_expm1}
 \title{Expm1}
+\usage{
+torch_expm1(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Expm1
 }
-\section{expm1(input, out=None) -> Tensor }{
+\section{expm1(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the exponential of the elements minus 1
@@ -24,7 +25,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_expm1(torch_tensor(c(0, log(2))))
 }
diff --git a/man/torch_eye.Rd b/man/torch_eye.Rd
index 74e7595fcb0849eeb052ae47296fa9ad60afff2c..1933cad236d8918eb71d4da50806d69fe4ad9d33 100644
--- a/man/torch_eye.Rd
+++ b/man/torch_eye.Rd
@@ -1,35 +1,43 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_eye}
 \alias{torch_eye}
 \title{Eye}
+\usage{
+torch_eye(
+  n,
+  m = n,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{n}{(int) the number of rows}
 
 \item{m}{(int, optional) the number of columns with default being \code{n}}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Eye
 }
-\section{eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_eye(3)
 }
diff --git a/man/torch_fft.Rd b/man/torch_fft.Rd
index 507083d1e0610aae2f05941c90585bd58b0a64c1..9c07a4172dabe1c78a342b992e79f6f49189bf42 100644
--- a/man/torch_fft.Rd
+++ b/man/torch_fft.Rd
@@ -1,15 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_fft}
 \alias{torch_fft}
 \title{Fft}
+\usage{
+torch_fft(self, signal_ndim, normalized = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 }
 \description{
 Fft
@@ -42,7 +45,7 @@ by \code{signal_ndim}. \code{input} must be a tensor with last dimension
 of size 2, representing the real and imaginary components of complex
 numbers, and should have at least \code{signal_ndim + 1} dimensions with optionally
 arbitrary number of leading batch dimensions. If \code{normalized} is set to
-\code{True}, this normalizes the result by dividing it with
+\code{TRUE}, this normalizes the result by dividing it with
 \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary.
 
 Returns the real and the imaginary parts together as one tensor of the same
@@ -58,7 +61,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # unbatched 2D FFT
 x = torch_randn(c(4, 3, 2))
diff --git a/man/torch_finfo.Rd b/man/torch_finfo.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..3fcd9ea76fce384f48b350f09126ac5c2330c906
--- /dev/null
+++ b/man/torch_finfo.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/type-info.R
+\name{torch_finfo}
+\alias{torch_finfo}
+\title{Floating point type info}
+\usage{
+torch_finfo(dtype)
+}
+\arguments{
+\item{dtype}{dtype to check information}
+}
+\description{
+A list that represents the numerical properties of a
+floating point torch.dtype
+}
+\concept{tensor-attributes}
diff --git a/man/torch_flatten.Rd b/man/torch_flatten.Rd
index 1573d85526449e2d52388ba3027aae58cc2d3195..7e75d7330d4daafe4503c59fa889d24a1339ff47 100644
--- a/man/torch_flatten.Rd
+++ b/man/torch_flatten.Rd
@@ -1,15 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_flatten}
 \alias{torch_flatten}
 \title{Flatten}
+\usage{
+torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
+
+\item{dims}{if tensor is named you can pass the name of the dimensions to
+flatten}
 
 \item{start_dim}{(int) the first dim to flatten}
 
 \item{end_dim}{(int) the last dim to flatten}
+
+\item{out_dim}{the name of the resulting dimension if a named tensor.}
 }
 \description{
 Flatten
@@ -21,7 +29,7 @@ Flattens a contiguous range of dims in a tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_tensor(matrix(c(1, 2), ncol = 2))
 torch_flatten(t)
diff --git a/man/torch_flip.Rd b/man/torch_flip.Rd
index 3121f3b7ad6ceb760bb67d68dcce7bbe8b0c1814..c478daf4899c72f08a930e1333a65da788b572c6 100644
--- a/man/torch_flip.Rd
+++ b/man/torch_flip.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_flip}
 \alias{torch_flip}
 \title{Flip}
+\usage{
+torch_flip(self, dims)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dims}{(a list or tuple) axis to flip on}
 }
@@ -19,7 +22,7 @@ Reverse the order of a n-D tensor along given axis in dims.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(0, 8)$view(c(2, 2, 2))
 x
diff --git a/man/torch_floor.Rd b/man/torch_floor.Rd
index 1100decd949f045bc7a898919b6e0cfcbad585f1..ed7351411c2c66d18e38b3a8a92d036d4d41f07d 100644
--- a/man/torch_floor.Rd
+++ b/man/torch_floor.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_floor}
 \alias{torch_floor}
 \title{Floor}
+\usage{
+torch_floor(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Floor
 }
-\section{floor(input, out=None) -> Tensor }{
+\section{floor(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the floor of the elements of \code{input},
@@ -24,7 +25,7 @@ the largest integer less than or equal to each element.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_floor_divide.Rd b/man/torch_floor_divide.Rd
index efba31aae1b5e25548c1631efc0fa3ad056c84e5..85f1c2f885f118dee2149b07e0838c995ef5057c 100644
--- a/man/torch_floor_divide.Rd
+++ b/man/torch_floor_divide.Rd
@@ -1,18 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_floor_divide}
 \alias{torch_floor_divide}
 \title{Floor_divide}
+\usage{
+torch_floor_divide(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the numerator tensor}
+\item{self}{(Tensor) the numerator tensor}
 
 \item{other}{(Tensor or Scalar) the denominator}
 }
 \description{
 Floor_divide
 }
-\section{floor_divide(input, other, out=None) -> Tensor }{
+\section{floor_divide(input, other, out=NULL) -> Tensor }{
 
 
 Return the division of the inputs rounded down to the nearest integer. See \code{\link{torch_div}}
@@ -24,7 +27,7 @@ for type promotion and broadcasting rules.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(c(4.0, 3.0))
 b = torch_tensor(c(2.0, 2.0))
diff --git a/man/torch_fmod.Rd b/man/torch_fmod.Rd
index a53db0ec76d06ca0413985c6d8e5e019d2c469b8..c6b3a6a8f59f73d5766c99c056b5e648edc60c37 100644
--- a/man/torch_fmod.Rd
+++ b/man/torch_fmod.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_fmod}
 \alias{torch_fmod}
 \title{Fmod}
+\usage{
+torch_fmod(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the dividend}
+\item{self}{(Tensor) the dividend}
 
 \item{other}{(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Fmod
 }
-\section{fmod(input, other, out=None) -> Tensor }{
+\section{fmod(input, other, out=NULL) -> Tensor }{
 
 
 Computes the element-wise remainder of division.
@@ -27,7 +28,7 @@ When \code{other} is a tensor, the shapes of \code{input} and
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
 torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
diff --git a/man/torch_frac.Rd b/man/torch_frac.Rd
index 182e09bdaf517d0f311aaa9c6bb7b08135f812ab..761f3281fb4e96046aa7ad827c3ca1b3e80fa7ca 100644
--- a/man/torch_frac.Rd
+++ b/man/torch_frac.Rd
@@ -1,13 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_frac}
 \alias{torch_frac}
 \title{Frac}
+\usage{
+torch_frac(self)
+}
+\arguments{
+\item{self}{the input tensor.}
+}
 \description{
 Frac
 }
-\section{frac(input, out=None) -> Tensor }{
+\section{frac(input, out=NULL) -> Tensor }{
 
 
 Computes the fractional portion of each element in \code{input}.
@@ -18,7 +24,7 @@ Computes the fractional portion of each element in \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_frac(torch_tensor(c(1, 2.5, -3.2)))
 }
diff --git a/man/torch_full.Rd b/man/torch_full.Rd
index 4687d1eab7e390b75ed3cbc4bbb3a175f52b1542..c54b7cbdfa7947de18412389473d540eb5171490 100644
--- a/man/torch_full.Rd
+++ b/man/torch_full.Rd
@@ -1,28 +1,39 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_full}
 \alias{torch_full}
 \title{Full}
+\usage{
+torch_full(
+  size,
+  fill_value,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{size}{(int...) a list, tuple, or \code{torch_Size} of integers defining the        shape of the output tensor.}
 
 \item{fill_value}{NA the number to fill the output tensor with.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{names}{optional names of the dimensions}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Full
 }
-\section{full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor of size \code{size} filled with \code{fill_value}.
@@ -38,7 +49,7 @@ and an integral \code{fill_value} will return a tensor of torch.long dtype.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_full(list(2, 3), 3.141592)
 }
diff --git a/man/torch_full_like.Rd b/man/torch_full_like.Rd
index 625370ec4258c373801313668ea8f3075a4b23e9..a9a7a2a482cb21faa8f7a502c47fabefa22d9a82 100644
--- a/man/torch_full_like.Rd
+++ b/man/torch_full_like.Rd
@@ -1,28 +1,39 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_full_like}
 \alias{torch_full_like}
 \title{Full_like}
+\usage{
+torch_full_like(
+  input,
+  fill_value,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{fill_value}{NA the number to fill the output tensor with.}
+\item{fill_value}{the number to fill the output tensor with.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Full_like
 }
-\section{full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, }{
+\section{full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, }{
 
 
 memory_format=torch.preserve_format) -> Tensor
diff --git a/man/torch_gather.Rd b/man/torch_gather.Rd
index c5379030f68a76b59ded0f3ef8f0c5fb602952ac..efb6bf96ee8e6dd18926e7a5916b3ecb06e046d5 100644
--- a/man/torch_gather.Rd
+++ b/man/torch_gather.Rd
@@ -1,24 +1,25 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_gather}
 \alias{torch_gather}
 \title{Gather}
+\usage{
+torch_gather(self, dim, index, sparse_grad = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the source tensor}
+\item{self}{(Tensor) the source tensor}
 
 \item{dim}{(int) the axis along which to index}
 
 \item{index}{(LongTensor) the indices of elements to gather}
 
-\item{out}{(Tensor, optional) the destination tensor}
-
-\item{sparse_grad}{(bool,optional) If \code{True}, gradient w.r.t. \code{input} will be a sparse tensor.}
+\item{sparse_grad}{(bool,optional) If \code{TRUE}, gradient w.r.t. \code{input} will be a sparse tensor.}
 }
 \description{
 Gather
 }
-\section{gather(input, dim, index, out=None, sparse_grad=False) -> Tensor }{
+\section{gather(input, dim, index, sparse_grad=FALSE) -> Tensor }{
 
 
 Gathers values along an axis specified by \code{dim}.
@@ -36,7 +37,7 @@ and \code{out} will have the same size as \code{index}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 t = torch_tensor(matrix(c(1,2,3,4), ncol = 2, byrow = TRUE))
 torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64()))
diff --git a/man/torch_ge.Rd b/man/torch_ge.Rd
index 479b5196cd66c31fe16079f5ae309b804531c345..77599e3329f9454e72f11d03e6d4520344ae0dad 100644
--- a/man/torch_ge.Rd
+++ b/man/torch_ge.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ge}
 \alias{torch_ge}
 \title{Ge}
+\usage{
+torch_ge(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Ge
 }
-\section{ge(input, other, out=None) -> Tensor }{
+\section{ge(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} \geq \mbox{other}} element-wise.
@@ -24,7 +25,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_generator.Rd b/man/torch_generator.Rd
index 6d042a5a5c8451c977c0b00d6e22ccb066418c73..64667c8ed659c5272202bd9db917585f18eb21a9 100644
--- a/man/torch_generator.Rd
+++ b/man/torch_generator.Rd
@@ -12,7 +12,7 @@ that produces pseudo random numbers. Used as a keyword argument in many
 In-place random sampling functions.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # Via string
 generator <- torch_generator()
diff --git a/man/torch_geqrf.Rd b/man/torch_geqrf.Rd
index 997e72843c013f382926561726542ca003da7de3..326af5e972cbcec77f0746228fc5835e10791a07 100644
--- a/man/torch_geqrf.Rd
+++ b/man/torch_geqrf.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_geqrf}
 \alias{torch_geqrf}
 \title{Geqrf}
+\usage{
+torch_geqrf(self)
+}
 \arguments{
-\item{input}{(Tensor) the input matrix}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, Tensor)}
+\item{self}{(Tensor) the input matrix}
 }
 \description{
 Geqrf
 }
-\section{geqrf(input, out=None) -> (Tensor, Tensor) }{
+\section{geqrf(input, out=NULL) -> (Tensor, Tensor) }{
 
 
 This is a low-level function for calling LAPACK directly. This function
diff --git a/man/torch_ger.Rd b/man/torch_ger.Rd
index 0d03fc5daa4ab0b17a3aaa6ea305fc22eda99d5f..8517af35418886f06d94c8ddd9abfbf9a0cd7578 100644
--- a/man/torch_ger.Rd
+++ b/man/torch_ger.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ger}
 \alias{torch_ger}
 \title{Ger}
+\usage{
+torch_ger(self, vec2)
+}
 \arguments{
-\item{input}{(Tensor) 1-D input vector}
+\item{self}{(Tensor) 1-D input vector}
 
 \item{vec2}{(Tensor) 1-D input vector}
-
-\item{out}{(Tensor, optional) optional output matrix}
 }
 \description{
 Ger
@@ -17,7 +18,7 @@ Ger
 \note{
 This function does not broadcast .
 }
-\section{ger(input, vec2, out=None) -> Tensor }{
+\section{ger(input, vec2, out=NULL) -> Tensor }{
 
 
 Outer product of \code{input} and \code{vec2}.
@@ -26,7 +27,7 @@ size \eqn{m}, then \code{out} must be a matrix of size \eqn{(n \times m)}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 v1 = torch_arange(1., 5.)
 v2 = torch_arange(1., 4.)
diff --git a/man/torch_gt.Rd b/man/torch_gt.Rd
index eed4e66ee51898f84380ac0012c11108b5160eeb..df54138d2df0337fb1dae6bfe80f7e9dd2536425 100644
--- a/man/torch_gt.Rd
+++ b/man/torch_gt.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_gt}
 \alias{torch_gt}
 \title{Gt}
+\usage{
+torch_gt(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Gt
 }
-\section{gt(input, other, out=None) -> Tensor }{
+\section{gt(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} > \mbox{other}} element-wise.
@@ -24,7 +25,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_hamming_window.Rd b/man/torch_hamming_window.Rd
index 0225361a943f40105587ecd6f27f93cdf4599a88..1d02d9154fb0135ecfab476a147d13b2e8143cef 100644
--- a/man/torch_hamming_window.Rd
+++ b/man/torch_hamming_window.Rd
@@ -1,25 +1,37 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_hamming_window}
 \alias{torch_hamming_window}
 \title{Hamming_window}
+\usage{
+torch_hamming_window(
+  window_length,
+  periodic = TRUE,
+  alpha = 0.54,
+  beta = 0.46,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
 \item{alpha}{(float, optional) The coefficient \eqn{\alpha} in the equation above}
 
 \item{beta}{(float, optional) The coefficient \eqn{\beta} in the equation above}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Hamming_window
@@ -31,7 +43,7 @@ Hamming_window
 \preformatted{This is a generalized version of `torch_hann_window`.
 }
 }
-\section{hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Hamming window function.
@@ -47,7 +59,7 @@ window trims off the last duplicate value from the symmetric window and is
 ready to be used as a periodic window with functions like
 \code{torch_stft}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_hamming_window(L, periodic=True)} equal to
+\code{torch_hamming_window(L, periodic=TRUE)} equal to
 \verb{torch_hamming_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_hann_window.Rd b/man/torch_hann_window.Rd
index a92e60667eb263a875bb7008bef6a9d0e3369d0b..4a21ee5a86a8b88e0845d7deaeb34562c31dcc2c 100644
--- a/man/torch_hann_window.Rd
+++ b/man/torch_hann_window.Rd
@@ -1,21 +1,31 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_hann_window}
 \alias{torch_hann_window}
 \title{Hann_window}
+\usage{
+torch_hann_window(
+  window_length,
+  periodic = TRUE,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{window_length}{(int) the size of returned window}
 
-\item{periodic}{(bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.}
+\item{periodic}{(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). Only floating point types are supported.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned window tensor. Only          \code{torch_strided} (dense layout) is supported.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Hann_window
@@ -24,7 +34,7 @@ Hann_window
 \preformatted{If `window_length` \eqn{=1}, the returned window contains a single value 1.
 }
 }
-\section{hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Hann window function.
@@ -41,7 +51,7 @@ window trims off the last duplicate value from the symmetric window and is
 ready to be used as a periodic window with functions like
 \code{torch_stft}. Therefore, if \code{periodic} is true, the \eqn{N} in
 above formula is in fact \eqn{\mbox{window\_length} + 1}. Also, we always have
-\code{torch_hann_window(L, periodic=True)} equal to
+\code{torch_hann_window(L, periodic=TRUE)} equal to
 \verb{torch_hann_window(L + 1, periodic=False)[:-1])}.
 }
 
diff --git a/man/torch_histc.Rd b/man/torch_histc.Rd
index 8d6a2973651c98ddbb6e51df662ac481248c7a26..3bc8e35522f5d824284b9ecde1d896f9f48a0969 100644
--- a/man/torch_histc.Rd
+++ b/man/torch_histc.Rd
@@ -1,24 +1,25 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_histc}
 \alias{torch_histc}
 \title{Histc}
+\usage{
+torch_histc(self, bins = 100L, min = 0L, max = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{bins}{(int) number of histogram bins}
 
 \item{min}{(int) lower end of the range (inclusive)}
 
 \item{max}{(int) upper end of the range (inclusive)}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Histc
 }
-\section{histc(input, bins=100, min=0, max=0, out=None) -> Tensor }{
+\section{histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor }{
 
 
 Computes the histogram of a tensor.
@@ -29,7 +30,7 @@ maximum values of the data are used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3)
 }
diff --git a/man/torch_ifft.Rd b/man/torch_ifft.Rd
index 1de2da601f01e3a667e95f6c563fbe8e4b6fed20..c6fc27071c7245cd6f8e79653df26512bb780cb0 100644
--- a/man/torch_ifft.Rd
+++ b/man/torch_ifft.Rd
@@ -1,15 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ifft}
 \alias{torch_ifft}
 \title{Ifft}
+\usage{
+torch_ifft(self, signal_ndim, normalized = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 }
 \description{
 Ifft
@@ -39,7 +42,7 @@ where \eqn{d} = \code{signal_ndim} is number of dimensions for the
 signal, and \eqn{N_i} is the size of signal dimension \eqn{i}.
 
 The argument specifications are almost identical with \code{\link{torch_fft}}.
-However, if \code{normalized} is set to \code{True}, this instead returns the
+However, if \code{normalized} is set to \code{TRUE}, this instead returns the
 results multiplied by \eqn{\sqrt{\prod_{i=1}^d N_i}}, to become a unitary
 operator. Therefore, to invert a \code{\link{torch_fft}}, the \code{normalized}
 argument should be set identically for \code{\link{torch_fft}}.
@@ -57,7 +60,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 3, 2))
 x
diff --git a/man/torch_iinfo.Rd b/man/torch_iinfo.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..e2dd40dd58ff536c28b256743ea0a53f3b9fc399
--- /dev/null
+++ b/man/torch_iinfo.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/type-info.R
+\name{torch_iinfo}
+\alias{torch_iinfo}
+\title{Integer type info}
+\usage{
+torch_iinfo(dtype)
+}
+\arguments{
+\item{dtype}{dtype to get information from.}
+}
+\description{
+A list that represents the numerical properties of a integer
+type.
+}
+\concept{tensor-attributes}
diff --git a/man/torch_imag.Rd b/man/torch_imag.Rd
index 2a1b1aebafeda0720c3c942db6f3ea6cce089ba1..b54d3840ca5b86e0a18d497d5b9c49eb614ca655 100644
--- a/man/torch_imag.Rd
+++ b/man/torch_imag.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_imag}
 \alias{torch_imag}
 \title{Imag}
+\usage{
+torch_imag(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Imag
 }
-\section{imag(input, out=None) -> Tensor }{
+\section{imag(input) -> Tensor }{
 
 
 Returns the imaginary part of the \code{input} tensor.
@@ -28,7 +29,7 @@ Not yet implemented.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_imag(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
 }
diff --git a/man/torch_index_select.Rd b/man/torch_index_select.Rd
index c3bb4d5195973fbdca6d0aa61e53028342147742..8a7a586abcbae9ba62bf50496050c3b55c10e794 100644
--- a/man/torch_index_select.Rd
+++ b/man/torch_index_select.Rd
@@ -1,17 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_index_select}
 \alias{torch_index_select}
 \title{Index_select}
+\usage{
+torch_index_select(self, dim, index)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension in which we index}
 
 \item{index}{(LongTensor) the 1-D tensor containing the indices to index}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Index_select
@@ -22,7 +23,7 @@ tensor.  If \code{out} has a different shape than expected, we
 silently change it to the correct shape, reallocating the underlying
 storage if necessary.
 }
-\section{index_select(input, dim, index, out=None) -> Tensor }{
+\section{index_select(input, dim, index, out=NULL) -> Tensor }{
 
 
 Returns a new tensor which indexes the \code{input} tensor along dimension
@@ -34,7 +35,7 @@ of \code{index}; other dimensions have the same size as in the original tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 4))
 x
diff --git a/man/torch_inverse.Rd b/man/torch_inverse.Rd
index f14f5ef2b946b3b9733b36e9ce604cf9a7ac9e16..468e3998494df6b45fc5caf6fcfc0af7075d0d45 100644
--- a/man/torch_inverse.Rd
+++ b/man/torch_inverse.Rd
@@ -1,13 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_inverse}
 \alias{torch_inverse}
 \title{Inverse}
+\usage{
+torch_inverse(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions}
 }
 \description{
 Inverse
@@ -17,7 +18,7 @@ Inverse
 transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
 }
 }
-\section{inverse(input, out=None) -> Tensor }{
+\section{inverse(input, out=NULL) -> Tensor }{
 
 
 Takes the inverse of the square matrix \code{input}. \code{input} can be batches
@@ -26,7 +27,7 @@ individual inverses.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 x = torch_rand(c(4, 4))
 y = torch_inverse(x)
diff --git a/man/torch_irfft.Rd b/man/torch_irfft.Rd
index 4a90548f63b795f3237ddeab80a91e6db038b042..367b6eac9482f02c59fc328053028f27be7feacd 100644
--- a/man/torch_irfft.Rd
+++ b/man/torch_irfft.Rd
@@ -1,19 +1,28 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_irfft}
 \alias{torch_irfft}
 \title{Irfft}
+\usage{
+torch_irfft(
+  self,
+  signal_ndim,
+  normalized = FALSE,
+  onesided = TRUE,
+  signal_sizes = list()
+)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} \code{+ 1}        dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 
-\item{onesided}{(bool, optional) controls whether \code{input} was halfed to avoid        redundancy, e.g., by \code{\link[=torch_rfft]{torch_rfft()}}. Default: \code{True}}
+\item{onesided}{(bool, optional) controls whether \code{input} was halfed to avoid        redundancy, e.g., by \code{\link[=torch_rfft]{torch_rfft()}}. Default: \code{TRUE}}
 
-\item{signal_sizes}{(list or \code{torch.Size}, optional) the size of the original        signal (without batch dimension). Default: \code{None}}
+\item{signal_sizes}{(list or \code{torch.Size}, optional) the size of the original        signal (without batch dimension). Default: \code{NULL}}
 }
 \description{
 Irfft
@@ -22,8 +31,8 @@ Irfft
 \preformatted{Due to the conjugate symmetry, `input` do not need to contain the full
 complex frequency values. Roughly half of the values will be sufficient, as
 is the case when `input` is given by [`~torch.rfft`] with
-``rfft(signal, onesided=True)``. In such case, set the `onesided`
-argument of this method to ``True``. Moreover, the original signal shape
+`rfft(signal, onesided=TRUE)`. In such case, set the `onesided`
+argument of this method to `TRUE`. Moreover, the original signal shape
 information can sometimes be lost, optionally set `signal_sizes` to be
 the size of the original signal (without the batch dimensions if in batched
 mode) to recover it with correct shape.
@@ -44,7 +53,7 @@ configuration. See cufft-plan-cache for more details on how to
 monitor and control the cache.
 }
 }
-\section{irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor }{
+\section{irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -> Tensor }{
 
 
 Complex-to-real Inverse Discrete Fourier Transform
@@ -54,7 +63,7 @@ It is mathematically equivalent with \code{\link{torch_ifft}} with differences o
 formats of the input and output.
 
 The argument specifications are almost identical with \code{\link{torch_ifft}}.
-Similar to \code{\link{torch_ifft}}, if \code{normalized} is set to \code{True},
+Similar to \code{\link{torch_ifft}}, if \code{normalized} is set to \code{TRUE},
 this normalizes the result by multiplying it with
 \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is unitary, where
 \eqn{N_i} is the size of signal dimension \eqn{i}.
@@ -64,7 +73,7 @@ this normalizes the result by multiplying it with
 
 Generally speaking, input to this function should contain values
 following conjugate symmetry. Note that even if \code{onesided} is
-\code{True}, often symmetry on some part is still needed. When this
+\code{TRUE}, often symmetry on some part is still needed. When this
 requirement is not satisfied, the behavior of \code{\link{torch_irfft}} is
 undefined. Since \code{torch_autograd.gradcheck} estimates numerical
 Jacobian with point perturbations, \code{\link{torch_irfft}} will almost
@@ -76,7 +85,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(4, 4))
 torch_rfft(x, 2, onesided=TRUE)
diff --git a/man/torch_is_complex.Rd b/man/torch_is_complex.Rd
index 3a972733e35d3c9c765a9c535d4b6afd1509f102..cfff9724aec0827bcd148e8a2034914d5bd32b58 100644
--- a/man/torch_is_complex.Rd
+++ b/man/torch_is_complex.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_is_complex}
 \alias{torch_is_complex}
 \title{Is_complex}
+\usage{
+torch_is_complex(self)
+}
 \arguments{
-\item{input}{(Tensor) the PyTorch tensor to test}
+\item{self}{(Tensor) the PyTorch tensor to test}
 }
 \description{
 Is_complex
@@ -13,7 +16,7 @@ Is_complex
 \section{is_complex(input) -> (bool) }{
 
 
-Returns True if the data type of \code{input} is a complex data type i.e.,
+Returns TRUE if the data type of \code{input} is a complex data type i.e.,
 one of \code{torch_complex64}, and \code{torch.complex128}.
 }
 
diff --git a/man/torch_is_floating_point.Rd b/man/torch_is_floating_point.Rd
index 7e537ca022d1863e861ff1681cf22b2ccb17bff6..c8e609bc127b4fe03365b186dbd4d800ca77640e 100644
--- a/man/torch_is_floating_point.Rd
+++ b/man/torch_is_floating_point.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_is_floating_point}
 \alias{torch_is_floating_point}
 \title{Is_floating_point}
+\usage{
+torch_is_floating_point(self)
+}
 \arguments{
-\item{input}{(Tensor) the PyTorch tensor to test}
+\item{self}{(Tensor) the PyTorch tensor to test}
 }
 \description{
 Is_floating_point
@@ -13,7 +16,7 @@ Is_floating_point
 \section{is_floating_point(input) -> (bool) }{
 
 
-Returns True if the data type of \code{input} is a floating point data type i.e.,
+Returns TRUE if the data type of \code{input} is a floating point data type i.e.,
 one of \code{torch_float64}, \code{torch.float32} and \code{torch.float16}.
 }
 
diff --git a/man/torch_is_installed.Rd b/man/torch_is_installed.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..46acb15f3887ddc0c4745b3d20990cb7aa0025c8
--- /dev/null
+++ b/man/torch_is_installed.Rd
@@ -0,0 +1,11 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/install.R
+\name{torch_is_installed}
+\alias{torch_is_installed}
+\title{Verifies if torch is installed}
+\usage{
+torch_is_installed()
+}
+\description{
+Verifies if torch is installed
+}
diff --git a/man/torch_isfinite.Rd b/man/torch_isfinite.Rd
index b08b8e5015df2e25b6a22b43b94907ce328441eb..1e620295c1928795e9e41c8e06c795c7859ab53e 100644
--- a/man/torch_isfinite.Rd
+++ b/man/torch_isfinite.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_isfinite}
 \alias{torch_isfinite}
 \title{Isfinite}
+\usage{
+torch_isfinite(self)
+}
 \arguments{
-\item{tensor}{(Tensor) A tensor to check}
+\item{self}{(Tensor) A tensor to check}
 }
 \description{
 Isfinite
@@ -17,7 +20,7 @@ Returns a new tensor with boolean elements representing if each element is \code
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
 }
diff --git a/man/torch_isinf.Rd b/man/torch_isinf.Rd
index 08adc3579209e8c902884a85672cac3c74bf5bbe..3f43dd8c9e5c0979798b844ab4c5b5de87915f12 100644
--- a/man/torch_isinf.Rd
+++ b/man/torch_isinf.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_isinf}
 \alias{torch_isinf}
 \title{Isinf}
+\usage{
+torch_isinf(self)
+}
 \arguments{
-\item{tensor}{(Tensor) A tensor to check}
+\item{self}{(Tensor) A tensor to check}
 }
 \description{
 Isinf
@@ -17,7 +20,7 @@ Returns a new tensor with boolean elements representing if each element is \verb
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
 }
diff --git a/man/torch_isnan.Rd b/man/torch_isnan.Rd
index 742cebad1a2ac04236058a9aeb25aac6f0624a71..1c321274fa564fc2656d04315fb02b99e411f594 100644
--- a/man/torch_isnan.Rd
+++ b/man/torch_isnan.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_isnan}
 \alias{torch_isnan}
 \title{Isnan}
+\usage{
+torch_isnan(self)
+}
 \arguments{
-\item{input}{(Tensor) A tensor to check}
+\item{self}{(Tensor) A tensor to check}
 }
 \description{
 Isnan
@@ -17,7 +20,7 @@ Returns a new tensor with boolean elements representing if each element is \code
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_isnan(torch_tensor(c(1, NaN, 2)))
 }
diff --git a/man/torch_kthvalue.Rd b/man/torch_kthvalue.Rd
index ee744246d91376ad83a709edda92efca67939d25..8e687ccaecc200da0cb40e2f03cfe5b1716d04c9 100644
--- a/man/torch_kthvalue.Rd
+++ b/man/torch_kthvalue.Rd
@@ -1,24 +1,25 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_kthvalue}
 \alias{torch_kthvalue}
 \title{Kthvalue}
+\usage{
+torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{k}{(int) k for the k-th smallest element}
 
 \item{dim}{(int, optional) the dimension to find the kth value along}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, LongTensor)                           can be optionally given to be used as output buffers}
 }
 \description{
 Kthvalue
 }
-\section{kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the \code{k} th
@@ -27,7 +28,7 @@ smallest element of each row of the \code{input} tensor in the given dimension
 
 If \code{dim} is not given, the last dimension of the \code{input} is chosen.
 
-If \code{keepdim} is \code{True}, both the \code{values} and \code{indices} tensors
+If \code{keepdim} is \code{TRUE}, both the \code{values} and \code{indices} tensors
 are the same size as \code{input}, except in the dimension \code{dim} where
 they are of size 1. Otherwise, \code{dim} is squeezed
 (see \code{\link{torch_squeeze}}), resulting in both the \code{values} and
@@ -35,7 +36,7 @@ they are of size 1. Otherwise, \code{dim} is squeezed
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(1., 6.)
 x
diff --git a/man/torch_le.Rd b/man/torch_le.Rd
index 69a6bd6d9b4f47c4f16763dc111842adaeb5efd1..b5d3e1ea5c4e91b1c21d0cc5814245f5a1238bac 100644
--- a/man/torch_le.Rd
+++ b/man/torch_le.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_le}
 \alias{torch_le}
 \title{Le}
+\usage{
+torch_le(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Le
 }
-\section{le(input, other, out=None) -> Tensor }{
+\section{le(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} \leq \mbox{other}} element-wise.
@@ -24,7 +25,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_lerp.Rd b/man/torch_lerp.Rd
index 64a468bef03eddb33a49d82fc0727affe285d1da..2ed8cf208fc4335894b55f84545e351731d9a0c8 100644
--- a/man/torch_lerp.Rd
+++ b/man/torch_lerp.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lerp}
 \alias{torch_lerp}
 \title{Lerp}
+\usage{
+torch_lerp(self, end, weight)
+}
 \arguments{
-\item{input}{(Tensor) the tensor with the starting points}
+\item{self}{(Tensor) the tensor with the starting points}
 
 \item{end}{(Tensor) the tensor with the ending points}
 
 \item{weight}{(float or tensor) the weight for the interpolation formula}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Lerp
 }
-\section{lerp(input, end, weight, out=None) }{
+\section{lerp(input, end, weight, out=NULL) }{
 
 
 Does a linear interpolation of two tensors \code{start} (given by \code{input}) and \code{end} based
@@ -31,7 +32,7 @@ the shapes of \code{weight}, \code{start}, and \code{end} must be broadcastable
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 start = torch_arange(1., 5.)
 end = torch_empty(4)$fill_(10)
diff --git a/man/torch_lgamma.Rd b/man/torch_lgamma.Rd
index 02fd194fbfcb9193dd0ae912b0fa91181179f4e0..a13098dbcabd89c8387630b60588d5dd8fd10e10 100644
--- a/man/torch_lgamma.Rd
+++ b/man/torch_lgamma.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lgamma}
 \alias{torch_lgamma}
 \title{Lgamma}
+\usage{
+torch_lgamma(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Lgamma
 }
-\section{lgamma(input, out=None) -> Tensor }{
+\section{lgamma(input, out=NULL) -> Tensor }{
 
 
 Computes the logarithm of the gamma function on \code{input}.
@@ -23,7 +24,7 @@ Computes the logarithm of the gamma function on \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(0.5, 2, 0.5)
 torch_lgamma(a)
diff --git a/man/torch_linspace.Rd b/man/torch_linspace.Rd
index 678c62247543d011b3ef7346ccf6ce11b8ec6eb3..b624775ed8a0c12d7a7263ed4524a22dbfab168f 100644
--- a/man/torch_linspace.Rd
+++ b/man/torch_linspace.Rd
@@ -1,9 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_linspace}
 \alias{torch_linspace}
 \title{Linspace}
+\usage{
+torch_linspace(
+  start,
+  end,
+  steps = 100,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(float) the starting value for the set of points}
 
@@ -11,20 +22,18 @@
 
 \item{steps}{(int) number of points to sample between \code{start}        and \code{end}. Default: \code{100}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Linspace
 }
-\section{linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a one-dimensional tensor of \code{steps}
@@ -34,7 +43,7 @@ The output tensor is 1-D of size \code{steps}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_linspace(3, 10, steps=5)
 torch_linspace(-10, 10, steps=5)
diff --git a/man/torch_load.Rd b/man/torch_load.Rd
index 68df052617f7954a80c8fc6dadbcbde74762a61a..de6f5405eb35941773551bf948d7e87de8750bd6 100644
--- a/man/torch_load.Rd
+++ b/man/torch_load.Rd
@@ -16,4 +16,5 @@ Loads a saved object
 Other torch_save: 
 \code{\link{torch_save}()}
 }
+\concept{serialization}
 \concept{torch_save}
diff --git a/man/torch_log.Rd b/man/torch_log.Rd
index c3daf84b8866303ff538066f72d5dfffacfe2afa..f91a1b1cbd1eabe9c68bebe413d4b5728a8b521d 100644
--- a/man/torch_log.Rd
+++ b/man/torch_log.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log}
 \alias{torch_log}
 \title{Log}
+\usage{
+torch_log(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Log
 }
-\section{log(input, out=None) -> Tensor }{
+\section{log(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the natural logarithm of the elements
@@ -24,7 +25,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_log10.Rd b/man/torch_log10.Rd
index 7e09dabe0b4aea3959781a8e2a35e6874fe7839e..7919429b17614621f4d4e2a42f09fee13e8aa230 100644
--- a/man/torch_log10.Rd
+++ b/man/torch_log10.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log10}
 \alias{torch_log10}
 \title{Log10}
+\usage{
+torch_log10(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Log10
 }
-\section{log10(input, out=None) -> Tensor }{
+\section{log10(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the logarithm to the base 10 of the elements
@@ -24,7 +25,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_rand(5)
 a
diff --git a/man/torch_log1p.Rd b/man/torch_log1p.Rd
index 72aae87dbb6735bcc2107ac4a0daf71844be1134..835b2d1834b8cd6614693e4664d53d4bfdc4842d 100644
--- a/man/torch_log1p.Rd
+++ b/man/torch_log1p.Rd
@@ -1,13 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log1p}
 \alias{torch_log1p}
 \title{Log1p}
+\usage{
+torch_log1p(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Log1p
@@ -16,7 +17,7 @@ Log1p
 This function is more accurate than \code{\link{torch_log}} for small
 values of \code{input}
 }
-\section{log1p(input, out=None) -> Tensor }{
+\section{log1p(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the natural logarithm of (1 + \code{input}).
@@ -27,7 +28,7 @@ Returns a new tensor with the natural logarithm of (1 + \code{input}).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_log2.Rd b/man/torch_log2.Rd
index f23ba906a1e6134fb3c1fe76d9c83f3da62e8c49..e58088d6b2a662e20908d6d61c9409dd023312a2 100644
--- a/man/torch_log2.Rd
+++ b/man/torch_log2.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_log2}
 \alias{torch_log2}
 \title{Log2}
+\usage{
+torch_log2(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Log2
 }
-\section{log2(input, out=None) -> Tensor }{
+\section{log2(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the logarithm to the base 2 of the elements
@@ -24,7 +25,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_rand(5)
 a
diff --git a/man/torch_logdet.Rd b/man/torch_logdet.Rd
index f9367f6cd94ee67a4cc599eb14a2305c5d07b963..84a269e23147caf22040a00c8fb15d56e18f7e74 100644
--- a/man/torch_logdet.Rd
+++ b/man/torch_logdet.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logdet}
 \alias{torch_logdet}
 \title{Logdet}
+\usage{
+torch_logdet(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
+\item{self}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
 }
 \description{
 Logdet
 }
 \note{
-\preformatted{Result is ``-inf`` if `input` has zero log determinant, and is ``nan`` if
+\preformatted{Result is `-inf` if `input` has zero log determinant, and is `NaN` if
 `input` has negative determinant.
 }
 
@@ -28,7 +31,7 @@ Calculates log determinant of a square matrix or batches of square matrices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(3, 3))
 torch_det(A)
diff --git a/man/torch_logical_and.Rd b/man/torch_logical_and.Rd
index 2575f28131ebdbfe6f9ad39b3d2aa51463dbab0d..bc3e31e12f868b55a033dc5470108b6d782fc50c 100644
--- a/man/torch_logical_and.Rd
+++ b/man/torch_logical_and.Rd
@@ -1,28 +1,29 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logical_and}
 \alias{torch_logical_and}
 \title{Logical_and}
+\usage{
+torch_logical_and(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute AND with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logical_and
 }
-\section{logical_and(input, other, out=None) -> Tensor }{
+\section{logical_and(input, other, out=NULL) -> Tensor }{
 
 
-Computes the element-wise logical AND of the given input tensors. Zeros are treated as \code{False} and nonzeros are
-treated as \code{True}.
+Computes the element-wise logical AND of the given input tensors. Zeros are treated as \code{FALSE} and nonzeros are
+treated as \code{TRUE}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_and(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
 a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
diff --git a/man/torch_logical_not.Rd b/man/torch_logical_not.Rd
index 21d5e7145f3c88c4f21f04a71acff151dbea1b48..b0ff17929c8d2bbe073be1fce75a3db0e5f19231 100644
--- a/man/torch_logical_not.Rd
+++ b/man/torch_logical_not.Rd
@@ -5,22 +5,20 @@
 \alias{torch_logical_not}
 \title{Logical_not}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Logical_not
 }
-\section{logical_not(input, out=None) -> Tensor }{
+\section{logical_not(input, out=NULL) -> Tensor }{
 
 
 Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
-dtype. If the input tensor is not a bool tensor, zeros are treated as \code{False} and non-zeros are treated as \code{True}.
+dtype. If the input tensor is not a bool tensor, zeros are treated as \code{FALSE} and non-zeros are treated as \code{TRUE}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_not(torch_tensor(c(TRUE, FALSE)))
 torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8()))
diff --git a/man/torch_logical_or.Rd b/man/torch_logical_or.Rd
index d79615a82250778b86c5cacac8d3ea5d81ab8794..6b35e1f6e93c654bdc3a5327c3d813e392f973e1 100644
--- a/man/torch_logical_or.Rd
+++ b/man/torch_logical_or.Rd
@@ -1,28 +1,29 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logical_or}
 \alias{torch_logical_or}
 \title{Logical_or}
+\usage{
+torch_logical_or(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute OR with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logical_or
 }
-\section{logical_or(input, other, out=None) -> Tensor }{
+\section{logical_or(input, other, out=NULL) -> Tensor }{
 
 
-Computes the element-wise logical OR of the given input tensors. Zeros are treated as \code{False} and nonzeros are
-treated as \code{True}.
+Computes the element-wise logical OR of the given input tensors. Zeros are treated as \code{FALSE} and nonzeros are
+treated as \code{TRUE}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_or(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
 a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
diff --git a/man/torch_logical_xor.Rd b/man/torch_logical_xor.Rd
index d3730c90455b3b1556cb7b600a748ccf320d5f44..452921212ecd99c6266641058b7cb8b77b986e1d 100644
--- a/man/torch_logical_xor.Rd
+++ b/man/torch_logical_xor.Rd
@@ -1,28 +1,29 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logical_xor}
 \alias{torch_logical_xor}
 \title{Logical_xor}
+\usage{
+torch_logical_xor(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{other}{(Tensor) the tensor to compute XOR with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logical_xor
 }
-\section{logical_xor(input, other, out=None) -> Tensor }{
+\section{logical_xor(input, other, out=NULL) -> Tensor }{
 
 
-Computes the element-wise logical XOR of the given input tensors. Zeros are treated as \code{False} and nonzeros are
-treated as \code{True}.
+Computes the element-wise logical XOR of the given input tensors. Zeros are treated as \code{FALSE} and nonzeros are
+treated as \code{TRUE}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE)))
 a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
diff --git a/man/torch_logspace.Rd b/man/torch_logspace.Rd
index 0b15c08813aca5b415e242edb2418916dce9fafd..a71e7a16f53b43fcab10a5a231776e088077958f 100644
--- a/man/torch_logspace.Rd
+++ b/man/torch_logspace.Rd
@@ -1,9 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_logspace}
 \alias{torch_logspace}
 \title{Logspace}
+\usage{
+torch_logspace(
+  start,
+  end,
+  steps = 100,
+  base = 10,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(float) the starting value for the set of points}
 
@@ -13,20 +25,18 @@
 
 \item{base}{(float) base of the logarithm function. Default: \code{10.0}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Logspace
 }
-\section{logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a one-dimensional tensor of \code{steps} points
@@ -37,7 +47,7 @@ The output tensor is 1-D of size \code{steps}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_logspace(start=-10, end=10, steps=5)
 torch_logspace(start=0.1, end=1.0, steps=5)
diff --git a/man/torch_logsumexp.Rd b/man/torch_logsumexp.Rd
index b737d58f08b74aa1e054376bf9081459f5bc8d0c..9bd2548f65b184173f9d8a4138a9f9e540fe57a4 100644
--- a/man/torch_logsumexp.Rd
+++ b/man/torch_logsumexp.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_logsumexp}
 \alias{torch_logsumexp}
 \title{Logsumexp}
+\usage{
+torch_logsumexp(self, dim, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Logsumexp
 }
-\section{logsumexp(input, dim, keepdim=False, out=None) }{
+\section{logsumexp(input, dim, keepdim=False, out=NULL) }{
 
 
 Returns the log of summed exponentials of each row of the \code{input}
@@ -29,14 +30,14 @@ For summation index \eqn{j} given by \code{dim} and other indices \eqn{i}, the r
         \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
 }
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 torch_logsumexp(a, 1)
diff --git a/man/torch_lstsq.Rd b/man/torch_lstsq.Rd
index f357b66a001de44cc8efa472360b176071e3a3d6..9fcae8a32ffb0d70aba60a22fcd9696549efa6ba 100644
--- a/man/torch_lstsq.Rd
+++ b/man/torch_lstsq.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lstsq}
 \alias{torch_lstsq}
 \title{Lstsq}
+\usage{
+torch_lstsq(self, A)
+}
 \arguments{
-\item{input}{(Tensor) the matrix \eqn{B}}
+\item{self}{(Tensor) the matrix \eqn{B}}
 
 \item{A}{(Tensor) the \eqn{m} by \eqn{n} matrix \eqn{A}}
-
-\item{out}{(tuple, optional) the optional destination tensor}
 }
 \description{
 Lstsq
@@ -18,7 +19,7 @@ Lstsq
 \preformatted{The case when \eqn{m < n} is not supported on the GPU.
 }
 }
-\section{lstsq(input, A, out=None) -> Tensor }{
+\section{lstsq(input, A, out=NULL) -> Tensor }{
 
 
 Computes the solution to the least squares and least norm problems for a full
@@ -46,7 +47,7 @@ remaining \eqn{m - n} rows of that column.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_tensor(rbind(
  c(1,1,1),
diff --git a/man/torch_lt.Rd b/man/torch_lt.Rd
index e56577eaeed0af18d9b089ea27bbda9eb5dfa062..dad4d06f32582bdd90768c2cc315ef6a7da9b63b 100644
--- a/man/torch_lt.Rd
+++ b/man/torch_lt.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lt}
 \alias{torch_lt}
 \title{Lt}
+\usage{
+torch_lt(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Lt
 }
-\section{lt(input, other, out=None) -> Tensor }{
+\section{lt(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{\mbox{input} < \mbox{other}} element-wise.
@@ -24,7 +25,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_lu.Rd b/man/torch_lu.Rd
index 904b7d5e95c16bf3bc5cf655e7cd508485ef4cfb..039e1ba1a187d3169e0740ffae47c29542d890ce 100644
--- a/man/torch_lu.Rd
+++ b/man/torch_lu.Rd
@@ -23,7 +23,7 @@ tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
 is set to True.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(2, 3, 3))
 torch_lu(A)
diff --git a/man/torch_lu_solve.Rd b/man/torch_lu_solve.Rd
index 66c05b1342ff91ec996eb6cbf9b4910e67f03464..f5675ce124bf65032021763ddbac5dcfca0d8e08 100644
--- a/man/torch_lu_solve.Rd
+++ b/man/torch_lu_solve.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_lu_solve}
 \alias{torch_lu_solve}
 \title{Lu_solve}
+\usage{
+torch_lu_solve(self, LU_data, LU_pivots)
+}
 \arguments{
-\item{b}{(Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.}
+\item{self}{(Tensor) the RHS tensor of size \eqn{(*, m, k)}, where \eqn{*}                is zero or more batch dimensions.}
 
 \item{LU_data}{(Tensor) the pivoted LU factorization of A from \code{torch_lu} of size \eqn{(*, m, m)},                       where \eqn{*} is zero or more batch dimensions.}
 
 \item{LU_pivots}{(IntTensor) the pivots of the LU factorization from \code{torch_lu} of size \eqn{(*, m)},                           where \eqn{*} is zero or more batch dimensions.                           The batch dimensions of \code{LU_pivots} must be equal to the batch dimensions of                           \code{LU_data}.}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Lu_solve
 }
-\section{lu_solve(input, LU_data, LU_pivots, out=None) -> Tensor }{
+\section{lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor }{
 
 
 Returns the LU solve of the linear system \eqn{Ax = b} using the partially pivoted
@@ -24,7 +25,7 @@ LU factorization of A from \code{torch_lu}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 A = torch_randn(c(2, 3, 3))
 b = torch_randn(c(2, 3, 1))
 out = torch_lu(A)
diff --git a/man/torch_manual_seed.Rd b/man/torch_manual_seed.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..9ed92be43738f73d8765b5a4c02a2eb1fa14c666
--- /dev/null
+++ b/man/torch_manual_seed.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/generator.R
+\name{torch_manual_seed}
+\alias{torch_manual_seed}
+\title{Sets the seed for generating random numbers.}
+\usage{
+torch_manual_seed(seed)
+}
+\arguments{
+\item{seed}{integer seed.}
+}
+\description{
+Sets the seed for generating random numbers.
+}
diff --git a/man/torch_masked_select.Rd b/man/torch_masked_select.Rd
index 29492eb81e79d96853e7ed4e5fe3403a83827e97..fbad7f2955fbc717c9a0ce0f9dd1b30d8acd957f 100644
--- a/man/torch_masked_select.Rd
+++ b/man/torch_masked_select.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_masked_select}
 \alias{torch_masked_select}
 \title{Masked_select}
+\usage{
+torch_masked_select(self, mask)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{mask}{(BoolTensor) the tensor containing the binary mask to index with}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Masked_select
@@ -18,7 +19,7 @@ Masked_select
 The returned tensor does \strong{not} use the same storage
 as the original tensor
 }
-\section{masked_select(input, mask, out=None) -> Tensor }{
+\section{masked_select(input, mask, out=NULL) -> Tensor }{
 
 
 Returns a new 1-D tensor which indexes the \code{input} tensor according to
@@ -29,7 +30,7 @@ to match, but they must be broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 4))
 x
diff --git a/man/torch_matmul.Rd b/man/torch_matmul.Rd
index 82fbb2dccb56a240feba6d059742ecb2757cddf8..aa6e08f81dd6b8ded8be5869b958ef113c115a53 100644
--- a/man/torch_matmul.Rd
+++ b/man/torch_matmul.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_matmul}
 \alias{torch_matmul}
 \title{Matmul}
+\usage{
+torch_matmul(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the first tensor to be multiplied}
+\item{self}{(Tensor) the first tensor to be multiplied}
 
 \item{other}{(Tensor) the second tensor to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Matmul
@@ -18,7 +19,7 @@ Matmul
 \preformatted{The 1-dimensional dot product version of this function does not support an `out` parameter.
 }
 }
-\section{matmul(input, other, out=None) -> Tensor }{
+\section{matmul(input, other, out=NULL) -> Tensor }{
 
 
 Matrix product of two tensors.
@@ -45,7 +46,7 @@ tensor, \code{out} will be an \eqn{(j \times k \times n \times p)} tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 # vector x vector
 tensor1 = torch_randn(c(3))
diff --git a/man/torch_matrix_power.Rd b/man/torch_matrix_power.Rd
index 46f8d91750d78d71ff7d8604cf7f5f3e3342bb15..2c4a899c644320f1b46e831b38dd34709a7773b0 100644
--- a/man/torch_matrix_power.Rd
+++ b/man/torch_matrix_power.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_matrix_power}
 \alias{torch_matrix_power}
 \title{Matrix_power}
+\usage{
+torch_matrix_power(self, n)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{n}{(int) the power to raise the matrix to}
 }
@@ -25,7 +28,7 @@ is returned.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(2, 2, 2))
 a
diff --git a/man/torch_matrix_rank.Rd b/man/torch_matrix_rank.Rd
index 6443ffc8bd50c46a9b9bab486b6cb7933cfb363c..fdad763391895e0808d5affbbd6f3d2b892a0ebe 100644
--- a/man/torch_matrix_rank.Rd
+++ b/man/torch_matrix_rank.Rd
@@ -1,36 +1,39 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_matrix_rank}
 \alias{torch_matrix_rank}
 \title{Matrix_rank}
+\usage{
+torch_matrix_rank(self, tol, symmetric = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input 2-D tensor}
+\item{self}{(Tensor) the input 2-D tensor}
 
-\item{tol}{(float, optional) the tolerance value. Default: \code{None}}
+\item{tol}{(float, optional) the tolerance value. Default: \code{NULL}}
 
-\item{symmetric}{(bool, optional) indicates whether \code{input} is symmetric.                               Default: \code{False}}
+\item{symmetric}{(bool, optional) indicates whether \code{input} is symmetric.                               Default: \code{FALSE}}
 }
 \description{
 Matrix_rank
 }
-\section{matrix_rank(input, tol=None, symmetric=False) -> Tensor }{
+\section{matrix_rank(input, tol=NULL, symmetric=False) -> Tensor }{
 
 
 Returns the numerical rank of a 2-D tensor. The method to compute the
-matrix rank is done using SVD by default. If \code{symmetric} is \code{True},
+matrix rank is done using SVD by default. If \code{symmetric} is \code{TRUE},
 then \code{input} is assumed to be symmetric, and the computation of the
 rank is done by obtaining the eigenvalues.
 
 \code{tol} is the threshold below which the singular values (or the eigenvalues
-when \code{symmetric} is \code{True}) are considered to be 0. If \code{tol} is not
+when \code{symmetric} is \code{TRUE}) are considered to be 0. If \code{tol} is not
 specified, \code{tol} is set to \code{S.max() * max(S.size()) * eps} where \code{S} is the
-singular values (or the eigenvalues when \code{symmetric} is \code{True}), and \code{eps}
+singular values (or the eigenvalues when \code{symmetric} is \code{TRUE}), and \code{eps}
 is the epsilon value for the datatype of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_eye(10)
 torch_matrix_rank(a)
diff --git a/man/torch_max.Rd b/man/torch_max.Rd
index 1de50eee29bf741fe69369e65d3589117b60dc6e..52214e44b041cea16060e581d9acd4d41c8816fe 100644
--- a/man/torch_max.Rd
+++ b/man/torch_max.Rd
@@ -5,11 +5,11 @@
 \alias{torch_max}
 \title{Max}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Default: \code{False}.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not. Default: \code{FALSE}.}
 
 \item{out}{(tuple, optional) the result tuple of two output tensors (max, max_indices)}
 
@@ -28,7 +28,7 @@ follows the broadcasting rules .
 Returns the maximum value of all elements in the \code{input} tensor.
 }
 
-\section{max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the maximum
@@ -44,13 +44,13 @@ maximal value found, unless it is unique.
 The exact implementation details are device-specific.
 Do not expect the same result when run on CPU and GPU in general.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size
 as \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting
 in the output tensors having 1 fewer dimension than \code{input}.
 }
 
-\section{max(input, other, out=None) -> Tensor }{
+\section{max(input, other, out=NULL) -> Tensor }{
 
 
 Each element of the tensor \code{input} is compared with the corresponding
@@ -65,7 +65,7 @@ but they must be broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_mean.Rd b/man/torch_mean.Rd
index 52c6a45c1e790af8d41b9e62a7f7f4e2b2159a26..c67738ada80219b7748045f1e84fdb63f0eab08f 100644
--- a/man/torch_mean.Rd
+++ b/man/torch_mean.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mean}
 \alias{torch_mean}
 \title{Mean}
+\usage{
+torch_mean(self, dim, keepdim = FALSE, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{dtype}{the resulting data type.}
 }
 \description{
 Mean
@@ -22,21 +25,21 @@ Mean
 Returns the mean value of all elements in the \code{input} tensor.
 }
 
-\section{mean(input, dim, keepdim=False, out=None) -> Tensor }{
+\section{mean(input, dim, keepdim=False, out=NULL) -> Tensor }{
 
 
 Returns the mean value of each row of the \code{input} tensor in the given
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_median.Rd b/man/torch_median.Rd
index 44525ab7d3d0ff010f7c3a8b45e67ced27ed6176..c01b50b12d7d69b833bb05d9f838c51b003750a3 100644
--- a/man/torch_median.Rd
+++ b/man/torch_median.Rd
@@ -1,17 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_median}
 \alias{torch_median}
 \title{Median}
+\usage{
+torch_median(self, dim, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (max, max_indices)}
 }
 \description{
 Median
@@ -22,7 +23,7 @@ Median
 Returns the median value of all elements in the \code{input} tensor.
 }
 
-\section{median(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the median
@@ -31,14 +32,14 @@ value of each row of the \code{input} tensor in the given dimension
 
 By default, \code{dim} is the last dimension of the \code{input} tensor.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size
 as \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in
 the outputs tensor having 1 fewer dimension than \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_meshgrid.Rd b/man/torch_meshgrid.Rd
index c4b4a12f2f40d161cce9c78422f621409392201f..d65be929418bd8061e9023ac4d8f633fc3b75d9b 100644
--- a/man/torch_meshgrid.Rd
+++ b/man/torch_meshgrid.Rd
@@ -1,13 +1,15 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_meshgrid}
 \alias{torch_meshgrid}
 \title{Meshgrid}
+\usage{
+torch_meshgrid(tensors)
+}
 \arguments{
-\item{tensors}{(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be}
-
-\item{treated}{(1,)}
+\item{tensors}{(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
+treated (1,).}
 }
 \description{
 Meshgrid
@@ -21,7 +23,7 @@ expanding the \eqn{i} \code{th} input over dimensions defined by other inputs.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(c(1, 2, 3))
 y = torch_tensor(c(4, 5, 6))
diff --git a/man/torch_min.Rd b/man/torch_min.Rd
index 3f5cb9f7615c0b9104a54c9ba99ffb9f4c745c6d..cae165f1d7e36f24663cfc461e1ce6f77cfda308 100644
--- a/man/torch_min.Rd
+++ b/man/torch_min.Rd
@@ -5,7 +5,7 @@
 \alias{torch_min}
 \title{Min}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
@@ -28,7 +28,7 @@ follows the broadcasting rules .
 Returns the minimum value of all elements in the \code{input} tensor.
 }
 
-\section{min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the minimum
@@ -44,13 +44,13 @@ minimal value found, unless it is unique.
 The exact implementation details are device-specific.
 Do not expect the same result when run on CPU and GPU in general.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size as
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size as
 \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in
 the output tensors having 1 fewer dimension than \code{input}.
 }
 
-\section{min(input, other, out=None) -> Tensor }{
+\section{min(input, other, out=NULL) -> Tensor }{
 
 
 Each element of the tensor \code{input} is compared with the corresponding
@@ -66,7 +66,7 @@ but they must be broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_mm.Rd b/man/torch_mm.Rd
index 898ae45d120af523d718f71ef1855619d5613fa9..15f215198f97ab398f77d560b08a509a8c74e2d3 100644
--- a/man/torch_mm.Rd
+++ b/man/torch_mm.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mm}
 \alias{torch_mm}
 \title{Mm}
+\usage{
+torch_mm(self, mat2)
+}
 \arguments{
-\item{input}{(Tensor) the first matrix to be multiplied}
+\item{self}{(Tensor) the first matrix to be multiplied}
 
 \item{mat2}{(Tensor) the second matrix to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Mm
@@ -18,7 +19,7 @@ Mm
 This function does not broadcast .
 For broadcasting matrix products, see \code{\link{torch_matmul}}.
 }
-\section{mm(input, mat2, out=None) -> Tensor }{
+\section{mm(input, mat2, out=NULL) -> Tensor }{
 
 
 Performs a matrix multiplication of the matrices \code{input} and \code{mat2}.
@@ -28,7 +29,7 @@ If \code{input} is a \eqn{(n \times m)} tensor, \code{mat2} is a
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 mat1 = torch_randn(c(2, 3))
 mat2 = torch_randn(c(3, 3))
diff --git a/man/torch_mode.Rd b/man/torch_mode.Rd
index 571689badbec70ab58c4aae058c12783dfc23632..546a6f656fcf47f2c6f7b9a2b4c2329f32905394 100644
--- a/man/torch_mode.Rd
+++ b/man/torch_mode.Rd
@@ -1,17 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mode}
 \alias{torch_mode}
 \title{Mode}
+\usage{
+torch_mode(self, dim = -1L, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
-
-\item{out}{(tuple, optional) the result tuple of two output tensors (values, indices)}
 }
 \description{
 Mode
@@ -19,7 +20,7 @@ Mode
 \note{
 This function is not defined for \code{torch_cuda.Tensor} yet.
 }
-\section{mode(input, dim=-1, keepdim=False, out=None) -> (Tensor, LongTensor) }{
+\section{mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor) }{
 
 
 Returns a namedtuple \verb{(values, indices)} where \code{values} is the mode
@@ -29,14 +30,14 @@ in that row, and \code{indices} is the index location of each mode value found.
 
 By default, \code{dim} is the last dimension of the \code{input} tensor.
 
-If \code{keepdim} is \code{True}, the output tensors are of the same size as
+If \code{keepdim} is \code{TRUE}, the output tensors are of the same size as
 \code{input} except in the dimension \code{dim} where they are of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting
 in the output tensors having 1 fewer dimension than \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randint(0, 50, size = list(5))
 a
diff --git a/man/torch_mul.Rd b/man/torch_mul.Rd
index e31f6f8a3f1bdf541c8a19930b84021a2349a278..cde66445a12e001123df5a684ccb9880d42bf2bb 100644
--- a/man/torch_mul.Rd
+++ b/man/torch_mul.Rd
@@ -1,26 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mul}
 \alias{torch_mul}
 \title{Mul}
+\usage{
+torch_mul(self, other)
+}
 \arguments{
-\item{{input}}{NA}
-
-\item{value}{(Number) the number to be multiplied to each element of \code{input}}
-
-\item{{out}}{NA}
-
-\item{input}{(Tensor) the first multiplicand tensor}
+\item{self}{(Tensor) the first multiplicand tensor}
 
 \item{other}{(Tensor) the second multiplicand tensor}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Mul
 }
-\section{mul(input, other, out=None) }{
+\section{mul(input, other, out=NULL) }{
 
 
 Multiplies each element of the input \code{input} with the scalar
@@ -46,7 +41,7 @@ broadcastable .
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3))
 a
diff --git a/man/torch_multinomial.Rd b/man/torch_multinomial.Rd
index a12552845ea6aac7b71d999d877aacab5830180e..ae67022089498f4418e54268899c792f001d3224 100644
--- a/man/torch_multinomial.Rd
+++ b/man/torch_multinomial.Rd
@@ -1,19 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_multinomial}
 \alias{torch_multinomial}
 \title{Multinomial}
+\usage{
+torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor containing probabilities}
+\item{self}{(Tensor) the input tensor containing probabilities}
 
 \item{num_samples}{(int) number of samples to draw}
 
 \item{replacement}{(bool, optional) whether to draw with replacement or not}
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Multinomial
@@ -32,7 +33,7 @@ If \code{input} is a vector, \code{out} is a vector of size \code{num_samples}.
 If \code{input} is a matrix with \code{m} rows, \code{out} is an matrix of shape
 \eqn{(m \times \mbox{num\_samples})}.
 
-If replacement is \code{True}, samples are drawn with replacement.
+If replacement is \code{TRUE}, samples are drawn with replacement.
 
 If not, they are drawn without replacement, which means that when a
 sample index is drawn for a row, it cannot be drawn again for that row.
@@ -42,7 +43,7 @@ number of non-zero elements in `input` (or the min number of non-zero
 elements in each row of `input` if it is a matrix).
 }
 }
-\section{multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor }{
+\section{multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor }{
 
 
 Returns a tensor where each row contains \code{num_samples} indices sampled
@@ -51,7 +52,7 @@ of tensor \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights
 torch_multinomial(weights, 2)
diff --git a/man/torch_mv.Rd b/man/torch_mv.Rd
index 3b6a76a41e7f5a2777135a58d22e68940442eab6..d02c3f20c0cb5e83b09e8395a1f25158b84f1682 100644
--- a/man/torch_mv.Rd
+++ b/man/torch_mv.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mv}
 \alias{torch_mv}
 \title{Mv}
+\usage{
+torch_mv(self, vec)
+}
 \arguments{
-\item{input}{(Tensor) matrix to be multiplied}
+\item{self}{(Tensor) matrix to be multiplied}
 
 \item{vec}{(Tensor) vector to be multiplied}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Mv
@@ -17,7 +18,7 @@ Mv
 \note{
 This function does not broadcast .
 }
-\section{mv(input, vec, out=None) -> Tensor }{
+\section{mv(input, vec, out=NULL) -> Tensor }{
 
 
 Performs a matrix-vector product of the matrix \code{input} and the vector
@@ -28,7 +29,7 @@ size \eqn{m}, \code{out} will be 1-D of size \eqn{n}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 mat = torch_randn(c(2, 3))
 vec = torch_randn(c(3))
diff --git a/man/torch_mvlgamma.Rd b/man/torch_mvlgamma.Rd
index 5c9d1d91128618d0973c3059c807a8fd20d553ba..913346d3b6934cfec066a53b1574cddaba266521 100644
--- a/man/torch_mvlgamma.Rd
+++ b/man/torch_mvlgamma.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_mvlgamma}
 \alias{torch_mvlgamma}
 \title{Mvlgamma}
+\usage{
+torch_mvlgamma(self, p)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compute the multivariate log-gamma function}
+\item{self}{(Tensor) the tensor to compute the multivariate log-gamma function}
 
 \item{p}{(int) the number of dimensions}
 }
@@ -27,7 +30,7 @@ All elements must be greater than \eqn{\frac{p - 1}{2}}, otherwise an error woul
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_empty(c(2, 3))$uniform_(1, 2)
 a
diff --git a/man/torch_narrow.Rd b/man/torch_narrow.Rd
index afad9d4df35b81b8e44ec2a643627a7ec3544fc5..5934d04c110195acaabd93f96329dab463a62465 100644
--- a/man/torch_narrow.Rd
+++ b/man/torch_narrow.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_narrow}
 \alias{torch_narrow}
 \title{Narrow}
+\usage{
+torch_narrow(self, dim, start, length)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to narrow}
+\item{self}{(Tensor) the tensor to narrow}
 
 \item{dim}{(int) the dimension along which to narrow}
 
@@ -25,7 +28,7 @@ returned tensor and \code{input} tensor share the same underlying storage.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE))
 torch_narrow(x, 1, torch_tensor(0L)$sum(dim = 1), 2)
diff --git a/man/torch_ne.Rd b/man/torch_ne.Rd
index 366a50104767361cc8a134eb952f54e2fcc02f49..cc8c81d2341bc527a50f9040127ec2424652025f 100644
--- a/man/torch_ne.Rd
+++ b/man/torch_ne.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ne}
 \alias{torch_ne}
 \title{Ne}
+\usage{
+torch_ne(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to compare}
+\item{self}{(Tensor) the tensor to compare}
 
 \item{other}{(Tensor or float) the tensor or value to compare}
-
-\item{out}{(Tensor, optional) the output tensor that must be a \code{BoolTensor}}
 }
 \description{
 Ne
 }
-\section{ne(input, other, out=None) -> Tensor }{
+\section{ne(input, other, out=NULL) -> Tensor }{
 
 
 Computes \eqn{input \neq other} element-wise.
@@ -24,7 +25,7 @@ broadcastable  with the first argument.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)), 
          torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE)))
diff --git a/man/torch_neg.Rd b/man/torch_neg.Rd
index 677b43d7af9c1e6d0d92ea2795058e26a054a3f8..13a05bc3e7e87b5387e4e2de0a0f0622ca225a51 100644
--- a/man/torch_neg.Rd
+++ b/man/torch_neg.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_neg}
 \alias{torch_neg}
 \title{Neg}
+\usage{
+torch_neg(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Neg
 }
-\section{neg(input, out=None) -> Tensor }{
+\section{neg(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the negative of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the negative of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5))
 a
diff --git a/man/torch_nonzero.Rd b/man/torch_nonzero.Rd
index 5a41654203e7a924e5dce45290d84d44680e9bed..dcd929d6962f32ef371f682ec88f2a014e44fd30 100644
--- a/man/torch_nonzero.Rd
+++ b/man/torch_nonzero.Rd
@@ -1,13 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_nonzero}
 \alias{torch_nonzero}
 \title{Nonzero}
+\usage{
+torch_nonzero(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(LongTensor, optional) the output tensor containing indices}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Nonzero
@@ -16,18 +17,18 @@ Nonzero
 \preformatted{[`torch_nonzero(..., as_tuple=False) `] (default) returns a
 2-D tensor where each row is the index for a nonzero value.
 
-[`torch_nonzero(..., as_tuple=True) `] returns a tuple of 1-D
-index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]``
-gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor
+[`torch_nonzero(..., as_tuple=TRUE) `] returns a tuple of 1-D
+index tensors, allowing for advanced indexing, so `x[x.nonzero(as_tuple=TRUE)]`
+gives all nonzero values of tensor `x`. Of the returned tuple, each index tensor
 contains nonzero indices for a certain dimension.
 
 See below for more details on the two behaviors.
 }
 }
-\section{nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors }{
+\section{nonzero(input, *, out=NULL, as_tuple=False) -> LongTensor or tuple of LongTensors }{
 
 
-\strong{When} \code{as_tuple} \strong{is \code{False} (default)}:
+\strong{When} \code{as_tuple} \strong{is \code{FALSE} (default)}:
 
 Returns a tensor containing the indices of all non-zero elements of
 \code{input}.  Each row in the result contains the indices of a non-zero
@@ -38,7 +39,7 @@ If \code{input} has \eqn{n} dimensions, then the resulting indices tensor
 \code{out} is of size \eqn{(z \times n)}, where \eqn{z} is the total number of
 non-zero elements in the \code{input} tensor.
 
-\strong{When} \code{as_tuple} \strong{is \code{True}}:
+\strong{When} \code{as_tuple} \strong{is \code{TRUE}}:
 
 Returns a tuple of 1-D tensors, one for each dimension in \code{input},
 each containing the indices (in that dimension) of all non-zero elements of
@@ -53,7 +54,7 @@ value, it is treated as a one-dimensional tensor with one element.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1)))
 }
diff --git a/man/torch_norm.Rd b/man/torch_norm.Rd
index 6873517e5b862739e86a151d1b83c24c20d4edb8..cb8bc741d940abb1cb2f080340bc57d29d2ee321 100644
--- a/man/torch_norm.Rd
+++ b/man/torch_norm.Rd
@@ -1,21 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_norm}
 \alias{torch_norm}
 \title{Norm}
+\usage{
+torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor}
-
-\item{p}{(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: \code{'fro'}        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        None   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is None  sum(abs(x)\strong{ord)}(1./ord)        =====  ============================  ==========================}
+\item{self}{(Tensor) the input tensor}
 
-\item{dim}{(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is None, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.}
+\item{p}{(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: \code{'fro'}        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)\strong{ord)}(1./ord)        =====  ============================  ==========================}
 
-\item{keepdim}{(bool, optional) whether the output tensors have \code{dim}        retained or not. Ignored if \code{dim} = \code{None} and        \code{out} = \code{None}. Default: \code{False}}
+\item{dim}{(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.}
 
-\item{out}{(Tensor, optional) the output tensor. Ignored if        \code{dim} = \code{None} and \code{out} = \code{None}.}
+\item{keepdim}{(bool, optional) whether the output tensors have \code{dim}        retained or not. Ignored if \code{dim} = \code{NULL} and        \code{out} = \code{NULL}. Default: \code{FALSE}
+Ignored if        \code{dim} = \code{NULL} and \code{out} = \code{NULL}.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: None.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.}
 }
 \description{
 Norm
@@ -27,7 +29,7 @@ Returns the matrix norm or vector norm of a given tensor.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(0, 9, dtype = torch_float())
 b = a$reshape(list(3, 3))
diff --git a/man/torch_normal.Rd b/man/torch_normal.Rd
index ec43d79b2c2a4cad4781b55e416e9531c79d24b3..080f7bb871945411ff241cc946e60183eac76109 100644
--- a/man/torch_normal.Rd
+++ b/man/torch_normal.Rd
@@ -1,19 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_normal}
 \alias{torch_normal}
 \title{Normal}
+\usage{
+torch_normal(mean, std = 1L, size, generator = NULL)
+}
 \arguments{
 \item{mean}{(Tensor) the tensor of per-element means}
 
 \item{std}{(Tensor) the tensor of per-element standard deviations}
 
-\item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{size}{(int...) a sequence of integers defining the shape of the output tensor.}
+
+\item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 }
 \description{
 Normal
@@ -22,7 +23,7 @@ Normal
 When the shapes do not match, the shape of \code{mean}
 is used as the shape for the returned output tensor
 }
-\section{normal(mean, std, *, generator=None, out=None) -> Tensor }{
+\section{normal(mean, std, *, generator=NULL, out=NULL) -> Tensor }{
 
 
 Returns a tensor of random numbers drawn from separate normal distributions
@@ -38,21 +39,21 @@ The shapes of \code{mean} and \code{std} don't need to match, but the
 total number of elements in each tensor need to be the same.
 }
 
-\section{normal(mean=0.0, std, out=None) -> Tensor }{
+\section{normal(mean=0.0, std, out=NULL) -> Tensor }{
 
 
 Similar to the function above, but the means are shared among all drawn
 elements.
 }
 
-\section{normal(mean, std=1.0, out=None) -> Tensor }{
+\section{normal(mean, std=1.0, out=NULL) -> Tensor }{
 
 
 Similar to the function above, but the standard-deviations are shared among
 all drawn elements.
 }
 
-\section{normal(mean, std, size, *, out=None) -> Tensor }{
+\section{normal(mean, std, size, *, out=NULL) -> Tensor }{
 
 
 Similar to the function above, but the means and standard deviations are shared
@@ -60,7 +61,7 @@ among all drawn elements. The resulting tensor has size given by \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 torch_normal(mean=0, std=torch_arange(1, 0, -0.1))
diff --git a/man/torch_ones.Rd b/man/torch_ones.Rd
index 98be2a116457f9c947d4baddd130bc7412b3ff65..eaf9bbae8dc7cc209c7c4febbe1cd2b36d1e5ab3 100644
--- a/man/torch_ones.Rd
+++ b/man/torch_ones.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_ones}
 \alias{torch_ones}
 \title{Ones}
+\usage{
+torch_ones(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
+\item{...}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{names}{optional names for the dimensions}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Ones
 }
-\section{ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{1}, with the shape defined
@@ -28,7 +38,7 @@ by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_ones(c(2, 3))
 torch_ones(c(5))
diff --git a/man/torch_ones_like.Rd b/man/torch_ones_like.Rd
index 072991da8e92ad63787d33206c9cd3115cc7d2fa..5ebdd0a61771eaca5c415d997fc236cb3ab4214e 100644
--- a/man/torch_ones_like.Rd
+++ b/man/torch_ones_like.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_ones_like}
 \alias{torch_ones_like}
 \title{Ones_like}
+\usage{
+torch_ones_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Ones_like
 }
-\section{ones_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{1}, with the same size as
@@ -36,7 +46,7 @@ the old \code{torch_ones_like(input, out=output)} is equivalent to
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_empty(c(2, 3))
 torch_ones_like(input)
diff --git a/man/torch_orgqr.Rd b/man/torch_orgqr.Rd
index df61b8795d4381400bfce60c42a0cbef9ab2dd70..bf22176b6affff38ca6b9b9c9c2277d3ee5ea677 100644
--- a/man/torch_orgqr.Rd
+++ b/man/torch_orgqr.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_orgqr}
 \alias{torch_orgqr}
 \title{Orgqr}
+\usage{
+torch_orgqr(self, input2)
+}
 \arguments{
-\item{input}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
+\item{self}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
 
 \item{input2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
 }
diff --git a/man/torch_ormqr.Rd b/man/torch_ormqr.Rd
index f519b4506bb7a5815c805bd70f1715fa6306c3cb..d62c3342693370e6a475760a40b668bdeb3b1780 100644
--- a/man/torch_ormqr.Rd
+++ b/man/torch_ormqr.Rd
@@ -1,26 +1,33 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_ormqr}
 \alias{torch_ormqr}
 \title{Ormqr}
+\usage{
+torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
+\item{self}{(Tensor) the \code{a} from \code{\link{torch_geqrf}}.}
 
 \item{input2}{(Tensor) the \code{tau} from \code{\link{torch_geqrf}}.}
 
 \item{input3}{(Tensor) the matrix to be multiplied.}
+
+\item{left}{see LAPACK documentation}
+
+\item{transpose}{see LAPACK documentation}
 }
 \description{
 Ormqr
 }
-\section{ormqr(input, input2, input3, left=True, transpose=False) -> Tensor }{
+\section{ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor }{
 
 
 Multiplies \code{mat} (given by \code{input3}) by the orthogonal \code{Q} matrix of the QR factorization
-formed by \code{\link{torch_geqrf}} that is represented by \verb{(a, tau)} (given by (\code{input}, \code{input2})).
+formed by \code{\link[=torch_geqrf]{torch_geqrf()}} that is represented by \verb{(a, tau)} (given by (\code{input}, \code{input2})).
 
 This directly calls the underlying LAPACK function \code{?ormqr}.
-See \verb{LAPACK documentation for ormqr}_ for further details.
+See \href{https://software.intel.com/content/www/us/en/develop/documentation/mkl-developer-reference-c/top/scalapack-routines/scalapack-computational-routines/orthogonal-factorizations-scalapack-computational-routines/p-ormqr.html}{LAPACK documentation for ormqr} for further details.
 }
 
diff --git a/man/torch_pdist.Rd b/man/torch_pdist.Rd
index 7b0dcca0ed111bf566160c92a3af0afec20c12ad..e6f5c075ba6deb81750c7c8fade86ca9d34d2c38 100644
--- a/man/torch_pdist.Rd
+++ b/man/torch_pdist.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pdist}
 \alias{torch_pdist}
 \title{Pdist}
+\usage{
+torch_pdist(self, p = 2L)
+}
 \arguments{
-\item{input}{NA input tensor of shape \eqn{N \times M}.}
+\item{self}{NA input tensor of shape \eqn{N \times M}.}
 
 \item{p}{NA p value for the p-norm distance to calculate between each vector pair        \eqn{\in [0, \infty]}.}
 }
@@ -17,7 +20,7 @@ Pdist
 
 Computes the p-norm distance between every pair of row vectors in the input.
 This is identical to the upper triangular portion, excluding the diagonal, of
-\verb{torch_norm(input[:, None] - input, dim=2, p=p)}. This function will be faster
+\verb{torch_norm(input[:, NULL] - input, dim=2, p=p)}. This function will be faster
 if the rows are contiguous.
 
 If input has shape \eqn{N \times M} then the output will have shape
diff --git a/man/torch_pinverse.Rd b/man/torch_pinverse.Rd
index 360fc8227a4706f5af34ef1cd415c89fa5147512..c3bed5c29ab7dafac8b3491d76a7cf20e186be9c 100644
--- a/man/torch_pinverse.Rd
+++ b/man/torch_pinverse.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pinverse}
 \alias{torch_pinverse}
 \title{Pinverse}
+\usage{
+torch_pinverse(self, rcond = 0)
+}
 \arguments{
-\item{input}{(Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions}
+\item{self}{(Tensor) The input tensor of size \eqn{(*, m, n)} where \eqn{*} is zero or more batch dimensions}
 
 \item{rcond}{(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15}
 }
@@ -31,7 +34,7 @@ Please look at \verb{Moore-Penrose inverse}_ for more details
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randn(c(3, 5))
 input
diff --git a/man/torch_pixel_shuffle.Rd b/man/torch_pixel_shuffle.Rd
index 584b9f454f055458a2a22bc0e30d40ec3dad9454..5af008b54a12e0e260e1b70adb79a52d7d70210f 100644
--- a/man/torch_pixel_shuffle.Rd
+++ b/man/torch_pixel_shuffle.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pixel_shuffle}
 \alias{torch_pixel_shuffle}
 \title{Pixel_shuffle}
+\usage{
+torch_pixel_shuffle(self, upscale_factor)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor}
+\item{self}{(Tensor) the input tensor}
 
 \item{upscale_factor}{(int) factor to increase spatial resolution by}
 }
@@ -22,7 +25,7 @@ See \code{~torch.nn.PixelShuffle} for details.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_randn(c(1, 9, 4, 4))
 output = nnf_pixel_shuffle(input, 3)
diff --git a/man/torch_poisson.Rd b/man/torch_poisson.Rd
index 50e8b2c738d6da5d9c5a4b3fd4e2eff5cb019015..c5a33794e6a248d918485309569f201b4ddd5dca 100644
--- a/man/torch_poisson.Rd
+++ b/man/torch_poisson.Rd
@@ -1,18 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_poisson}
 \alias{torch_poisson}
 \title{Poisson}
+\usage{
+torch_poisson(self, generator = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor containing the rates of the Poisson distribution}
+\item{self}{(Tensor) the input tensor containing the rates of the Poisson distribution}
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 }
 \description{
 Poisson
 }
-\section{poisson(input *, generator=None) -> Tensor }{
+\section{poisson(input *, generator=NULL) -> Tensor }{
 
 
 Returns a tensor of the same size as \code{input} with each element
@@ -25,7 +28,7 @@ element in \code{input} i.e.,
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5
 torch_poisson(rates)
diff --git a/man/torch_polygamma.Rd b/man/torch_polygamma.Rd
index 360b0f6e9eb1e10e623419b137aae88c3c8a4d43..68fe882d2c4e63d1cace51f81f5d953b2a08ba8f 100644
--- a/man/torch_polygamma.Rd
+++ b/man/torch_polygamma.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_polygamma}
 \alias{torch_polygamma}
 \title{Polygamma}
+\usage{
+torch_polygamma(n, self)
+}
 \arguments{
 \item{n}{(int) the order of the polygamma function}
 
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Polygamma
@@ -18,7 +19,7 @@ Polygamma
 \preformatted{This function is not implemented for \eqn{n \geq 2}.
 }
 }
-\section{polygamma(n, input, out=None) -> Tensor }{
+\section{polygamma(n, input, out=NULL) -> Tensor }{
 
 
 Computes the \eqn{n^{th}} derivative of the digamma function on \code{input}.
@@ -30,7 +31,7 @@ Computes the \eqn{n^{th}} derivative of the digamma function on \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 a = torch_tensor(c(1, 0.5))
 torch_polygamma(1, a)
diff --git a/man/torch_pow.Rd b/man/torch_pow.Rd
index 567dc90399d4be9f93b87d0faa6ac42906a94b6a..01212160cc07c2610ca8847315f80d7f0a38bffb 100644
--- a/man/torch_pow.Rd
+++ b/man/torch_pow.Rd
@@ -1,22 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_pow}
 \alias{torch_pow}
 \title{Pow}
+\usage{
+torch_pow(self, exponent)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(float) the scalar base value for the power operation}
 
 \item{exponent}{(float or tensor) the exponent value}
-
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{self}{(float) the scalar base value for the power operation}
 }
 \description{
 Pow
 }
-\section{pow(input, exponent, out=None) -> Tensor }{
+\section{pow(input, exponent, out=NULL) -> Tensor }{
 
 
 Takes the power of each element in \code{input} with \code{exponent} and
@@ -39,7 +38,7 @@ When \code{exponent} is a tensor, the shapes of \code{input}
 and \code{exponent} must be broadcastable .
 }
 
-\section{pow(self, exponent, out=None) -> Tensor }{
+\section{pow(self, exponent, out=NULL) -> Tensor }{
 
 
 \code{self} is a scalar \code{float} value, and \code{exponent} is a tensor.
@@ -53,7 +52,7 @@ The operation applied is:
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_prod.Rd b/man/torch_prod.Rd
index cc0a9aa135806c0d7205d8f92b0c53f399f781e1..1fd3c582a9815426a808b1d4f437a62eb9b022f4 100644
--- a/man/torch_prod.Rd
+++ b/man/torch_prod.Rd
@@ -1,41 +1,44 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_prod}
 \alias{torch_prod}
 \title{Prod}
+\usage{
+torch_prod(self, dim, keepdim = FALSE, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the dimension to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
+
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Prod
 }
-\section{prod(input, dtype=None) -> Tensor }{
+\section{prod(input, dtype=NULL) -> Tensor }{
 
 
 Returns the product of all elements in the \code{input} tensor.
 }
 
-\section{prod(input, dim, keepdim=False, dtype=None) -> Tensor }{
+\section{prod(input, dim, keepdim=False, dtype=NULL) -> Tensor }{
 
 
 Returns the product of each row of the \code{input} tensor in the given
 dimension \code{dim}.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in
 the output tensor having 1 fewer dimension than \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_promote_types.Rd b/man/torch_promote_types.Rd
index 9d93b511ede51528c8f7f7641ea57d985630f260..11e5bed85bade479ce1c9d959b5e0cb2fe59766d 100644
--- a/man/torch_promote_types.Rd
+++ b/man/torch_promote_types.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_promote_types}
 \alias{torch_promote_types}
 \title{Promote_types}
+\usage{
+torch_promote_types(type1, type2)
+}
 \arguments{
 \item{type1}{(\code{torch.dtype})}
 
@@ -22,7 +25,7 @@ promotion logic.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_promote_types(torch_int32(), torch_float32())
 torch_promote_types(torch_uint8(), torch_long())
diff --git a/man/torch_qr.Rd b/man/torch_qr.Rd
index 2c6cf71e3bdf74b0b4330c720218a34ea57d3929..7c8b1b0d29491703b317cb5c42f85a231538734d 100644
--- a/man/torch_qr.Rd
+++ b/man/torch_qr.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_qr}
 \alias{torch_qr}
 \title{Qr}
+\usage{
+torch_qr(self, some = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.}
-
-\item{some}{(bool, optional) Set to \code{True} for reduced QR decomposition and \code{False} for                complete QR decomposition.}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                batch dimensions consisting of matrices of dimension \eqn{m \times n}.}
 
-\item{out}{(tuple, optional) tuple of \code{Q} and \code{R} tensors                satisfying \code{input = torch.matmul(Q, R)}.                The dimensions of \code{Q} and \code{R} are \eqn{(*, m, k)} and \eqn{(*, k, n)}                respectively, where \eqn{k = \min(m, n)} if \verb{some:} is \code{True} and                \eqn{k = m} otherwise.}
+\item{some}{(bool, optional) Set to \code{TRUE} for reduced QR decomposition and \code{FALSE} for                complete QR decomposition.}
 }
 \description{
 Qr
@@ -22,7 +23,7 @@ While it should always give you a valid decomposition, it may not
 give you the same one across platforms - it will depend on your
 LAPACK implementation.
 }
-\section{qr(input, some=True, out=None) -> (Tensor, Tensor) }{
+\section{qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor) }{
 
 
 Computes the QR decomposition of a matrix or a batch of matrices \code{input},
@@ -30,12 +31,12 @@ and returns a namedtuple (Q, R) of tensors such that \eqn{\mbox{input} = Q R}
 with \eqn{Q} being an orthogonal matrix or batch of orthogonal matrices and
 \eqn{R} being an upper triangular matrix or batch of upper triangular matrices.
 
-If \code{some} is \code{True}, then this function returns the thin (reduced) QR factorization.
-Otherwise, if \code{some} is \code{False}, this function returns the complete QR factorization.
+If \code{some} is \code{TRUE}, then this function returns the thin (reduced) QR factorization.
+Otherwise, if \code{some} is \code{FALSE}, this function returns the complete QR factorization.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE))
 out = torch_qr(a)
diff --git a/man/torch_qscheme.Rd b/man/torch_qscheme.Rd
index fa68e28767bf271dabe36faec233ae2ca12e2da7..2258be80be5144e3bdb8009cb33d8ddce8c30acd 100644
--- a/man/torch_qscheme.Rd
+++ b/man/torch_qscheme.Rd
@@ -19,3 +19,4 @@ torch_per_tensor_symmetric()
 \description{
 Creates the corresponding Scheme object
 }
+\concept{tensor-attributes}
diff --git a/man/torch_quantize_per_channel.Rd b/man/torch_quantize_per_channel.Rd
index f6027a8dd70c56c239a4006c48988d9e16608673..6011f1726a6e9a8a34716e7b248a08164ecbdf6a 100644
--- a/man/torch_quantize_per_channel.Rd
+++ b/man/torch_quantize_per_channel.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_quantize_per_channel}
 \alias{torch_quantize_per_channel}
 \title{Quantize_per_channel}
+\usage{
+torch_quantize_per_channel(self, scales, zero_points, axis, dtype)
+}
 \arguments{
-\item{input}{(Tensor) float tensor to quantize}
+\item{self}{(Tensor) float tensor to quantize}
 
 \item{scales}{(Tensor) float 1D tensor of scales to use, size should match \code{input.size(axis)}}
 
@@ -25,7 +28,7 @@ Converts a float tensor to per-channel quantized tensor with given scales and ze
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE))
 torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
                            torch_tensor(c(10L, 0L)), 0, torch_quint8())
diff --git a/man/torch_quantize_per_tensor.Rd b/man/torch_quantize_per_tensor.Rd
index e0e9db15c082a1a2c71735e53904b7225713d8c0..b327d5e424ce3fdf9e281cae9c5e3ecb00c402f3 100644
--- a/man/torch_quantize_per_tensor.Rd
+++ b/man/torch_quantize_per_tensor.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_quantize_per_tensor}
 \alias{torch_quantize_per_tensor}
 \title{Quantize_per_tensor}
+\usage{
+torch_quantize_per_tensor(self, scale, zero_point, dtype)
+}
 \arguments{
-\item{input}{(Tensor) float tensor to quantize}
+\item{self}{(Tensor) float tensor to quantize}
 
 \item{scale}{(float) scale to apply in quantization formula}
 
@@ -23,7 +26,7 @@ Converts a float tensor to quantized tensor with given scale and zero point.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())
 torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr()
 }
diff --git a/man/torch_rand.Rd b/man/torch_rand.Rd
index 8bf27242b42c5d3aeb33b8054e1c7bb8c71e84f9..a2f9f4e532e80a91268a79b470a13e3edf83f601 100644
--- a/man/torch_rand.Rd
+++ b/man/torch_rand.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_rand}
 \alias{torch_rand}
 \title{Rand}
+\usage{
+torch_rand(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
+\item{...}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{names}{optional dimension names}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Rand
 }
-\section{rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with random numbers from a uniform distribution
@@ -30,7 +40,7 @@ The shape of the tensor is defined by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_rand(4)
 torch_rand(c(2, 3))
diff --git a/man/torch_rand_like.Rd b/man/torch_rand_like.Rd
index e13f1a8e28cf1e27afa2956452fdabc2099df551..c4532e0fb714781675b4688317900ae55fc474a5 100644
--- a/man/torch_rand_like.Rd
+++ b/man/torch_rand_like.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_rand_like}
 \alias{torch_rand_like}
 \title{Rand_like}
+\usage{
+torch_rand_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Rand_like
 }
-\section{rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor with the same size as \code{input} that is filled with
diff --git a/man/torch_randint.Rd b/man/torch_randint.Rd
index 397bf569bc7cb2a879fe2641bd0eb884c327067c..6e75e16d332c4fae66226762d4152e58f1c0a84b 100644
--- a/man/torch_randint.Rd
+++ b/man/torch_randint.Rd
@@ -1,9 +1,22 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randint}
 \alias{torch_randint}
 \title{Randint}
+\usage{
+torch_randint(
+  low,
+  high,
+  size,
+  generator = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{low}{(int, optional) Lowest integer to be drawn from the distribution. Default: 0.}
 
@@ -13,23 +26,23 @@
 
 \item{generator}{(\code{torch.Generator}, optional) a pseudorandom number generator for sampling}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{memory_format}{memory format for the resulting tensor.}
 }
 \description{
 Randint
 }
-\section{randint(low=0, high, size, *, generator=None, out=None, \ }{
+\section{randint(low=0, high, size, *, generator=NULL, out=NULL, \ }{
 
 
-dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
+dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor
 
 Returns a tensor filled with random integers generated uniformly
 between \code{low} (inclusive) and \code{high} (exclusive).
@@ -42,7 +55,7 @@ a tensor with dtype \code{torch_int64}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_randint(3, 5, list(3))
 torch_randint(0, 10, size = list(2, 2))
diff --git a/man/torch_randint_like.Rd b/man/torch_randint_like.Rd
index 971f489d8085004db40b905473f96100b364d039..7d1c05a5745d1ddb87a92c317a6e4fc50e21f288 100644
--- a/man/torch_randint_like.Rd
+++ b/man/torch_randint_like.Rd
@@ -1,9 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randint_like}
 \alias{torch_randint_like}
 \title{Randint_like}
+\usage{
+torch_randint_like(
+  input,
+  low,
+  high,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
@@ -11,20 +22,18 @@
 
 \item{high}{(int) One above the highest integer to be drawn from the distribution.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
-
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Randint_like
 }
-\section{randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, }{
+\section{randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, }{
 
 
 memory_format=torch.preserve_format) -> Tensor
diff --git a/man/torch_randn.Rd b/man/torch_randn.Rd
index 25b695f012fc6524c82396c004dfff345d9d2659..49ce9feac7cd7f23034ba1f8f015fbca9fbf0c52 100644
--- a/man/torch_randn.Rd
+++ b/man/torch_randn.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randn}
 \alias{torch_randn}
 \title{Randn}
+\usage{
+torch_randn(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
+\item{...}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{names}{optional names for the dimensions}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Randn
 }
-\section{randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with random numbers from a normal distribution
@@ -34,7 +44,7 @@ The shape of the tensor is defined by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_randn(c(4))
 torch_randn(c(2, 3))
diff --git a/man/torch_randn_like.Rd b/man/torch_randn_like.Rd
index 7592a38e543e1ed114b707ba389077535d90e21e..c374cb08529d5d822f53ab2228c7a70d9ae9210f 100644
--- a/man/torch_randn_like.Rd
+++ b/man/torch_randn_like.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randn_like}
 \alias{torch_randn_like}
 \title{Randn_like}
+\usage{
+torch_randn_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Randn_like
 }
-\section{randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor with the same size as \code{input} that is filled with
diff --git a/man/torch_randperm.Rd b/man/torch_randperm.Rd
index 93d832ece492d34f9cef278ff41a6a136968dc25..705596370d3b2ccc78958905883c3693eaad1547 100644
--- a/man/torch_randperm.Rd
+++ b/man/torch_randperm.Rd
@@ -1,33 +1,40 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_randperm}
 \alias{torch_randperm}
 \title{Randperm}
+\usage{
+torch_randperm(
+  n,
+  dtype = torch_int64(),
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{n}{(int) the upper bound (exclusive)}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
 \item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: \code{torch_int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Randperm
 }
-\section{randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) -> LongTensor }{
+\section{randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor }{
 
 
 Returns a random permutation of integers from \code{0} to \code{n - 1}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_randperm(4)
 }
diff --git a/man/torch_range.Rd b/man/torch_range.Rd
index 352f3b5203c89003d4802fcd5a8b08c306f88ba6..078c1911375fdce9db5bd089fae9baf2b9200f3c 100644
--- a/man/torch_range.Rd
+++ b/man/torch_range.Rd
@@ -1,9 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_range}
 \alias{torch_range}
 \title{Range}
+\usage{
+torch_range(
+  start,
+  end,
+  step = 1,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{start}{(float) the starting value for the set of points. Default: \code{0}.}
 
@@ -11,20 +22,18 @@
 
 \item{step}{(float) the gap between each pair of adjacent points. Default: \code{1}.}
 
-\item{out}{(Tensor, optional) the output tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}). If \code{dtype} is not given, infer the data type from the other input        arguments. If any of \code{start}, \code{end}, or \code{stop} are floating-point, the        \code{dtype} is inferred to be the default dtype, see        \code{~torch.get_default_dtype}. Otherwise, the \code{dtype} is inferred to        be \code{torch.int64}.}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Range
 }
-\section{range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a 1-D tensor of size \eqn{\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1}
@@ -42,7 +51,7 @@ This function is deprecated in favor of \code{\link{torch_arange}}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_range(1, 4)
 torch_range(1, 4, 0.5)
diff --git a/man/torch_real.Rd b/man/torch_real.Rd
index 92b99560064353c38bb46db7035320fae122cbd1..15cdbc172ee45637a26da0e68b01b12d7ec82e0c 100644
--- a/man/torch_real.Rd
+++ b/man/torch_real.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_real}
 \alias{torch_real}
 \title{Real}
+\usage{
+torch_real(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Real
 }
-\section{real(input, out=None) -> Tensor }{
+\section{real(input) -> Tensor }{
 
 
 Returns the real part of the \code{input} tensor. If
@@ -30,7 +31,7 @@ Not yet implemented for complex tensors.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 torch_real(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))
 }
diff --git a/man/torch_reciprocal.Rd b/man/torch_reciprocal.Rd
index cb24bfbda027c53d625965c6d58bc4f9f363a033..f064f2b0ff421cb4f08a7fd23c32ebabc49dc884 100644
--- a/man/torch_reciprocal.Rd
+++ b/man/torch_reciprocal.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_reciprocal}
 \alias{torch_reciprocal}
 \title{Reciprocal}
+\usage{
+torch_reciprocal(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Reciprocal
 }
-\section{reciprocal(input, out=None) -> Tensor }{
+\section{reciprocal(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the reciprocal of the elements of \code{input}
@@ -23,7 +24,7 @@ Returns a new tensor with the reciprocal of the elements of \code{input}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_reduction.Rd b/man/torch_reduction.Rd
index 65c7f9e28a320abae97a84e4327bb2ad4a829c27..ead83db58fcab0a6f13e87e0a5f8b5d333d7d087 100644
--- a/man/torch_reduction.Rd
+++ b/man/torch_reduction.Rd
@@ -16,3 +16,4 @@ torch_reduction_none()
 \description{
 Creates the reduction objet
 }
+\concept{tensor-attributes}
diff --git a/man/torch_relu.Rd b/man/torch_relu.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..b9a415aec4a958b0f12941ddb32c0ce7dc86daa5
--- /dev/null
+++ b/man/torch_relu.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen-namespace-docs.R, R/gen-namespace.R
+\name{torch_relu}
+\alias{torch_relu}
+\title{Relu}
+\usage{
+torch_relu(self)
+}
+\arguments{
+\item{self}{the input tensor}
+}
+\description{
+Relu
+}
+\section{relu(input) -> Tensor }{
+
+
+Computes the relu tranformation.
+}
+
diff --git a/man/torch_relu_.Rd b/man/torch_relu_.Rd
index a8e54ff0f971e2bf7cdc444a59f881d9df8d800c..6687e4d1c3a6392fd79bb2beea1dd3560cfd6c29 100644
--- a/man/torch_relu_.Rd
+++ b/man/torch_relu_.Rd
@@ -1,15 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_relu_}
 \alias{torch_relu_}
 \title{Relu_}
+\usage{
+torch_relu_(self)
+}
+\arguments{
+\item{self}{the input tensor}
+}
 \description{
 Relu_
 }
 \section{relu_(input) -> Tensor }{
 
 
-In-place version of \code{torch_relu}.
+In-place version of \code{\link[=torch_relu]{torch_relu()}}.
 }
 
diff --git a/man/torch_remainder.Rd b/man/torch_remainder.Rd
index 0d1e071efb62f1e638175cc7d9ed78f218a50e5e..326e05928191373d191b2b4e240404636846ce9d 100644
--- a/man/torch_remainder.Rd
+++ b/man/torch_remainder.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_remainder}
 \alias{torch_remainder}
 \title{Remainder}
+\usage{
+torch_remainder(self, other)
+}
 \arguments{
-\item{input}{(Tensor) the dividend}
+\item{self}{(Tensor) the dividend}
 
 \item{other}{(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Remainder
 }
-\section{remainder(input, other, out=None) -> Tensor }{
+\section{remainder(input, other, out=NULL) -> Tensor }{
 
 
 Computes the element-wise remainder of division.
@@ -27,7 +28,7 @@ When \code{other} is a tensor, the shapes of \code{input} and
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
 torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
diff --git a/man/torch_renorm.Rd b/man/torch_renorm.Rd
index 6e5b1de2b89297856fa9fd5d9a35a8471766910a..72e7b816febeaa338ee9d277e693c1b7306af9ee 100644
--- a/man/torch_renorm.Rd
+++ b/man/torch_renorm.Rd
@@ -1,19 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_renorm}
 \alias{torch_renorm}
 \title{Renorm}
+\usage{
+torch_renorm(self, p, dim, maxnorm)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{p}{(float) the power for the norm computation}
 
 \item{dim}{(int) the dimension to slice over to get the sub-tensors}
 
 \item{maxnorm}{(float) the maximum norm to keep each sub-tensor under}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Renorm
@@ -21,7 +22,7 @@ Renorm
 \note{
 If the norm of a row is lower than \code{maxnorm}, the row is unchanged
 }
-\section{renorm(input, p, dim, maxnorm, out=None) -> Tensor }{
+\section{renorm(input, p, dim, maxnorm, out=NULL) -> Tensor }{
 
 
 Returns a tensor where each sub-tensor of \code{input} along dimension
@@ -30,7 +31,7 @@ than the value \code{maxnorm}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x = torch_ones(c(3, 3))
 x[2,]$fill_(2)
 x[3,]$fill_(3)
diff --git a/man/torch_repeat_interleave.Rd b/man/torch_repeat_interleave.Rd
index 4c2393a4500398ae169984e9d8a22895f1cd936c..d7a37c7c359c1d2793a924fd739229074384d34e 100644
--- a/man/torch_repeat_interleave.Rd
+++ b/man/torch_repeat_interleave.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_repeat_interleave}
 \alias{torch_repeat_interleave}
 \title{Repeat_interleave}
+\usage{
+torch_repeat_interleave(self, repeats, dim = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{repeats}{(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.}
 
@@ -14,14 +17,14 @@
 \description{
 Repeat_interleave
 }
-\section{repeat_interleave(input, repeats, dim=None) -> Tensor }{
+\section{repeat_interleave(input, repeats, dim=NULL) -> Tensor }{
 
 
 Repeat elements of a tensor.
 }
 
 \section{Warning}{
-\preformatted{This is different from `torch_Tensor.repeat` but similar to ``numpy.repeat``.
+\preformatted{This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
 }
 }
 
@@ -34,7 +37,7 @@ If the \code{repeats} is \verb{tensor([n1, n2, n3, ...])}, then the output will
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 x = torch_tensor(c(1, 2, 3))
 x$repeat_interleave(2)
diff --git a/man/torch_reshape.Rd b/man/torch_reshape.Rd
index 01105c5f3fe6349221d047eca265f123c1f05d9c..a86e9efd8b56c845e0cea13e6c5587c307ae6cc1 100644
--- a/man/torch_reshape.Rd
+++ b/man/torch_reshape.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_reshape}
 \alias{torch_reshape}
 \title{Reshape}
+\usage{
+torch_reshape(self, shape)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to be reshaped}
+\item{self}{(Tensor) the tensor to be reshaped}
 
 \item{shape}{(tuple of ints) the new shape}
 }
@@ -28,7 +31,7 @@ dimensions and the number of elements in \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(0, 4)
 torch_reshape(a, list(2, 2))
diff --git a/man/torch_result_type.Rd b/man/torch_result_type.Rd
index 42eaa4c3e9df547f3e660b4118dba873e92dde78..ad3b913e8b9f47c0c4b2e56dce41df31d7871b65 100644
--- a/man/torch_result_type.Rd
+++ b/man/torch_result_type.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_result_type}
 \alias{torch_result_type}
 \title{Result_type}
+\usage{
+torch_result_type(tensor1, tensor2)
+}
 \arguments{
 \item{tensor1}{(Tensor or Number) an input tensor or number}
 
@@ -21,9 +24,8 @@ for more information on the type promotion logic.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
-torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
-# torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))
+torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1)
 }
 }
diff --git a/man/torch_rfft.Rd b/man/torch_rfft.Rd
index 99d095f5c1f74a563ab9034bc3cbdb8f1518a14f..a74c14e87184d5abe9a27063883d2b46a8de5f80 100644
--- a/man/torch_rfft.Rd
+++ b/man/torch_rfft.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rfft}
 \alias{torch_rfft}
 \title{Rfft}
+\usage{
+torch_rfft(self, signal_ndim, normalized = FALSE, onesided = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of at least \code{signal_ndim} dimensions}
+\item{self}{(Tensor) the input tensor of at least \code{signal_ndim} dimensions}
 
 \item{signal_ndim}{(int) the number of dimensions in each signal.        \code{signal_ndim} can only be 1, 2 or 3}
 
-\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return normalized results.        Default: \code{FALSE}}
 
-\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy. Default: \code{True}}
+\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy. Default: \code{TRUE}}
 }
 \description{
 Rfft
@@ -23,7 +26,7 @@ configuration. See cufft-plan-cache for more details on how to
 monitor and control the cache.
 }
 }
-\section{rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor }{
+\section{rfft(input, signal_ndim, normalized=False, onesided=TRUE) -> Tensor }{
 
 
 Real-to-complex Discrete Fourier Transform
@@ -35,7 +38,7 @@ formats of the input and output.
 This method supports 1D, 2D and 3D real-to-complex transforms, indicated
 by \code{signal_ndim}. \code{input} must be a tensor with at least
 \code{signal_ndim} dimensions with optionally arbitrary number of leading batch
-dimensions. If \code{normalized} is set to \code{True}, this normalizes the result
+dimensions. If \code{normalized} is set to \code{TRUE}, this normalizes the result
 by dividing it with \eqn{\sqrt{\prod_{i=1}^K N_i}} so that the operator is
 unitary, where \eqn{N_i} is the size of signal dimension \eqn{i}.
 
@@ -47,7 +50,7 @@ The real-to-complex Fourier transform results follow conjugate symmetry:
 where the index arithmetic is computed modulus the size of the corresponding
 dimension, \eqn{\ ^*} is the conjugate operator, and
 \eqn{d} = \code{signal_ndim}. \code{onesided} flag controls whether to avoid
-redundancy in the output results. If set to \code{True} (default), the output will
+redundancy in the output results. If set to \code{TRUE} (default), the output will
 not be full complex result of shape \eqn{(*, 2)}, where \eqn{*} is the shape
 of \code{input}, but instead the last dimension will be halfed as of size
 \eqn{\lfloor \frac{N_d}{2} \rfloor + 1}.
@@ -62,7 +65,7 @@ For CPU tensors, this method is currently only available with MKL. Use
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(5, 5))
 torch_rfft(x, 2)
diff --git a/man/torch_roll.Rd b/man/torch_roll.Rd
index d2e1e221868f9646c991947f73a07c8d8d8c0571..6ba28ae42ae89ef601352808ca6bff898fe71fb8 100644
--- a/man/torch_roll.Rd
+++ b/man/torch_roll.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_roll}
 \alias{torch_roll}
 \title{Roll}
+\usage{
+torch_roll(self, shifts, dims = list())
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{shifts}{(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value}
 
@@ -14,7 +17,7 @@
 \description{
 Roll
 }
-\section{roll(input, shifts, dims=None) -> Tensor }{
+\section{roll(input, shifts, dims=NULL) -> Tensor }{
 
 
 Roll the tensor along the given dimension(s). Elements that are shifted beyond the
@@ -24,7 +27,7 @@ to the original shape.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2))
 x
diff --git a/man/torch_rot90.Rd b/man/torch_rot90.Rd
index 4ddc51f2b484ec6fc3b4a111b6acfa63989a5238..bb14b1b9dd8b68fc65cca0b41a7c5f06414cd9e9 100644
--- a/man/torch_rot90.Rd
+++ b/man/torch_rot90.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rot90}
 \alias{torch_rot90}
 \title{Rot90}
+\usage{
+torch_rot90(self, k = 1L, dims = c(0, 1))
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{k}{(int) number of times to rotate}
 
@@ -22,7 +25,7 @@ Rotation direction is from the first towards the second axis if k > 0, and from
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(0, 4)$view(c(2, 2))
 x
diff --git a/man/torch_round.Rd b/man/torch_round.Rd
index faac60d6b782a355feb026b524c849159896a348..05551e9b9ef504a7cbc0bf0ece7217162ade70f1 100644
--- a/man/torch_round.Rd
+++ b/man/torch_round.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_round}
 \alias{torch_round}
 \title{Round}
+\usage{
+torch_round(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Round
 }
-\section{round(input, out=None) -> Tensor }{
+\section{round(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with each of the elements of \code{input} rounded
@@ -20,7 +21,7 @@ to the closest integer.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_rrelu_.Rd b/man/torch_rrelu_.Rd
index be9c9c01e68921e4f963faa378029c02a925ff63..50c434878f38ee4a8c092eeb7723e2d254dbc980 100644
--- a/man/torch_rrelu_.Rd
+++ b/man/torch_rrelu_.Rd
@@ -1,9 +1,29 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rrelu_}
 \alias{torch_rrelu_}
 \title{Rrelu_}
+\usage{
+torch_rrelu_(
+  self,
+  lower = 0.125,
+  upper = 0.333333,
+  training = FALSE,
+  generator = NULL
+)
+}
+\arguments{
+\item{self}{the input tensor}
+
+\item{lower}{lower bound of the uniform distribution. Default: 1/8}
+
+\item{upper}{upper bound of the uniform distribution. Default: 1/3}
+
+\item{training}{bool wether it's a training pass. DEfault: FALSE}
+
+\item{generator}{random number generator}
+}
 \description{
 Rrelu_
 }
diff --git a/man/torch_rsqrt.Rd b/man/torch_rsqrt.Rd
index d37448a8badfc150e3ffcf4f89fa91f2b7fe5030..3109c1fc5489fc7948ded53f405099fcfcdbe05d 100644
--- a/man/torch_rsqrt.Rd
+++ b/man/torch_rsqrt.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_rsqrt}
 \alias{torch_rsqrt}
 \title{Rsqrt}
+\usage{
+torch_rsqrt(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Rsqrt
 }
-\section{rsqrt(input, out=None) -> Tensor }{
+\section{rsqrt(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the reciprocal of the square-root of each of
@@ -24,7 +25,7 @@ the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_save.Rd b/man/torch_save.Rd
index 44c3065999e2e0ddfae1e045a4e47942230dcaf6..8736b4f09887ac25fd64b4611eb612ddbd468af3 100644
--- a/man/torch_save.Rd
+++ b/man/torch_save.Rd
@@ -21,4 +21,5 @@ term storage.
 Other torch_save: 
 \code{\link{torch_load}()}
 }
+\concept{serialization}
 \concept{torch_save}
diff --git a/man/torch_selu.Rd b/man/torch_selu.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..efcf2e2c8a2f5038d9b333c452b04c98492ef888
--- /dev/null
+++ b/man/torch_selu.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen-namespace-docs.R, R/gen-namespace.R
+\name{torch_selu}
+\alias{torch_selu}
+\title{Selu}
+\usage{
+torch_selu(self)
+}
+\arguments{
+\item{self}{the input tensor}
+}
+\description{
+Selu
+}
+\section{selu(input) -> Tensor }{
+
+
+Computes the selu transformation.
+}
+
diff --git a/man/torch_selu_.Rd b/man/torch_selu_.Rd
index e74d3d43962b30ba7a20722477efeaa0cb182c6c..81005438b85373ada30590157bb39973b7b4e7cd 100644
--- a/man/torch_selu_.Rd
+++ b/man/torch_selu_.Rd
@@ -1,15 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_selu_}
 \alias{torch_selu_}
 \title{Selu_}
+\usage{
+torch_selu_(self)
+}
+\arguments{
+\item{self}{the input tensor}
+}
 \description{
 Selu_
 }
 \section{selu_(input) -> Tensor }{
 
 
-In-place version of \code{toch_selu}.
+In-place version of \code{\link[=torch_selu]{torch_selu()}}.
 }
 
diff --git a/man/torch_sigmoid.Rd b/man/torch_sigmoid.Rd
index e67da88c551934b39297f1f31ccadd855ff7b425..f45b8442a2e28d10ab982fd0f332eb1d38930a94 100644
--- a/man/torch_sigmoid.Rd
+++ b/man/torch_sigmoid.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sigmoid}
 \alias{torch_sigmoid}
 \title{Sigmoid}
+\usage{
+torch_sigmoid(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Sigmoid
 }
-\section{sigmoid(input, out=None) -> Tensor }{
+\section{sigmoid(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the sigmoid of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the sigmoid of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_sign.Rd b/man/torch_sign.Rd
index 7c90ef229dee9ea47cfd161029b7d93b6290c181..992776e79ae11f2e7956e2764ea5db21a99dcf40 100644
--- a/man/torch_sign.Rd
+++ b/man/torch_sign.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sign}
 \alias{torch_sign}
 \title{Sign}
+\usage{
+torch_sign(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Sign
 }
-\section{sign(input, out=None) -> Tensor }{
+\section{sign(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the signs of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the signs of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_tensor(c(0.7, -1.2, 0., 2.3))
 a
diff --git a/man/torch_sin.Rd b/man/torch_sin.Rd
index 2f6739f40aeccfbe14ee20bee85329198e5b62c3..dbad49db566ba0bcfc64118119e20a4c2cd74742 100644
--- a/man/torch_sin.Rd
+++ b/man/torch_sin.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sin}
 \alias{torch_sin}
 \title{Sin}
+\usage{
+torch_sin(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Sin
 }
-\section{sin(input, out=None) -> Tensor }{
+\section{sin(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the sine of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the sine of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_sinh.Rd b/man/torch_sinh.Rd
index 857b3625e113bdb033a88183655bb71a96e584fb..b09e7cd7d9e95533ea73a127d688c17c170e2bdf 100644
--- a/man/torch_sinh.Rd
+++ b/man/torch_sinh.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sinh}
 \alias{torch_sinh}
 \title{Sinh}
+\usage{
+torch_sinh(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Sinh
 }
-\section{sinh(input, out=None) -> Tensor }{
+\section{sinh(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the hyperbolic sine of the elements of
@@ -24,7 +25,7 @@ Returns a new tensor with the hyperbolic sine of the elements of
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_slogdet.Rd b/man/torch_slogdet.Rd
index 0a9aaa01d00144f3ba81d79dec05b8072dee2a05..49767ea68846d7ee0e933967ba47e8808860848e 100644
--- a/man/torch_slogdet.Rd
+++ b/man/torch_slogdet.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_slogdet}
 \alias{torch_slogdet}
 \title{Slogdet}
+\usage{
+torch_slogdet(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
+\item{self}{(Tensor) the input tensor of size \verb{(*, n, n)} where \code{*} is zero or more                batch dimensions.}
 }
 \description{
 Slogdet
 }
 \note{
-\preformatted{If ``input`` has zero determinant, this returns ``(0, -inf)``.
+\preformatted{If `input` has zero determinant, this returns `(0, -inf)`.
 }
 
 \preformatted{Backward through `slogdet` internally uses SVD results when `input`
@@ -27,7 +30,7 @@ Calculates the sign and log absolute value of the determinant(s) of a square mat
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(3, 3))
 A
diff --git a/man/torch_solve.Rd b/man/torch_solve.Rd
index 9050afcb6f365d2c9522e47a32f63f5b4f4920d5..0d0a66bd46425d4c18b01c5d8680da8a1b246833 100644
--- a/man/torch_solve.Rd
+++ b/man/torch_solve.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_solve}
 \alias{torch_solve}
 \title{Solve}
+\usage{
+torch_solve(self, A)
+}
 \arguments{
-\item{input}{(Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.}
+\item{self}{(Tensor) input matrix \eqn{B} of size \eqn{(*, m, k)} , where \eqn{*}                is zero or more batch dimensions.}
 
 \item{A}{(Tensor) input square matrix of size \eqn{(*, m, m)}, where                \eqn{*} is zero or more batch dimensions.}
-
-\item{out}{((Tensor, Tensor) optional output tuple.}
 }
 \description{
 Solve
@@ -17,11 +18,11 @@ Solve
 \note{
 \preformatted{Irrespective of the original strides, the returned matrices
 `solution` and `LU` will be transposed, i.e. with strides like
-`B.contiguous().transpose(-1, -2).stride()` and
-`A.contiguous().transpose(-1, -2).stride()` respectively.
+`B$contiguous()$transpose(-1, -2)$stride()` and
+`A$contiguous()$transpose(-1, -2)$stride()` respectively.
 }
 }
-\section{torch.solve(input, A, out=None) -> (Tensor, Tensor) }{
+\section{solve(input, A) -> (Tensor, Tensor) }{
 
 
 This function returns the solution to the system of linear
@@ -36,7 +37,7 @@ batched outputs \verb{solution, LU}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_tensor(rbind(c(6.80, -2.11,  5.66,  5.97,  8.23),
                       c(-6.05, -3.30,  5.36, -4.44,  1.08),
diff --git a/man/torch_sort.Rd b/man/torch_sort.Rd
index 2238df664a88e0c563861e73305bed0b589392a1..eeb3e9e19ee5473029a6dad557399fabcd698320 100644
--- a/man/torch_sort.Rd
+++ b/man/torch_sort.Rd
@@ -1,22 +1,23 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sort}
 \alias{torch_sort}
 \title{Sort}
+\usage{
+torch_sort(self, dim = -1L, descending = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int, optional) the dimension to sort along}
 
 \item{descending}{(bool, optional) controls the sorting order (ascending or descending)}
-
-\item{out}{(tuple, optional) the output tuple of (\code{Tensor}, \code{LongTensor}) that can        be optionally given to be used as output buffers}
 }
 \description{
 Sort
 }
-\section{sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor) }{
+\section{sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor) }{
 
 
 Sorts the elements of the \code{input} tensor along a given dimension
@@ -24,7 +25,7 @@ in ascending order by value.
 
 If \code{dim} is not given, the last dimension of the \code{input} is chosen.
 
-If \code{descending} is \code{True} then the elements are sorted in descending
+If \code{descending} is \code{TRUE} then the elements are sorted in descending
 order by value.
 
 A namedtuple of (values, indices) is returned, where the \code{values} are the
@@ -33,7 +34,7 @@ sorted values and \code{indices} are the indices of the elements in the original
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(3, 4))
 out = torch_sort(x)
diff --git a/man/torch_sparse_coo_tensor.Rd b/man/torch_sparse_coo_tensor.Rd
index f9778a0d2e262ee23a3a2241f89352352d0413a6..5cddf5eafba003e18a41ac987b9308748a2dd6f1 100644
--- a/man/torch_sparse_coo_tensor.Rd
+++ b/man/torch_sparse_coo_tensor.Rd
@@ -1,9 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_sparse_coo_tensor}
 \alias{torch_sparse_coo_tensor}
 \title{Sparse_coo_tensor}
+\usage{
+torch_sparse_coo_tensor(
+  indices,
+  values,
+  size = NULL,
+  dtype = NULL,
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
 \item{indices}{(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy \code{ndarray}, scalar, and other types. Will be cast to a \code{torch_LongTensor}        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.}
 
@@ -11,16 +21,16 @@
 
 \item{size}{(list, tuple, or \code{torch.Size}, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if None, infers data type from \code{values}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from \code{values}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Sparse_coo_tensor
 }
-\section{sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor }{
+\section{sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given \code{indices}
@@ -30,7 +40,7 @@ coordinates in the indices, and the value at that index is the sum of all duplic
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64())
 v = torch_tensor(c(3, 4, 5), dtype=torch_float32())
diff --git a/man/torch_split.Rd b/man/torch_split.Rd
index b6c7e2943db14e45d9dcbffb67486a34de55063e..ca222f03bc374d2877f51e6b5bbf1387bf61561e 100644
--- a/man/torch_split.Rd
+++ b/man/torch_split.Rd
@@ -1,13 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_split}
 \alias{torch_split}
 \title{Split}
+\usage{
+torch_split(self, split_size, dim = 1L)
+}
 \arguments{
-\item{tensor}{(Tensor) tensor to split.}
+\item{self}{(Tensor) tensor to split.}
 
-\item{split_size_or_sections}{(int) size of a single chunk or        list of sizes for each chunk}
+\item{split_size}{(int) size of a single chunk or        list of sizes for each chunk}
 
 \item{dim}{(int) dimension along which to split the tensor.}
 }
@@ -23,7 +26,7 @@ the tensor size along the given dimension `dim` is not divisible by
 `split_size`.
 
 If `split_size_or_sections` is a list, then `tensor` will be split
-into ``len(split_size_or_sections)`` chunks with sizes in `dim` according
+into `len(split_size_or_sections)` chunks with sizes in `dim` according
 to `split_size_or_sections`.
 }
 }
diff --git a/man/torch_sqrt.Rd b/man/torch_sqrt.Rd
index 53a7d94db8061c840bb12cf6cca0478e77093606..618a548ecf64e2913038967e740eb04e74903c68 100644
--- a/man/torch_sqrt.Rd
+++ b/man/torch_sqrt.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sqrt}
 \alias{torch_sqrt}
 \title{Sqrt}
+\usage{
+torch_sqrt(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Sqrt
 }
-\section{sqrt(input, out=None) -> Tensor }{
+\section{sqrt(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the square-root of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the square-root of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_square.Rd b/man/torch_square.Rd
index 64c3bad931176bc1b14aea9d35e379022e92e7ec..b674e341bbf512e5f43eded24beb7a0d9d4cd9f3 100644
--- a/man/torch_square.Rd
+++ b/man/torch_square.Rd
@@ -1,25 +1,26 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_square}
 \alias{torch_square}
 \title{Square}
+\usage{
+torch_square(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Square
 }
-\section{square(input, out=None) -> Tensor }{
+\section{square(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the square of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_squeeze.Rd b/man/torch_squeeze.Rd
index 923276bbdcbaf673a22dddfa0f3d57c0b348d6b0..e2f57a8345009cda90f6dbdf49de2b1bb393a39f 100644
--- a/man/torch_squeeze.Rd
+++ b/man/torch_squeeze.Rd
@@ -1,15 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_squeeze}
 \alias{torch_squeeze}
 \title{Squeeze}
+\usage{
+torch_squeeze(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int, optional) if given, the input will be squeezed only in           this dimension}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Squeeze
@@ -18,7 +19,7 @@ Squeeze
 The returned tensor shares the storage with the input tensor,
 so changing the contents of one will change the contents of the other.
 }
-\section{squeeze(input, dim=None, out=None) -> Tensor }{
+\section{squeeze(input, dim=NULL, out=NULL) -> Tensor }{
 
 
 Returns a tensor with all the dimensions of \code{input} of size \code{1} removed.
@@ -34,7 +35,7 @@ will squeeze the tensor to the shape \eqn{(A \times B)}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_zeros(c(2, 1, 2, 1, 2))
 x
diff --git a/man/torch_stack.Rd b/man/torch_stack.Rd
index 929ad9950a12b322b8f695bc21c6c7600af432b7..7910c85352bf6843fe4c1b64650d83bca14fb99b 100644
--- a/man/torch_stack.Rd
+++ b/man/torch_stack.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_stack}
 \alias{torch_stack}
 \title{Stack}
+\usage{
+torch_stack(tensors, dim = 1L)
+}
 \arguments{
 \item{tensors}{(sequence of Tensors) sequence of tensors to concatenate}
 
 \item{dim}{(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Stack
 }
-\section{stack(tensors, dim=0, out=None) -> Tensor }{
+\section{stack(tensors, dim=0, out=NULL) -> Tensor }{
 
 
 Concatenates sequence of tensors along a new dimension.
diff --git a/man/torch_std.Rd b/man/torch_std.Rd
index d77a2b69c711e2456c447a09b1c0f0987d6d2c69..1d8d67d7ab8ead7bb749c5291719aa15fbd802bf 100644
--- a/man/torch_std.Rd
+++ b/man/torch_std.Rd
@@ -1,50 +1,51 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_std}
 \alias{torch_std}
 \title{Std}
+\usage{
+torch_std(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 }
 \description{
 Std
 }
-\section{std(input, unbiased=True) -> Tensor }{
+\section{std(input, unbiased=TRUE) -> Tensor }{
 
 
 Returns the standard-deviation of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor }{
+\section{std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor }{
 
 
 Returns the standard-deviation of each row of the \code{input} tensor in the
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_std_mean.Rd b/man/torch_std_mean.Rd
index 7e6e68bf6e002d0a531eea5838c0b56a1cffbc28..65b7470b6afc8a1ca86ede9f06b74d93d34436bc 100644
--- a/man/torch_std_mean.Rd
+++ b/man/torch_std_mean.Rd
@@ -1,48 +1,51 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_std_mean}
 \alias{torch_std_mean}
 \title{Std_mean}
+\usage{
+torch_std_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 }
 \description{
 Std_mean
 }
-\section{std_mean(input, unbiased=True) -> (Tensor, Tensor) }{
+\section{std_mean(input, unbiased=TRUE) -> (Tensor, Tensor) }{
 
 
 Returns the standard-deviation and mean of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{std_mean(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor) }{
+\section{std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor) }{
 
 
 Returns the standard-deviation and mean of each row of the \code{input} tensor in the
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the standard-deviation will be calculated
+If \code{unbiased} is \code{FALSE}, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_stft.Rd b/man/torch_stft.Rd
index 3757eb866f78fffc1b58697ef0e887bdef371d3c..69d10a8127718cc02bc505c87d90b9d5f57c79d4 100644
--- a/man/torch_stft.Rd
+++ b/man/torch_stft.Rd
@@ -1,27 +1,40 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_stft}
 \alias{torch_stft}
 \title{Stft}
+\usage{
+torch_stft(
+  input,
+  n_fft,
+  hop_length = NULL,
+  win_length = NULL,
+  window = NULL,
+  center = TRUE,
+  pad_mode = "reflect",
+  normalized = FALSE,
+  onesided = TRUE
+)
+}
 \arguments{
 \item{input}{(Tensor) the input tensor}
 
 \item{n_fft}{(int) size of Fourier transform}
 
-\item{hop_length}{(int, optional) the distance between neighboring sliding window        frames. Default: \code{None} (treated as equal to \code{floor(n_fft / 4)})}
+\item{hop_length}{(int, optional) the distance between neighboring sliding window        frames. Default: \code{NULL} (treated as equal to \code{floor(n_fft / 4)})}
 
-\item{win_length}{(int, optional) the size of window frame and STFT filter.        Default: \code{None}  (treated as equal to \code{n_fft})}
+\item{win_length}{(int, optional) the size of window frame and STFT filter.        Default: \code{NULL}  (treated as equal to \code{n_fft})}
 
-\item{window}{(Tensor, optional) the optional window function.        Default: \code{None} (treated as window of all \eqn{1} s)}
+\item{window}{(Tensor, optional) the optional window function.        Default: \code{NULL} (treated as window of all \eqn{1} s)}
 
-\item{center}{(bool, optional) whether to pad \code{input} on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: \code{True}}
+\item{center}{(bool, optional) whether to pad \code{input} on both sides so        that the \eqn{t}-th frame is centered at time \eqn{t \times \mbox{hop\_length}}.        Default: \code{TRUE}}
 
-\item{pad_mode}{(string, optional) controls the padding method used when        \code{center} is \code{True}. Default: \code{"reflect"}}
+\item{pad_mode}{(string, optional) controls the padding method used when        \code{center} is \code{TRUE}. Default: \code{"reflect"}}
 
-\item{normalized}{(bool, optional) controls whether to return the normalized STFT results         Default: \code{False}}
+\item{normalized}{(bool, optional) controls whether to return the normalized STFT results         Default: \code{FALSE}}
 
-\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy Default: \code{True}}
+\item{onesided}{(bool, optional) controls whether to return half of results to        avoid redundancy Default: \code{TRUE}}
 }
 \description{
 Stft
@@ -40,36 +53,36 @@ expression:
 }
 where \eqn{m} is the index of the sliding window, and \eqn{\omega} is
 the frequency that \eqn{0 \leq \omega < \mbox{n\_fft}}. When
-\code{onesided} is the default value \code{True},\preformatted{* `input` must be either a 1-D time sequence or a 2-D batch of time
+\code{onesided} is the default value \code{TRUE},\preformatted{* `input` must be either a 1-D time sequence or a 2-D batch of time
   sequences.
 
-* If `hop_length` is ``None`` (default), it is treated as equal to
-  ``floor(n_fft / 4)``.
+* If `hop_length` is `NULL` (default), it is treated as equal to
+  `floor(n_fft / 4)`.
 
-* If `win_length` is ``None`` (default), it is treated as equal to
+* If `win_length` is `NULL` (default), it is treated as equal to
   `n_fft`.
 
 * `window` can be a 1-D tensor of size `win_length`, e.g., from
-  `torch_hann_window`. If `window` is ``None`` (default), it is
+  `torch_hann_window`. If `window` is `NULL` (default), it is
   treated as if having \eqn{1} everywhere in the window. If
   \eqn{\mbox{win\_length} < \mbox{n\_fft}}, `window` will be padded on
   both sides to length `n_fft` before being applied.
 
-* If `center` is ``True`` (default), `input` will be padded on
+* If `center` is `TRUE` (default), `input` will be padded on
   both sides so that the \eqn{t}-th frame is centered at time
   \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
   begins at time  \eqn{t \times \mbox{hop\_length}}.
 
 * `pad_mode` determines the padding method used on `input` when
-  `center` is ``True``. See `torch_nn.functional.pad` for
-  all available options. Default is ``"reflect"``.
+  `center` is `TRUE`. See `torch_nn.functional.pad` for
+  all available options. Default is `"reflect"`.
 
-* If `onesided` is ``True`` (default), only values for \eqn{\omega}
+* If `onesided` is `TRUE` (default), only values for \eqn{\omega}
   in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
   are returned because the real-to-complex Fourier transform satisfies the
   conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
 
-* If `normalized` is ``True`` (default is ``False``), the function
+* If `normalized` is `TRUE` (default is `FALSE`), the function
   returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
 
 Returns the real and the imaginary parts together as one tensor of size
@@ -78,10 +91,12 @@ batch size of `input`, \eqn{N} is the number of frequencies where
 STFT is applied, \eqn{T} is the total number of frames used, and each pair
 in the last dimension represents a complex number as the real part and the
 imaginary part.
-
-.. warning::
-  This function changed signature at version 0.4.1. Calling with the
-  previous signature may cause error or return incorrect result.
 }
 }
 
+\section{Warning}{
+
+This function changed signature at version 0.4.1. Calling with the
+previous signature may cause error or return incorrect result.
+}
+
diff --git a/man/torch_sum.Rd b/man/torch_sum.Rd
index 990de622fec91281d837b9fec8acd38858ab8738..5f9b06c8952b18b66d75d9a8a44c1452d0fca800 100644
--- a/man/torch_sum.Rd
+++ b/man/torch_sum.Rd
@@ -1,42 +1,45 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_sum}
 \alias{torch_sum}
 \title{Sum}
+\usage{
+torch_sum(self, dim, keepdim = FALSE, dtype = NULL)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: None.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
+
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to \code{dtype} before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.}
 }
 \description{
 Sum
 }
-\section{sum(input, dtype=None) -> Tensor }{
+\section{sum(input, dtype=NULL) -> Tensor }{
 
 
 Returns the sum of all elements in the \code{input} tensor.
 }
 
-\section{sum(input, dim, keepdim=False, dtype=None) -> Tensor }{
+\section{sum(input, dim, keepdim=False, dtype=NULL) -> Tensor }{
 
 
 Returns the sum of each row of the \code{input} tensor in the given
 dimension \code{dim}. If \code{dim} is a list of dimensions,
 reduce over all of them.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_svd.Rd b/man/torch_svd.Rd
index 1ae3bdca99cbac054dc089ed4a2d562bc6d68e7b..924ae245e86cf536e166fd20779cec6b92df8e00 100644
--- a/man/torch_svd.Rd
+++ b/man/torch_svd.Rd
@@ -1,17 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_svd}
 \alias{torch_svd}
 \title{Svd}
+\usage{
+torch_svd(self, some = TRUE, compute_uv = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, m, n)} where \code{*} is zero or more                    batch dimensions consisting of \eqn{m \times n} matrices.}
 
 \item{some}{(bool, optional) controls the shape of returned \code{U} and \code{V}}
 
 \item{compute_uv}{(bool, optional) option whether to compute \code{U} and \code{V} or not}
-
-\item{out}{(tuple, optional) the output tuple of tensors}
 }
 \description{
 Svd
@@ -34,30 +35,30 @@ appear as the gradients are not properly defined. Also, notice that
 double backward will usually do an additional backward through \code{U} and
 \code{V} even if the original backward is only on \code{S}.
 
-When \code{some} = \code{False}, the gradients on \verb{U[..., :, min(m, n):]}
+When \code{some} = \code{FALSE}, the gradients on \verb{U[..., :, min(m, n):]}
 and \verb{V[..., :, min(m, n):]} will be ignored in backward as those vectors
 can be arbitrary bases of the subspaces.
 
-When \code{compute_uv} = \code{False}, backward cannot be performed since \code{U} and \code{V}
+When \code{compute_uv} = \code{FALSE}, backward cannot be performed since \code{U} and \code{V}
 from the forward pass is required for the backward operation.
 }
-\section{svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) }{
+\section{svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) }{
 
 
 This function returns a namedtuple \verb{(U, S, V)} which is the singular value
 decomposition of a input real matrix or batches of real matrices \code{input} such that
 \eqn{input = U \times diag(S) \times V^T}.
 
-If \code{some} is \code{True} (default), the method returns the reduced singular value decomposition
+If \code{some} is \code{TRUE} (default), the method returns the reduced singular value decomposition
 i.e., if the last two dimensions of \code{input} are \code{m} and \code{n}, then the returned
 \code{U} and \code{V} matrices will contain only \eqn{min(n, m)} orthonormal columns.
 
-If \code{compute_uv} is \code{False}, the returned \code{U} and \code{V} matrices will be zero matrices
+If \code{compute_uv} is \code{FALSE}, the returned \code{U} and \code{V} matrices will be zero matrices
 of shape \eqn{(m \times m)} and \eqn{(n \times n)} respectively. \code{some} will be ignored here.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5, 3))
 a
diff --git a/man/torch_symeig.Rd b/man/torch_symeig.Rd
index dd68211187ae5942c07f71976be7c4145347aa46..6be0c5944a10d6697835fc735be3889bfb92a4f4 100644
--- a/man/torch_symeig.Rd
+++ b/man/torch_symeig.Rd
@@ -1,17 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_symeig}
 \alias{torch_symeig}
 \title{Symeig}
+\usage{
+torch_symeig(self, eigenvectors = FALSE, upper = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions consisting of symmetric matrices.}
+\item{self}{(Tensor) the input tensor of size \eqn{(*, n, n)} where \code{*} is zero or more                    batch dimensions consisting of symmetric matrices.}
 
 \item{eigenvectors}{(boolean, optional) controls whether eigenvectors have to be computed}
 
 \item{upper}{(boolean, optional) controls whether to consider upper-triangular or lower-triangular region}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, Tensor)}
 }
 \description{
 Symeig
@@ -27,7 +28,7 @@ Extra care needs to be taken when backward through outputs. Such
 operation is really only stable when all eigenvalues are distinct.
 Otherwise, \code{NaN} can appear as the gradients are not properly defined.
 }
-\section{symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) }{
+\section{symeig(input, eigenvectors=False, upper=TRUE) -> (Tensor, Tensor) }{
 
 
 This function returns eigenvalues and eigenvectors
@@ -40,17 +41,17 @@ such that \eqn{\mbox{input} = V \mbox{diag}(e) V^T}.
 The boolean argument \code{eigenvectors} defines computation of
 both eigenvectors and eigenvalues or eigenvalues only.
 
-If it is \code{False}, only eigenvalues are computed. If it is \code{True},
+If it is \code{FALSE}, only eigenvalues are computed. If it is \code{TRUE},
 both eigenvalues and eigenvectors are computed.
 
 Since the input matrix \code{input} is supposed to be symmetric,
 only the upper triangular portion is used by default.
 
-If \code{upper} is \code{False}, then lower triangular portion is used.
+If \code{upper} is \code{FALSE}, then lower triangular portion is used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(5, 5))
 a = a + a$t()  # To make a symmetric
diff --git a/man/torch_t.Rd b/man/torch_t.Rd
index e09c651f6e225c09fd4b323e04285385efa874dc..ccaa48f7cfe414f72a004abd37a96955dcc71ecf 100644
--- a/man/torch_t.Rd
+++ b/man/torch_t.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_t}
 \alias{torch_t}
 \title{T}
+\usage{
+torch_t(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 T
@@ -21,7 +24,7 @@ is equivalent to \code{transpose(input, 0, 1)}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(2,3))
 x
diff --git a/man/torch_take.Rd b/man/torch_take.Rd
index 3c5ae3c8342b5d033b4cdb14a95cbfe4c8a0ec1e..b7442a56a98934c0c1c8a81c2af22e60a1868443 100644
--- a/man/torch_take.Rd
+++ b/man/torch_take.Rd
@@ -1,13 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_take}
 \alias{torch_take}
 \title{Take}
+\usage{
+torch_take(self, index)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
-\item{indices}{(LongTensor) the indices into tensor}
+\item{index}{(LongTensor) the indices into tensor}
 }
 \description{
 Take
@@ -21,9 +24,9 @@ takes the same shape as the indices.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
-torch_take(src, torch_tensor(c(0, 2, 5), dtype = torch_int64()))
+torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64()))
 }
 }
diff --git a/man/torch_tan.Rd b/man/torch_tan.Rd
index 6a0a52ed6e8ed0421e4f0d439935e477c81272ac..63e59d3e3665783df907292798bcd5f7c2b654c7 100644
--- a/man/torch_tan.Rd
+++ b/man/torch_tan.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tan}
 \alias{torch_tan}
 \title{Tan}
+\usage{
+torch_tan(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Tan
 }
-\section{tan(input, out=None) -> Tensor }{
+\section{tan(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the tangent of the elements of \code{input}.
@@ -23,7 +24,7 @@ Returns a new tensor with the tangent of the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_tanh.Rd b/man/torch_tanh.Rd
index 3093397bb79e3f78464d4eb13e7bbe56ff415512..10aebdbc6eb9e8321f6215420e4f11a22751a7b1 100644
--- a/man/torch_tanh.Rd
+++ b/man/torch_tanh.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tanh}
 \alias{torch_tanh}
 \title{Tanh}
+\usage{
+torch_tanh(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Tanh
 }
-\section{tanh(input, out=None) -> Tensor }{
+\section{tanh(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the hyperbolic tangent of the elements
@@ -24,7 +25,7 @@ of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_tensor.Rd b/man/torch_tensor.Rd
index e68c9d0a40998b04c5121f10cddaa2300f73315c..ae1312ce5ab47366ac1813fc015422be9d965710 100644
--- a/man/torch_tensor.Rd
+++ b/man/torch_tensor.Rd
@@ -27,7 +27,7 @@ torch_tensor(
 Converts R objects to a torch tensor
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 torch_tensor(c(1,2,3,4))
 torch_tensor(c(1,2,3,4), dtype = torch_int())
 
diff --git a/man/torch_tensordot.Rd b/man/torch_tensordot.Rd
index 3eb123238003bbf12114397d642eeab63ab1a042..362089c9a5890ad71f5bbdb022f731ac6b858419 100644
--- a/man/torch_tensordot.Rd
+++ b/man/torch_tensordot.Rd
@@ -1,9 +1,12 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_tensordot}
 \alias{torch_tensordot}
 \title{Tensordot}
+\usage{
+torch_tensordot(a, b, dims = 2)
+}
 \arguments{
 \item{a}{(Tensor) Left tensor to contract}
 
@@ -12,21 +15,15 @@
 \item{dims}{(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for \code{a} and     \code{b} respectively}
 }
 \description{
-Tensordot
-}
-\section{TEST }{
-
-
-Returns a contraction of a and b over multiple dimensions.\preformatted{`tensordot` implements a generalized matrix product.
+Returns a contraction of a and b over multiple dimensions.
+\code{tensordot} implements a generalized matrix product.
 }
-}
-
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
 b = torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-torch_tensordot(a, b, dims_self=c(1, 0), dims_other = c(0, 1))
+torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2)))
 \dontrun{
 a = torch_randn(3, 4, 5, device='cuda')
 b = torch_randn(4, 5, 6, device='cuda')
diff --git a/man/torch_threshold_.Rd b/man/torch_threshold_.Rd
index f93e84d64374c53884955454eefb77b5475cd7da..f09c561991beaaddf93c8b7321593ad1a4ab347a 100644
--- a/man/torch_threshold_.Rd
+++ b/man/torch_threshold_.Rd
@@ -1,9 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_threshold_}
 \alias{torch_threshold_}
 \title{Threshold_}
+\usage{
+torch_threshold_(self, threshold, value)
+}
+\arguments{
+\item{self}{input tensor}
+
+\item{threshold}{The value to threshold at}
+
+\item{value}{The value to replace with}
+}
 \description{
 Threshold_
 }
diff --git a/man/torch_topk.Rd b/man/torch_topk.Rd
index c3aa8d7e5b96041c0e049bfda2d28cf1a1a2edfb..cd138938c2f8070164b2bcf35caa08e05958eda5 100644
--- a/man/torch_topk.Rd
+++ b/man/torch_topk.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_topk}
 \alias{torch_topk}
 \title{Topk}
+\usage{
+torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{k}{(int) the k in "top-k"}
 
@@ -14,13 +17,11 @@
 \item{largest}{(bool, optional) controls whether to return largest or           smallest elements}
 
 \item{sorted}{(bool, optional) controls whether to return the elements           in sorted order}
-
-\item{out}{(tuple, optional) the output tuple of (Tensor, LongTensor) that can be        optionally given to be used as output buffers}
 }
 \description{
 Topk
 }
-\section{topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) }{
+\section{topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) }{
 
 
 Returns the \code{k} largest elements of the given \code{input} tensor along
@@ -28,17 +29,17 @@ a given dimension.
 
 If \code{dim} is not given, the last dimension of the \code{input} is chosen.
 
-If \code{largest} is \code{False} then the \code{k} smallest elements are returned.
+If \code{largest} is \code{FALSE} then the \code{k} smallest elements are returned.
 
 A namedtuple of \verb{(values, indices)} is returned, where the \code{indices} are the indices
 of the elements in the original \code{input} tensor.
 
-The boolean option \code{sorted} if \code{True}, will make sure that the returned
+The boolean option \code{sorted} if \code{TRUE}, will make sure that the returned
 \code{k} elements are themselves sorted
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(1., 6.)
 x
diff --git a/man/torch_trace.Rd b/man/torch_trace.Rd
index b35f4a2194ac144085ac594d21a1d733667700d7..23e2b02613af7255affc814b88b60c26c08baddd 100644
--- a/man/torch_trace.Rd
+++ b/man/torch_trace.Rd
@@ -1,9 +1,15 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_trace}
 \alias{torch_trace}
 \title{Trace}
+\usage{
+torch_trace(self)
+}
+\arguments{
+\item{self}{the input tensor}
+}
 \description{
 Trace
 }
@@ -14,7 +20,7 @@ Returns the sum of the elements of the diagonal of the input 2-D matrix.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_arange(1., 10.)$view(c(3, 3))
 x
diff --git a/man/torch_transpose.Rd b/man/torch_transpose.Rd
index b7e2e5d7f8f0c2e0a688ed2fa1ceb56f00118d87..425ea49e560dbd463c24cd5256a2530e5827524d 100644
--- a/man/torch_transpose.Rd
+++ b/man/torch_transpose.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_transpose}
 \alias{torch_transpose}
 \title{Transpose}
+\usage{
+torch_transpose(self, dim0, dim1)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim0}{(int) the first dimension to be transposed}
 
@@ -26,7 +29,7 @@ of the other.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_randn(c(2, 3))
 x
diff --git a/man/torch_trapz.Rd b/man/torch_trapz.Rd
index 824a276e632f079dc4cc3bb9e5fd4327bf032ee9..8c3916657f64b13077ba8a97feca38a777b879d7 100644
--- a/man/torch_trapz.Rd
+++ b/man/torch_trapz.Rd
@@ -1,17 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_trapz}
 \alias{torch_trapz}
 \title{Trapz}
+\usage{
+torch_trapz(y, dx = 1L, x, dim = -1L)
+}
 \arguments{
 \item{y}{(Tensor) The values of the function to integrate}
 
+\item{dx}{(float) The distance between points at which \code{y} is sampled.}
+
 \item{x}{(Tensor) The points at which the function \code{y} is sampled.        If \code{x} is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \eqn{\int_a^b f = -\int_b^a f} is followed).}
 
 \item{dim}{(int) The dimension along which to integrate.        By default, use the last dimension.}
-
-\item{dx}{(float) The distance between points at which \code{y} is sampled.}
 }
 \description{
 Trapz
@@ -29,7 +32,7 @@ As above, but the sample points are spaced uniformly at a distance of \code{dx}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 y = torch_randn(list(2, 3))
 y
diff --git a/man/torch_triangular_solve.Rd b/man/torch_triangular_solve.Rd
index c5c7bf0d4a0af920ef26cb6dd781224701e17c7d..2bef20a901892d883e7248c13a973abac67f81da 100644
--- a/man/torch_triangular_solve.Rd
+++ b/man/torch_triangular_solve.Rd
@@ -1,24 +1,33 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_triangular_solve}
 \alias{torch_triangular_solve}
 \title{Triangular_solve}
+\usage{
+torch_triangular_solve(
+  self,
+  A,
+  upper = TRUE,
+  transpose = FALSE,
+  unitriangular = FALSE
+)
+}
 \arguments{
-\item{input}{(Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where                \eqn{*} is zero of more batch dimensions (\eqn{b})}
+\item{self}{(Tensor) multiple right-hand sides of size \eqn{(*, m, k)} where                \eqn{*} is zero of more batch dimensions (\eqn{b})}
 
 \item{A}{(Tensor) the input triangular coefficient matrix of size \eqn{(*, m, m)}                where \eqn{*} is zero or more batch dimensions}
 
-\item{upper}{(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: \code{True}.}
+\item{upper}{(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: \code{TRUE}.}
 
-\item{transpose}{(bool, optional) whether \eqn{A} should be transposed before        being sent into the solver. Default: \code{False}.}
+\item{transpose}{(bool, optional) whether \eqn{A} should be transposed before        being sent into the solver. Default: \code{FALSE}.}
 
-\item{unitriangular}{(bool, optional) whether \eqn{A} is unit triangular.        If True, the diagonal elements of \eqn{A} are assumed to be        1 and not referenced from \eqn{A}. Default: \code{False}.}
+\item{unitriangular}{(bool, optional) whether \eqn{A} is unit triangular.        If TRUE, the diagonal elements of \eqn{A} are assumed to be        1 and not referenced from \eqn{A}. Default: \code{FALSE}.}
 }
 \description{
 Triangular_solve
 }
-\section{triangular_solve(input, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor) }{
+\section{triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor) }{
 
 
 Solves a system of equations with a triangular coefficient matrix \eqn{A}
@@ -33,7 +42,7 @@ batched outputs \code{X}
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 A = torch_randn(c(2, 2))$triu()
 A
diff --git a/man/torch_tril.Rd b/man/torch_tril.Rd
index fa1ff34e8e2366ec773fde5ba9469fcf9bf0d8ca..700e50b019b6f8428299a999c8853781c890db0b 100644
--- a/man/torch_tril.Rd
+++ b/man/torch_tril.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_tril}
 \alias{torch_tril}
 \title{Tril}
+\usage{
+torch_tril(self, diagonal = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Tril
 }
-\section{tril(input, diagonal=0, out=None) -> Tensor }{
+\section{tril(input, diagonal=0, out=NULL) -> Tensor }{
 
 
 Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
@@ -33,7 +34,7 @@ the main diagonal. The main diagonal are the set of indices
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a
diff --git a/man/torch_tril_indices.Rd b/man/torch_tril_indices.Rd
index 92982a1be206430b458dcbd71fe72cc597c07e10..7fe8400294c617d4734cb9a251c14c35c325d4dc 100644
--- a/man/torch_tril_indices.Rd
+++ b/man/torch_tril_indices.Rd
@@ -1,9 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_tril_indices}
 \alias{torch_tril_indices}
 \title{Tril_indices}
+\usage{
+torch_tril_indices(
+  row,
+  col,
+  offset = 0,
+  dtype = torch_long(),
+  device = "cpu",
+  layout = torch_strided()
+)
+}
 \arguments{
 \item{row}{(\code{int}) number of rows in the 2-D matrix.}
 
@@ -11,9 +21,9 @@
 
 \item{offset}{(\code{int}) diagonal offset from the main diagonal.        Default: if not provided, 0.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, \code{torch_long}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, \code{torch_long}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{layout}{(\code{torch.layout}, optional) currently only support \code{torch_strided}.}
 }
@@ -21,7 +31,7 @@
 Tril_indices
 }
 \note{
-\preformatted{When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
+\preformatted{When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 prevent overflow during calculation.
 }
 }
@@ -46,7 +56,7 @@ where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 a = torch_tril_indices(3, 3)
 a
diff --git a/man/torch_triu.Rd b/man/torch_triu.Rd
index b2579cf1ad801606e385e5662eaf5867b067ef58..c0e5f26bdd6954521d914d845c0aab63c0158cfa 100644
--- a/man/torch_triu.Rd
+++ b/man/torch_triu.Rd
@@ -1,20 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_triu}
 \alias{torch_triu}
 \title{Triu}
+\usage{
+torch_triu(self, diagonal = 0L)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{diagonal}{(int, optional) the diagonal to consider}
-
-\item{out}{(Tensor, optional) the output tensor.}
 }
 \description{
 Triu
 }
-\section{triu(input, diagonal=0, out=None) -> Tensor }{
+\section{triu(input, diagonal=0, out=NULL) -> Tensor }{
 
 
 Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
@@ -33,7 +34,7 @@ the main diagonal. The main diagonal are the set of indices
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(3, 3))
 a
diff --git a/man/torch_triu_indices.Rd b/man/torch_triu_indices.Rd
index 60438b283c853eb5a0ea9c11b754895dcde4e53c..64e2515888e4e168d82337a1aaf033db202185f7 100644
--- a/man/torch_triu_indices.Rd
+++ b/man/torch_triu_indices.Rd
@@ -1,9 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/wrapers.R
 \name{torch_triu_indices}
 \alias{torch_triu_indices}
 \title{Triu_indices}
+\usage{
+torch_triu_indices(
+  row,
+  col,
+  offset = 0,
+  dtype = torch_long(),
+  device = "cpu",
+  layout = torch_strided()
+)
+}
 \arguments{
 \item{row}{(\code{int}) number of rows in the 2-D matrix.}
 
@@ -11,9 +21,9 @@
 
 \item{offset}{(\code{int}) diagonal offset from the main diagonal.        Default: if not provided, 0.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, \code{torch_long}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, \code{torch_long}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
 \item{layout}{(\code{torch.layout}, optional) currently only support \code{torch_strided}.}
 }
@@ -21,7 +31,7 @@
 Triu_indices
 }
 \note{
-\preformatted{When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
+\preformatted{When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 prevent overflow during calculation.
 }
 }
@@ -46,7 +56,7 @@ where \eqn{d_{1}, d_{2}} are the dimensions of the matrix.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 \dontrun{
 a = torch_triu_indices(3, 3)
 a
diff --git a/man/torch_true_divide.Rd b/man/torch_true_divide.Rd
index 7e5a27e5ab80b631520c80edbc4c4f462152e8bd..667c9a69d24b7d13db15321ee0fdcaf2fae12a29 100644
--- a/man/torch_true_divide.Rd
+++ b/man/torch_true_divide.Rd
@@ -1,16 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_true_divide}
 \alias{torch_true_divide}
-\title{True_divide}
+\title{TRUE_divide}
+\usage{
+torch_true_divide(self, other)
+}
 \arguments{
-\item{dividend}{(Tensor) the dividend}
+\item{self}{(Tensor) the dividend}
 
-\item{divisor}{(Tensor or Scalar) the divisor}
+\item{other}{(Tensor or Scalar) the divisor}
 }
 \description{
-True_divide
+TRUE_divide
 }
 \section{true_divide(dividend, divisor) -> Tensor }{
 
@@ -26,7 +29,7 @@ in which case they are cast to the default (floating) scalar type before the div
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 dividend = torch_tensor(c(5, 3), dtype=torch_int())
 divisor = torch_tensor(c(3, 2), dtype=torch_int())
diff --git a/man/torch_trunc.Rd b/man/torch_trunc.Rd
index 27ec1c2fd66961f5a9a53f11691bac92779890c6..95665a1606c43afa0d3b9a44649a2cbf960ddc7a 100644
--- a/man/torch_trunc.Rd
+++ b/man/torch_trunc.Rd
@@ -1,18 +1,19 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_trunc}
 \alias{torch_trunc}
 \title{Trunc}
+\usage{
+torch_trunc(self)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{out}{(Tensor, optional) the output tensor.}
+\item{self}{(Tensor) the input tensor.}
 }
 \description{
 Trunc
 }
-\section{trunc(input, out=None) -> Tensor }{
+\section{trunc(input, out=NULL) -> Tensor }{
 
 
 Returns a new tensor with the truncated integer values of
@@ -20,7 +21,7 @@ the elements of \code{input}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(4))
 a
diff --git a/man/torch_unbind.Rd b/man/torch_unbind.Rd
index da95839a4712d3df3c61e340c8eb08184b6716da..5a47d648ad9449666d8a0017308f0c0807949be6 100644
--- a/man/torch_unbind.Rd
+++ b/man/torch_unbind.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_unbind}
 \alias{torch_unbind}
 \title{Unbind}
+\usage{
+torch_unbind(self, dim = 1L)
+}
 \arguments{
-\item{input}{(Tensor) the tensor to unbind}
+\item{self}{(Tensor) the tensor to unbind}
 
 \item{dim}{(int) dimension to remove}
 }
@@ -21,7 +24,7 @@ Returns a tuple of all slices along a given dimension, already without it.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE)))
 }
diff --git a/man/torch_unique_consecutive.Rd b/man/torch_unique_consecutive.Rd
index 3c55f9964fd5d5f3f2faf6938b41417b4457e5c5..b34752edd66c20763943604a8dc9d4b50c31ffe5 100644
--- a/man/torch_unique_consecutive.Rd
+++ b/man/torch_unique_consecutive.Rd
@@ -1,17 +1,25 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_unique_consecutive}
 \alias{torch_unique_consecutive}
 \title{Unique_consecutive}
+\usage{
+torch_unique_consecutive(
+  self,
+  return_inverse = FALSE,
+  return_counts = FALSE,
+  dim = NULL
+)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor}
+\item{self}{(Tensor) the input tensor}
 
 \item{return_inverse}{(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.}
 
 \item{return_counts}{(bool) Whether to also return the counts for each unique        element.}
 
-\item{dim}{(int) the dimension to apply unique. If \code{None}, the unique of the        flattened input is returned. default: \code{None}}
+\item{dim}{(int) the dimension to apply unique. If \code{NULL}, the unique of the        flattened input is returned. default: \code{NULL}}
 }
 \description{
 Unique_consecutive
@@ -26,7 +34,7 @@ Eliminates all but the first element from every consecutive group of equivalent
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2))
 output = torch_unique_consecutive(x)
 output
diff --git a/man/torch_unsqueeze.Rd b/man/torch_unsqueeze.Rd
index 8f082b84d7fadf3ecfb2a51ce0b148ab5483623b..12154c973c7d6c6c6c30dc869faa0c48230ef161 100644
--- a/man/torch_unsqueeze.Rd
+++ b/man/torch_unsqueeze.Rd
@@ -1,11 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_unsqueeze}
 \alias{torch_unsqueeze}
 \title{Unsqueeze}
+\usage{
+torch_unsqueeze(self, dim)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int) the index at which to insert the singleton dimension}
 }
@@ -26,7 +29,7 @@ applied at \code{dim} = \code{dim + input.dim() + 1}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x = torch_tensor(c(1, 2, 3, 4))
 torch_unsqueeze(x, 1)
diff --git a/man/torch_var.Rd b/man/torch_var.Rd
index c3a8fbc62781a2634a814382a440dd6586ffd4b3..ad5462719482537d99d1fa2031fc10c388852477 100644
--- a/man/torch_var.Rd
+++ b/man/torch_var.Rd
@@ -1,49 +1,50 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_var}
 \alias{torch_var}
 \title{Var}
+\usage{
+torch_var(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
-\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 }
 \description{
 Var
 }
-\section{var(input, unbiased=True) -> Tensor }{
+\section{var(input, unbiased=TRUE) -> Tensor }{
 
 
 Returns the variance of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor }{
+\section{var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor }{
 
 
 Returns the variance of each row of the \code{input} tensor in the given
 dimension \code{dim}.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_var_mean.Rd b/man/torch_var_mean.Rd
index 9e959a0f71b7c8450913d972dbd42305699db5f6..4cda19876ae09fdd428f68dcfd0c4e5a79e265d4 100644
--- a/man/torch_var_mean.Rd
+++ b/man/torch_var_mean.Rd
@@ -1,47 +1,50 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_var_mean}
 \alias{torch_var_mean}
 \title{Var_mean}
+\usage{
+torch_var_mean(self, dim, unbiased = TRUE, keepdim = FALSE)
+}
 \arguments{
-\item{input}{(Tensor) the input tensor.}
-
-\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+\item{self}{(Tensor) the input tensor.}
 
 \item{dim}{(int or tuple of ints) the dimension or dimensions to reduce.}
 
+\item{unbiased}{(bool) whether to use the unbiased estimation or not}
+
 \item{keepdim}{(bool) whether the output tensor has \code{dim} retained or not.}
 }
 \description{
 Var_mean
 }
-\section{var_mean(input, unbiased=True) -> (Tensor, Tensor) }{
+\section{var_mean(input, unbiased=TRUE) -> (Tensor, Tensor) }{
 
 
 Returns the variance and mean of all elements in the \code{input} tensor.
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
-\section{var_mean(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor) }{
+\section{var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor) }{
 
 
 Returns the variance and mean of each row of the \code{input} tensor in the given
 dimension \code{dim}.
 
-If \code{keepdim} is \code{True}, the output tensor is of the same size
+If \code{keepdim} is \code{TRUE}, the output tensor is of the same size
 as \code{input} except in the dimension(s) \code{dim} where it is of size 1.
 Otherwise, \code{dim} is squeezed (see \code{\link{torch_squeeze}}), resulting in the
 output tensor having 1 (or \code{len(dim)}) fewer dimension(s).
 
-If \code{unbiased} is \code{False}, then the variance will be calculated via the
+If \code{unbiased} is \code{FALSE}, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 a = torch_randn(c(1, 3))
 a
diff --git a/man/torch_where.Rd b/man/torch_where.Rd
index f368ad083126bf7443badfe2c16221abce325e50..e3937156aced40fb2ddec527f78a03630983ed4c 100644
--- a/man/torch_where.Rd
+++ b/man/torch_where.Rd
@@ -1,15 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/gen-namespace-docs.R,
-%   R/gen-namespace-examples.R
+%   R/gen-namespace-examples.R, R/gen-namespace.R
 \name{torch_where}
 \alias{torch_where}
 \title{Where}
+\usage{
+torch_where(condition, self, other)
+}
 \arguments{
-\item{condition}{(BoolTensor) When True (nonzero), yield x, otherwise yield y}
+\item{condition}{(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y}
 
-\item{x}{(Tensor) values selected at indices where \code{condition} is \code{True}}
+\item{self}{(Tensor) values selected at indices where \code{condition} is \code{TRUE}}
 
-\item{y}{(Tensor) values selected at indices where \code{condition} is \code{False}}
+\item{other}{(Tensor) values selected at indices where \code{condition} is \code{FALSE}}
 }
 \description{
 Where
@@ -18,8 +21,7 @@ Where
 \preformatted{The tensors `condition`, `x`, `y` must be broadcastable .
 }
 
-\preformatted{See also [`torch_nonzero`].
-}
+See also \code{\link[=torch_nonzero]{torch_nonzero()}}.
 }
 \section{where(condition, x, y) -> Tensor }{
 
@@ -41,11 +43,11 @@ The operation is defined as:
 
 
 \code{torch_where(condition)} is identical to
-\code{torch_nonzero(condition, as_tuple=True)}.
+\code{torch_nonzero(condition, as_tuple=TRUE)}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 \dontrun{
 x = torch_randn(c(3, 2))
diff --git a/man/torch_zeros.Rd b/man/torch_zeros.Rd
index 46b4ea41ec8e85f7e2a34227dfe7352b055468c0..af44a5138752309696d12b34faeb1f38347892fe 100644
--- a/man/torch_zeros.Rd
+++ b/man/torch_zeros.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_zeros}
 \alias{torch_zeros}
 \title{Zeros}
+\usage{
+torch_zeros(
+  ...,
+  names = NULL,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE
+)
+}
 \arguments{
-\item{size}{(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
+\item{...}{a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.}
 
-\item{out}{(Tensor, optional) the output tensor.}
+\item{names}{optional dimension names}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{None}, uses a global default (see \code{torch_set_default_tensor_type}).}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned tensor.        Default: if \code{NULL}, uses a global default (see \code{torch_set_default_tensor_type}).}
 
 \item{layout}{(\code{torch.layout}, optional) the desired layout of returned Tensor.        Default: \code{torch_strided}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, uses the current device for the default tensor type        (see \code{torch_set_default_tensor_type}). \code{device} will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 }
 \description{
 Zeros
 }
-\section{zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor }{
+\section{zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{0}, with the shape defined
@@ -28,7 +38,7 @@ by the variable argument \code{size}.
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 torch_zeros(c(2, 3))
 torch_zeros(c(5))
diff --git a/man/torch_zeros_like.Rd b/man/torch_zeros_like.Rd
index cc5915865e82d0f1f1116a91df5186305aa844a8..bdbd430bc782fd07498c3d3462372d674a3296b4 100644
--- a/man/torch_zeros_like.Rd
+++ b/man/torch_zeros_like.Rd
@@ -1,26 +1,36 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gen-namespace-docs.R,
+% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
 %   R/gen-namespace-examples.R
 \name{torch_zeros_like}
 \alias{torch_zeros_like}
 \title{Zeros_like}
+\usage{
+torch_zeros_like(
+  input,
+  dtype = NULL,
+  layout = torch_strided(),
+  device = NULL,
+  requires_grad = FALSE,
+  memory_format = torch_preserve_format()
+)
+}
 \arguments{
 \item{input}{(Tensor) the size of \code{input} will determine size of the output tensor.}
 
-\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{None}, defaults to the dtype of \code{input}.}
+\item{dtype}{(\code{torch.dtype}, optional) the desired data type of returned Tensor.        Default: if \code{NULL}, defaults to the dtype of \code{input}.}
 
-\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{None}, defaults to the layout of \code{input}.}
+\item{layout}{(\code{torch.layout}, optional) the desired layout of returned tensor.        Default: if \code{NULL}, defaults to the layout of \code{input}.}
 
-\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{None}, defaults to the device of \code{input}.}
+\item{device}{(\code{torch.device}, optional) the desired device of returned tensor.        Default: if \code{NULL}, defaults to the device of \code{input}.}
 
-\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{False}.}
+\item{requires_grad}{(bool, optional) If autograd should record operations on the        returned tensor. Default: \code{FALSE}.}
 
 \item{memory_format}{(\code{torch.memory_format}, optional) the desired memory format of        returned Tensor. Default: \code{torch_preserve_format}.}
 }
 \description{
 Zeros_like
 }
-\section{zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
+\section{zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor }{
 
 
 Returns a tensor filled with the scalar value \code{0}, with the same size as
@@ -36,7 +46,7 @@ the old \code{torch_zeros_like(input, out=output)} is equivalent to
 }
 
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 input = torch_empty(c(2, 3))
 torch_zeros_like(input)
diff --git a/man/with_enable_grad.Rd b/man/with_enable_grad.Rd
index 11fd30cb626d926810edddbea5142e115748b3a5..3b93fd56061efe8dae9ff608ae29d14c1c13b5c1 100644
--- a/man/with_enable_grad.Rd
+++ b/man/with_enable_grad.Rd
@@ -18,7 +18,7 @@ This context manager is thread local; it will not affect computation in
 other threads.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 
 x <- torch_tensor(1, requires_grad=TRUE)
 with_no_grad({
diff --git a/man/with_no_grad.Rd b/man/with_no_grad.Rd
index 251abf77a5c30db5736f5336f7cb63e27c5d542c..7565b5ebdc7d1c769a68d3acf8a10c5ddf79f52c 100644
--- a/man/with_no_grad.Rd
+++ b/man/with_no_grad.Rd
@@ -13,7 +13,7 @@ with_no_grad(code)
 Temporarily modify gradient recording.
 }
 \examples{
-\dontrun{
+if (torch_is_installed()) {
 x <- torch_tensor(runif(5), requires_grad = TRUE)
 with_no_grad({
   x$sub_(torch_tensor(as.numeric(1:5)))
diff --git a/pkgdown/extra.css b/pkgdown/extra.css
new file mode 100644
index 0000000000000000000000000000000000000000..d8c4f4705152cda4cafbca72f23b5e79aedbc1b7
--- /dev/null
+++ b/pkgdown/extra.css
@@ -0,0 +1,44 @@
+.navbar-default {
+  background-color: #7e1f77;
+  border-color: #7e1f77;
+}
+
+.navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus {
+  background-color: #a953a2;
+}
+
+.navbar-default .navbar-collapse, .navbar-default .navbar-form {
+  border-color: #ffffff;
+}
+
+.navbar-default .navbar-nav .open .dropdown-menu>.dropdown-header {
+  border-color: #ffffff !important;
+}
+
+.navbar-default .navbar-nav>li>a:hover, .navbar-default .navbar-nav>li>a:focus {
+  color: #ffffff;
+  background-color: #a953a2;
+}
+
+.navbar-default .navbar-nav>.active>a, .navbar-default .navbar-nav>.active>a:hover, .navbar-default .navbar-nav>.active>a:focus {
+  background-color: #a953a2;
+}
+
+.navbar-default .navbar-nav>.open>a, .navbar-default .navbar-nav>.open>a:hover, .navbar-default .navbar-nav>.open>a:focus {
+  background-color: #a953a2;
+}
+
+.nav-pills>li.active>a, .nav-pills>li.active>a:hover, .nav-pills>li.active>a:focus {
+  background-color: #a953a2;
+}
+
+.dropdown-menu>.active>a, 
+.dropdown-menu>.active>a:hover, 
+.dropdown-menu>.active>a:focus {
+    background-color: #7e1f77;
+}
+
+.dropdown-menu>a:hover, 
+.dropdown-menu>a:focus {
+    background-color: #7e1f77;
+}
\ No newline at end of file
diff --git a/pkgdown/favicon.ico b/pkgdown/favicon.ico
new file mode 100644
index 0000000000000000000000000000000000000000..f32f988f0f6b3878e76b909d4f703761c00a1761
Binary files /dev/null and b/pkgdown/favicon.ico differ
diff --git a/src/Makevars b/src/Makevars
index 552a48e8f97442903ff0bcc2f7a87a77ad3d5be5..94dd6bcd7d52e1194f9c581423124ec203c80a12 100644
--- a/src/Makevars
+++ b/src/Makevars
@@ -3,3 +3,6 @@ all: $(SHLIB) rename
 rename:
 	@echo "Renaming torch lib to torchpkg"
 	"${R_HOME}/bin${R_ARCH_BIN}/Rscript" "../tools/renamelib.R"
+
+# in order to rename SHLIb must be done.
+rename : $(SHLIB)
\ No newline at end of file
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index c28c9836dc65b44a6f04cc5d1c7420516ff94ba4..fdf00e1d28796e494f5c3feb6b84c41da806dc2a 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -22980,7 +22980,7 @@ BEGIN_RCPP
 END_RCPP
 }
 // cpp_generator_current_seed
-Rcpp::NumericVector cpp_generator_current_seed(Rcpp::XPtr generator);
+std::string cpp_generator_current_seed(Rcpp::XPtr generator);
 RcppExport SEXP _torch_cpp_generator_current_seed(SEXP generatorSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
@@ -22991,16 +22991,26 @@ BEGIN_RCPP
 END_RCPP
 }
 // cpp_generator_set_current_seed
-void cpp_generator_set_current_seed(Rcpp::XPtr generator, std::uint64_t seed);
+void cpp_generator_set_current_seed(Rcpp::XPtr generator, std::string seed);
 RcppExport SEXP _torch_cpp_generator_set_current_seed(SEXP generatorSEXP, SEXP seedSEXP) {
 BEGIN_RCPP
     Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::XPtr >::type generator(generatorSEXP);
-    Rcpp::traits::input_parameter< std::uint64_t >::type seed(seedSEXP);
+    Rcpp::traits::input_parameter< std::string >::type seed(seedSEXP);
     cpp_generator_set_current_seed(generator, seed);
     return R_NilValue;
 END_RCPP
 }
+// cpp_torch_manual_seed
+void cpp_torch_manual_seed(std::string seed);
+RcppExport SEXP _torch_cpp_torch_manual_seed(SEXP seedSEXP) {
+BEGIN_RCPP
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< std::string >::type seed(seedSEXP);
+    cpp_torch_manual_seed(seed);
+    return R_NilValue;
+END_RCPP
+}
 // enquos0
 std::vector enquos0(Rcpp::Environment env);
 RcppExport SEXP _torch_enquos0(SEXP envSEXP) {
@@ -23416,6 +23426,17 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
+// cpp_load_state_dict
+Rcpp::List cpp_load_state_dict(std::string path);
+RcppExport SEXP _torch_cpp_load_state_dict(SEXP pathSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< std::string >::type path(pathSEXP);
+    rcpp_result_gen = Rcpp::wrap(cpp_load_state_dict(path));
+    return rcpp_result_gen;
+END_RCPP
+}
 // cpp_torch_scalar
 Rcpp::XPtr cpp_torch_scalar(SEXP x);
 RcppExport SEXP _torch_cpp_torch_scalar(SEXP xSEXP) {
@@ -23537,8 +23558,8 @@ BEGIN_RCPP
 END_RCPP
 }
 // cpp_torch_tensor
-Rcpp::XPtr cpp_torch_tensor(SEXP x, std::vector dim, Rcpp::XPtr options, bool requires_grad);
-RcppExport SEXP _torch_cpp_torch_tensor(SEXP xSEXP, SEXP dimSEXP, SEXP optionsSEXP, SEXP requires_gradSEXP) {
+Rcpp::XPtr cpp_torch_tensor(SEXP x, std::vector dim, Rcpp::XPtr options, bool requires_grad, bool is_integer64);
+RcppExport SEXP _torch_cpp_torch_tensor(SEXP xSEXP, SEXP dimSEXP, SEXP optionsSEXP, SEXP requires_gradSEXP, SEXP is_integer64SEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
@@ -23546,7 +23567,8 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< std::vector >::type dim(dimSEXP);
     Rcpp::traits::input_parameter< Rcpp::XPtr >::type options(optionsSEXP);
     Rcpp::traits::input_parameter< bool >::type requires_grad(requires_gradSEXP);
-    rcpp_result_gen = Rcpp::wrap(cpp_torch_tensor(x, dim, options, requires_grad));
+    Rcpp::traits::input_parameter< bool >::type is_integer64(is_integer64SEXP);
+    rcpp_result_gen = Rcpp::wrap(cpp_torch_tensor(x, dim, options, requires_grad, is_integer64));
     return rcpp_result_gen;
 END_RCPP
 }
@@ -25488,6 +25510,7 @@ static const R_CallMethodDef CallEntries[] = {
     {"_torch_cpp_torch_generator", (DL_FUNC) &_torch_cpp_torch_generator, 0},
     {"_torch_cpp_generator_current_seed", (DL_FUNC) &_torch_cpp_generator_current_seed, 1},
     {"_torch_cpp_generator_set_current_seed", (DL_FUNC) &_torch_cpp_generator_set_current_seed, 2},
+    {"_torch_cpp_torch_manual_seed", (DL_FUNC) &_torch_cpp_torch_manual_seed, 1},
     {"_torch_enquos0", (DL_FUNC) &_torch_enquos0, 1},
     {"_torch_evaluate_slices", (DL_FUNC) &_torch_evaluate_slices, 2},
     {"_torch_Tensor_slice", (DL_FUNC) &_torch_Tensor_slice, 4},
@@ -25526,6 +25549,7 @@ static const R_CallMethodDef CallEntries[] = {
     {"_torch_cpp_torch_reduction_sum", (DL_FUNC) &_torch_cpp_torch_reduction_sum, 0},
     {"_torch_cpp_tensor_save", (DL_FUNC) &_torch_cpp_tensor_save, 1},
     {"_torch_cpp_tensor_load", (DL_FUNC) &_torch_cpp_tensor_load, 1},
+    {"_torch_cpp_load_state_dict", (DL_FUNC) &_torch_cpp_load_state_dict, 1},
     {"_torch_cpp_torch_scalar", (DL_FUNC) &_torch_cpp_torch_scalar, 1},
     {"_torch_cpp_torch_scalar_dtype", (DL_FUNC) &_torch_cpp_torch_scalar_dtype, 1},
     {"_torch_cpp_torch_scalar_to_int", (DL_FUNC) &_torch_cpp_torch_scalar_to_int, 1},
@@ -25537,7 +25561,7 @@ static const R_CallMethodDef CallEntries[] = {
     {"_torch_cpp_Storage_data_ptr", (DL_FUNC) &_torch_cpp_Storage_data_ptr, 1},
     {"_torch_cpp_torch_tensor_print", (DL_FUNC) &_torch_cpp_torch_tensor_print, 1},
     {"_torch_cpp_torch_tensor_dtype", (DL_FUNC) &_torch_cpp_torch_tensor_dtype, 1},
-    {"_torch_cpp_torch_tensor", (DL_FUNC) &_torch_cpp_torch_tensor, 4},
+    {"_torch_cpp_torch_tensor", (DL_FUNC) &_torch_cpp_torch_tensor, 5},
     {"_torch_cpp_as_array", (DL_FUNC) &_torch_cpp_as_array, 1},
     {"_torch_cpp_tensor_dim", (DL_FUNC) &_torch_cpp_tensor_dim, 1},
     {"_torch_cpp_tensor_numel", (DL_FUNC) &_torch_cpp_tensor_numel, 1},
diff --git a/src/generator.cpp b/src/generator.cpp
index 4af28882ff4ec20c1a73e2b6dec99dbb926c34e7..dc36c8a629bc785129320e42fc35e5a3fa3051ab 100644
--- a/src/generator.cpp
+++ b/src/generator.cpp
@@ -11,15 +11,30 @@ Rcpp::XPtr cpp_torch_generator () {
 }
 
 // [[Rcpp::export]]
-Rcpp::NumericVector cpp_generator_current_seed (Rcpp::XPtr generator) {
-  Rcpp::NumericVector out(1);
+std::string cpp_generator_current_seed (Rcpp::XPtr generator) {
   uint64_t seed = lantern_Generator_current_seed(generator->get());
-  std::memcpy(&(out[0]), &(seed), sizeof(double));
-  out.attr("class") = "integer64";
-  return out;
+  auto seed_str = std::to_string(seed);
+  return seed_str;
 }
 
 // [[Rcpp::export]]
-void cpp_generator_set_current_seed (Rcpp::XPtr generator, std::uint64_t seed) {
-  lantern_Generator_set_current_seed(generator->get(), seed);
+void cpp_generator_set_current_seed (Rcpp::XPtr generator, std::string seed) {
+  
+  uint64_t value;
+  std::istringstream iss(seed);
+  iss >> value;
+  
+  lantern_Generator_set_current_seed(generator->get(), value);
+}
+
+// [[Rcpp::export]]
+void cpp_torch_manual_seed (std::string seed)
+{
+ 
+  int64_t value;
+  std::istringstream iss(seed);
+  iss >> value;
+  
+  lantern_manual_seed(value);
+  
 }
diff --git a/src/indexing.cpp b/src/indexing.cpp
index 06a1f9219e9f35489da4cd0f88906d42c214c010..0b2021fe35bdc2c9219056ea67cc413dd184e524 100644
--- a/src/indexing.cpp
+++ b/src/indexing.cpp
@@ -86,7 +86,7 @@ std::vector evaluate_slices (std::vector quosures,
 
 Rcpp::XPtr cpp_torch_tensor (SEXP x, std::vector dim,
                                               Rcpp::XPtr options,
-                                              bool requires_grad);
+                                              bool requires_grad, bool is_integer64);
 
 XPtrTorchTensorIndex slices_to_index (std::vector slices, bool drop)
 {
@@ -192,7 +192,7 @@ XPtrTorchTensorIndex slices_to_index (std::vector slices, bool dr
       options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_int64()).get());
       std::vector dim = {LENGTH(slice)};
       
-      Rcpp::XPtr tensor = cpp_torch_tensor(v, dim, make_xptr(options), false);
+      Rcpp::XPtr tensor = cpp_torch_tensor(v, dim, make_xptr(options), false, false);
       
       lantern_TensorIndex_append_tensor(index.get(), tensor->get());
       continue;
@@ -209,7 +209,7 @@ XPtrTorchTensorIndex slices_to_index (std::vector slices, bool dr
       options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_bool()).get());
       std::vector dim = {LENGTH(slice)};
       
-      Rcpp::XPtr tensor = cpp_torch_tensor(v, dim, make_xptr(options), false);
+      Rcpp::XPtr tensor = cpp_torch_tensor(v, dim, make_xptr(options), false, false);
       
       lantern_TensorIndex_append_tensor(index.get(), tensor->get());
       continue;
diff --git a/src/lantern/lantern.h b/src/lantern/lantern.h
index 6f086249d3ebd31a1d2ae741a5b267d1447a200b..d1fa85f297c886a49d1a87e24b17d884cf2f93a1 100644
--- a/src/lantern/lantern.h
+++ b/src/lantern/lantern.h
@@ -468,6 +468,53 @@ extern "C"
   HOST_API void lantern_Tensor_index_put_tensor_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_tensor_(self, index, rhs); LANTERN_HOST_HANDLER}
   LANTERN_API void (LANTERN_PTR _lantern_Tensor_index_put_scalar_) (void* self, void* index, void* rhs);
   HOST_API void lantern_Tensor_index_put_scalar_ (void* self, void* index, void* rhs) { _lantern_Tensor_index_put_scalar_(self, index, rhs); LANTERN_HOST_HANDLER}
+  LANTERN_API void (LANTERN_PTR _lantern_manual_seed) (int64_t seed);
+  HOST_API void lantern_manual_seed (int64_t seed) {_lantern_manual_seed(seed); LANTERN_HOST_HANDLER}
+
+  LANTERN_API void* (LANTERN_PTR _lantern_load_state_dict) (const char * path);
+  HOST_API void * lantern_load_state_dict (const char * path)
+  {
+    void * ret = _lantern_load_state_dict(path);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_keys) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_keys (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_keys(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void* (LANTERN_PTR _lantern_get_state_dict_values) (void * ivalue);
+  HOST_API void* lantern_get_state_dict_values (void* ivalue)
+  {
+    void * ret = _lantern_get_state_dict_values(ivalue);
+    LANTERN_HOST_HANDLER return ret;
+  }
+
+  LANTERN_API void (LANTERN_PTR _lantern_IValue_delete) (void * x);
+  HOST_API void lantern_IValue_delete (void* x)
+  {
+    _lantern_get_state_dict_values(x);
+    LANTERN_HOST_HANDLER;
+  }
+
+  LANTERN_API void (LANTERN_PTR _lantern_vector_string_delete) (void * x);
+  HOST_API void lantern_vector_string_delete (void* x)
+  {
+    _lantern_vector_string_delete(x);
+    LANTERN_HOST_HANDLER;
+  }
+
+  LANTERN_API int64_t *(LANTERN_PTR _lantern_Tensor_data_ptr_int64_t)(void *self);
+  HOST_API int64_t* lantern_Tensor_data_ptr_int64_t (void* self)
+  {
+    int64_t* ret = _lantern_Tensor_data_ptr_int64_t(self);
+    LANTERN_HOST_HANDLER;
+    return ret;
+  }
+
+
   /* Autogen Headers -- Start */
   LANTERN_API void* (LANTERN_PTR _lantern__cast_byte_tensor_bool)(void* self, void* non_blocking);
   HOST_API void* lantern__cast_byte_tensor_bool(void* self, void* non_blocking) { void* ret = _lantern__cast_byte_tensor_bool(self, non_blocking); LANTERN_HOST_HANDLER return ret; }
@@ -4175,6 +4222,13 @@ bool lanternInit(const std::string &libPath, std::string *pError)
   LOAD_SYMBOL(_lantern_const_char_delete);
   LOAD_SYMBOL(_lantern_Tensor_index_put_tensor_);
   LOAD_SYMBOL(_lantern_Tensor_index_put_scalar_);
+  LOAD_SYMBOL(_lantern_manual_seed);
+  LOAD_SYMBOL(_lantern_load_state_dict);
+  LOAD_SYMBOL(_lantern_get_state_dict_keys);
+  LOAD_SYMBOL(_lantern_get_state_dict_values);
+  LOAD_SYMBOL(_lantern_IValue_delete);
+  LOAD_SYMBOL(_lantern_vector_string_delete);
+  LOAD_SYMBOL(_lantern_Tensor_data_ptr_int64_t);
   /* Autogen Symbols -- Start */
   LOAD_SYMBOL(_lantern__cast_byte_tensor_bool)
   LOAD_SYMBOL(_lantern__cast_char_tensor_bool)
diff --git a/src/save.cpp b/src/save.cpp
index db26fc7c2656e6ace43bd7a111954597fd1e2078..8d08ae791cccb2b1de959795ab58164c75faede8 100644
--- a/src/save.cpp
+++ b/src/save.cpp
@@ -16,3 +16,29 @@ Rcpp::XPtr cpp_tensor_load (std::string s)
   XPtrTorchTensor t = lantern_tensor_load(s.c_str());
   return make_xptr(t);
 }
+
+// [[Rcpp::export]]
+Rcpp::List cpp_load_state_dict (std::string path)
+{
+  XPtrTorchIValue v = lantern_load_state_dict(path.c_str());
+  
+  XPtrTorchTensorList values = lantern_get_state_dict_values(v.get());
+  
+  XPtrTorchvector_string s = lantern_get_state_dict_keys(v.get());
+  int size = lantern_vector_string_size(s.get());
+  
+  std::vector keys;
+  for (int i = 0; i < size; i++)
+  {
+    const char * k = lantern_vector_string_at(s.get(), i);
+    keys.push_back(std::string(k));
+    lantern_const_char_delete(k);
+  }
+  
+  Rcpp::List L = Rcpp::List::create(
+    Rcpp::Named("keys") = keys, 
+    Rcpp::Named("values") = make_xptr(values)
+  );
+  
+  return L;
+}
diff --git a/src/tensor.cpp b/src/tensor.cpp
index f694d9ebb325f4b86654a6177154f835c6e5e917..f6df6ec8880f572420832ed560707739ee6f62f3 100644
--- a/src/tensor.cpp
+++ b/src/tensor.cpp
@@ -29,10 +29,17 @@ XPtrTorchTensor tensor_from_r_array (const SEXP x, std::vector dim, std
 
   XPtrTorchTensorOptions options = lantern_TensorOptions();
   
-  if (dtype == "double") {
+  if (dtype == "double") 
+  {
     options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_float64()).get());
-  } else if (dtype == "int") {
+  } 
+  else if (dtype == "int") 
+  {
     options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_int32()).get());
+  } 
+  else if (dtype == "int64")
+  {
+    options = lantern_TensorOptions_dtype(options.get(), XPtrTorchDtype(lantern_Dtype_int64()).get());
   }
 
   options = lantern_TensorOptions_device(options.get(), XPtrTorchDevice(lantern_Device("cpu", 0, false)).get());
@@ -55,17 +62,28 @@ XPtrTorchTensor tensor_from_r_array (const SEXP x, std::vector dim, std
 // [[Rcpp::export]]
 Rcpp::XPtr cpp_torch_tensor (SEXP x, std::vector dim,
                                             Rcpp::XPtr options,
-                                            bool requires_grad) {
+                                            bool requires_grad, bool is_integer64) {
 
   XPtrTorchTensor tensor(nullptr);
 
-  if (TYPEOF(x) == INTSXP) {
+  if (TYPEOF(x) == INTSXP) 
+  {
     tensor = tensor_from_r_array(x, dim, "int");
-  } else if (TYPEOF(x) == REALSXP) {
+  } 
+  else if (TYPEOF(x) == REALSXP && !is_integer64) 
+  {
     tensor = tensor_from_r_array(x, dim, "double");
-  } else if (TYPEOF(x) == LGLSXP) {
+  } 
+  else if (TYPEOF(x) == REALSXP && is_integer64)
+  {
+    tensor = tensor_from_r_array(x, dim, "int64");
+  }
+  else if (TYPEOF(x) == LGLSXP) 
+  {
     tensor = tensor_from_r_array(x, dim, "int");
-  } else {
+  } 
+  else 
+  {
     Rcpp::stop("R type not handled");
   };
   
@@ -105,6 +123,22 @@ Rcpp::List tensor_to_r_array_int32_t (XPtrTorchTensor x) {
   return Rcpp::List::create(Rcpp::Named("vec") = vec, Rcpp::Named("dim") = tensor_dimensions(x));
 }
 
+Rcpp::List tensor_to_r_array_int64_t (XPtrTorchTensor x)
+{
+  XPtrTorchTensor ten = lantern_Tensor_contiguous(x.get());
+  auto d_ptr = lantern_Tensor_data_ptr_int64_t(ten.get());
+  
+  int64_t len = lantern_Tensor_numel(ten.get());
+  Rcpp::NumericVector vec(len);         // storage vehicle we return them in
+  
+  // transfers values 'keeping bits' but changing type
+  // using reinterpret_cast would get us a warning
+  std::memcpy(&(vec[0]), d_ptr, len * sizeof(double));
+  
+  vec.attr("class") = "integer64"; 
+  return Rcpp::List::create(Rcpp::Named("vec") = vec, Rcpp::Named("dim") = tensor_dimensions(x));
+}
+
 Rcpp::List tensor_to_r_array_bool (XPtrTorchTensor x) {
   XPtrTorchTensor ten = lantern_Tensor_contiguous(x.get());
   auto d_ptr = lantern_Tensor_data_ptr_bool(ten.get());
@@ -142,7 +176,7 @@ Rcpp::List cpp_as_array (Rcpp::XPtr x) {
   }
   
   if (dtype == "Long") {
-    return tensor_to_r_array_int32_t(*x.get());
+    return tensor_to_r_array_int64_t(*x.get());
   }
   
   Rcpp::stop("dtype not handled");
diff --git a/src/torch_types.h b/src/torch_types.h
index e55d0a6ae7c6c8f9f3457fe0d7fb261ae5cf763a..ce363bf7edbb36eee527b8069a1f349f1cee773e 100644
--- a/src/torch_types.h
+++ b/src/torch_types.h
@@ -194,6 +194,20 @@ public:
   }
 };
 
+class XPtrTorchIValue : public XPtrTorch {
+public:
+  XPtrTorchIValue (void * x) : XPtrTorch {NULL} {
+    this->set(std::shared_ptr(x, lantern_IValue_delete));
+  }
+};
+
+class XPtrTorchvector_string : public XPtrTorch {
+public:
+  XPtrTorchvector_string (void * x) : XPtrTorch {NULL} {
+    this->set(std::shared_ptr(x, lantern_vector_string_delete));
+  }
+};
+
 template
 class nullable {
 public:
diff --git a/tests/testthat/assets/state_dict.pth b/tests/testthat/assets/state_dict.pth
new file mode 100644
index 0000000000000000000000000000000000000000..7b88a21fb8124fc244e957dadf85352fbe56bcbf
Binary files /dev/null and b/tests/testthat/assets/state_dict.pth differ
diff --git a/tests/testthat/test-cuda.R b/tests/testthat/test-cuda.R
index f348ea1ae1ba8001598886f01b17adfe0385959f..552f4aee5d6e40c0ffd553999891da3da3816f5e 100644
--- a/tests/testthat/test-cuda.R
+++ b/tests/testthat/test-cuda.R
@@ -13,6 +13,6 @@ test_that("cuda tensors", {
   
   x <- torch_randn(10, 10, device = torch_device("cuda"))
   
-  expect_equal(x$device()$type, "cuda")
-  expect_equal(x$device()$index, 0)
+  expect_equal(x$device$type, "cuda")
+  expect_equal(x$device$index, 0)
 })
diff --git a/tests/testthat/test-device.R b/tests/testthat/test-device.R
index bc141ae468c334c7e9168a0901a544be822bcd63..0faa3d3f691eadee6d38f3977713a87ffecd81fe 100644
--- a/tests/testthat/test-device.R
+++ b/tests/testthat/test-device.R
@@ -20,19 +20,19 @@ test_that("Can create devices", {
   skip_if_cuda_not_available()
   
   x <- torch_tensor(1, device = torch_device("cuda:0"))
-  expect_equal(x$device()$type, "cuda")
+  expect_equal(x$device$type, "cuda")
 })
 
 test_that("use string to define the device", {
   
   x <- torch_randn(10, 10, device = "cpu")
-  expect_equal(x$device()$type, "cpu")
+  expect_equal(x$device$type, "cpu")
   
   x <- torch_tensor(1, device = "cpu")
-  expect_equal(x$device()$type, "cpu")
+  expect_equal(x$device$type, "cpu")
   
   skip_if_cuda_not_available()
   
   x <- torch_tensor(1, device = "cuda")
-  expect_equal(x$device()$type, "cuda")
+  expect_equal(x$device$type, "cuda")
 })
diff --git a/tests/testthat/test-dim-error-messages.R b/tests/testthat/test-dim-error-messages.R
index caa8656e50bb25530660affe3646764b049f1324..5a4444288b8ce1611a25fc41ef3d0df7258257d6 100644
--- a/tests/testthat/test-dim-error-messages.R
+++ b/tests/testthat/test-dim-error-messages.R
@@ -166,9 +166,22 @@ test_that("tensordot error message", {
   b <- torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
   
   expect_error(
-    torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 3)),
+    torch_tensordot(a, b, list(c(2, 1), c(1, 3))),
     regex = "contracted dimensions need to match, but first has size 3 in dim 1 and second has size 2 in dim 3",
     fixed = TRUE
   )
   
+})
+
+test_that("embedding returns a better error message", {
+  
+  e <- nn_embedding(10, 3)
+  x <- torch_tensor(c(0, 1, 2, 3), dtype = torch_long())
+  
+  expect_error(
+    e(x),
+    regex = "Indices/Index start at 1 and got a 0.",
+    class = "runtime_error"
+  )
+  
 })
\ No newline at end of file
diff --git a/tests/testthat/test-dtype.R b/tests/testthat/test-dtype.R
index d6d7df9826c9a5ec88f74a2bf825b8024c7d3277..396a182c0242429b7cedfdf4469bdb631568d2bf 100644
--- a/tests/testthat/test-dtype.R
+++ b/tests/testthat/test-dtype.R
@@ -26,14 +26,14 @@ test_that("Can compare dtypes", {
 test_that("Default dtype", {
   
   x <- torch_randn(10)
-  expect_true(x$dtype() == torch_float())
+  expect_true(x$dtype == torch_float())
   expect_true(torch_get_default_dtype() == torch_float())
   
   torch_set_default_dtype(torch_float64())
   expect_true(torch_get_default_dtype() == torch_float64())
   x <- torch_randn(10)
-  expect_true(x$dtype() == torch_float64())
+  expect_true(x$dtype == torch_float64())
   
   torch_set_default_dtype(torch_float())
   
-})
\ No newline at end of file
+})
diff --git a/tests/testthat/test-gen-namespace.R b/tests/testthat/test-gen-namespace.R
index 4f04748a8b46e014765725e4539cebb6e61b8c61..d4c3710ee608388cdc9f824dd85fcfe84f351193 100644
--- a/tests/testthat/test-gen-namespace.R
+++ b/tests/testthat/test-gen-namespace.R
@@ -165,7 +165,7 @@ test_that("tensordot", {
   
   a <- torch_arange(start = 0, end = 60.)$reshape(c(3, 4, 5))
   b <- torch_arange(start = 0, end = 24.)$reshape(c(4, 3, 2))
-  out <- torch_tensordot(a, b, dims_self=c(2, 1), dims_other = c(1, 2))
+  out <- torch_tensordot(a, b, list(c(2, 1), c(1, 2)))
   
   expect_tensor_shape(out, c(5,2))
   
diff --git a/tests/testthat/test-generator.R b/tests/testthat/test-generator.R
index cdea3d5c40eb5b5fccaedce494526c7ba9e1d927..74b9bf76342106416c86f8bfdd152952c34a937f 100644
--- a/tests/testthat/test-generator.R
+++ b/tests/testthat/test-generator.R
@@ -8,3 +8,14 @@ test_that("can create and use simple generators", {
   x$set_current_seed(bit64::as.integer64(123456789101112))
   expect_equal(x$current_seed(), bit64::as.integer64(123456789101112))
 })
+
+test_that("manual_seed works", {
+  
+  torch_manual_seed(1L)
+  a <- torch_randn(1)
+  torch_manual_seed(1L)
+  b <- torch_randn(1)
+  
+  expect_equal_to_tensor(a, b)
+  
+})
\ No newline at end of file
diff --git a/tests/testthat/test-indexing.R b/tests/testthat/test-indexing.R
index e87b08c4865e7ba5f1a4f03323a9b9f82ed477fe..8cd1b1afc51a3b1ba19f239642ecac2d4b7639de 100644
--- a/tests/testthat/test-indexing.R
+++ b/tests/testthat/test-indexing.R
@@ -8,9 +8,9 @@ test_that("[ works", {
   expect_equal(as_array(x[1:10:2,,]), as_array(x)[seq(1,10, by = 2),,])
   
   x <- torch_tensor(0:9)
-  expect_equal(as_array(x[-1]), 9)
-  expect_equal(as_array(x[-2:10]), c(8,9))
-  expect_equal(as_array(x[2:N]), c(1:9))
+  expect_equal(as_array(x[-1]$to(dtype = torch_int())), 9)
+  expect_equal(as_array(x[-2:10]$to(dtype = torch_int())), c(8,9))
+  expect_equal(as_array(x[2:N]$to(dtype = torch_int())), c(1:9))
   
   x <- torch_randn(c(10,10,10,10))
   expect_equal(as_array(x[1,..]), as_array(x)[1,,,])
@@ -26,10 +26,10 @@ test_that("[ works", {
   
   x <- torch_tensor(1:10)
   y <- 1:10
-  expect_equal_to_r(x[c(1,3,2,5)], y[c(1,3,2,5)])
+  expect_equal_to_r(x[c(1,3,2,5)]$to(dtype = torch_int()), y[c(1,3,2,5)])
   
   index <- 1:3
-  expect_equal_to_r(x[index], y[index])
+  expect_equal_to_r(x[index]$to(dtype = torch_int()), y[index])
   
   x <- torch_randn(10, 10)
   x[c(2,3,1), c(3,2,1)]
diff --git a/tests/testthat/test-nn-batchnorm.R b/tests/testthat/test-nn-batchnorm.R
index 923a216a0727fdb65052b921dfc4f5e04c64c7c8..c26f60f449c990ca21d9cdb517141b9aaead39fe 100644
--- a/tests/testthat/test-nn-batchnorm.R
+++ b/tests/testthat/test-nn-batchnorm.R
@@ -19,3 +19,12 @@ test_that("nn_batch_norm2d", {
   x <- torch_randn(10, 10, 10)
   expect_error(m(x))
 })
+
+test_that("load state dict for batch norm", {
+  m <- nn_batch_norm2d(10)
+  s <- m$state_dict()
+  s <- s[!names(s) %in% "num_batches_tracked"]
+  m$load_state_dict(s)
+  
+  expect_length(m$state_dict(), 5)
+})
diff --git a/tests/testthat/test-nn-loss.R b/tests/testthat/test-nn-loss.R
index 54b39013cdff876192de12668bf47b78bacf1030..20881b0816cc3cc77fb6d740e323c9eb85965a50 100644
--- a/tests/testthat/test-nn-loss.R
+++ b/tests/testthat/test-nn-loss.R
@@ -19,3 +19,101 @@ test_that("nn_cross_entropy_loss", {
   
   expect_tensor(output)
 })
+
+test_that("nn_kl_div_loss", {
+  
+  loss <- nn_kl_div_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5, requires_grad = TRUE)
+  
+  expect_warning(
+    output <- loss(input, target)  
+  )
+  
+  output$backward()
+  
+  expect_tensor(output)
+})
+
+test_that("nn_hinge_embedding_loss", {
+  
+  loss <- nn_hinge_embedding_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5, requires_grad = TRUE)
+  
+  out <- loss(input, target)
+  out$backward()
+  
+  expect_length(out$shape, 0)
+  
+})
+
+test_that("multilabel margin loss", {
+  
+  loss <- nn_multilabel_margin_loss()
+  x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1,4))
+  # for target y, only consider labels 4 and 1, not after label -1
+  y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1,4))
+  o <- loss(x, y)
+  expect_equal(as.numeric(o), 0.85, tol = 1e-5)
+  
+  expect_length(o$shape, 0)
+  y <- torch_tensor(c(4, 0, -1, 2), dtype = torch_long())$view(c(1,4))
+  expect_error(o <- loss(x, y))
+  
+})
+
+test_that("smooth_l1_loss", {
+  
+  loss <- nn_smooth_l1_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5)
+  o <- loss(input, target)
+  
+  expect_length(o$shape, 0)
+  
+})
+
+test_that("soft_margin loss", {
+  
+  loss <- nn_soft_margin_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5)
+  o <- loss(input, target)
+  
+  expect_length(o$shape, 0)
+  
+})
+
+test_that("multilabel_soft_margin loss", {
+  
+  loss <- nn_multilabel_soft_margin_loss()
+  input <- torch_randn(3, 5, requires_grad=TRUE)
+  target <- torch_randn(3, 5)
+  o <- loss(input, target)
+  
+  expect_length(o$shape, 0)
+  
+})
+
+test_that("cosine_embedding loss", {
+  
+  loss <- nn_cosine_embedding_loss()
+  input1 <- torch_randn(5, 5, requires_grad=TRUE)
+  input2 <- torch_randn(5, 5, requires_grad=TRUE)
+  target <- torch_randn(5, 5)
+  o <- loss(input1, input2, target)
+  
+  expect_length(o$shape, 0)
+  
+})
+
+test_that("nn_multi_margin_loss", {
+  
+  loss <- nn_multi_margin_loss()
+  input <- torch_randn(100, 5, requires_grad=TRUE)
+  target <- torch_randint(low = 1, high = 5, size = c(100), dtype = torch_long())
+  o <- loss(input, target)
+
+  expect_length(o$shape, 0)  
+})
diff --git a/tests/testthat/test-nn.R b/tests/testthat/test-nn.R
index 0caf8daf186bada6fbf97ea8a91c98f0171a7fac..7b5f36c540d6bee42edd5c2c9d532ce184e933b2 100644
--- a/tests/testthat/test-nn.R
+++ b/tests/testthat/test-nn.R
@@ -117,8 +117,8 @@ test_that("to", {
   net <- nn_linear(10, 10)
   net$to(dtype = torch_double())
   
-  expect_true(net$weight$dtype() == torch_double())
-  expect_true(net$bias$dtype() == torch_double())
+  expect_true(net$weight$dtype == torch_double())
+  expect_true(net$bias$dtype == torch_double())
   
   
   Net <- nn_module(
@@ -140,20 +140,20 @@ test_that("to", {
   
   net$to(dtype = torch_double())
   
-  expect_true(net$linear$weight$dtype() == torch_double())
-  expect_true(net$linear$bias$dtype() == torch_double())
-  expect_true(net$norm$running_mean$dtype() == torch_double())
-  expect_true(net$norm$running_var$dtype() == torch_double())
-  expect_true(net$linear$weight$grad$dtype() == torch_double())
+  expect_true(net$linear$weight$dtype == torch_double())
+  expect_true(net$linear$bias$dtype == torch_double())
+  expect_true(net$norm$running_mean$dtype == torch_double())
+  expect_true(net$norm$running_var$dtype == torch_double())
+  expect_true(net$linear$weight$grad$dtype == torch_double())
   
   skip_if_cuda_not_available()
   net$cuda()
-  expect_equal(net$linear$weight$device()$type, "cuda")
-  expect_equal(net$linear$bias$device()$type, "cuda")
+  expect_equal(net$linear$weight$device$type, "cuda")
+  expect_equal(net$linear$bias$device$type, "cuda")
   
   net$cpu()
-  expect_equal(net$linear$weight$device()$type, "cpu")
-  expect_equal(net$linear$bias$device()$type,"cpu")
+  expect_equal(net$linear$weight$device$type, "cpu")
+  expect_equal(net$linear$bias$device$type,"cpu")
   
 })
 
@@ -191,7 +191,7 @@ test_that("state_dict for modules", {
   expect_equal_to_tensor(s[[6]], net$norm$running_var)
   
   
-  s <- s[-7]
+  s <- s[-6]
   expect_error(net2$load_state_dict(s), class = "value_error")
   
 })
@@ -294,3 +294,52 @@ test_that("moodule$apply", {
   expect_equal_to_tensor(net$norm$weight, torch_zeros_like(net$norm$weight))
   
 })
+
+test_that("$<-  works for instances", {
+  
+  m <- nn_module(
+    initialize = function() {
+      self$mymodule <- nn_linear(10, 10)
+      self$n <- nn_linear(15, 15)
+    }
+  )
+  
+  model <- m()
+  expect_s3_class(model, "nn_module")
+  model$mymodule <- nn_linear(2,2)
+  expect_s3_class(model, "nn_module")
+  expect_equal(model$mymodule$out_feature, 2)
+  model$new_module <- nn_linear(5,5)
+  expect_s3_class(model, "nn_module")
+  
+  pars <- model$parameters
+  expect_length(pars, 6)
+  expect_tensor_shape(pars$mymodule.weight, c(2,2))
+  expect_tensor_shape(pars$new_module.weight, c(5,5))
+
+})
+
+test_that("[[<- works for instances", {
+  
+  m <- nn_module(
+    initialize = function() {
+      self$mymodule <- nn_linear(10, 10)
+      self$n <- nn_linear(15, 15)
+    }
+  )
+  
+  model <- m()
+  expect_s3_class(model, "nn_module")
+  model[["mymodule"]] <- nn_linear(2,2)
+  expect_s3_class(model, "nn_module")
+  expect_equal(model$mymodule$out_feature, 2)
+  model[["new_module"]] <- nn_linear(5,5)
+  expect_s3_class(model, "nn_module")
+  
+  pars <- model$parameters
+  expect_length(pars, 6)
+  expect_tensor_shape(pars$mymodule.weight, c(2,2))
+  expect_tensor_shape(pars$new_module.weight, c(5,5))
+  
+  
+})
diff --git a/tests/testthat/test-nnf-loss.R b/tests/testthat/test-nnf-loss.R
new file mode 100644
index 0000000000000000000000000000000000000000..221123f953eca2adc9ecc2fe5972b2fbdf4559c9
--- /dev/null
+++ b/tests/testthat/test-nnf-loss.R
@@ -0,0 +1,17 @@
+test_that("nnf_mse_loss", {
+
+  x <- torch_tensor(c(1,2,3))
+  y <- torch_tensor(c(2,3,4))
+  
+  o <- nnf_mse_loss(x, y)
+  
+  expect_equal_to_r(o, 1)
+  
+  y <- y$unsqueeze(2)
+  
+  expect_warning(
+    nnf_mse_loss(x, y),
+    regexp = "target size"
+  )
+  
+})
diff --git a/tests/testthat/test-nnf-upsampling.R b/tests/testthat/test-nnf-upsampling.R
new file mode 100644
index 0000000000000000000000000000000000000000..7240a40649250b180da05b16d80fe8f3d0810740
--- /dev/null
+++ b/tests/testthat/test-nnf-upsampling.R
@@ -0,0 +1,12 @@
+test_that("interpolate", {
+  
+  img <- torch_ones(3, 32, 32)
+  
+  expect_tensor_shape(nnf_interpolate(img, size = 40), c(3, 32, 40))
+  
+  img <- torch_ones(1, 3, 32, 32)
+  o <- nnf_interpolate(img, size = c(40, 40), mode = "bilinear")
+  expect_tensor_shape(o, c(1, 3, 40, 40))
+  expect_true(!any(as_array(torch_isnan(o)))) # no nans
+  
+})
diff --git a/tests/testthat/test-save.R b/tests/testthat/test-save.R
index dd8636fce183ea2cc40b431ea4e2ad1261a8c8b2..68ce43d98e9a1e298fe1f276b9ba19712cd5eef5 100644
--- a/tests/testthat/test-save.R
+++ b/tests/testthat/test-save.R
@@ -135,3 +135,18 @@ test_that("save alexnet like model", {
   
 })
 
+test_that("load a state dict created in python", {
+
+  # the state dict was create in python with  
+  # ones = torch.ones(3, 5)
+  # twos = torch.ones(3, 5) * 2
+  # value = {'ones': ones, 'twos': twos}
+  # torch.save(value, "assets/state_dict.pth", _use_new_zipfile_serialization=True)
+  
+  dict <- load_state_dict("assets/state_dict.pth")
+  expect_equal(names(dict), c("ones", "twos"))
+  expect_equal_to_tensor(dict$ones, torch_ones(3, 5))
+  expect_equal_to_tensor(dict$twos, torch_ones(3, 5) * 2)
+  
+})
+
diff --git a/tests/testthat/test-tensor.R b/tests/testthat/test-tensor.R
index 72bda733ae2fe64dfb0f1fa3ca7412bb22e3ac1c..cd882b17dddf34cc1fc9ec92974a49fb1265860b 100644
--- a/tests/testthat/test-tensor.R
+++ b/tests/testthat/test-tensor.R
@@ -11,12 +11,12 @@ test_that("Can create a tensor", {
   expect_equal(dim(x), 0)
   
   x <- torch_tensor(1)
-  expect_true(x$dtype() == torch_float32())
+  expect_true(x$dtype == torch_float32())
   
   x <- torch_tensor(1, dtype = torch_double())
-  expect_true(x$dtype() == torch_double())
+  expect_true(x$dtype == torch_double())
   
-  device <- x$device()
+  device <- x$device
   expect_equal(device$type, "cpu")
 })
 
@@ -39,7 +39,16 @@ test_that("Numeric tensors", {
 test_that("Integer tensors", {
   
   x <- 1:4
-  expect_equal_to_r(torch_tensor(x), x)
+  expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
+  
+  x <- matrix(c(1:4), ncol = 2)
+  expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
+  
+  x <- array(c(1:8), dim = c(2,2,2))
+  expect_equal_to_r(torch_tensor(x)$to(dtype = torch_int()), x)
+  
+  x <- 1:5
+  expect_equal_to_r(torch_tensor(1:5), x)
   
   x <- matrix(c(1:4), ncol = 2)
   expect_equal_to_r(torch_tensor(x), x)
@@ -47,6 +56,21 @@ test_that("Integer tensors", {
   x <- array(c(1:8), dim = c(2,2,2))
   expect_equal_to_r(torch_tensor(x), x)
   
+  x <- 1:5
+  expect_equal_to_r(torch_tensor(bit64::as.integer64(x)), x)
+  
+  x <- array(c(1:8), dim = c(2,2,2))
+  o <- as.integer64(torch_tensor(x))
+  expect_s3_class(o, "integer64")
+  expect_s3_class(o, "array")
+  expect_equal(dim(o), dim(x))
+  
+  x <- as.integer64(.Machine$integer)*2
+  y <- torch_tensor(x)
+  z <- as.integer64(y)
+  
+  expect_equal(as.integer64(z), x)
+  
 })
 
 test_that("Logical tensors", {
@@ -80,7 +104,7 @@ test_that("Pass only device argument to `to`", {
   
   y <- torch_tensor(1, dtype = torch_long())
   k <- x$to(other = y)
-  expect_true(k$dtype() == torch_long())
+  expect_true(k$dtype == torch_long())
 })
 
 test_that("cuda and cpu methods", {
@@ -89,15 +113,15 @@ test_that("cuda and cpu methods", {
   x <- torch_tensor(1)
   y <- x$cuda()
   
-  expect_true(y$device()$type == "cuda")
+  expect_true(y$device$type == "cuda")
   
   # calling twice dont error
   y$cuda()
-  expect_true(y$device()$type == "cuda")
+  expect_true(y$device$type == "cuda")
   
   k <- y$cpu()
   
-  expect_true(k$device()$type == "cpu")
+  expect_true(k$device$type == "cpu")
   
 })
 
@@ -116,3 +140,20 @@ test_that("is_contiguous", {
   expect_true(!x$is_contiguous())
   
 })
+
+test_that("is_cuda", {
+  x <- torch_randn(10, 10)
+  expect_true(!x$is_cuda)
+  
+  skip_if_cuda_not_available()
+  
+  x <- torch_randn(10, 10, device = torch_device("cuda"))
+  expect_true(X$is_cuda)
+})
+
+test_that("ndim", {
+  
+  x <- torch_randn(10, 10)
+  expect_equal(x$ndim, 2)
+  
+})
diff --git a/tests/testthat/test-wrapers.R b/tests/testthat/test-wrapers.R
new file mode 100644
index 0000000000000000000000000000000000000000..19ac33a79fd2c8e88eca127198256f0f9585afa1
--- /dev/null
+++ b/tests/testthat/test-wrapers.R
@@ -0,0 +1,46 @@
+test_that("result_type", {
+  
+  x <- torch_result_type(
+    tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), 
+    tensor2 = 1
+  )
+  
+  expect_true(x == torch_float())
+  
+  x <- torch_result_type(
+    tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), 
+    tensor2 = torch_tensor(1:2)
+  )
+  
+  expect_true(x == torch_long())
+  
+  x <- torch_result_type(
+    tensor1 = 1, 
+    tensor2 = torch_tensor(1:2)
+  )
+  
+  expect_true(x == torch_float())
+  
+  x <- torch_result_type(
+    tensor1 = 1, 
+    tensor2 = 2L
+  )
+  
+  expect_true(x == torch_float())
+  
+})
+
+test_that("torch_multi_margin_loss", {
+  
+  x <- torch_randn(3, 2)
+  y <- torch_tensor(c(1, 2, 3), dtype = torch_long())
+  
+  expect_error(torch_multi_margin_loss(x, y))
+  
+  x <- torch_randn(3, 3)
+  expect_tensor(torch_multi_margin_loss(x, y))
+  
+  y <- torch_tensor(c(0, 1, 2))
+  expect_error(torch_multi_margin_loss(x, y))
+  
+})
diff --git a/tools/document.R b/tools/document.R
index 8bfbec6960ecc18258380b1146db119f61de4e04..91fe76e0c35987f40bfb4f81885222b8129655b4 100644
--- a/tools/document.R
+++ b/tools/document.R
@@ -1,5 +1,9 @@
 library(roxygen2)
 
+
+# Patch examples ----------------------------------------------------------
+# we path examples so we add the `if` conditional.
+
 .S3method("roxy_tag_parse", "roxy_tag_examples", function(x) {
   roxygen2::tag_examples(x)
 })
@@ -11,10 +15,44 @@ library(roxygen2)
 .S3method("format", "rd_section_examples", function (x, ...) {
   value <- paste0(x$value, collapse = "\n")
   roxygen2:::rd_macro("examples",
-    roxygen2:::rd_macro("dontrun", value, space = TRUE),
+    c("if (torch_is_installed()) {", value, "}"),
     space = TRUE
   )
 })
 
+# Subsection --------------------------------------------------------------
+
+.S3method("roxy_tag_parse", "roxy_tag_subsection", function(x) {
+  roxygen2::tag_markdown(x)
+})
+
+.S3method("roxy_tag_rd", "roxy_tag_subsection", function(x, base_path, env) {
+  pieces <- stringr::str_split(x$val, ":", n = 2)[[1]]
+  title <- stringr::str_split(pieces[1], "\n")[[1]]
+  
+  if (length(title) > 1) {
+    roxygen2:::roxy_tag_warning(x, paste0(
+      "Section title spans multiple lines.\n",
+      "Did you forget a colon (:) at the end of the title?"
+    ))
+    return()
+  }
+  
+  roxygen2:::rd_section_section(pieces[1], pieces[2])
+})
+
+.S3method("format", "rd_section_subsection", function(x, ...) {
+  paste0(
+    "\\subsection{", x$value$title, "}{\n", x$value$content, "\n}\n",
+    collapse = "\n"
+  )
+})
+
+# Params ------------------------------------------------------------------
+# avoids using the \arguments{} so we can have two sections with parameters
+# for different signatures.
+
+
+
 
 devtools::document()
\ No newline at end of file
diff --git a/tools/renamelib.R b/tools/renamelib.R
index 9d225c750702f5ec6df60d9148787191b89052ee..a8003a6ee205dc9373c47d2e02ff7fc2cb6f4bf6 100644
--- a/tools/renamelib.R
+++ b/tools/renamelib.R
@@ -1,17 +1,5 @@
 libs_path <- getwd()
 
-# wait, because of it's built in parallel this script might run before
-# finishing.
-start <- Sys.time()
-timeout <- 180
-
-while(length(dir(libs_path, pattern = "torch\\.")) == 0) {
-  Sys.sleep(4)
-  cat("Waiting for compilation...\n")
-  if (as.numeric(Sys.time() - start) > timeout)
-    break
-}
-
 for (lib in dir(libs_path, pattern = "torch\\.")) {
   file.copy(file.path(libs_path, lib),
             file.path(libs_path, gsub("torch", "torchpkg", lib)),
diff --git a/tools/torchgen/R/r.R b/tools/torchgen/R/r.R
index 32e5693022f94447f5d63c5bf9cc92ff9ad4abcf..ad893131a3e51215ee338c7810a09e4b5175a521 100644
--- a/tools/torchgen/R/r.R
+++ b/tools/torchgen/R/r.R
@@ -81,6 +81,7 @@ r_namespace <- function(decls) {
 
   glue::glue("
 
+#' @rdname {r_namespace_name(decls)}
 {r_namespace_name(decls)} <- function({r_namespace_signature(decls)}) {{
   {r_namespace_body(decls)}
 }}
@@ -99,7 +100,12 @@ creation_ops <- c("ones", "ones_like", "rand", "rand_like", "randint",
 internal_funs <- c("logical_not", "max_pool1d_with_indices", "max_pool2d_with_indices",
                    "max_pool2d_with_indices_out", "max_pool3d_with_indices",
                    "max_pool3d_with_indices_out", "max", "min", "max_out", "min_out",
-                   "nll_loss", "nll_loss2d")
+                   "nll_loss", "nll_loss2d", "bartlett_window", "blackman_window",
+                   "hamming_window", "hann_window", "normal",
+                   "result_type", "sparse_coo_tensor", "stft",
+                   "tensordot", "tril_indices", "triu_indices",
+                   "multilabel_margin_loss", "multi_margin_loss")
+
 internal_funs <- c(internal_funs, creation_ops)
 
 
diff --git a/vignettes/.gitignore b/vignettes/.gitignore
index 2d19fc766d98a08d9d1437896bfb008a7b15f340..3037d85db3608b91b374723836573fd74d7e9c8f 100644
--- a/vignettes/.gitignore
+++ b/vignettes/.gitignore
@@ -1 +1,3 @@
 *.html
+tensor.pt
+model.pt
diff --git a/vignettes/examples/basic-autograd.R b/vignettes/examples/basic-autograd.R
new file mode 100644
index 0000000000000000000000000000000000000000..b666f281249c190b0e42d7c731d052f515940b52
--- /dev/null
+++ b/vignettes/examples/basic-autograd.R
@@ -0,0 +1,16 @@
+library(torch)
+
+# creates example tensors. x requires_grad = TRUE tells that 
+# we are going to take derivatives over it.
+x <- torch_tensor(3, requires_grad = TRUE)
+y <- torch_tensor(2)
+
+# executes the forward operation x^2
+o <- x^2
+
+# computes the backward operation for each tensor that is marked with
+# requires_grad = TRUE
+o$backward()
+
+# get do/dx = 2 * x (at x = 3)
+x$grad
diff --git a/vignettes/examples/mnist-cnn.Rmd b/vignettes/examples/basic-autograd.Rmd
similarity index 64%
rename from vignettes/examples/mnist-cnn.Rmd
rename to vignettes/examples/basic-autograd.Rmd
index 2d2010e0d5dceaeeae8109f8c6842f2bbfdd26d7..f4a1310585aa1301dee7cbc4da2c5c831ebaa354 100644
--- a/vignettes/examples/mnist-cnn.Rmd
+++ b/vignettes/examples/basic-autograd.Rmd
@@ -1,9 +1,9 @@
 ---
-title: mnist-cnn
+title: basic-autograd
 type: docs
 ---
 
 ```{r, echo = FALSE}
-knitr::opts_chunk$set(eval = FALSE)
+knitr::opts_chunk$set(eval = TRUE)
 knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
 ```
\ No newline at end of file
diff --git a/vignettes/examples/basic-nn-module.R b/vignettes/examples/basic-nn-module.R
new file mode 100644
index 0000000000000000000000000000000000000000..b57e2c202377ce64017ac96c0710bab78e09288c
--- /dev/null
+++ b/vignettes/examples/basic-nn-module.R
@@ -0,0 +1,39 @@
+library(torch)
+
+# creates example tensors. x requires_grad = TRUE tells that 
+# we are going to take derivatives over it.
+dense <- nn_module(
+  clasname = "dense",
+  # the initialize function tuns whenever we instantiate the model
+  initialize = function(in_features, out_features) {
+    
+    # just for you to see when this function is called
+    cat("Calling initialize!") 
+    
+    # we use nn_parameter to indicate that those tensors are special
+    # and should be treated as parameters by `nn_module`.
+    self$w <- nn_parameter(torch_randn(in_features, out_features))
+    self$b <- nn_parameter(torch_zeros(out_features))
+    
+  },
+  # this function is called whenever we call our model on input.
+  forward = function(x) {
+    cat("Calling forward!")
+    torch_mm(x, self$w) + self$b
+  }
+)
+
+model <- dense(3, 1)
+
+# you can get all parameters 
+model$parameters
+
+# or individually
+model$w
+model$b
+
+# create an input tensor
+x <- torch_randn(10, 3)
+y_pred <- model(x)
+y_pred
+
diff --git a/vignettes/examples/mnist-dcgan.Rmd b/vignettes/examples/basic-nn-module.Rmd
similarity index 63%
rename from vignettes/examples/mnist-dcgan.Rmd
rename to vignettes/examples/basic-nn-module.Rmd
index db79ca61dfb493bf03ed9e0f887eb1aa9957f915..3027ce43289209385412bcc2cfc82d8aaef4e52b 100644
--- a/vignettes/examples/mnist-dcgan.Rmd
+++ b/vignettes/examples/basic-nn-module.Rmd
@@ -1,9 +1,9 @@
 ---
-title: mnist-dcgan
+title: basic-nn-module
 type: docs
 ---
 
 ```{r, echo = FALSE}
-knitr::opts_chunk$set(eval = FALSE)
+knitr::opts_chunk$set(eval = TRUE)
 knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
 ```
\ No newline at end of file
diff --git a/vignettes/examples/dataset.R b/vignettes/examples/dataset.R
new file mode 100644
index 0000000000000000000000000000000000000000..c6350673aa0fc013ad3eceab5f8feaf1022cc402
--- /dev/null
+++ b/vignettes/examples/dataset.R
@@ -0,0 +1,73 @@
+library(torch)
+
+# In deep learning models you don't usually have all your data in RAM
+# because you are usually training using mini-batch gradient descent
+# thus only needing a mini-batch on RAM each time.
+
+# In torch we use the `datasets` abstraction to define the process of
+# loading data. Once you have defined your dataset you can use torch
+# dataloaders that allows you to iterate over this dataset in batches.
+
+# Note that datasets are optional in torch. They are jut there as a
+# recommended way to load data.
+
+# Below you will see an example of how to create a simple torch dataset
+# that pre-process a data.frame into tensors so you can feed them to
+# a model.
+
+df_dataset <- dataset(
+  "mydataset",
+  
+  # the input data to your dataset goes in the initialize function.
+  # our dataset will take a dataframe and the name of the response
+  # variable.
+  initialize = function(df, response_variable) {
+    self$df <- df[,-which(names(df) == response_variable)]
+    self$response_variable <- df[[response_variable]]
+  },
+  
+  # the .getitem method takes an index as input and returns the
+  # corresponding item from the dataset.
+  # the index could be anything. the dataframe could have many
+  # rows for each index and the .getitem method would do some
+  # kind of aggregation before returning the element.
+  # in our case the index will be a row of the data.frame,
+  .getitem = function(index) {
+    response <- torch_tensor(self$response_variable[index])
+    x <- torch_tensor(as.numeric(self$df[index,]))
+    
+    # note that the dataloaders will automatically stack tensors
+    # creating a new dimension
+    list(x = x, y = response)
+  },
+  
+  # It's optional, but helpful to define the .length method returning 
+  # the number of elements in the dataset. This is needed if you want 
+  # to shuffle your dataset.
+  .length = function() {
+    length(self$response_variable)
+  }
+  
+)
+
+
+# we can now initialize an instance of our dataset.
+# for example
+mtcars_dataset <- df_dataset(mtcars, "mpg")
+
+# now we can get an item with
+mtcars_dataset$.getitem(1)
+
+# Given a dataset you can create a dataloader with
+dl <- dataloader(mtcars_dataset, batch_size = 15, shuffle = TRUE)
+
+# we can then loop trough the elements of the dataloader with
+for(batch in enumerate(dl)) {
+  cat("X size:  ")
+  print(batch[[1]]$size())
+  cat("Y size:  ")
+  print(batch[[2]]$size())
+}
+
+
+
diff --git a/vignettes/examples/mnist-mlp.Rmd b/vignettes/examples/dataset.Rmd
similarity index 63%
rename from vignettes/examples/mnist-mlp.Rmd
rename to vignettes/examples/dataset.Rmd
index 06cd5402b104ed8bdc6084b6a5739ce5d9713535..3027ce43289209385412bcc2cfc82d8aaef4e52b 100644
--- a/vignettes/examples/mnist-mlp.Rmd
+++ b/vignettes/examples/dataset.Rmd
@@ -1,9 +1,9 @@
 ---
-title: mnist-mlp
+title: basic-nn-module
 type: docs
 ---
 
 ```{r, echo = FALSE}
-knitr::opts_chunk$set(eval = FALSE)
+knitr::opts_chunk$set(eval = TRUE)
 knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
 ```
\ No newline at end of file
diff --git a/vignettes/examples/index.Rmd b/vignettes/examples/index.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..f9e7c93a60098acd80d38ef95490821c6203847e
--- /dev/null
+++ b/vignettes/examples/index.Rmd
@@ -0,0 +1,17 @@
+---
+title: Examples
+type: docs
+---
+
+Gallery of scripts demonstrating `torch` functionality.
+
+```{r, results='asis', echo=FALSE}
+files <- list.files(pattern = "*.R$")
+files <- gsub("\\.R$", "", files)
+
+o <- data.frame(
+  `Examples` = paste0("[", files, "]", "(../../articles/examples/", files, ".html)")
+)
+knitr::kable(o)
+```
+
diff --git a/vignettes/examples/mnist-alexnet.R b/vignettes/examples/mnist-alexnet.R
deleted file mode 100644
index e4758617649ae200697d7c047914f65534e2cdfe..0000000000000000000000000000000000000000
--- a/vignettes/examples/mnist-alexnet.R
+++ /dev/null
@@ -1,89 +0,0 @@
-dir <- "~/Downloads/tiny-imagenet"
-
-ds <- tiny_imagenet_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- magick::image_resize(x, "224x224")
-    x <- as.integer(magick::image_data(x, "rgb"))  
-    x <- torch_tensor(x)
-    x <- x/256
-    x <- x$permute(c(3, 1, 2))
-  },
-  target_transform = function(x) {
-    x <- torch_tensor(x, dtype = torch_long())
-    x$squeeze(1)
-  }
-)
-
-dl <- dataloader(ds, batch_size = 128, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function(num_classes = 1000) {
-    self$features <- nn_sequential(
-      nn_conv2d(3, 64, kernel_size = 11, stride = 4, padding = 2),
-      nn_relu(),
-      nn_max_pool2d(kernel_size = 3, stride = 2),
-      nn_conv2d(64, 192, kernel_size = 5, padding = 2),
-      nn_relu(),
-      nn_max_pool2d(kernel_size = 3, stride = 2),
-      nn_conv2d(192, 384, kernel_size = 3, padding = 1),
-      nn_relu(),
-      nn_conv2d(384, 256, kernel_size = 3, padding = 1),
-      nn_relu(),
-      nn_max_pool2d(kernel_size = 3, stride = 2)
-    )
-    self$avgpool <- nn_max_pool2d(c(6,6))
-    self$classifier <- nn_sequential(
-      nn_dropout(),
-      nn_linear(256, 4096),
-      nn_relu(),
-      nn_dropout(),
-      nn_linear(4096, 4096),
-      nn_relu(),
-      nn_linear(4096, num_classes)
-    )
-  },
-  forward = function(x) {
-    x <- self$features(x)
-    x <- self$avgpool(x)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$classifier(x)
-  }
-)
-
-if (cuda_is_available()) {
-  device <- torch_device("cuda")
-} else {
-  device <- torch_device("cpu")
-}
-  
-model <- net(num_classes = 200)
-model$to(device = device)
-optimizer <- optim_adam(model$parameters)
-loss_fun <- nn_cross_entropy_loss()
-
-epochs <- 10
-
-for (epoch in 1:50) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-  
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]]$to(device = device))
-    loss <- loss_fun(output, b[[2]]$to(device = device))
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-  
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
diff --git a/vignettes/examples/mnist-cnn.R b/vignettes/examples/mnist-cnn.R
deleted file mode 100644
index 4482c395bc22d4c48e71752e2518110504627473..0000000000000000000000000000000000000000
--- a/vignettes/examples/mnist-cnn.R
+++ /dev/null
@@ -1,65 +0,0 @@
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$conv1 <- nn_conv2d(1, 32, 3, 1)
-    self$conv2 <- nn_conv2d(32, 64, 3, 1)
-    self$dropout1 <- nn_dropout2d(0.25)
-    self$dropout2 <- nn_dropout2d(0.5)
-    self$fc1 <- nn_linear(9216, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x <- self$conv1(x)
-    x <- nnf_relu(x)
-    x <- self$conv2(x)
-    x <- nnf_relu(x)
-    x <- nnf_max_pool2d(x, 2)
-    x <- self$dropout1(x)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$fc1(x)
-    x <- nnf_relu(x)
-    x <- self$dropout2(x)
-    x <- self$fc2(x)
-    output <- nnf_log_softmax(x, dim=1)
-    output
-  }
-)
-
-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
-epochs <- 10
-
-for (epoch in 1:10) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-  
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-  
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
diff --git a/vignettes/examples/mnist-dcgan.R b/vignettes/examples/mnist-dcgan.R
deleted file mode 100644
index ee16299e8bb3b4910526115f365c64ec5aff2581..0000000000000000000000000000000000000000
--- a/vignettes/examples/mnist-dcgan.R
+++ /dev/null
@@ -1,145 +0,0 @@
-library(torch)
-
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x <- 2*(x - 0.5)
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-generator <- nn_module(
-  "generator", 
-  initialize = function(latent_dim, out_channels) {
-    self$main <- nn_sequential(
-      nn_conv_transpose2d(latent_dim, 512, kernel_size = 4, 
-                          stride = 1, padding = 0, bias = FALSE),
-      nn_batch_norm2d(512),
-      nn_relu(),
-      nn_conv_transpose2d(512, 256, kernel_size = 4, 
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(256),
-      nn_relu(),
-      nn_conv_transpose2d(256, 128, kernel_size = 4, 
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(128),
-      nn_relu(),
-      nn_conv_transpose2d(128, out_channels, kernel_size = 4, 
-                          stride = 2, padding = 3, bias = FALSE),
-      nn_tanh()
-    )
-  },
-  forward = function(input) {
-    self$main(input)
-  }
-)
-
-discriminator <- nn_module(
-  "discriminator",
-  initialize = function(in_channels) {
-    self$main <- nn_sequential(
-      nn_conv2d(in_channels, 16, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(16, 32, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(32),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(32, 64, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(64),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(64, 128, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE)
-    )
-    self$linear <- nn_linear(128, 1)
-    self$sigmoid <- nn_sigmoid()
-  },
-  forward = function(input) {
-    x <- self$main(input)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$linear(x)
-    self$sigmoid(x)
-  }
-)
-
-plot_gen <- function(noise) {
-  img <- G(noise)
-  img <- img$cpu()
-  img <- img[1,1,,,newaxis]/2 + 0.5
-  img <- torch_stack(list(img, img, img), dim = 2)[..,1]
-  img <- as.raster(as_array(img))
-  plot(img)
-}
-
-device <- torch_device(ifelse(cuda_is_available(),  "cuda", "cpu"))
-
-G <- generator(latent_dim = 100, out_channels = 1)
-D <- discriminator(in_channels = 1)
-
-init_weights <- function(m) {
-  if (grepl("conv", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 0.0, 0.02)
-  } else if (grepl("batch_norm", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 1.0, 0.02)
-    nn_init_constant_(m$bias$data(), 0)
-  } 
-}
-
-G[[1]]$apply(init_weights)
-D[[1]]$apply(init_weights)
-
-G$to(device = device)
-D$to(device = device)
-
-G_optimizer <- optim_adam(G$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-D_optimizer <- optim_adam(D$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-
-fixed_noise <- torch_randn(1, 100, 1, 1, device = device)
-
-loss <- nn_bce_loss()
-
-for (epoch in 1:10) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss D: :lossd Loss G: :lossg"
-  )
-  lossg <- c()
-  lossd <- c()
-  
-  for (b in enumerate(dl)) {
-    
-    y_real <- torch_ones(32, device = device)
-    y_fake <- torch_zeros(32, device = device)
-    
-    noise <- torch_randn(32, 100, 1, 1, device = device)
-    fake <- G(noise)
-    
-    img <- b[[1]]$to(device = device)
-    
-    # train the discriminator ---
-    D_loss <- loss(D(img), y_real) + loss(D(fake$detach()), y_fake)
-    
-    D_optimizer$zero_grad()
-    D_loss$backward()
-    D_optimizer$step()
-    
-    # train the generator ---
-    
-    G_loss <- loss(D(fake), y_real)
-    
-    G_optimizer$zero_grad()
-    G_loss$backward()
-    G_optimizer$step()
-    
-    lossd <- c(lossd, D_loss$item())
-    lossg <- c(lossg, G_loss$item())
-    pb$tick(tokens = list(lossd = mean(lossd), lossg = mean(lossg)))
-  }
-  plot_gen(fixed_noise)
-  
-  cat(sprintf("Epoch %d - Loss D: %3f Loss G: %3f\n", epoch, mean(lossd), mean(lossg)))
-}
diff --git a/vignettes/examples/mnist-mlp.R b/vignettes/examples/mnist-mlp.R
deleted file mode 100644
index 1a1fc0b17eefd95e59b9472adf06b8c992fc1571..0000000000000000000000000000000000000000
--- a/vignettes/examples/mnist-mlp.R
+++ /dev/null
@@ -1,53 +0,0 @@
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x$to(dtype = torch_float())/256
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$fc1 <- nn_linear(784, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x %>% 
-      torch_flatten(start_dim = 2) %>% 
-      self$fc1() %>% 
-      nnf_relu() %>% 
-      self$fc2() %>% 
-      nnf_log_softmax(dim = 1)
-  }
-)
-
-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
-epochs <- 10
-
-for (epoch in 1:10) {
-  
-  pb <- progress::progress_bar$new(
-    total = length(dl), 
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-  
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-  
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
diff --git a/vignettes/extending-autograd.Rmd b/vignettes/extending-autograd.Rmd
index 424d84758b876745a972f92dee59b4bd13fbd6f2..97f7f60bee790fb9e0544e30e6778c21ba7adf52 100644
--- a/vignettes/extending-autograd.Rmd
+++ b/vignettes/extending-autograd.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/getting-started/assets/mnist.png b/vignettes/getting-started/assets/mnist.png
new file mode 100644
index 0000000000000000000000000000000000000000..53c876a89d53ccb3ae4fb5167460e84248ad3672
Binary files /dev/null and b/vignettes/getting-started/assets/mnist.png differ
diff --git a/vignettes/getting-started/autograd.Rmd b/vignettes/getting-started/autograd.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..a67c814ee74c86147189762474399596bf8cea02
--- /dev/null
+++ b/vignettes/getting-started/autograd.Rmd
@@ -0,0 +1,181 @@
+---
+title: 'Autograd: automatic differentiation'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py). All credits goes to [Soumith Chintala](http://soumith.ch/).
+
+```{r setup}
+library(torch)
+```
+
+Central to all neural networks in torch is the autograd functionality. Let's first briefly visit this, and we will then go to training our first neural network.
+
+Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.
+
+Let us see this in more simple terms with some examples.
+
+## Tensor
+
+`torch_tensor` is the central class of the package. If you set its attribute `$requires_grad` as `TRUE`, it starts to track all operations on it. When you finish your computation you can call `$backward()` and have all the gradients computed automatically. The gradient for this tensor will be accumulated into `$grad` attribute.
+
+To stop a tensor from tracking history, you can call `$detach()` to detach it from the computation history, and to prevent future computation from being tracked.
+
+To prevent tracking history (and using memory), you can also wrap the code block in `with_no_grad({})`. This can be particularly helpful when evaluating a model because the model may have trainable parameters with `requires_grad=TRUE`, but for which we don't need the gradients.
+
+There's one more class which is very important for autograd implementation - a `autograd_function`.
+
+Tensor and Function are interconnected and build up an acyclic graph, that encodes a complete history of computation. Each tensor has a `$grad_fn` attribute that references an `autograd_function` that has created the Tensor (except for Tensors created by the user - their `grad_fn` is `NULL`).
+
+If you want to compute the derivatives, you can call `$backward()` on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don't need to specify any arguments to `backward()`, however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.
+
+Create a tensor and set `requires_grad=TRUE` to track computation with it:
+
+```{r}
+x <- torch_ones(2, 2, requires_grad = TRUE)
+x
+```
+
+Do a tensor operation:
+
+```{r}
+y <- x + 2
+y
+```
+
+`y` was created as a result of an operation, so it has a `grad_fn`.
+
+```{r}
+y$grad_fn
+```
+
+Do more operations on `y`
+
+```{r}
+z <- y * y * 3
+z
+out <- z$mean()
+out
+```
+
+`$requires_grad_( ... )` changes an existing Tensor's `requires_grad` flag in-place. The input flag defaults to `FALSE` if not given.
+
+```{r}
+a <- torch_randn(2, 2)
+a <- (a * 3) / (a - 1)
+a$requires_grad
+a$requires_grad_(TRUE)
+a$requires_grad
+b <- (a * a)$sum()
+b$grad_fn
+```
+
+## Gradients
+
+Let's backprop now. Because out contains a single scalar, `out$backward()` is equivalent to `out$backward(torch.tensor(1.))`.
+
+```{r}
+out$backward()
+```
+
+Print gradients d(out)/dx
+
+```{r}
+x$grad
+```
+
+You should have got a matrix of `4.5`. Let's call the `out` *Tensor* $o$.
+
+We have that $o = \frac{1}{4}\sum_i z_i$, $z_i = 3(x_i+2)^2$ and $z_i\bigr\rvert_{x_i=1} = 27$. Therefore, $\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)$, hence $\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5$.
+
+Mathematically, if you have a vector valued function $\vec{y}=f(\vec{x})$,
+then the gradient of $\vec{y}$ with respect to $\vec{x}$
+is a Jacobian matrix:
+
+$$
+  J=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{1}}{\partial x_{n}}\\
+   \vdots & \ddots & \vdots\\
+   \frac{\partial y_{m}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)
+$$
+
+Generally speaking, `autograd` is an engine for computing
+vector-Jacobian product. That is, given any vector
+$v=\left(\begin{array}{cccc} v_{1} & v_{2} & \cdots & v_{m}\end{array}\right)^{T}$,
+compute the product $v^{T}\cdot J$. If $v$ happens to be
+the gradient of a scalar function $l=g\left(\vec{y}\right)$,
+that is,
+$v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} & \cdots & \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}$,
+then by the chain rule, the vector-Jacobian product would be the
+gradient of $l$ with respect to $\vec{x}$:
+
+$$
+  J^{T}\cdot v=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{1}}\\
+   \vdots & \ddots & \vdots\\
+   \frac{\partial y_{1}}{\partial x_{n}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)\left(\begin{array}{c}
+   \frac{\partial l}{\partial y_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial y_{m}}
+   \end{array}\right)=\left(\begin{array}{c}
+   \frac{\partial l}{\partial x_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial x_{n}}
+   \end{array}\right)
+$$
+
+(Note that $v^{T}\cdot J$ gives a row vector which can be
+treated as a column vector by taking $J^{T}\cdot v$.)
+
+This characteristic of vector-Jacobian product makes it very
+convenient to feed external gradients into a model that has
+non-scalar output.
+
+Now let's take a look at an example of vector-Jacobian product:
+
+```{r}
+x <- torch_randn(3, requires_grad=TRUE)
+y <- 100 * x
+y
+```
+
+Now in this case y is no longer a scalar. `autograd` could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:
+
+```{r}
+v <- torch_tensor(c(0.1, 1.0, 0.0001))
+y$backward(v)
+
+x$grad
+```
+
+You can also stop autograd from tracking history on Tensors with `$requires_grad=TRUE` either by wrapping the code block in with `with_no_grad()`:
+
+```{r}
+x$requires_grad
+(x ** 2)$requires_grad
+
+with_no_grad({
+  print((x ** 2)$requires_grad)
+})
+```
+
+```{r}
+x$requires_grad
+y <- x$detach()
+y$requires_grad
+x$eq(y)$all()
+```
+
+Read Later:
+
+Document about `help(autograd_function)`, `vignette("using-autograd")`, `vignette("extending-autograd")`.
diff --git a/vignettes/getting-started/control-flow-and-weight-sharing.Rmd b/vignettes/getting-started/control-flow-and-weight-sharing.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..8f23d96485fe59729cb6668aa2587a90cb4afb0e
--- /dev/null
+++ b/vignettes/getting-started/control-flow-and-weight-sharing.Rmd
@@ -0,0 +1,121 @@
+---
+title: 'Control flow & Weight sharing'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.
+
+For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.
+
+We can easily implement this model using `nn_module`:
+
+```{r}
+dynamic_net <- nn_module(
+   "dynamic_net",
+   # In the constructor we construct three nn_linear instances that we will use
+   # in the forward pass.
+   initialize = function(D_in, H, D_out) {
+      self$input_linear <- nn_linear(D_in, H)
+      self$middle_linear <- nn_linear(H, H)
+      self$output_linear <- nn_linear(H, D_out)
+   },
+   # For the forward pass of the model, we randomly choose either 0, 1, 2, or 3
+   # and reuse the middle_linear Module that many times to compute hidden layer
+   # representations.
+   # 
+   # Since each forward pass builds a dynamic computation graph, we can use normal
+   # R control-flow operators like loops or conditional statements when
+   # defining the forward pass of the model.
+   # 
+   # Here we also see that it is perfectly safe to reuse the same Module many
+   # times when defining a computational graph. This is a big improvement from Lua
+   # Torch, where each Module could be used only once.
+   forward = function(x) {
+      h_relu <- self$input_linear(x)$clamp(min = 0)
+      for (i in seq_len(sample.int(4, size = 1))) {
+         h_relu <- self$middle_linear(h_relu)$clamp(min=0)
+      }
+      y_pred <- self$output_linear(h_relu)
+      y_pred
+   }
+)
+
+
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Construct our model by instantiating the class defined above
+model <- dynamic_net(D_in, H, D_out)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+# Use the optim package to define an Optimizer that will update the weights of
+# the model for us. Here we will use Adam; the optim package contains many other
+# optimization algorithms. The first argument to the Adam constructor tells the
+# optimizer which Tensors it should update.
+learning_rate <- 1e-4
+optimizer <- optim_sgd(model$parameters, lr=learning_rate, momentum = 0.9)
+
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Before the backward pass, use the optimizer object to zero all of the
+   # gradients for the variables it will update (which are the learnable
+   # weights of the model). This is because by default, gradients are
+   # accumulated in buffers( i.e, not overwritten) whenever $backward()
+   # is called. Checkout docs of `autograd_backward` for more details.
+   optimizer$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to model
+   # parameters
+   loss$backward()
+
+   # Calling the step function on an Optimizer makes an update to its
+   # parameters
+   optimizer$step()
+}
+```
+
+
diff --git a/vignettes/getting-started/custom-nn.Rmd b/vignettes/getting-started/custom-nn.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..752ae0aedffb18dc72adb9e85764d79d7407820b
--- /dev/null
+++ b/vignettes/getting-started/custom-nn.Rmd
@@ -0,0 +1,103 @@
+---
+title: 'Custom nn modules'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using `nn_module` function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.
+
+In this example we implement our two-layer network as a custom Module subclass:
+
+```{r}
+two_layer_net <- nn_module(
+   "two_layer_net",
+   initialize = function(D_in, H, D_out) {
+      self$linear1 <- nn_linear(D_in, H)
+      self$linear2 <- nn_linear(H, D_out)
+   },
+   forward = function(x) {
+      x %>% 
+         self$linear1() %>% 
+         nnf_relu() %>% 
+         self$linear2()
+   }
+)
+
+
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Construct our model by instantiating the class defined above
+model <- two_layer_net(D_in, H, D_out)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+# Use the optim package to define an Optimizer that will update the weights of
+# the model for us. Here we will use Adam; the optim package contains many other
+# optimization algorithms. The first argument to the Adam constructor tells the
+# optimizer which Tensors it should update.
+learning_rate <- 1e-4
+optimizer <- optim_sgd(model$parameters, lr=learning_rate)
+
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Before the backward pass, use the optimizer object to zero all of the
+   # gradients for the variables it will update (which are the learnable
+   # weights of the model). This is because by default, gradients are
+   # accumulated in buffers( i.e, not overwritten) whenever $backward()
+   # is called. Checkout docs of `autograd_backward` for more details.
+   optimizer$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to model
+   # parameters
+   loss$backward()
+
+   # Calling the step function on an Optimizer makes an update to its
+   # parameters
+   optimizer$step()
+}
+```
+
+In the [next example](control-flow-and-weight-sharing.html) we will about dynamic graphs in torch.
diff --git a/vignettes/getting-started/neural-networks.Rmd b/vignettes/getting-started/neural-networks.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..c6298ea6d42dd7d40528cd74b454724c1de53070
--- /dev/null
+++ b/vignettes/getting-started/neural-networks.Rmd
@@ -0,0 +1,216 @@
+---
+title: Neural networks
+type: docs
+---
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py). All credits goes to [Soumith Chintala](http://soumith.ch/).
+
+```{r setup}
+library(torch)
+```
+
+Neural networks can be constructed using the `nn` functionality.
+
+Now that you had a glimpse of `autograd`, `nn` depends on `autograd` to define models and differentiate them. An nn.Module contains layers, and a method `forward(input)` that returns the output.
+
+For example, look at this network that classifies digit images:
+
+![Convnet for mnist classification](assets/mnist.png)
+
+It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output.
+
+A typical training procedure for a neural network is as follows:
+
+- Define the neural network that has some learnable parameters (or weights)
+- Iterate over a dataset of inputs
+- Process input through the network
+- Compute the loss (how far is the output from being correct)
+- Propagate gradients back into the network’s parameters
+- Update the weights of the network, typically using a simple update rule: `weight = weight - learning_rate * gradient`.
+
+## Define the network
+
+Let's define this network:
+
+```{r}
+Net <- nn_module(
+  initialize = function() {
+    self$conv1 = nn_conv2d(1, 6, 3)
+    self$conv2 = nn_conv2d(6, 16, 3)
+    # an affine operation: y = Wx + b
+    self$fc1 = nn_linear(16 * 6 * 6, 120)  # 6*6 from image dimension
+    self$fc2 = nn_linear(120, 84)
+    self$fc3 = nn_linear(84, 10)
+  },
+  forward = function(x) {
+    x %>% 
+      
+      self$conv1() %>% 
+      nnf_relu() %>% 
+      nnf_max_pool2d(c(2,2)) %>% 
+      
+      self$conv2() %>% 
+      nnf_relu() %>% 
+      nnf_max_pool2d(c(2,2)) %>% 
+      
+      torch_flatten(start_dim = 2) %>% 
+      
+      self$fc1() %>% 
+      nnf_relu() %>% 
+      
+      self$fc2() %>% 
+      nnf_relu() %>% 
+      
+      self$fc3()
+  }
+)
+
+net <- Net()
+```
+
+You just have to define the `forward` function, and the `backward` function (where gradients are computed) is automatically defined for you using `autograd.` You can use any of the Tensor operations in the `forward` function.
+
+The learnable parameters of a model are returned by `net$parameters`.
+
+```{r}
+str(net$parameters)
+```
+
+Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.
+
+```{r}
+input <- torch_randn(1, 1, 32, 32)
+out <- net(input)
+out
+```
+
+Zero the gradient buffers of all parameters and backprops with random gradients:
+
+```{r}
+net$zero_grad()
+out$backward(torch_randn(1, 10))
+```
+
+> **Note**: `nn` only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, `nn_conv2d` will take in a 4D Tensor of nSamples x nChannels x Height x Width.
+If you have a single sample, just use `input$unsqueeze(1)` to add a fake batch dimension.
+
+Before proceeding further, let’s recap all the classes you’ve seen so far.
+
+### Recap
+
+- `torch_tensor` - A multi-dimensional array with support for autograd operations like `backward()`. Also holds the gradient w.r.t. the tensor.
+
+- `nn_module` - Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.
+
+- `nn_parameter` - A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to a Module.
+
+- `autograd_function` - Implements forward and backward definitions of an autograd operation. Every Tensor operation creates at least a single Function node that connects to functions that created a Tensor and encodes its history.
+
+### At this point, we covered
+
+- Defining a neural network
+- Processing inputs and calling backward
+
+### Still left
+
+- Computing the loss
+- Updating the weights of the network
+
+## Loss function
+
+A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target.
+
+There are several different loss functions under the nn package . A simple loss is: `nnf_mse_loss` which computes the mean-squared error between the input and the target.
+
+For example:
+
+```{r}
+output <- net(input)
+target <- torch_randn(10)  # a dummy target, for example
+target <- target$view(c(1, -1))  # make it the same shape as output
+
+loss <- nnf_mse_loss(output, target)
+loss
+```
+
+Now, if you follow loss in the backward direction, using its `$grad_fn` attribute, you will see a graph of computations that looks like this:
+
+```
+input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d
+      -> view -> linear -> relu -> linear -> relu -> linear
+      -> MSELoss
+      -> loss
+```
+
+So, when we call `loss$backward()`, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their `#grad` Tensor accumulated with the gradient.
+
+For illustration, let us follow a few steps backward:
+
+```{r}
+loss$grad_fn
+loss$grad_fn$next_functions[[1]]
+loss$grad_fn$next_functions[[1]]$next_functions[[1]]
+```
+
+## Backprop
+
+To backpropagate the error all we have to do is to `loss$backward()`. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.
+
+Now we shall call `loss$backward()`, and have a look at conv1’s bias gradients before and after the backward.
+
+```{r}
+net$zero_grad()     # zeroes the gradient buffers of all parameters
+
+# conv1.bias.grad before backward
+net$conv1$bias$grad
+
+loss$backward()
+
+# conv1.bias.grad after backward
+net$conv1$bias$grad
+```
+
+Now, we have seen how to use loss functions.
+
+## Update the weights
+
+The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):
+
+$$weight = weight - learning_rate * gradient$$ 
+
+We can implement this using simple R code:
+
+```{r}
+learning_rate <- 0.01
+for (f in net$parameters) {
+  with_no_grad({
+    f$sub_(f$grad * learning_rate)
+  })
+}
+```
+
+> **Note:** Weight updates here is wraped around `with_no_grad` as we don't the updates to be tracked by the autograd engine. 
+
+However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc. 
+
+```{r}
+# create your optimizer
+optimizer <- optim_sgd(net$parameters, lr = 0.01)
+
+# in your training loop:
+optimizer$zero_grad()   # zero the gradient buffers
+output <- net(input)
+loss <- nnf_mse_loss(output, target)
+loss$backward()
+optimizer$step()    # Does the update
+```
+
+> **Note:** Observe how gradient buffers had to be manually set to zero using `optimizer$zero_grad()`. This is because gradients are accumulated as explained in the Backprop section.
+
diff --git a/vignettes/getting-started/new-autograd-functions.Rmd b/vignettes/getting-started/new-autograd-functions.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..c4dbbcaede8289720fa31f6e7e84e5da1fc3c020
--- /dev/null
+++ b/vignettes/getting-started/new-autograd-functions.Rmd
@@ -0,0 +1,112 @@
+---
+title: 'Defining new autograd functions'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.
+
+In torch we can easily define our own autograd operator by defining a subclass of `autograd_function` and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.
+
+In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:
+
+```{r}
+# We can implement our own custom autograd Functions by subclassing
+# autograd_functioon and implementing the forward and backward passes
+# which operate on Tensors.
+my_relu <- autograd_function(
+   # In the forward pass we receive a Tensor containing the input and return
+   # a Tensor containing the output. ctx is a context object that can be used
+   # to stash information for backward computation. You can cache arbitrary
+   # objects for use in the backward pass using the ctx$save_for_backward method.
+   forward = function(ctx, input) {
+      ctx$save_for_backward(input = input)
+      input$clamp(min = 0)
+   },
+   # In the backward pass we receive a Tensor containing the gradient of the loss
+   # with respect to the output, and we need to compute the gradient of the loss
+   # with respect to the input.
+   backward = function(ctx, grad_output) {
+      v <- ctx$saved_variables
+      grad_input <- grad_output$clone()
+      grad_input[v$input < 0] <- 0
+      list(input = grad_input)
+   }
+)
+
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Randomly initialize weights
+# Setting requires_grad=TRUE indicates that we want to compute gradients with
+# respect to these Tensors during the backward pass.
+w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE)
+w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE)
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y using operations on Tensors; these
+   # are exactly the same operations we used to compute the forward pass using
+   # Tensors, but we do not need to keep references to intermediate values since
+   # we are not implementing the backward pass by hand.
+   y_pred <- my_relu(x$mm(w1))$mm(w2)
+   
+   # Compute and print loss using operations on Tensors.
+   # Now loss is a Tensor of shape (1,)
+   loss <- (y_pred - y)$pow(2)$sum()
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Use autograd to compute the backward pass. This call will compute the
+   # gradient of loss with respect to all Tensors with requires_grad=True.
+   # After this call w1$grad and w2$grad will be Tensors holding the gradient
+   # of the loss with respect to w1 and w2 respectively.
+   loss$backward()
+   
+   # Manually update weights using gradient descent. Wrap in `with_no_grad`
+   # because weights have requires_grad=TRUE, but we don't need to track this
+   # in autograd.
+   # You can also use optim_sgd to achieve this.
+   with_no_grad({
+      
+      # operations suffixed with an `_` operates on in-place on the tensor.
+      w1$sub_(learning_rate * w1$grad)
+      w2$sub_(learning_rate * w2$grad)
+      
+      # Manually zero the gradients after updating weights
+      w1$grad$zero_()
+      w2$grad$zero_()
+   })
+}
+```
+
+In the [next example](nn.html) we will learn how to use the neural networks abstractions in torch.
diff --git a/vignettes/getting-started/nn.Rmd b/vignettes/getting-started/nn.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..4a6659be48f6e5ac92449089bb5cc6b5722659c0
--- /dev/null
+++ b/vignettes/getting-started/nn.Rmd
@@ -0,0 +1,97 @@
+---
+title: 'nn: neural networks with torch'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+Computational graphs and autograd are a very powerful paradigm for defining complex operators and automatically taking derivatives; however for large neural networks raw autograd can be a bit too low-level.
+
+When building neural networks we frequently think of arranging the computation into layers, some of which have learnable parameters which will be optimized during learning.
+
+In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.
+
+In torch, the nn functionality serves this same purpose. The nn feature defines a set of Modules, which are roughly equivalent to neural network layers. A Module receives input Tensors and computes output Tensors, but may also hold internal state such as Tensors containing learnable parameters. The nn collection also defines a set of useful loss functions that are commonly used when training neural networks.
+
+In this example we use nn to implement our two-layer network:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Use the nn package to define our model as a sequence of layers. nn_sequential
+# is a Module which contains other Modules, and applies them in sequence to
+# produce its output. Each Linear Module computes output from input using a
+# linear function, and holds internal Tensors for its weight and bias.
+model <- nn_sequential(
+    nn_linear(D_in, H),
+    nn_relu(),
+    nn_linear(H, D_out)
+)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Zero the gradients before running the backward pass.
+   model$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to all the learnable
+   # parameters of the model. Internally, the parameters of each Module are stored
+   # in Tensors with requires_grad=TRUE, so this call will compute gradients for
+   # all learnable parameters in the model.
+   loss$backward()
+   
+   # Update the weights using gradient descent. Each parameter is a Tensor, so
+   # we can access its gradients like we did before.
+   with_no_grad({
+      for (param in model$parameters) {
+         param$sub_(learning_rate * param$grad)
+      }
+   })
+}
+```
+
+In the [next example](optim.html) we will learn how to use optimizers implemented in torch.
diff --git a/vignettes/getting-started/optim.Rmd b/vignettes/getting-started/optim.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..4c90996c8be292a4df2a9746d074d4bbc0cff396
--- /dev/null
+++ b/vignettes/getting-started/optim.Rmd
@@ -0,0 +1,97 @@
+---
+title: 'optim: optimizers in torch'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with `with_no_grad` to avoid tracking history in autograd). This is not a huge burden for simple optimization algorithms like stochastic gradient descent, but in practice we often train neural networks using more sophisticated optimizers like AdaGrad, RMSProp, Adam, etc.
+
+The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.
+
+In this example we will use the nn package to define our model as before, but we will optimize the model using the Adam algorithm provided by `optim`:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Use the nn package to define our model as a sequence of layers. nn_sequential
+# is a Module which contains other Modules, and applies them in sequence to
+# produce its output. Each Linear Module computes output from input using a
+# linear function, and holds internal Tensors for its weight and bias.
+model <- nn_sequential(
+    nn_linear(D_in, H),
+    nn_relu(),
+    nn_linear(H, D_out)
+)
+
+# The nn package also contains definitions of popular loss functions; in this
+# case we will use Mean Squared Error (MSE) as our loss function.
+loss_fn <- nnf_mse_loss
+
+# Use the optim package to define an Optimizer that will update the weights of
+# the model for us. Here we will use Adam; the optim package contains many other
+# optimization algorithms. The first argument to the Adam constructor tells the
+# optimizer which Tensors it should update.
+learning_rate <- 1e-4
+optimizer <- optim_adam(model$parameters, lr=learning_rate)
+
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y by passing x to the model. Module objects
+   # can be called like functions. When doing so you pass a Tensor of input
+   # data to the Module and it produces a Tensor of output data.
+   y_pred <- model(x)
+   
+   # Compute and print loss. We pass Tensors containing the predicted and true
+   # values of y, and the loss function returns a Tensor containing the
+   # loss.
+   loss <- loss_fn(y_pred, y)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Before the backward pass, use the optimizer object to zero all of the
+   # gradients for the variables it will update (which are the learnable
+   # weights of the model). This is because by default, gradients are
+   # accumulated in buffers( i.e, not overwritten) whenever $backward()
+   # is called. Checkout docs of `autograd_backward` for more details.
+   optimizer$zero_grad()
+
+   # Backward pass: compute gradient of the loss with respect to model
+   # parameters
+   loss$backward()
+
+   # Calling the step function on an Optimizer makes an update to its
+   # parameters
+   optimizer$step()
+}
+```
+
+In the [next example](custom-nn.html) we will learn how to create custom `nn_modules`.
diff --git a/vignettes/getting-started/tensors-and-autograd.Rmd b/vignettes/getting-started/tensors-and-autograd.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..62a12e2ba14b17f08d00d1c2907eeac803f171cc
--- /dev/null
+++ b/vignettes/getting-started/tensors-and-autograd.Rmd
@@ -0,0 +1,91 @@
+---
+title: 'Tensors and autograd'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.
+
+Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.
+
+This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has `x$requires_grad=TRUE` then `x$grad` is another Tensor holding the gradient of x with respect to some scalar value.
+
+Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+# Setting requires_grad=FALSE (the default) indicates that we do not need to 
+# compute gradients with respect to these Tensors during the backward pass.
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Randomly initialize weights
+# Setting requires_grad=TRUE indicates that we want to compute gradients with
+# respect to these Tensors during the backward pass.
+w1 <- torch_randn(D_in, H, device=device, requires_grad = TRUE)
+w2 <- torch_randn(H, D_out, device=device, requires_grad = TRUE)
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y using operations on Tensors; these
+   # are exactly the same operations we used to compute the forward pass using
+   # Tensors, but we do not need to keep references to intermediate values since
+   # we are not implementing the backward pass by hand.
+   y_pred <- x$mm(w1)$clamp(min=0)$mm(w2)
+   
+   # Compute and print loss using operations on Tensors.
+   # Now loss is a Tensor of shape (1,)
+   loss <- (y_pred - y)$pow(2)$sum()
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", as.numeric(loss), "\n")
+   
+   # Use autograd to compute the backward pass. This call will compute the
+   # gradient of loss with respect to all Tensors with requires_grad=True.
+   # After this call w1$grad and w2$grad will be Tensors holding the gradient
+   # of the loss with respect to w1 and w2 respectively.
+   loss$backward()
+   
+   # Manually update weights using gradient descent. Wrap in `with_no_grad`
+   # because weights have requires_grad=TRUE, but we don't need to track this
+   # in autograd.
+   # You can also use optim_sgd to achieve this.
+   with_no_grad({
+      
+      # operations suffixed with an `_` operates on in-place on the tensor.
+      w1$sub_(learning_rate * w1$grad)
+      w2$sub_(learning_rate * w2$grad)
+      
+      # Manually zero the gradients after updating weights
+      w1$grad$zero_()
+      w2$grad$zero_()
+   })
+}
+```
+
+In the [next example](new-autograd-functions.html) we will learn how to create new autograd functions.
diff --git a/vignettes/getting-started/tensors.Rmd b/vignettes/getting-started/tensors.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..367ed5d99b2fc3dee0577ee1eec22e0af9f06426
--- /dev/null
+++ b/vignettes/getting-started/tensors.Rmd
@@ -0,0 +1,76 @@
+---
+title: 'torch Tensors'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+R arrays are great, but they cannot utilize GPUs to accelerate its numerical computations. For modern deep neural networks, GPUs often provide speedups of 50x or greater, so unfortunately pure R won’t be enough for modern deep learning.
+
+Here we introduce the most fundamental torch concept: the Tensor. A torch Tensor is conceptually similar to an R array: a Tensor is an n-dimensional array, and torch provides many functions for operating on these Tensors. Behind the scenes, Tensors can keep track of a computational graph and gradients, but they’re also useful as a generic tool for scientific computing.
+
+Also unlike R, torch Tensors can utilize GPUs to accelerate their numeric computations. To run a torch Tensor on GPU, you simply need to cast it to a new datatype.
+
+Here we use torch Tensors to fit a two-layer network to random data. Like the R before we need to manually implement the forward and backward passes through the network:
+
+```{r}
+if (cuda_is_available()) {
+   device <- torch_device("cuda")
+} else {
+   device <- torch_device("cpu")
+}
+   
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+x <- torch_randn(N, D_in, device=device)
+y <- torch_randn(N, D_out, device=device)
+
+# Randomly initialize weights
+w1 <- torch_randn(D_in, H, device=device)
+w2 <- torch_randn(H, D_out, device=device)
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y
+   h <- x$mm(w1)
+   h_relu <- h$clamp(min=0)
+   y_pred <- h_relu$mm(w2)
+   
+   # Compute and print loss
+   loss <- as.numeric((y_pred - y)$pow(2)$sum())
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", loss, "\n")
+   
+   # Backprop to compute gradients of w1 and w2 with respect to loss
+   grad_y_pred <- 2.0 * (y_pred - y)
+   grad_w2 <- h_relu$t()$mm(grad_y_pred)
+   grad_h_relu <- grad_y_pred$mm(w2$t())
+   grad_h <- grad_h_relu$clone()
+   grad_h[h < 0] <- 0
+   grad_w1 <- x$t()$mm(grad_h)
+   
+   # Update weights using gradient descent
+   w1 <- w1 - learning_rate * grad_w1
+   w2 <- w2 - learning_rate * grad_w2
+}
+```
+
+In the [next example](tensors-and-autograd.html) we will use autograd instead of computing the gradients manually.
diff --git a/vignettes/getting-started/warmup.Rmd b/vignettes/getting-started/warmup.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..f0b2abc3498f18ad226a88c96a6f4c8684d920fc
--- /dev/null
+++ b/vignettes/getting-started/warmup.Rmd
@@ -0,0 +1,70 @@
+---
+title: 'Warm-up'
+type: docs
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py). All credits goes to [Justin Johnson](https://github.com/jcjohnson/pytorch-examples).
+
+```{r setup}
+library(torch)
+```
+
+A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.
+
+This implementation uses pure R to manually compute the forward pass, loss, and backward pass.
+
+An R array is a generic n-dimensional array; it does not know anything about deep learning or gradients or computational graphs, and is just a way to perform generic numeric computations.
+
+```{r}
+# N is batch size; D_in is input dimension;
+# H is hidden dimension; D_out is output dimension.
+N <- 64
+D_in <- 1000
+H <- 100
+D_out <- 10
+
+# Create random input and output data
+x <- array(rnorm(N*D_in), dim = c(N, D_in))
+y <- array(rnorm(N*D_out), dim = c(N, D_out))
+
+# Randomly initialize weights
+w1 <- array(rnorm(D_in*H), dim = c(D_in, H))
+w2 <- array(rnorm(H*D_out), dim = c(H, D_out))
+
+learning_rate <- 1e-6
+for (t in seq_len(500)) {
+   # Forward pass: compute predicted y
+   h <- x %*% w1
+   h_relu <- ifelse(h < 0, 0, h)
+   y_pred <- h_relu %*% w2
+   
+   # Compute and print loss
+   loss <- sum((y_pred - y)^2)
+   if (t %% 100 == 0 || t == 1)
+      cat("Step:", t, ":", loss, "\n")
+   
+   # Backprop to compute gradients of w1 and w2 with respect to loss
+   grad_y_pred <- 2 * (y_pred - y)
+   grad_w2 <- t(h_relu) %*% grad_y_pred
+   grad_h_relu <- grad_y_pred %*% t(w2)
+   grad_h <- grad_h_relu
+   grad_h[h < 0] <- 0
+   grad_w1 <- t(x) %*% grad_h
+   
+   # Update weights
+   w1 <- w1 - learning_rate * grad_w1
+   w2 <- w2 - learning_rate * grad_w2
+}
+```
+
+In the [next example](tensors.html) we will replace the R array for a torch Tensor.
+
+
diff --git a/vignettes/getting-started/what-is-torch.Rmd b/vignettes/getting-started/what-is-torch.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..5a8d5f4e724f5683d0b896078490c6da5a351a0e
--- /dev/null
+++ b/vignettes/getting-started/what-is-torch.Rmd
@@ -0,0 +1,169 @@
+---
+title: What is torch?
+type: docs
+---
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+> Note: This is an R port of the official tutorial available [here](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py). All credits goes to [Soumith Chintala](http://soumith.ch/).
+
+```{r setup}
+library(torch)
+```
+
+It’s a scientific computing package targeted at two sets of audiences:
+
+- An array library to use the power of GPUs
+- a deep learning research platform that provides maximum flexibility and speed
+
+## Getting started
+
+### Tensors
+
+Tensors are similar to R arrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.
+
+> Note: An uninitialized matrix is declared, but does not contain definite known values before it is used. When an uninitialized matrix is created, whatever values were in the allocated memory at the time will appear as the initial values.
+
+Construct a 5x3 matrix, uninitialized:
+
+```{r}
+x <- torch_empty(5, 3)
+x
+```
+
+Construct a randomly initialized matrix:
+
+```{r}
+x <- torch_rand(5, 3)
+x
+```
+
+Construct a matrix filled zeros and of dtype long:
+
+```{r}
+x <- torch_zeros(5, 3, dtype = torch_long())
+x
+```
+
+Construct a tensor directly from data:
+
+```{r}
+x <- torch_tensor(c(5.5, 3))
+x
+```
+
+or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user
+
+```{r}
+x <- torch_randn_like(x, dtype = torch_float()) # override dtype!
+x                                               # result has the same size
+```
+
+Get its size:
+
+```{r}
+x$size()
+```
+
+### Operations
+
+There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.
+
+Addition: syntax 1
+
+```{r}
+x <- torch_rand(5, 3)
+y <- torch_rand(5, 3)
+x + y
+```
+
+Addition: syntax 2
+
+```{r}
+torch_add(x, y)
+```
+
+Addition: in-place
+
+```{r}
+y$add_(x)
+y
+```
+
+> Note: Any operation that mutates a tensor in-place is post-fixed with an `_`. For example: `x$copy_(y)`, `x$t_()`, will change x.
+
+You can use standard R-like indexing with all bells and whistles! See more about indexing with `vignette("indexing")`.
+
+```{r}
+x[, 1]
+```
+
+Resizing: If you want to resize/reshape tensor, you can use `torch_view`:
+
+```{r}
+x <- torch_randn(4, 4)
+y <- x$view(16)
+z <- x$view(size = c(-1, 8))  # the size -1 is inferred from other dimensions
+x$size()
+y$size()
+z$size()
+```
+If you have a one element tensor, use `$item()` to get the value as an R number
+
+```{r}
+x <- torch_randn(1)
+x
+x$item()
+```
+
+You can find a complete list of operations in the reference page.
+
+
+## R bridge
+
+Converting a Torch Tensor to an R array and vice versa is a breeze.
+
+### Converting a torch tensor into an R array
+
+```{r}
+a <- torch_ones(5)
+a
+```
+
+```{r}
+b <- as_array(a)
+b
+```
+
+### Converting R arrays to torch tensors
+
+```{r}
+a <- rep(1, 5)
+a
+b <- torch_tensor(a)
+b
+```
+
+Currently supported types are numerics and boolean types.
+
+## CUDA tensors
+
+Tensors can be moved onto any device using the `$to` method.
+
+```{r}
+if (cuda_is_available()) {
+  device <- torch_device("cuda")
+  y <- torch_ones_like(x, device = device)  # directly create a tensor on GPU
+  x <- x$to(device)                       # or just use strings ``.to("cuda")``
+  z <- x + y
+  print(z)
+  print(z$to(device = "cpu", torch_double())) # `$to` can also change dtype together!
+}
+```
+
+
diff --git a/vignettes/indexing.Rmd b/vignettes/indexing.Rmd
index d11d71fc330ed2078a485b8cfbc9012966e43a94..2bf47d2e5248b380392c71b20a7e88650fa945c2 100644
--- a/vignettes/indexing.Rmd
+++ b/vignettes/indexing.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/loading-data.Rmd b/vignettes/loading-data.Rmd
index bb4e6e9d03164bcf02fdb60fa24d6ae1b4e3940d..7a2aa90aa41f973b76b11972fa2c307a59c95157 100644
--- a/vignettes/loading-data.Rmd
+++ b/vignettes/loading-data.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
@@ -154,7 +154,7 @@ one-hot-encode or embed it.)
 net <- nn_module(
   "PenguinNet",
   initialize = function() {
-    self$fc1 <- nn_linear(6, 32)
+    self$fc1 <- nn_linear(7, 32)
     self$fc2 <- nn_linear(32, 3)
   },
   forward = function(x) {
diff --git a/vignettes/serialization.Rmd b/vignettes/serialization.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..cc272a62a03229202c28362eacd21a3827de58d5
--- /dev/null
+++ b/vignettes/serialization.Rmd
@@ -0,0 +1,95 @@
+---
+title: "Serialization"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Serialization}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
+)
+```
+
+```{r setup}
+library(torch)
+```
+
+Torch tensors in R are pointers to Tensors allocated by LibTorch.
+This has one major consequence for serialization. One cannot simply
+use `saveRDS` for serializing tensors, as you would save the pointer but not the data itself. When reloading a tensor saved with `saveRDS` the pointer might have been deleted in LibTorch and you would get wrong results.
+
+To solve this problem, `torch` implements specialized functions for serializing tensors to the disk:
+
+- `torch_save()`: to save tensors and models to the disk.
+- `torch_load()`: to load the models or tensors back to the session.
+
+Please note that this format is still experimental and you shouldn't use it for long term storage.
+
+## Saving tensors
+
+You can save any object of type `torch_tensor` to the disk using:
+
+```{r}
+x <- torch_randn(10, 10)
+torch_save(x, "tensor.pt")
+x_ <- torch_load("tensor.pt")
+
+torch_allclose(x, x_)
+```
+## Saving modules
+
+The `torch_save` and `torch_load` functions also work for `nn_modules` objects.
+
+When saving an `nn_module`, all the object is serialized including the model structure and it's state.
+
+```{r}
+module <- nn_module(
+  "my_module",
+  initialize = function() {
+    self$fc1 <- nn_linear(10, 10)
+    self$fc2 <- nn_linear(10, 1)
+  },
+  forward = function(x) {
+    x %>% 
+      self$fc1() %>% 
+      self$fc2()
+  }
+)
+
+model <- module()
+torch_save(model, "model.pt")
+model_ <- torch_load("model.pt")
+
+# input tensor
+x <- torch_randn(50, 10)
+torch_allclose(model(x), model_(x))
+```
+## Loading models saved in python
+
+Currently the only way to load models from python is to rewrite the model architecture in R. All the parameter names must be identical.
+
+You can then save the PyTorch model state_dict using:
+
+```
+torch.save(model, fpath, _use_new_zipfile_serialization=True)
+```
+
+You can then reload the state dict in R and reload it into the model with:
+
+```{r eval = FALSE}
+state_dict <- load_state_dict(fpath)
+model <- Model()
+model$load_state_dict(state_dict)
+```
+
+You can find working examples in `torchvision`. For example [this](https://github.com/mlverse/torchvision/blob/main/R/models-alexnet.R#L2-L63) is what we do for the AlexNet model.
+
+
+
+
+
diff --git a/vignettes/tensor-creation.Rmd b/vignettes/tensor-creation.Rmd
index 7f043babb0c9a03115cce628ce7b43a4f33cdd80..2586814e53bf4121e09c905a7a5eef991e1b03cc 100644
--- a/vignettes/tensor-creation.Rmd
+++ b/vignettes/tensor-creation.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
diff --git a/vignettes/tensor/index.Rmd b/vignettes/tensor/index.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..f32c33c7dfe5d466bb32d91b56cfc4701b202910
--- /dev/null
+++ b/vignettes/tensor/index.Rmd
@@ -0,0 +1,3636 @@
+---
+title: "Tensor objects"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Tensor objects}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1"),
+  results = "hide"
+)
+```
+
+```{r setup}
+library(torch)
+```
+
+Central to torch is the `torch_tensor` objects. `torch_tensor`'s are R objects
+very similar to R6 instances. Tensors have a large amount of methods that can
+be called using the `$` operator.
+
+Following is a list of all methods that can be called by tensor objects and their
+documentation. You can also look at [PyTorch's documentation](https://pytorch.org/docs/stable/tensors.html) for additional details. 
+
+
+## T
+
+Is this Tensor with its dimensions reversed.
+
+If `n` is the number of dimensions in `x`,
+`x$T` is equivalent to `x$permute(n-1, n-2, ..., 0)`.
+
+## abs
+
+abs() -> Tensor
+
+See `?torch_abs`
+
+## abs_
+
+abs_() -> Tensor
+
+In-place version of `$abs`
+
+## absolute
+
+absolute() -> Tensor
+
+Alias for [$abs()]
+
+## absolute_
+
+absolute_() -> Tensor
+
+In-place version of `$absolute`
+Alias for [$abs_()]
+
+## acos
+
+acos() -> Tensor
+
+See `?torch_acos`
+
+## acos_
+
+acos_() -> Tensor
+
+In-place version of `$acos`
+
+## acosh
+
+acosh() -> Tensor
+
+See `?torch_acosh`
+
+## acosh_
+
+acosh_() -> Tensor
+
+In-place version of `$acosh`
+
+## add
+
+add(other, *, alpha=1) -> Tensor
+
+Add a scalar or tensor to `self` tensor. If both `alpha`
+and `other` are specified, each element of `other` is scaled by
+`alpha` before being used.
+
+When `other` is a tensor, the shape of `other` must be
+broadcastable with the shape of the underlying
+tensor
+
+See `?torch_add`
+
+## add_
+
+add_(other, *, alpha=1) -> Tensor
+
+In-place version of `$add`
+
+## addbmm
+
+addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+See `?torch_addbmm`
+
+## addbmm_
+
+addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addbmm`
+
+## addcdiv
+
+addcdiv(tensor1, tensor2, *, value=1) -> Tensor
+
+See `?torch_addcdiv`
+
+## addcdiv_
+
+addcdiv_(tensor1, tensor2, *, value=1) -> Tensor
+
+In-place version of `$addcdiv`
+
+## addcmul
+
+addcmul(tensor1, tensor2, *, value=1) -> Tensor
+
+See `?torch_addcmul`
+
+## addcmul_
+
+addcmul_(tensor1, tensor2, *, value=1) -> Tensor
+
+In-place version of `$addcmul`
+
+## addmm
+
+addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor
+
+See `?torch_addmm`
+
+## addmm_
+
+addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addmm`
+
+## addmv
+
+addmv(mat, vec, *, beta=1, alpha=1) -> Tensor
+
+See `?torch_addmv`
+
+## addmv_
+
+addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addmv`
+
+## addr
+
+addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor
+
+See `?torch_addr`
+
+## addr_
+
+addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$addr`
+
+## align_as
+
+align_as(other) -> Tensor
+
+Permutes the dimensions of the `self` tensor to match the dimension order
+in the `other` tensor, adding size-one dims for any new names.
+
+This operation is useful for explicit broadcasting by names (see examples).
+
+All of the dims of `self` must be named in order to use this method.
+The resulting tensor is a view on the original tensor.
+
+All dimension names of `self` must be present in `other$names`.
+`other` may contain named dimensions that are not in `self$names`;
+the output tensor has a size-one dimension for each of those new names.
+
+To align a tensor to a specific order, use `$align_to`.
+
+### Examples:
+
+```{r eval=FALSE}
+# Example 1: Applying a mask
+mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H'))
+imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C'))
+imgs$masked_fill_(mask$align_as(imgs), 0)
+
+# Example 2: Applying a per-channel-scale
+scale_channels <- function(input, scale) {
+  scale <- scale$refine_names("C")
+  input * scale$align_as(input)
+}
+
+num_channels <- 3
+scale <- torch_randn(num_channels, names='C')
+imgs <- torch_rand(32, 128, 128, num_channels, names=c('N', 'H', 'W', 'C'))
+more_imgs = torch_rand(32, num_channels, 128, 128, names=c('N', 'C', 'H', 'W'))
+videos = torch_randn(3, num_channels, 128, 128, 128, names=c('N', 'C', 'H', 'W', 'D'))
+
+# scale_channels is agnostic to the dimension order of the input
+scale_channels(imgs, scale)
+scale_channels(more_imgs, scale)
+scale_channels(videos, scale)
+```
+
+### Warning:
+    
+The named tensor API is experimental and subject to change.
+
+
+## align_to
+
+Permutes the dimensions of the `self` tensor to match the order
+specified in `names`, adding size-one dims for any new names.
+
+All of the dims of `self` must be named in order to use this method.
+The resulting tensor is a view on the original tensor.
+
+All dimension names of `self` must be present in `names`.
+`names` may contain additional names that are not in `self$names`;
+the output tensor has a size-one dimension for each of those new names.
+
+### Arguments:
+
+* names (iterable of str): The desired dimension ordering of the
+  output tensor. May contain up to one Ellipsis that is expanded
+  to all unmentioned dim names of `self`.
+
+### Examples:
+
+```{r echo = FALSE}
+tensor <- torch_randn(2, 2, 2, 2, 2, 2)
+named_tensor <- tensor$refine_names(names = c('A', 'B', 'C', 'D', 'E', 'F'))
+
+# Move the F and E dims to the front while keeping the rest in order
+named_tensor$align_to(c("A", "B", "F", "C", "E", "D"))
+```
+
+#### Warning:
+            
+The named tensor API is experimental and subject to change.
+
+        
+## all
+
+all() -> bool
+
+Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(1, 2)$to(dtype = torch_bool())
+a
+a$all()
+```
+
+all(dim, keepdim=FALSE, out=NULL) -> Tensor
+
+Returns TRUE if all elements in each row of the tensor in the given
+dimension `dim` are TRUE, FALSE otherwise.
+
+If `keepdim` is `TRUE`, the output tensor is of the same size as
+`input` except in the dimension `dim` where it is of size 1.
+Otherwise, `dim` is squeezed (see `?torch_squeeze()),` resulting
+in the output tensor having 1 fewer dimension than `input`.
+
+#### Arguments: 
+
+* dim (int): the dimension to reduce
+* keepdim (bool): whether the output tensor has `dim` retained or not
+* out (Tensor, optional): the output tensor
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(4, 2)$to(dtype = torch_bool())
+a
+a$all(dim=2)
+a$all(dim=1)
+```
+    
+## allclose
+
+allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
+
+See `?torch_allclose`
+
+## angle
+
+angle() -> Tensor
+
+See `?torch_angle`
+
+## any
+
+any() -> bool
+
+Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(1, 2)$to(dtype = torch_bool())
+a
+a$any()
+```
+
+any(dim, keepdim=FALSE, out=NULL) -> Tensor
+
+Returns TRUE if any elements in each row of the tensor in the given
+dimension `dim` are TRUE, FALSE otherwise.
+
+If `keepdim` is `TRUE`, the output tensor is of the same size as
+`input` except in the dimension `dim` where it is of size 1.
+Otherwise, `dim` is squeezed (see `?torch_squeeze()),` resulting
+in the output tensor having 1 fewer dimension than `input`.
+
+#### Arguments: 
+
+* dim (int): the dimension to reduce
+* keepdim (bool): whether the output tensor has `dim` retained or not
+* out (Tensor, optional): the output tensor
+
+#### Examples: 
+
+```{r}
+a <- torch_randn(4, 2) < 0
+a
+a$any(2)
+a$any(1)
+```
+
+## apply_
+
+apply_(callable) -> Tensor
+
+Applies the function `callable` to each element in the tensor, replacing
+each element with the value returned by `callable`.
+
+#### Note: 
+
+This function only works with CPU tensors and should not be used in code
+sections that require high performance.
+
+## argmax
+
+argmax(dim=NULL, keepdim=FALSE) -> LongTensor
+
+See `?torch_argmax`
+
+## argmin
+
+argmin(dim=NULL, keepdim=FALSE) -> LongTensor
+
+See `?torch_argmin`
+
+## argsort
+
+argsort(dim=-1, descending=FALSE) -> LongTensor
+
+See `?torch_argsort`
+
+## as_strided
+
+as_strided(size, stride, storage_offset=0) -> Tensor
+
+See [torch_as_strided()]
+
+## as_subclass
+
+as_subclass(cls) -> Tensor
+
+Makes a `cls` instance with the same data pointer as `self`. Changes
+in the output mirror changes in `self`, and the output stays attached
+to the autograd graph. `cls` must be a subclass of `Tensor`.
+
+## asin
+
+asin() -> Tensor
+
+See `?torch_asin`
+
+## asin_
+
+asin_() -> Tensor
+
+In-place version of `$asin`
+
+## asinh
+
+asinh() -> Tensor
+
+See `?torch_asinh`
+
+## asinh_
+
+asinh_() -> Tensor
+
+In-place version of `$asinh`
+
+## atan
+
+atan() -> Tensor
+
+See `?torch_atan`
+
+## atan2
+
+atan2(other) -> Tensor
+
+See [torch_atan2()]
+
+## atan2_
+
+atan2_(other) -> Tensor
+
+In-place version of `$atan2`
+
+## atan_
+
+atan_() -> Tensor
+
+In-place version of `$atan`
+
+## atanh
+
+atanh() -> Tensor
+
+See `?torch_atanh`
+
+## atanh_
+
+In-place version of `$atanh`
+
+## backward
+Computes the gradient of current tensor w.r.t. graph leaves.
+
+The graph is differentiated using the chain rule. If the tensor is
+non-scalar (i.e. its data has more than one element) and requires
+gradient, the function additionally requires specifying `gradient`.
+It should be a tensor of matching type and location, that contains
+the gradient of the differentiated function w.r.t. `self`.
+
+This function accumulates gradients in the leaves - you might need to zero
+`$grad` attributes or set them to `NULL` before calling it.
+See `Default gradient layouts`
+for details on the memory layout of accumulated gradients.
+
+#### Arguments:
+
+* gradient (Tensor or NULL): Gradient w.r.t. the
+    tensor. If it is a tensor, it will be automatically converted
+    to a Tensor that does not require grad unless `create_graph` is TRUE.
+    NULL values can be specified for scalar Tensors or ones that
+    don't require grad. If a NULL value would be acceptable then
+    this argument is optional.
+* retain_graph (bool, optional): If `FALSE`, the graph used to compute
+    the grads will be freed. Note that in nearly all cases setting
+    this option to TRUE is not needed and often can be worked around
+    in a much more efficient way. Defaults to the value of
+    `create_graph`.
+* create_graph (bool, optional): If `TRUE`, graph of the derivative will
+    be constructed, allowing to compute higher order derivative
+    products. Defaults to `FALSE`.
+        
+## baddbmm
+
+baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+See `?torch_baddbmm`
+
+## baddbmm_
+
+baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor
+
+In-place version of `$baddbmm`
+
+## bernoulli
+
+bernoulli(*, generator=NULL) -> Tensor
+
+Returns a result tensor where each $\texttt{result[i]}$ is independently
+sampled from $\text{Bernoulli}(\texttt{self[i]})$. `self` must have
+floating point `dtype`, and the result will have the same `dtype`.
+
+See `?torch_bernoulli`
+
+## bernoulli_
+
+bernoulli_(p=0.5, *, generator=NULL) -> Tensor
+
+Fills each location of `self` with an independent sample from
+$\text{Bernoulli}(\texttt{p})$. `self` can have integral
+`dtype`.
+
+bernoulli_(p_tensor, *, generator=NULL) -> Tensor
+
+`p_tensor` should be a tensor containing probabilities to be used for
+drawing the binary random number.
+
+The $\text{i}^{th}$ element of `self` tensor will be set to a
+value sampled from $\text{Bernoulli}(\texttt{p\_tensor[i]})$.
+
+`self` can have integral `dtype`, but `p_tensor` must have
+floating point `dtype`.
+
+See also `$bernoulli` and `?torch_bernoulli`
+
+## bfloat16
+
+bfloat16(memory_format=torch_preserve_format) -> Tensor
+`self$bfloat16()` is equivalent to `self$to(torch_bfloat16)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## bincount
+
+bincount(weights=NULL, minlength=0) -> Tensor
+
+See `?torch_bincount`
+
+## bitwise_and
+
+bitwise_and() -> Tensor
+
+See [torch_bitwise_and()]
+
+## bitwise_and_
+
+bitwise_and_() -> Tensor
+
+In-place version of `$bitwise_and`
+
+## bitwise_not
+
+bitwise_not() -> Tensor
+
+See [torch_bitwise_not()]
+
+## bitwise_not_
+
+bitwise_not_() -> Tensor
+
+In-place version of `$bitwise_not`
+
+## bitwise_or
+
+bitwise_or() -> Tensor
+
+See [torch_bitwise_or()]
+
+## bitwise_or_
+
+bitwise_or_() -> Tensor
+
+In-place version of `$bitwise_or`
+
+## bitwise_xor
+
+bitwise_xor() -> Tensor
+
+See [torch_bitwise_xor()]
+
+## bitwise_xor_
+
+bitwise_xor_() -> Tensor
+
+In-place version of `$bitwise_xor`
+
+## bmm
+
+bmm(batch2) -> Tensor
+
+See `?torch_bmm`
+
+## bool
+
+bool(memory_format=torch_preserve_format) -> Tensor
+
+`self$bool()` is equivalent to `self$to(torch_bool)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## byte
+
+byte(memory_format=torch_preserve_format) -> Tensor
+
+`self$byte()` is equivalent to `self$to(torch_uint8)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cauchy_
+
+cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor
+
+Fills the tensor with numbers drawn from the Cauchy distribution:
+
+$$
+f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
+$$
+
+## ceil
+
+ceil() -> Tensor
+
+See `?torch_ceil`
+
+## ceil_
+
+ceil_() -> Tensor
+
+In-place version of `$ceil`
+
+## char
+
+char(memory_format=torch_preserve_format) -> Tensor
+
+`self$char()` is equivalent to `self$to(torch_int8)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cholesky
+
+cholesky(upper=FALSE) -> Tensor
+
+See `?torch_cholesky`
+
+## cholesky_inverse
+
+cholesky_inverse(upper=FALSE) -> Tensor
+
+See [torch_cholesky_inverse()]
+
+## cholesky_solve
+
+cholesky_solve(input2, upper=FALSE) -> Tensor
+
+See [torch_cholesky_solve()]
+
+## chunk
+
+chunk(chunks, dim=0) -> List of Tensors
+
+See `?torch_chunk`
+
+## clamp
+
+clamp(min, max) -> Tensor
+
+See `?torch_clamp`
+
+## clamp_
+
+clamp_(min, max) -> Tensor
+
+In-place version of `$clamp`
+
+## clone
+
+clone(memory_format=torch_preserve_format) -> Tensor
+
+Returns a copy of the `self` tensor. The copy has the same size and data
+type as `self`.
+
+#### Note: 
+
+Unlike `copy_()`, this function is recorded in the computation graph. Gradients
+propagating to the cloned tensor will propagate to the original tensor.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## conj
+
+conj() -> Tensor
+
+See `?torch_conj`
+
+## contiguous
+
+contiguous(memory_format=torch_contiguous_format) -> Tensor
+
+Returns a contiguous in memory tensor containing the same data as `self` tensor. If
+`self` tensor is already in the specified memory format, this function returns the
+`self` tensor.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_contiguous_format`.
+
+## copy_
+
+copy_(src, non_blocking=FALSE) -> Tensor
+
+Copies the elements from `src` into `self` tensor and returns
+`self`.
+
+The `src` tensor must be :ref:`broadcastable `
+with the `self` tensor. It may be of a different data type or reside on a
+different device.
+
+#### Arguments: 
+
+* src (Tensor): the source tensor to copy from
+* non_blocking (bool): if `TRUE` and this copy is between CPU and GPU,
+* the copy may occur asynchronously with respect to the host. For other
+* cases, this argument has no effect.
+
+## cos
+
+cos() -> Tensor
+
+See `?torch_cos`
+
+## cos_
+
+cos_() -> Tensor
+
+In-place version of `$cos`
+
+## cosh
+
+cosh() -> Tensor
+
+See `?torch_cosh`
+
+## cosh_
+
+cosh_() -> Tensor
+
+In-place version of `$cosh`
+
+## cpu
+
+cpu(memory_format=torch_preserve_format) -> Tensor
+
+Returns a copy of this object in CPU memory.
+
+If this object is already in CPU memory and on the correct device,
+then no copy is performed and the original object is returned.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cross
+
+cross(other, dim=-1) -> Tensor
+
+See `?torch_cross`
+
+## cuda
+
+cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -> Tensor
+
+Returns a copy of this object in CUDA memory.
+
+If this object is already in CUDA memory and on the correct device,
+then no copy is performed and the original object is returned.
+
+#### Arguments: 
+
+* device (`torch_device`): The destination GPU device.
+  Defaults to the current CUDA device.
+* non_blocking (bool): If `TRUE` and the source is in pinned memory,
+  the copy will be asynchronous with respect to the host.
+  Otherwise, the argument has no effect. Default: `FALSE`.
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## cummax
+
+cummax(dim) -> (Tensor, Tensor)
+
+See `?torch_cummax`
+
+## cummin
+
+cummin(dim) -> (Tensor, Tensor)
+
+See `?torch_cummin`
+
+## cumprod
+
+cumprod(dim, dtype=NULL) -> Tensor
+
+See `?torch_cumprod`
+
+## cumsum
+
+cumsum(dim, dtype=NULL) -> Tensor
+
+See `?torch_cumsum`
+
+## data_ptr
+
+data_ptr() -> int
+
+Returns the address of the first element of `self` tensor.
+
+## deg2rad
+
+deg2rad() -> Tensor
+
+See [torch_deg2rad()]
+
+## deg2rad_
+
+deg2rad_() -> Tensor
+
+In-place version of `$deg2rad`
+
+## dense_dim
+
+dense_dim() -> int
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns the number of dense dimensions. Otherwise, this throws an error.
+
+See also `$sparse_dim`.
+
+## dequantize
+
+dequantize() -> Tensor
+
+Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
+
+## det
+
+det() -> Tensor
+
+See `?torch_det`
+
+## detach
+
+Returns a new Tensor, detached from the current graph.
+
+The result will never require gradient.
+
+#### Note:
+
+Returned Tensor shares the same storage with the original one.
+In-place modifications on either of them will be seen, and may trigger
+errors in correctness checks.
+IMPORTANT NOTE: Previously, in-place size / stride / storage changes
+(such as `resize_` / `resize_as_` / `set_` / `transpose_`) to the returned tensor
+also update the original tensor. Now, these in-place changes will not update the
+original tensor anymore, and will instead trigger an error.
+For sparse tensors:
+In-place indices / values changes (such as `zero_` / `copy_` / `add_`) to the
+returned tensor will not update the original tensor anymore, and will instead
+trigger an error.
+  
+## detach_
+
+Detaches the Tensor from the graph that created it, making it a leaf.
+Views cannot be detached in-place.
+    
+## device
+
+Is the `torch_device` where this Tensor is.
+
+## diag
+
+diag(diagonal=0) -> Tensor
+
+See `?torch_diag`
+
+## diag_embed
+
+diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor
+
+See [torch_diag_embed()]
+
+## diagflat
+
+diagflat(offset=0) -> Tensor
+
+See `?torch_diagflat`
+
+## diagonal
+
+diagonal(offset=0, dim1=0, dim2=1) -> Tensor
+
+See `?torch_diagonal`
+
+## digamma
+
+digamma() -> Tensor
+
+See `?torch_digamma`
+
+## digamma_
+
+digamma_() -> Tensor
+
+In-place version of `$digamma`
+
+## dim
+
+dim() -> int
+
+Returns the number of dimensions of `self` tensor.
+
+## dist
+
+dist(other, p=2) -> Tensor
+
+See `?torch_dist`
+
+## div
+
+div(value) -> Tensor
+
+See `?torch_div`
+
+## div_
+
+div_(value) -> Tensor
+
+In-place version of `$div`
+
+## dot
+
+dot(tensor2) -> Tensor
+
+See `?torch_dot`
+
+## double
+
+double(memory_format=torch_preserve_format) -> Tensor
+
+`self$double()` is equivalent to `self$to(torch_float64)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## eig
+
+eig(eigenvectors=FALSE) -> (Tensor, Tensor)
+
+See `?torch_eig`
+
+## element_size
+
+element_size() -> int
+
+Returns the size in bytes of an individual element.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+torch_tensor(c(1))$element_size()
+```
+
+## eq
+
+eq(other) -> Tensor
+
+See `?torch_eq`
+
+## eq_
+
+eq_(other) -> Tensor
+
+In-place version of `$eq`
+
+## equal
+
+equal(other) -> bool
+
+See `?torch_equal`
+
+## erf
+
+erf() -> Tensor
+
+See `?torch_erf`
+
+## erf_
+
+erf_() -> Tensor
+
+In-place version of `$erf`
+
+## erfc
+
+erfc() -> Tensor
+
+See `?torch_erfc`
+
+## erfc_
+
+erfc_() -> Tensor
+
+In-place version of `$erfc`
+
+## erfinv
+
+erfinv() -> Tensor
+
+See `?torch_erfinv`
+
+## erfinv_
+
+erfinv_() -> Tensor
+
+In-place version of `$erfinv`
+
+## exp
+
+exp() -> Tensor
+
+See `?torch_exp`
+
+## exp_
+
+exp_() -> Tensor
+
+In-place version of `$exp`
+
+## expand
+
+expand(*sizes) -> Tensor
+
+Returns a new view of the `self` tensor with singleton dimensions expanded
+to a larger size.
+
+Passing -1 as the size for a dimension means not changing the size of
+that dimension.
+
+Tensor can be also expanded to a larger number of dimensions, and the
+new ones will be appended at the front. For the new dimensions, the
+size cannot be set to -1.
+
+Expanding a tensor does not allocate new memory, but only creates a
+new view on the existing tensor where a dimension of size one is
+expanded to a larger size by setting the `stride` to 0. Any dimension
+of size 1 can be expanded to an arbitrary value without allocating new
+memory.
+
+#### Arguments: 
+
+* sizes (torch_Size or int...): the desired expanded size
+
+#### Warning:
+
+More than one element of an expanded tensor may refer to a single
+memory location. As a result, in-place operations (especially ones that
+are vectorized) may result in incorrect behavior. If you need to write
+to the tensors, please clone them first.
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(c(1,2,3), ncol = 1))
+x$size()
+x$expand(c(3, 4))
+x$expand(c(-1, 4))  # -1 means not changing the size of that dimension
+```
+
+## expand_as
+
+expand_as(other) -> Tensor
+
+Expand this tensor to the same size as `other`.
+`self$expand_as(other)` is equivalent to `self$expand(other.size())`.
+
+Please see `$expand` for more information about `expand`.
+
+#### Arguments: 
+
+* other (`$): The result tensor has the same size
+* as `other`.
+
+## expm1
+
+expm1() -> Tensor
+
+See [torch_expm1()]
+
+## expm1_
+
+expm1_() -> Tensor
+
+In-place version of `$expm1`
+
+## exponential_
+
+exponential_(lambd=1, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with elements drawn from the exponential distribution:
+
+$$
+f(x) = \lambda e^{-\lambda x}
+$$
+
+## fft
+
+fft(signal_ndim, normalized=FALSE) -> Tensor
+
+See `?torch_fft`
+
+## fill_
+
+fill_(value) -> Tensor
+
+Fills `self` tensor with the specified value.
+
+## fill_diagonal_
+
+fill_diagonal_(fill_value, wrap=FALSE) -> Tensor
+
+Fill the main diagonal of a tensor that has at least 2-dimensions.
+When dims>2, all dimensions of input must be of equal length.
+This function modifies the input tensor in-place, and returns the input tensor.
+
+#### Arguments:
+    
+* fill_value (Scalar): the fill value
+* wrap (bool): the diagonal 'wrapped' after N columns for tall matrices.
+
+#### Examples: 
+
+```{r}
+a <- torch_zeros(3, 3)
+a$fill_diagonal_(5)
+b <- torch_zeros(7, 3)
+b$fill_diagonal_(5)
+c <- torch_zeros(7, 3)
+c$fill_diagonal_(5, wrap=TRUE)
+```
+    
+## flatten
+
+flatten(input, start_dim=0, end_dim=-1) -> Tensor
+
+see `?torch_flatten`
+
+## flip
+
+flip(dims) -> Tensor
+
+See `?torch_flip`
+
+## fliplr
+
+fliplr() -> Tensor
+
+See `?torch_fliplr`
+
+## flipud
+
+flipud() -> Tensor
+
+See `?torch_flipud`
+
+## float
+
+float(memory_format=torch_preserve_format) -> Tensor
+
+`self$float()` is equivalent to `self$to(torch_float32)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## floor
+
+floor() -> Tensor
+
+See `?torch_floor`
+
+## floor_
+
+floor_() -> Tensor
+
+In-place version of `$floor`
+
+## floor_divide
+
+floor_divide(value) -> Tensor
+
+See [torch_floor_divide()]
+
+## floor_divide_
+
+floor_divide_(value) -> Tensor
+
+In-place version of `$floor_divide`
+
+## fmod
+
+fmod(divisor) -> Tensor
+
+See `?torch_fmod`
+
+## fmod_
+
+fmod_(divisor) -> Tensor
+
+In-place version of `$fmod`
+
+## frac
+
+frac() -> Tensor
+
+See `?torch_frac`
+
+## frac_
+
+frac_() -> Tensor
+
+In-place version of `$frac`
+
+## gather
+
+gather(dim, index) -> Tensor
+
+See `?torch_gather`
+
+## ge
+
+ge(other) -> Tensor
+
+See `?torch_ge`
+
+## ge_
+
+ge_(other) -> Tensor
+
+In-place version of `$ge`
+
+## geometric_
+
+geometric_(p, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with elements drawn from the geometric distribution:
+
+$$
+f(X=k) = p^{k - 1} (1 - p)
+$$
+
+## geqrf
+
+geqrf() -> (Tensor, Tensor)
+
+See `?torch_geqrf`
+
+## ger
+
+ger(vec2) -> Tensor
+
+See `?torch_ger`
+
+## get_device
+
+get_device() -> Device ordinal (Integer)
+
+For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.
+For CPU tensors, an error is thrown.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_randn(3, 4, 5, device='cuda:0')
+x$get_device()
+x$cpu()$get_device()  # RuntimeError: get_device is not implemented for type torch_FloatTensor
+```
+## grad
+
+This attribute is `NULL` by default and becomes a Tensor the first time a call to
+`backward` computes gradients for `self`.
+The attribute will then contain the gradients computed and future calls to
+[backward()] will accumulate (add) gradients into it.
+        
+## gt
+
+gt(other) -> Tensor
+
+See `?torch_gt`
+
+## gt_
+
+gt_(other) -> Tensor
+
+In-place version of `$gt`
+
+## half
+
+half(memory_format=torch_preserve_format) -> Tensor
+
+`self$half()` is equivalent to `self$to(torch_float16)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## hardshrink
+
+hardshrink(lambd=0.5) -> Tensor
+
+See [torch_nn.functional.hardshrink()]
+
+## has_names
+
+Is `TRUE` if any of this tensor's dimensions are named. Otherwise, is `FALSE`.
+
+## histc
+
+histc(bins=100, min=0, max=0) -> Tensor
+
+See `?torch_histc`
+
+## ifft
+
+ifft(signal_ndim, normalized=FALSE) -> Tensor
+
+See `?torch_ifft`
+
+## imag
+
+Returns a new tensor containing imaginary values of the `self` tensor.
+The returned tensor and `self` share the same underlying storage.
+
+#### Warning:
+
+[imag()] is only supported for tensors with complex dtypes.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_randn(4, dtype=torch_cfloat())
+x
+x$imag
+```
+## index_add
+
+index_add(tensor1, dim, index, tensor2) -> Tensor
+
+Out-of-place version of `$index_add_`.
+`tensor1` corresponds to `self` in `$index_add_`.
+
+## index_add_
+
+index_add_(dim, index, tensor) -> Tensor
+
+Accumulate the elements of `tensor` into the `self` tensor by adding
+to the indices in the order given in `index`. For example, if `dim == 0`
+and `index[i] == j`, then the `i`\ th row of `tensor` is added to the
+`j`\ th row of `self`.
+
+The `dim`\ th dimension of `tensor` must have the same size as the
+length of `index` (which must be a vector), and all other dimensions must
+match `self`, or an error will be raised.
+
+#### Note:
+
+In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting `torch_backends.cudnn.deterministic =
+TRUE`.
+
+#### Arguments: 
+
+* dim (int): dimension along which to index
+* index (LongTensor): indices of `tensor` to select from
+* tensor (Tensor): the tensor containing values to add
+
+#### Examples: 
+
+```{r}
+x <- torch_ones(5, 3)
+t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
+index <- torch_tensor(c(1L, 4L, 3L))
+x$index_add_(1, index, t)
+```
+
+## index_copy
+
+index_copy(tensor1, dim, index, tensor2) -> Tensor
+
+Out-of-place version of `$index_copy_`.
+`tensor1` corresponds to `self` in `$index_copy_`.
+
+## index_copy_
+
+index_copy_(dim, index, tensor) -> Tensor
+
+Copies the elements of `tensor` into the `self` tensor by selecting
+the indices in the order given in `index`. For example, if `dim == 0`
+and `index[i] == j`, then the `i`\ th row of `tensor` is copied to the
+`j`\ th row of `self`.
+
+The `dim`\ th dimension of `tensor` must have the same size as the
+length of `index` (which must be a vector), and all other dimensions must
+match `self`, or an error will be raised.
+
+#### Arguments: 
+
+* dim (int): dimension along which to index
+* index (LongTensor): indices of `tensor` to select from
+* tensor (Tensor): the tensor containing values to copy
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_zeros(5, 3)
+t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
+index <- torch_tensor(c(1, 5, 3))
+x$index_copy_(1, index, t)
+```
+    
+
+## index_fill
+
+index_fill(tensor1, dim, index, value) -> Tensor
+
+Out-of-place version of `$index_fill_`.
+`tensor1` corresponds to `self` in `$index_fill_`.
+
+## index_fill_
+
+index_fill_(dim, index, val) -> Tensor
+
+Fills the elements of the `self` tensor with value `val` by
+selecting the indices in the order given in `index`.
+
+#### Arguments: 
+
+* dim (int): dimension along which to index
+* index (LongTensor): indices of `self` tensor to fill in
+* val (float): the value to fill with
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
+index <- torch_tensor(c(1, 3), dtype = torch_long())
+x$index_fill_(1, index, -1)
+```
+## index_put
+
+index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor
+
+Out-place version of `$index_put_`.
+`tensor1` corresponds to `self` in `$index_put_`.
+
+## index_put_
+
+index_put_(indices, value, accumulate=FALSE) -> Tensor
+
+Puts values from the tensor `value` into the tensor `self` using
+the indices specified in `indices` (which is a tuple of Tensors). The
+expression `tensor.index_put_(indices, value)` is equivalent to
+`tensor[indices] = value`. Returns `self`.
+
+If `accumulate` is `TRUE`, the elements in `value` are added to
+`self`. If accumulate is `FALSE`, the behavior is undefined if indices
+contain duplicate elements.
+
+#### Arguments: 
+
+* indices (tuple of LongTensor): tensors used to index into `self`.
+* value (Tensor): tensor of same dtype as `self`.
+* accumulate (bool): whether to accumulate into self
+
+## index_select
+
+index_select(dim, index) -> Tensor
+
+See [torch_index_select()]
+
+## indices
+
+indices() -> Tensor
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns a view of the contained indices tensor. Otherwise, this throws an
+error.
+
+See also `Tensor.values`.
+
+#### Note: 
+
+
+This method can only be called on a coalesced sparse tensor. See
+`Tensor.coalesce` for details.
+
+## int
+
+int(memory_format=torch_preserve_format) -> Tensor
+
+`self$int()` is equivalent to `self$to(torch_int32)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## int_repr
+
+int_repr() -> Tensor
+
+Given a quantized Tensor,
+`self$int_repr()` returns a CPU Tensor with uint8_t as data type that stores the
+underlying uint8_t values of the given Tensor.
+
+## inverse
+
+inverse() -> Tensor
+
+See `?torch_inverse`
+
+## irfft
+
+irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor
+
+See `?torch_irfft`
+
+## is_complex
+
+is_complex() -> bool
+
+Returns TRUE if the data type of `self` is a complex data type.
+
+## is_contiguous
+
+is_contiguous(memory_format=torch_contiguous_format) -> bool
+
+Returns TRUE if `self` tensor is contiguous in memory in the order specified
+by memory format.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): Specifies memory allocation
+* order. Default: `torch_contiguous_format`.
+
+## is_cuda
+
+Is `TRUE` if the Tensor is stored on the GPU, `FALSE` otherwise.
+
+## is_floating_point
+
+is_floating_point() -> bool
+
+Returns TRUE if the data type of `self` is a floating point data type.
+
+## is_leaf
+
+All Tensors that have `requires_grad` which is `FALSE` will be leaf Tensors by convention.
+
+For Tensors that have `requires_grad` which is `TRUE`, they will be leaf Tensors if they were
+created by the user. This means that they are not the result of an operation and so
+`grad_fn` is NULL.
+
+Only leaf Tensors will have their `grad` populated during a call to [backward()].
+To get `grad` populated for non-leaf Tensors, you can use [retain_grad()].
+
+#### Examples: 
+
+```{r}
+a <- torch_rand(10, requires_grad=TRUE)
+a$is_leaf()
+
+# b <- torch_rand(10, requires_grad=TRUE)$cuda()
+# b$is_leaf()
+# FALSE
+# b was created by the operation that cast a cpu Tensor into a cuda Tensor
+
+c <- torch_rand(10, requires_grad=TRUE) + 2
+c$is_leaf()
+# c was created by the addition operation
+
+# d <- torch_rand(10)$cuda()
+# d$is_leaf()
+# TRUE
+# d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
+
+# e <- torch_rand(10)$cuda()$requires_grad_()
+# e$is_leaf()
+# TRUE
+# e requires gradients and has no operations creating it
+
+# f <- torch_rand(10, requires_grad=TRUE, device="cuda")
+# f$is_leaf
+# TRUE
+# f requires grad, has no operation creating it
+```
+
+## is_meta
+
+Is `TRUE` if the Tensor is a meta tensor, `FALSE` otherwise.  Meta tensors
+are like normal tensors, but they carry no data.
+
+## is_pinned
+
+Returns true if this tensor resides in pinned memory.
+
+## is_quantized
+
+Is `TRUE` if the Tensor is quantized, `FALSE` otherwise.
+
+## is_set_to
+
+is_set_to(tensor) -> bool
+
+Returns TRUE if this object refers to the same `THTensor` object from the
+Torch C API as the given tensor.
+
+## is_shared
+
+Checks if tensor is in shared memory.
+
+This is always `TRUE` for CUDA tensors.
+        
+## is_signed
+
+is_signed() -> bool
+
+Returns TRUE if the data type of `self` is a signed data type.
+
+## isclose
+
+isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor
+
+See `?torch_isclose`
+
+## isfinite
+
+isfinite() -> Tensor
+
+See `?torch_isfinite`
+
+## isinf
+
+isinf() -> Tensor
+
+See `?torch_isinf`
+
+## isnan
+
+isnan() -> Tensor
+
+See `?torch_isnan`
+
+## istft
+See `?torch_istft`
+## item
+
+item() -> number
+
+Returns the value of this tensor as a standard Python number. This only works
+for tensors with one element. For other cases, see `$tolist`.
+
+This operation is not differentiable.
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(1.0)
+x$item()
+```
+    
+## kthvalue
+
+kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
+
+See `?torch_kthvalue`
+
+## le
+
+le(other) -> Tensor
+
+See `?torch_le`
+
+## le_
+
+le_(other) -> Tensor
+
+In-place version of `$le`
+
+## lerp
+
+lerp(end, weight) -> Tensor
+
+See `?torch_lerp`
+
+## lerp_
+
+lerp_(end, weight) -> Tensor
+
+In-place version of `$lerp`
+
+## lgamma
+
+lgamma() -> Tensor
+
+See `?torch_lgamma`
+
+## lgamma_
+
+lgamma_() -> Tensor
+
+In-place version of `$lgamma`
+
+## log
+
+log() -> Tensor
+
+See `?torch_log`
+
+## log10
+
+log10() -> Tensor
+
+See [torch_log10()]
+
+## log10_
+
+log10_() -> Tensor
+
+In-place version of `$log10`
+
+## log1p
+
+log1p() -> Tensor
+
+See [torch_log1p()]
+
+## log1p_
+
+log1p_() -> Tensor
+
+In-place version of `$log1p`
+
+## log2
+
+log2() -> Tensor
+
+See [torch_log2()]
+
+## log2_
+
+log2_() -> Tensor
+
+In-place version of `$log2`
+
+## log_
+
+log_() -> Tensor
+
+In-place version of `$log`
+
+## log_normal_
+
+log_normal_(mean=1, std=2, *, generator=NULL)
+
+Fills `self` tensor with numbers samples from the log-normal distribution
+parameterized by the given mean `\mu` and standard deviation
+`\sigma`. Note that `mean` and `std` are the mean and
+standard deviation of the underlying normal distribution, and not of the
+returned distribution:
+
+$$
+f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
+$$
+
+## logaddexp
+
+logaddexp(other) -> Tensor
+
+See `?torch_logaddexp`
+
+## logaddexp2
+
+logaddexp2(other) -> Tensor
+
+See [torch_logaddexp2()]
+
+## logcumsumexp
+
+logcumsumexp(dim) -> Tensor
+
+See `?torch_logcumsumexp`
+
+## logdet
+
+logdet() -> Tensor
+
+See `?torch_logdet`
+
+## logical_and
+
+logical_and() -> Tensor
+
+See [torch_logical_and()]
+
+## logical_and_
+
+logical_and_() -> Tensor
+
+In-place version of `$logical_and`
+
+## logical_not
+
+logical_not() -> Tensor
+
+See [torch_logical_not()]
+
+## logical_not_
+
+logical_not_() -> Tensor
+
+In-place version of `$logical_not`
+
+## logical_or
+
+logical_or() -> Tensor
+
+See [torch_logical_or()]
+
+## logical_or_
+
+logical_or_() -> Tensor
+
+In-place version of `$logical_or`
+
+## logical_xor
+
+logical_xor() -> Tensor
+
+See [torch_logical_xor()]
+
+## logical_xor_
+
+logical_xor_() -> Tensor
+
+In-place version of `$logical_xor`
+
+## logsumexp
+
+logsumexp(dim, keepdim=FALSE) -> Tensor
+
+See `?torch_logsumexp`
+
+## long
+
+long(memory_format=torch_preserve_format) -> Tensor
+
+`self$long()` is equivalent to `self$to(torch_int64)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## lstsq
+
+lstsq(A) -> (Tensor, Tensor)
+
+See `?torch_lstsq`
+
+## lt
+
+lt(other) -> Tensor
+
+See `?torch_lt`
+
+## lt_
+
+lt_(other) -> Tensor
+
+In-place version of `$lt`
+
+## lu
+See `?torch_lu`
+## lu_solve
+
+lu_solve(LU_data, LU_pivots) -> Tensor
+
+See [torch_lu_solve()]
+
+## map_
+
+map_(tensor, callable)
+
+Applies `callable` for each element in `self` tensor and the given
+`tensor` and stores the results in `self` tensor. `self` tensor and
+the given `tensor` must be broadcastable.
+
+The `callable` should have the signature:
+
+`callable(a, b) -> number`
+
+## masked_fill
+
+masked_fill(mask, value) -> Tensor
+
+Out-of-place version of `$masked_fill_`
+
+## masked_fill_
+
+masked_fill_(mask, value)
+
+Fills elements of `self` tensor with `value` where `mask` is
+TRUE. The shape of `mask` must be
+`broadcastable ` with the shape of the underlying
+tensor.
+
+#### Arguments: 
+
+* mask (BoolTensor): the boolean mask
+* value (float): the value to fill in with
+
+## masked_scatter
+
+masked_scatter(mask, tensor) -> Tensor
+
+Out-of-place version of `$masked_scatter_`
+
+## masked_scatter_
+
+masked_scatter_(mask, source)
+
+Copies elements from `source` into `self` tensor at positions where
+the `mask` is TRUE.
+The shape of `mask` must be :ref:`broadcastable `
+with the shape of the underlying tensor. The `source` should have at least
+as many elements as the number of ones in `mask`
+
+#### Arguments: 
+
+* mask (BoolTensor): the boolean mask
+* source (Tensor): the tensor to copy from
+
+#### Note: 
+
+The `mask` operates on the `self` tensor, not on the given
+`source` tensor.
+
+## masked_select
+
+masked_select(mask) -> Tensor
+
+See [torch_masked_select()]
+
+## matmul
+
+matmul(tensor2) -> Tensor
+
+See `?torch_matmul`
+
+## matrix_power
+
+matrix_power(n) -> Tensor
+
+See [torch_matrix_power()]
+
+## max
+
+max(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
+
+See `?torch_max`
+
+## mean
+
+mean(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
+
+See `?torch_mean`
+
+## median
+
+median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
+
+See `?torch_median`
+
+## min
+
+min(dim=NULL, keepdim=FALSE) -> Tensor or (Tensor, Tensor)
+
+See `?torch_min`
+
+## mm
+
+mm(mat2) -> Tensor
+
+See `?torch_mm`
+
+## mode
+
+mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor)
+
+See `?torch_mode`
+
+## mul
+
+mul(value) -> Tensor
+
+See `?torch_mul`
+
+## mul_
+
+mul_(value)
+
+In-place version of `$mul`
+
+## multinomial
+
+multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor
+
+See `?torch_multinomial`
+
+## mv
+
+mv(vec) -> Tensor
+
+See `?torch_mv`
+
+## mvlgamma
+
+mvlgamma(p) -> Tensor
+
+See `?torch_mvlgamma`
+
+## mvlgamma_
+
+mvlgamma_(p) -> Tensor
+
+In-place version of `$mvlgamma`
+
+## names
+
+Stores names for each of this tensor's dimensions.
+
+`names[idx]` corresponds to the name of tensor dimension `idx`.
+Names are either a string if the dimension is named or `NULL` if the
+dimension is unnamed.
+
+Dimension names may contain characters or underscore. Furthermore, a dimension
+name must be a valid Python variable name (i.e., does not start with underscore).
+
+Tensors may not have two named dimensions with the same name.
+
+#### Warning:
+
+The named tensor API is experimental and subject to change.
+
+
+## narrow
+
+narrow(dimension, start, length) -> Tensor
+
+See `?torch_narrow`
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_tensor(matrix(1:9, ncol = 3))
+x$narrow(1, 1, 3)
+x$narrow(1, 1, 2)
+```
+
+## narrow_copy
+
+narrow_copy(dimension, start, length) -> Tensor
+
+Same as `Tensor.narrow` except returning a copy rather
+than shared storage.  This is primarily for sparse tensors, which
+do not have a shared-storage narrow method.  Calling ``narrow_copy`
+with ``dimemsion > self$sparse_dim()`` will return a copy with the
+relevant dense dimension narrowed, and ``self$shape`` updated accordingly.
+
+## ndim
+
+Alias for `$dim()`
+
+## ndimension
+
+ndimension() -> int
+
+Alias for `$dim()`
+
+## ne
+
+ne(other) -> Tensor
+
+See `?torch_ne`
+
+## ne_
+
+ne_(other) -> Tensor
+
+In-place version of `$ne`
+
+## neg
+
+neg() -> Tensor
+
+See `?torch_neg`
+
+## neg_
+
+neg_() -> Tensor
+
+In-place version of `$neg`
+
+## nelement
+
+nelement() -> int
+
+Alias for `$numel`
+
+## new_empty
+
+new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with uninitialized data.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_ones(5)
+tensor$new_empty(c(2, 3))
+```
+
+## new_full
+
+new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with `fill_value`.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* fill_value (scalar): the number to fill the output tensor with.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_ones(c(2), dtype=torch_float64())
+tensor$new_full(c(3, 4), 3.141592)
+```
+## new_ones
+
+new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with `1`.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* size (int...): a list, tuple, or `torch_Size` of integers defining the
+* shape of the output tensor.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+tensor <- torch_tensor(c(2), dtype=torch_int32())
+tensor$new_ones(c(2, 3))
+```
+
+## new_tensor
+
+new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a new Tensor with `data` as the tensor data.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Warning: 
+
+`new_tensor` always copies `data(). If you have a Tensor
+`data` and want to avoid a copy, use [$requires_grad_()]
+or [$detach()].
+If you have a numpy array and want to avoid a copy, use
+[torch_from_numpy()].
+
+
+When data is a tensor `x`, [new_tensor()()] reads out 'the data' from whatever it is passed,
+and constructs a leaf variable. Therefore `tensor$new_tensor(x)` is equivalent to `x$clone()$detach()`
+and `tensor$new_tensor(x, requires_grad=TRUE)` is equivalent to `x$clone()$detach()$requires_grad_(TRUE)`.
+The equivalents using `clone()` and `detach()` are recommended.
+
+#### Arguments: 
+
+* data (array_like): The returned Tensor copies `data`.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+tensor <- torch_ones(c(2), dtype=torch_int8)
+data <- matrix(1:4, ncol = 2)
+tensor$new_tensor(data)
+```
+## new_zeros
+
+new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor
+
+Returns a Tensor of size `size` filled with `0`.
+By default, the returned Tensor has the same `torch_dtype` and
+`torch_device` as this tensor.
+
+#### Arguments: 
+
+* size (int...): a list, tuple, or `torch_Size` of integers defining the
+* shape of the output tensor.
+* dtype (`torch_dtype`, optional): the desired type of returned tensor.
+        Default: if NULL, same `torch_dtype` as this tensor.
+* device (`torch_device`, optional): the desired device of returned tensor.
+        Default: if NULL, same `torch_device` as this tensor.
+* requires_grad (bool, optional): If autograd should record operations on the
+* returned tensor. Default: `FALSE`.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_tensor(c(1), dtype=torch_float64())
+tensor$new_zeros(c(2, 3))
+```
+
+## nonzero
+
+nonzero() -> LongTensor
+
+See `?torch_nonzero`
+
+## norm
+See `?torch_norm`
+## normal_
+
+normal_(mean=0, std=1, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with elements samples from the normal distribution
+parameterized by `mean` and `std`.
+
+## numel
+
+numel() -> int
+
+See `?torch_numel`
+
+## numpy
+
+numpy() -> numpy.ndarray
+
+Returns `self` tensor as a NumPy :class:`ndarray`. This tensor and the
+returned `ndarray` share the same underlying storage. Changes to
+`self` tensor will be reflected in the :class:`ndarray` and vice versa.
+
+## orgqr
+
+orgqr(input2) -> Tensor
+
+See `?torch_orgqr`
+
+## ormqr
+
+ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor
+
+See `?torch_ormqr`
+
+## permute
+
+permute(*dims) -> Tensor
+
+Returns a view of the original tensor with its dimensions permuted.
+
+#### Arguments: 
+
+* dims (int...): The desired ordering of dimensions
+
+#### Examples:
+
+```{r}
+x <- torch_randn(2, 3, 5)
+x$size()
+x$permute(c(3, 1, 2))$size()
+```
+
+## pin_memory
+
+pin_memory() -> Tensor
+
+Copies the tensor to pinned memory, if it's not already pinned.
+
+## pinverse
+
+pinverse() -> Tensor
+
+See `?torch_pinverse`
+
+## polygamma
+
+polygamma(n) -> Tensor
+
+See `?torch_polygamma`
+
+## polygamma_
+
+polygamma_(n) -> Tensor
+
+In-place version of `$polygamma`
+
+## pow
+
+pow(exponent) -> Tensor
+
+See `?torch_pow`
+
+## pow_
+
+pow_(exponent) -> Tensor
+
+In-place version of `$pow`
+
+## prod
+
+prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
+
+See `?torch_prod`
+
+## put_
+
+put_(indices, tensor, accumulate=FALSE) -> Tensor
+
+Copies the elements from `tensor` into the positions specified by
+indices. For the purpose of indexing, the `self` tensor is treated as if
+it were a 1-D tensor.
+
+If `accumulate` is `TRUE`, the elements in `tensor` are added to
+`self`. If accumulate is `FALSE`, the behavior is undefined if indices
+contain duplicate elements.
+
+#### Arguments: 
+
+* indices (LongTensor): the indices into self
+* tensor (Tensor): the tensor containing values to copy from
+* accumulate (bool): whether to accumulate into self
+
+#### Examples: 
+
+```{r}
+src <- torch_tensor(matrix(3:8, ncol = 3))
+src$put_(torch_tensor(1:2), torch_tensor(9:10))
+```
+   
+## q_per_channel_axis
+
+q_per_channel_axis() -> int
+
+Given a Tensor quantized by linear (affine) per-channel quantization,
+returns the index of dimension on which per-channel quantization is applied.
+
+## q_per_channel_scales
+
+q_per_channel_scales() -> Tensor
+
+Given a Tensor quantized by linear (affine) per-channel quantization,
+returns a Tensor of scales of the underlying quantizer. It has the number of
+elements that matches the corresponding dimensions (from q_per_channel_axis) of
+the tensor.
+
+## q_per_channel_zero_points
+
+q_per_channel_zero_points() -> Tensor
+
+Given a Tensor quantized by linear (affine) per-channel quantization,
+returns a tensor of zero_points of the underlying quantizer. It has the number of
+elements that matches the corresponding dimensions (from q_per_channel_axis) of
+the tensor.
+
+## q_scale
+
+q_scale() -> float
+
+Given a Tensor quantized by linear(affine) quantization,
+returns the scale of the underlying quantizer().
+
+## q_zero_point
+
+q_zero_point() -> int
+
+Given a Tensor quantized by linear(affine) quantization,
+returns the zero_point of the underlying quantizer().
+
+## qr
+
+qr(some=TRUE) -> (Tensor, Tensor)
+
+See `?torch_qr`
+
+## qscheme
+
+qscheme() -> torch_qscheme
+
+Returns the quantization scheme of a given QTensor.
+
+## rad2deg
+
+rad2deg() -> Tensor
+
+See [torch_rad2deg()]
+
+## rad2deg_
+
+rad2deg_() -> Tensor
+
+In-place version of `$rad2deg`
+
+## random_
+
+random_(from=0, to=NULL, *, generator=NULL) -> Tensor
+
+Fills `self` tensor with numbers sampled from the discrete uniform
+distribution over `[from, to - 1]`. If not specified, the values are usually
+only bounded by `self` tensor's data type. However, for floating point
+types, if unspecified, range will be `[0, 2^mantissa]` to ensure that every
+value is representable. For example, `torch_tensor(1, dtype=torch_double).random_()`
+will be uniform in `[0, 2^53]`.
+
+## real
+
+Returns a new tensor containing real values of the `self` tensor.
+The returned tensor and `self` share the same underlying storage.
+
+#### Warning:
+
+[real()] is only supported for tensors with complex dtypes.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_randn(4, dtype=torch_cfloat())
+x
+x$real
+```
+
+## reciprocal
+
+reciprocal() -> Tensor
+
+See `?torch_reciprocal`
+
+## reciprocal_
+
+reciprocal_() -> Tensor
+
+In-place version of `$reciprocal`
+
+## record_stream
+
+record_stream(stream)
+
+Ensures that the tensor memory is not reused for another tensor until all
+current work queued on `stream` are complete.
+
+#### Note: 
+
+The caching allocator is aware of only the stream where a tensor was
+allocated. Due to the awareness, it already correctly manages the life
+cycle of tensors on only one stream. But if a tensor is used on a stream
+different from the stream of origin, the allocator might reuse the memory
+unexpectedly. Calling this method lets the allocator know which streams
+have used the tensor.
+
+
+## refine_names
+
+Refines the dimension names of `self` according to `names`.
+
+Refining is a special case of renaming that "lifts" unnamed dimensions.
+A `NULL` dim can be refined to have any name; a named dim can only be
+refined to have the same name.
+
+Because named tensors can coexist with unnamed tensors, refining names
+gives a nice way to write named-tensor-aware code that works with both
+named and unnamed tensors.
+
+`names` may contain up to one Ellipsis (`...`).
+The Ellipsis is expanded greedily; it is expanded in-place to fill
+`names` to the same length as `self$dim()` using names from the
+corresponding indices of `self$names`.
+
+#### Arguments:
+* names (iterable of str): The desired names of the output tensor. May
+  contain up to one Ellipsis.
+
+#### Examples:
+
+```{r, eval = FALSE}
+imgs <- torch_randn(32, 3, 128, 128)
+named_imgs <- imgs$refine_names(c('N', 'C', 'H', 'W'))
+named_imgs$names
+```
+        
+## register_hook
+Registers a backward hook.
+
+The hook will be called every time a gradient with respect to the
+Tensor is computed. The hook should have the following signature::
+
+hook(grad) -> Tensor or NULL
+
+The hook should not modify its argument, but it can optionally return
+a new gradient which will be used in place of `grad`.
+
+This function returns a handle with a method `handle$remove()`
+that removes the hook from the module.
+
+#### Example
+
+```{r}
+v <- torch_tensor(c(0., 0., 0.), requires_grad=TRUE)
+h <- v$register_hook(function(grad) grad * 2)  # double the gradient
+v$backward(torch_tensor(c(1., 2., 3.)))
+v$grad
+h$remove()
+```
+        
+## remainder
+
+remainder(divisor) -> Tensor
+
+See `?torch_remainder`
+
+## remainder_
+
+remainder_(divisor) -> Tensor
+
+In-place version of `$remainder`
+
+## rename
+
+Renames dimension names of `self`.
+
+There are two main usages:
+
+`self$rename(**rename_map)` returns a view on tensor that has dims
+renamed as specified in the mapping `rename_map`.
+
+`self$rename(*names)` returns a view on tensor, renaming all
+dimensions positionally using `names`.
+Use `self$rename(NULL)` to drop names on a tensor.
+
+One cannot specify both positional args `names` and keyword args
+`rename_map`.
+
+#### Examples:
+
+```{r}
+imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W'))
+renamed_imgs <- imgs$rename(c("Batch", "Channels", "Height", "Width"))
+```
+        
+## rename_
+
+In-place version of `$rename`.
+
+## renorm
+
+renorm(p, dim, maxnorm) -> Tensor
+
+See `?torch_renorm`
+
+## renorm_
+
+renorm_(p, dim, maxnorm) -> Tensor
+
+In-place version of `$renorm`
+
+## repeat
+
+repeat(*sizes) -> Tensor
+
+Repeats this tensor along the specified dimensions.
+
+Unlike `$expand`, this function copies the tensor's data.
+
+#### Arguments: 
+
+* sizes (torch_Size or int...): The number of times to repeat this tensor along each
+* dimension
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(c(1, 2, 3))
+x$`repeat`(c(4, 2))
+x$`repeat`(c(4, 2, 1))$size()
+```
+
+## repeat_interleave
+
+repeat_interleave(repeats, dim=NULL) -> Tensor
+
+See [torch_repeat_interleave()].
+
+## requires_grad
+
+Is `TRUE` if gradients need to be computed for this Tensor, `FALSE` otherwise.
+
+#### Note: 
+
+The fact that gradients need to be computed for a Tensor do not mean that the `grad`
+attribute will be populated, see `is_leaf` for more details.
+
+## requires_grad_
+
+requires_grad_(requires_grad=TRUE) -> Tensor
+
+Change if autograd should record operations on this tensor: sets this tensor's
+`requires_grad` attribute in-place. Returns this tensor.
+
+[requires_grad_()]'s main use case is to tell autograd to begin recording
+operations on a Tensor `tensor`. If `tensor` has `requires_grad=FALSE`
+(because it was obtained through a DataLoader, or required preprocessing or
+initialization), `tensor.requires_grad_()` makes it so that autograd will
+begin to record operations on `tensor`.
+
+#### Arguments: 
+
+* requires_grad (bool): If autograd should record operations on this tensor.
+        Default: `TRUE`.
+
+#### Examples: 
+
+```{r, eval = FALSE}
+# Let's say we want to preprocess some saved weights and use
+# the result as new weights.
+saved_weights <- c(0.1, 0.2, 0.3, 0.25)
+loaded_weights <- torch_tensor(saved_weights)
+weights <- preprocess(loaded_weights)  # some function
+weights
+
+# Now, start to record operations done to weights
+weights$requires_grad_()
+out <- weights$pow(2)$sum()
+out$backward()
+weights$grad
+```
+    
+## reshape
+
+reshape(*shape) -> Tensor
+
+Returns a tensor with the same data and number of elements as `self`
+but with the specified shape. This method returns a view if `shape` is
+compatible with the current shape. See `$view` on when it is
+possible to return a view.
+
+See `?torch_reshape`
+
+#### Arguments: 
+
+* shape (tuple of ints or int...): the desired shape
+ 
+## reshape_as
+
+reshape_as(other) -> Tensor
+
+Returns this tensor as the same shape as `other`.
+`self$reshape_as(other)` is equivalent to `self$reshape(other.sizes())`.
+This method returns a view if `other.sizes()` is compatible with the current
+shape. See `$view` on when it is possible to return a view.
+
+Please see `reshape` for more information about `reshape`.
+
+#### Arguments: 
+
+* other (`$): The result tensor has the same shape
+* as `other`.
+
+## resize_
+
+resize_(*sizes, memory_format=torch_contiguous_format) -> Tensor
+
+Resizes `self` tensor to the specified size. If the number of elements is
+larger than the current storage size, then the underlying storage is resized
+to fit the new number of elements. If the number of elements is smaller, the
+underlying storage is not changed. Existing elements are preserved but any new
+memory is uninitialized.
+
+#### Warning:
+
+This is a low-level method. The storage is reinterpreted as C-contiguous,
+ignoring the current strides (unless the target size equals the current
+size, in which case the tensor is left unchanged). For most purposes, you
+will instead want to use `$view()`, which checks for
+contiguity, or `$reshape()`, which copies data if needed. To
+change the size in-place with custom strides, see `$set_()`.
+
+#### Arguments: 
+
+* sizes (torch_Size or int...): the desired size
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+  Tensor. Default: `torch_contiguous_format`. Note that memory format of
+  `self` is going to be unaffected if `self$size()` matches `sizes`.
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(1:6, ncol = 2))
+x$resize_(c(2, 2))
+```
+    
+## resize_as_
+
+resize_as_(tensor, memory_format=torch_contiguous_format) -> Tensor
+
+Resizes the `self` tensor to be the same size as the specified
+`tensor`. This is equivalent to `self$resize_(tensor.size())`.
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+    Tensor. Default: `torch_contiguous_format`. Note that memory format of
+    `self` is going to be unaffected if `self$size()` matches `tensor.size()`.
+
+
+## retain_grad
+
+Enables `$grad` attribute for non-leaf Tensors.
+
+## rfft
+
+rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor
+
+See `?torch_rfft`
+
+## roll
+
+roll(shifts, dims) -> Tensor
+
+See `?torch_roll`
+
+## rot90
+
+rot90(k, dims) -> Tensor
+
+See [torch_rot90()]
+
+## round
+
+round() -> Tensor
+
+See `?torch_round`
+
+## round_
+
+round_() -> Tensor
+
+In-place version of `$round`
+
+## rsqrt
+
+rsqrt() -> Tensor
+
+See `?torch_rsqrt`
+
+## rsqrt_
+
+rsqrt_() -> Tensor
+
+In-place version of `$rsqrt`
+
+## scatter
+
+scatter(dim, index, src) -> Tensor
+
+Out-of-place version of `$scatter_`
+
+## scatter_
+
+scatter_(dim, index, src) -> Tensor
+
+Writes all values from the tensor `src` into `self` at the indices
+specified in the `index` tensor. For each value in `src`, its output
+index is specified by its index in `src` for `dimension != dim` and by
+the corresponding value in `index` for `dimension = dim`.
+
+For a 3-D tensor, `self` is updated as:
+
+```
+self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0
+self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1
+self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2
+```
+
+This is the reverse operation of the manner described in `$gather`.
+
+`self`, `index` and `src` (if it is a Tensor) should have same
+number of dimensions. It is also required that `index.size(d) <= src.size(d)`
+for all dimensions `d`, and that `index.size(d) <= self$size(d)` for all
+dimensions `d != dim`.
+
+Moreover, as for `$gather`, the values of `index` must be
+between `0` and `self$size(dim) - 1` inclusive, and all values in a row
+along the specified dimension `dim` must be unique.
+
+#### Arguments: 
+
+* dim (int): the axis along which to index
+* index (LongTensor): the indices of elements to scatter,
+* can be either empty or the same size of src.
+      When empty, the operation returns identity
+* src (Tensor): the source element(s) to scatter,
+* incase `value` is not specified
+* value (float): the source element(s) to scatter,
+* incase `src` is not specified
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_rand(2, 5)
+x
+torch_zeros(3, 5)$scatter_(
+        1, 
+        torch_tensor(rbind(c(2, 3, 3, 1, 1), c(3, 1, 1, 2, 3)), x)
+)
+
+z <- torch_zeros(2, 4)$scatter_(
+        2, 
+        torch_tensor(matrix(3:4, ncol = 1)), 1.23
+)
+```
+    
+
+## scatter_add
+
+scatter_add(dim, index, src) -> Tensor
+
+Out-of-place version of `$scatter_add_`
+
+## scatter_add_
+
+scatter_add_(dim, index, src) -> Tensor
+
+Adds all values from the tensor `other` into `self` at the indices
+specified in the `index` tensor in a similar fashion as
+`~$scatter_`. For each value in `src`, it is added to
+an index in `self` which is specified by its index in `src`
+for `dimension != dim` and by the corresponding value in `index` for
+`dimension = dim`.
+
+For a 3-D tensor, `self` is updated as::
+
+```
+self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0
+self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1
+self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2
+```
+
+`self`, `index` and `src` should have same number of
+dimensions. It is also required that `index.size(d) <= src.size(d)` for all
+dimensions `d`, and that `index.size(d) <= self$size(d)` for all dimensions
+`d != dim`.
+
+#### Note:
+    
+In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting `torch_backends.cudnn.deterministic = TRUE`.
+    
+#### Arguments: 
+
+* dim (int): the axis along which to index
+* index (LongTensor): the indices of elements to scatter and add,
+* can be either empty or the same size of src.
+      When empty, the operation returns identity.
+* src (Tensor): the source elements to scatter and add
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_rand(2, 5)
+x
+torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)
+```
+    
+## select
+
+select(dim, index) -> Tensor
+
+Slices the `self` tensor along the selected dimension at the given index.
+This function returns a view of the original tensor with the given dimension removed.
+
+#### Arguments: 
+
+* dim (int): the dimension to slice
+* index (int): the index to select with
+
+#### Note: 
+
+`select` is equivalent to slicing. For example,
+`tensor$select(0, index)` is equivalent to `tensor[index]` and
+`tensor$select(2, index)` is equivalent to `tensor[:,:,index]`.
+
+## set_
+
+set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> Tensor
+
+Sets the underlying storage, size, and strides. If `source` is a tensor,
+`self` tensor will share the same storage and have the same size and
+strides as `source`. Changes to elements in one tensor will be reflected
+in the other.
+
+#### Arguments: 
+
+* source (Tensor or Storage): the tensor or storage to use
+* storage_offset (int, optional): the offset in the storage
+* size (torch_Size, optional): the desired size. Defaults to the size of the source.
+* stride (tuple, optional): the desired stride. Defaults to C-contiguous strides.
+
+## share_memory_
+
+Moves the underlying storage to shared memory.
+
+This is a no-op if the underlying storage is already in shared memory
+and for CUDA tensors. Tensors in shared memory cannot be resized.
+        
+## short
+
+short(memory_format=torch_preserve_format) -> Tensor
+
+`self$short()` is equivalent to `self$to(torch_int16)`. See [to()].
+
+#### Arguments: 
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+* returned Tensor. Default: `torch_preserve_format`.
+
+## sigmoid
+
+sigmoid() -> Tensor
+
+See `?torch_sigmoid`
+
+## sigmoid_
+
+sigmoid_() -> Tensor
+
+In-place version of `$sigmoid`
+
+## sign
+
+sign() -> Tensor
+
+See `?torch_sign`
+
+## sign_
+
+sign_() -> Tensor
+
+In-place version of `$sign`
+
+## sin
+
+sin() -> Tensor
+
+See `?torch_sin`
+
+## sin_
+
+sin_() -> Tensor
+
+In-place version of `$sin`
+
+## sinh
+
+sinh() -> Tensor
+
+See `?torch_sinh`
+
+## sinh_
+
+sinh_() -> Tensor
+
+In-place version of `$sinh`
+
+## size
+
+size() -> torch_Size
+
+Returns the size of the `self` tensor. The returned value is a subclass of
+`tuple`.
+
+#### Examples: 
+
+```{r}
+torch_empty(3, 4, 5)$size()
+```
+    
+## slogdet
+
+slogdet() -> (Tensor, Tensor)
+
+See `?torch_slogdet`
+
+## solve
+
+solve(A) -> Tensor, Tensor
+
+See `?torch_solve`
+
+## sort
+
+sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor)
+
+See `?torch_sort`
+
+## sparse_dim
+
+sparse_dim() -> int
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns the number of sparse dimensions. Otherwise, this throws an error.
+
+See also `Tensor.dense_dim`.
+
+## sparse_mask
+
+sparse_mask(input, mask) -> Tensor
+
+Returns a new SparseTensor with values from Tensor `input` filtered
+by indices of `mask` and values are ignored. `input` and `mask`
+must have the same shape.
+
+#### Arguments: 
+
+* input (Tensor): an input Tensor
+* mask (SparseTensor): a SparseTensor which we filter `input` based on its indices
+
+## split
+See `?torch_split`
+        
+## sqrt
+
+sqrt() -> Tensor
+
+See `?torch_sqrt`
+
+## sqrt_
+
+sqrt_() -> Tensor
+
+In-place version of `$sqrt`
+
+## square
+
+square() -> Tensor
+
+See `?torch_square`
+
+## square_
+
+square_() -> Tensor
+
+In-place version of `$square`
+
+## squeeze
+
+squeeze(dim=NULL) -> Tensor
+
+See `?torch_squeeze`
+
+## squeeze_
+
+squeeze_(dim=NULL) -> Tensor
+
+In-place version of `$squeeze`
+
+## std
+
+std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
+
+See `?torch_std`
+
+## stft
+See `?torch_stft`
+        
+## storage
+
+storage() -> torch_Storage
+
+Returns the underlying storage.
+
+## storage_offset
+
+storage_offset() -> int
+
+Returns `self` tensor's offset in the underlying storage in terms of
+number of storage elements (not bytes).
+
+#### Examples: 
+
+```{r, eval = FALSE}
+x <- torch_tensor(c(1, 2, 3, 4, 5))
+x$storage_offset()
+x[3:N]$storage_offset()
+```
+
+## storage_type
+
+storage_type() -> type
+
+Returns the type of the underlying storage.
+
+## stride
+
+stride(dim) -> tuple or int
+
+Returns the stride of `self` tensor.
+
+Stride is the jump necessary to go from one element to the next one in the
+specified dimension `dim`. A tuple of all strides is returned when no
+argument is passed in. Otherwise, an integer value is returned as the stride in
+the particular dimension `dim`.
+
+#### Arguments: 
+
+* dim (int, optional): the desired dimension in which stride is required
+
+#### Examples: 
+
+```{r}
+x <- torch_tensor(matrix(1:10, nrow = 2))
+x$stride()
+x$stride(1)
+x$stride(-1)
+```
+    
+## sub
+
+sub(other, *, alpha=1) -> Tensor
+
+Subtracts a scalar or tensor from `self` tensor. If both `alpha`
+and `other` are specified, each element of `other` is scaled by
+`alpha` before being used.
+
+When `other` is a tensor, the shape of `other` must be
+`broadcastable ` with the shape of the underlying
+tensor.
+
+
+## sub_
+
+sub_(other, *, alpha=1) -> Tensor
+
+In-place version of `$sub`
+
+## sum
+
+sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor
+
+See `?torch_sum`
+
+## sum_to_size
+
+sum_to_size(*size) -> Tensor
+
+Sum `this` tensor to `size`.
+`size` must be broadcastable to `this` tensor size.
+
+#### Arguments: 
+
+* size (int...): a sequence of integers defining the shape of the output tensor.
+
+## svd
+
+svd(some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)
+
+See `?torch_svd`
+
+## symeig
+
+symeig(eigenvectors=FALSE, upper=TRUE) -> (Tensor, Tensor)
+
+See `?torch_symeig`
+
+## t
+
+t() -> Tensor
+
+See `?torch_t`
+
+## t_
+
+t_() -> Tensor
+
+In-place version of `$t`
+
+## take
+
+take(indices) -> Tensor
+
+See `?torch_take`
+
+## tan
+
+tan() -> Tensor
+
+See `?torch_tan`
+
+## tan_
+
+tan_() -> Tensor
+
+In-place version of `$tan`
+
+## tanh
+
+tanh() -> Tensor
+
+See `?torch_tanh`
+
+## tanh_
+
+tanh_() -> Tensor
+
+In-place version of `$tanh`
+
+## to
+
+to(*args, **kwargs) -> Tensor
+
+Performs Tensor dtype and/or device conversion. A `torch_dtype` and :class:`torch_device` are
+inferred from the arguments of `self$to(*args, **kwargs)`.
+
+#### Note: 
+
+If the `self` Tensor already
+has the correct `torch_dtype` and :class:`torch_device`, then `self` is returned.
+Otherwise, the returned tensor is a copy of `self` with the desired
+`torch_dtype` and :class:`torch_device`.
+
+Here are the ways to call `to`:
+
+to(dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor
+
+Returns a Tensor with the specified `dtype`
+
+#### Arguments:
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+  returned Tensor. Default: `torch_preserve_format`.
+
+to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor
+
+Returns a Tensor with the specified `device` and (optional)
+`dtype`. If `dtype` is `NULL` it is inferred to be `self$dtype`.
+When `non_blocking`, tries to convert asynchronously with respect to
+the host if possible, e.g., converting a CPU Tensor with pinned memory to a
+CUDA Tensor.
+
+When `copy` is set, a new Tensor is created even when the Tensor
+already matches the desired conversion.
+
+#### Arguments:
+
+* memory_format (`torch_memory_format`, optional): the desired memory format of
+  returned Tensor. Default: `torch_preserve_format`.
+
+function:: to(other, non_blocking=FALSE, copy=FALSE) -> Tensor
+
+Returns a Tensor with same `torch_dtype` and :class:`torch_device` as
+the Tensor `other`. When `non_blocking`, tries to convert
+asynchronously with respect to the host if possible, e.g., converting a CPU
+Tensor with pinned memory to a CUDA Tensor.
+
+When `copy` is set, a new Tensor is created even when the Tensor
+already matches the desired conversion.
+
+#### Examples: 
+
+```{r}
+tensor <- torch_randn(2, 2)  # Initially dtype=float32, device=cpu
+tensor$to(dtype = torch_float64())
+
+other <- torch_randn(1, dtype=torch_float64())
+tensor$to(other = other, non_blocking=TRUE)
+```
+    
+## to_mkldnn
+
+to_mkldnn() -> Tensor
+Returns a copy of the tensor in `torch_mkldnn` layout.
+
+
+## to_sparse
+
+to_sparse(sparseDims) -> Tensor
+Returns a sparse copy of the tensor.  PyTorch supports sparse tensors in
+`coordinate format `.
+
+#### Arguments: 
+
+* sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor
+
+## tolist
+
+tolist() -> list or number
+
+Returns the tensor as a (nested) list. For scalars, a standard
+Python number is returned, just like with `$item`.
+Tensors are automatically moved to the CPU first if necessary.
+
+This operation is not differentiable.
+
+## topk
+
+topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)
+
+See `?torch_topk`
+
+## trace
+
+trace() -> Tensor
+
+See `?torch_trace`
+
+## transpose
+
+transpose(dim0, dim1) -> Tensor
+
+See `?torch_transpose`
+
+## transpose_
+
+transpose_(dim0, dim1) -> Tensor
+
+In-place version of `$transpose`
+
+## triangular_solve
+
+triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor)
+
+See [torch_triangular_solve()]
+
+## tril
+
+tril(k=0) -> Tensor
+
+See `?torch_tril`
+
+## tril_
+
+tril_(k=0) -> Tensor
+
+In-place version of `$tril`
+
+## triu
+
+triu(k=0) -> Tensor
+
+See `?torch_triu`
+
+## triu_
+
+triu_(k=0) -> Tensor
+
+In-place version of `$triu`
+
+## true_divide
+
+true_divide(value) -> Tensor
+
+See [torch_true_divide()]
+
+## true_divide_
+
+true_divide_(value) -> Tensor
+
+In-place version of `$true_divide_`
+
+## trunc
+
+trunc() -> Tensor
+
+See `?torch_trunc`
+
+## trunc_
+
+trunc_() -> Tensor
+
+In-place version of `$trunc`
+
+## type
+
+type(dtype=NULL, non_blocking=FALSE, **kwargs) -> str or Tensor
+Returns the type if `dtype` is not provided, else casts this object to
+the specified type.
+
+If this is already of the correct type, no copy is performed and the
+original object is returned.
+
+#### Arguments: 
+
+* dtype (type or string): The desired type
+* non_blocking (bool): If `TRUE`, and the source is in pinned memory
+* and destination is on the GPU or vice versa, the copy is performed
+* asynchronously with respect to the host. Otherwise, the argument
+* has no effect.
+    **kwargs: For compatibility, may contain the key `async` in place of
+* the `non_blocking` argument. The `async` arg is deprecated.
+
+## type_as
+
+type_as(tensor) -> Tensor
+
+Returns this tensor cast to the type of the given tensor.
+
+This is a no-op if the tensor is already of the correct type. This is
+equivalent to `self$type(tensor.type())`
+
+#### Arguments: 
+
+* tensor (Tensor): the tensor which has the desired type
+
+## unbind
+
+unbind(dim=0) -> seq
+
+See `?torch_unbind`
+
+## unflatten
+
+Unflattens the named dimension `dim`, viewing it in the shape
+specified by `namedshape`.
+
+#### Arguments:
+
+* namedshape: (iterable of `(name, size)` tuples).
+        
+## unfold
+
+unfold(dimension, size, step) -> Tensor
+
+Returns a view of the original tensor which contains all slices of size `size` from
+`self` tensor in the dimension `dimension`.
+
+Step between two slices is given by `step`.
+
+If `sizedim` is the size of dimension `dimension` for `self`, the size of
+dimension `dimension` in the returned tensor will be
+`(sizedim - size) / step + 1`.
+
+An additional dimension of size `size` is appended in the returned tensor.
+
+#### Arguments: 
+
+* dimension (int): dimension in which unfolding happens
+* size (int): the size of each slice that is unfolded
+* step (int): the step between each slice
+
+## uniform_
+
+uniform_(from=0, to=1) -> Tensor
+
+Fills `self` tensor with numbers sampled from the continuous uniform
+distribution:
+
+$$
+P(x) = \dfrac{1}{\text{to} - \text{from}}
+$$
+
+## unique
+
+Returns the unique elements of the input tensor.
+
+See `?torch_unique`
+        
+## unique_consecutive
+
+Eliminates all but the first element from every consecutive group of equivalent elements.
+
+See [torch_unique_consecutive()]
+        
+## unsqueeze
+
+unsqueeze(dim) -> Tensor
+
+See `?torch_unsqueeze`
+
+## unsqueeze_
+
+unsqueeze_(dim) -> Tensor
+
+In-place version of `$unsqueeze`
+
+## values
+
+values() -> Tensor
+
+If `self` is a sparse COO tensor (i.e., with `torch_sparse_coo` layout),
+this returns a view of the contained values tensor. Otherwise, this throws an
+error.
+
+
+#### Note: 
+
+This method can only be called on a coalesced sparse tensor. See
+`Tensor$coalesce` for details.
+
+## var
+
+var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor
+
+See `?torch_var`
+
+## view
+
+view(*shape) -> Tensor
+
+Returns a new tensor with the same data as the `self` tensor but of a
+different `shape`.
+
+The returned tensor shares the same data and must have the same number
+of elements, but may have a different size. For a tensor to be viewed, the new
+view size must be compatible with its original size and stride, i.e., each new
+view dimension must either be a subspace of an original dimension, or only span
+across original dimensions `d, d+1, \dots, d+k` that satisfy the following
+contiguity-like condition that `\forall i = d, \dots, d+k-1`,
+
+$$
+\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
+$$
+
+Otherwise, it will not be possible to view `self` tensor as `shape`
+without copying it (e.g., via `contiguous`). When it is unclear whether a
+`view` can be performed, it is advisable to use :meth:`reshape`, which
+returns a view if the shapes are compatible, and copies (equivalent to calling
+`contiguous`) otherwise.
+
+#### Arguments: 
+
+* shape (torch_Size or int...): the desired size
+
+## view_as
+
+view_as(other) -> Tensor
+
+View this tensor as the same size as `other`.
+`self$view_as(other)` is equivalent to `self$view(other.size())`.
+
+Please see `$view` for more information about `view`.
+
+#### Arguments: 
+
+* other (`$): The result tensor has the same size
+* as `other`.
+
+## where
+
+where(condition, y) -> Tensor
+
+`self$where(condition, y)` is equivalent to `torch_where(condition, self, y)`.
+See `?torch_where`
+
+## zero_
+
+zero_() -> Tensor
+
+Fills `self` tensor with zeros.
+
diff --git a/vignettes/using-autograd.Rmd b/vignettes/using-autograd.Rmd
index 279e58acbc656726e1ebf86b03b887a0a0527c1f..6f7f79b7f94285cde9921668ccfeb7303482ec3f 100644
--- a/vignettes/using-autograd.Rmd
+++ b/vignettes/using-autograd.Rmd
@@ -11,7 +11,7 @@ vignette: >
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
-  eval = identical(Sys.getenv("TORCH_TEST", unset = 0), 1)
+  eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
 )
 ```
 
@@ -166,4 +166,4 @@ for (t in 1:200) {
  
 ```
 
-We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we'll see how these parts of the logic can be made more modular and reusable, as well.
\ No newline at end of file
+We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we'll see how these parts of the logic can be made more modular and reusable, as well.