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Add r_dataptr_ro() to avoid ALTREP COW when\n reading buffers via from_blob.\n- R/utils-data-dataloader.R: Add SHM transport as a third option\n alongside callr pipe and socket connections, controlled by the\n torch.dataloader_use_mori option.\n- bench/bench-shm.R: Benchmark comparing default vs SHM transport.\n- .github/workflows/main.yaml: Add SHM benchmark to CI.\n\n* Rename mori references to shm\n\nThe implementation uses POSIX shared memory directly, not mori.\nRename option to torch.dataloader_use_shm and internal variables\naccordingly.\n\n* Add test that re-runs all dataloader tests with SHM transport\n\n* Fix Windows build: move #ifdef inside function bodies\n\nThe Rcpp-generated exports need function definitions on all platforms.\nMove the _WIN32 guard inside each function body so the symbols are\nalways defined, but error at runtime on Windows. 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"nn_fractional_max_pool3d", "nn_gelu", "nn_glu", "nn_group_norm", "nn_gru", "nn_hardshrink", "nn_hardsigmoid", "nn_hardswish", "nn_hardtanh", "nn_hinge_embedding_loss", "nn_identity", "nn_init_calculate_gain", "nn_init_constant_", "nn_init_dirac_", "nn_init_eye_", "nn_init_kaiming_normal_", "nn_init_kaiming_uniform_", "nn_init_normal_", "nn_init_ones_", "nn_init_orthogonal_", "nn_init_sparse_", "nn_init_trunc_normal_", "nn_init_uniform_", "nn_init_xavier_normal_", "nn_init_xavier_uniform_", "nn_init_zeros_", "nn_kl_div_loss", "nn_l1_loss", "nn_layer_norm", "nn_leaky_relu", "nn_linear", "nn_log_sigmoid", "nn_log_softmax", "nn_lp_pool1d", "nn_lp_pool2d", "nn_lstm", "nn_margin_ranking_loss", "nn_max_pool1d", "nn_max_pool2d", "nn_max_pool3d", "nn_max_unpool1d", "nn_max_unpool2d", "nn_max_unpool3d", "nn_module", "nn_module_dict", "nn_module_list", "nn_mse_loss", "nn_multi_margin_loss", "nn_multihead_attention", "nn_multilabel_margin_loss", "nn_multilabel_soft_margin_loss", "nn_nll_loss", "nn_pairwise_distance", "nn_parameter", "nn_poisson_nll_loss", "nn_prelu", "nn_prune_head", "nn_relu", "nn_relu6", "nn_rnn", "nn_rrelu", "nn_selu", "nn_sequential", "nn_sigmoid", "nn_silu", "nn_smooth_l1_loss", "nn_soft_margin_loss", "nn_softmax", "nn_softmax2d", "nn_softmin", "nn_softplus", "nn_softshrink", "nn_softsign", "nn_tanh", "nn_tanhshrink", "nn_threshold", "nn_transformer_encoder", "nn_transformer_encoder_layer", "nn_triplet_margin_loss", "nn_triplet_margin_with_distance_loss", "nn_unflatten", "nn_upsample", "nn_utils_clip_grad_norm_", "nn_utils_clip_grad_value_", "nn_utils_rnn_pack_padded_sequence", "nn_utils_rnn_pack_sequence", "nn_utils_rnn_pad_packed_sequence", "nn_utils_rnn_pad_sequence", "nn_utils_weight_norm", "nnf_adaptive_avg_pool1d", "nnf_adaptive_avg_pool2d", "nnf_adaptive_avg_pool3d", "nnf_adaptive_max_pool1d", "nnf_adaptive_max_pool2d", "nnf_adaptive_max_pool3d", "nnf_affine_grid", "nnf_alpha_dropout", "nnf_area_under_min_fpr_fnr", "nnf_avg_pool1d", "nnf_avg_pool2d", "nnf_avg_pool3d", "nnf_batch_norm", "nnf_bilinear", "nnf_binary_cross_entropy", "nnf_binary_cross_entropy_with_logits", "nnf_celu", "nnf_celu_", "nnf_contrib_sparsemax", "nnf_conv_tbc", "nnf_conv_transpose1d", "nnf_conv_transpose2d", "nnf_conv_transpose3d", "nnf_conv1d", "nnf_conv2d", "nnf_conv3d", "nnf_cosine_embedding_loss", "nnf_cosine_similarity", "nnf_cross_entropy", "nnf_ctc_loss", "nnf_dropout", "nnf_dropout2d", "nnf_dropout3d", "nnf_elu", "nnf_elu_", "nnf_embedding", "nnf_embedding_bag", "nnf_fold", "nnf_fractional_max_pool2d", "nnf_fractional_max_pool3d", "nnf_gelu", "nnf_glu", "nnf_grid_sample", "nnf_group_norm", "nnf_gumbel_softmax", "nnf_hardshrink", "nnf_hardsigmoid", "nnf_hardswish", "nnf_hardtanh", "nnf_hardtanh_", "nnf_hinge_embedding_loss", "nnf_instance_norm", "nnf_interpolate", "nnf_kl_div", "nnf_l1_loss", "nnf_layer_norm", "nnf_leaky_relu", "nnf_linear", "nnf_local_response_norm", "nnf_log_softmax", "nnf_logsigmoid", "nnf_lp_pool1d", "nnf_lp_pool2d", "nnf_margin_ranking_loss", "nnf_max_pool1d", "nnf_max_pool2d", "nnf_max_pool3d", "nnf_max_unpool1d", "nnf_max_unpool2d", "nnf_max_unpool3d", "nnf_mse_loss", "nnf_multi_head_attention_forward", "nnf_multi_margin_loss", "nnf_multilabel_margin_loss", "nnf_multilabel_soft_margin_loss", "nnf_nll_loss", "nnf_normalize", "nnf_one_hot", "nnf_pad", "nnf_pairwise_distance", "nnf_pdist", "nnf_pixel_shuffle", "nnf_poisson_nll_loss", "nnf_prelu", "nnf_relu", "nnf_relu_", "nnf_relu6", "nnf_rrelu", "nnf_rrelu_", "nnf_selu", "nnf_selu_", "nnf_sigmoid", "nnf_silu", "nnf_smooth_l1_loss", "nnf_soft_margin_loss", "nnf_softmax", "nnf_softmin", "nnf_softplus", "nnf_softshrink", "nnf_softsign", "nnf_tanhshrink", "nnf_threshold", "nnf_threshold_", "nnf_triplet_margin_loss", "nnf_triplet_margin_with_distance_loss", "nnf_unfold", "optim_adadelta", "optim_adagrad", "optim_adam", "optim_adamw", "optim_asgd", "optim_ignite_adagrad", "optim_ignite_adam", "optim_ignite_adamw", "optim_ignite_rmsprop", "optim_ignite_sgd", "optim_lbfgs", "optim_required", "optim_rmsprop", "optim_rprop", "optim_sgd", "optimizer", "optimizer_ignite", "OptimizerIgnite", "sampler", "set_autocast", "set_cpu_allocator_config", "slc", "tensor_dataset", "torch_abs", "torch_absolute", "torch_acos", "torch_acosh", "torch_adaptive_avg_pool1d", "torch_add", "torch_addbmm", "torch_addcdiv", "torch_addcmul", "torch_addmm", "torch_addmv", "torch_addr", "torch_allclose", "torch_amax", "torch_amin", "torch_angle", "torch_arange", "torch_arccos", "torch_arccosh", "torch_arcsin", "torch_arcsinh", "torch_arctan", "torch_arctanh", "torch_argmax", "torch_argmin", "torch_argsort", "torch_as_strided", "torch_asin", "torch_asinh", "torch_atan", "torch_atan2", "torch_atanh", "torch_atleast_1d", "torch_atleast_2d", "torch_atleast_3d", "torch_avg_pool1d", "torch_baddbmm", "torch_bartlett_window", "torch_bernoulli", "torch_bfloat16", "torch_bincount", "torch_bitwise_and", "torch_bitwise_not", "torch_bitwise_or", "torch_bitwise_xor", "torch_blackman_window", "torch_block_diag", "torch_bmm", "torch_bool", "torch_broadcast_tensors", "torch_bucketize", "torch_can_cast", "torch_cartesian_prod", "torch_cat", "torch_cdist", "torch_cdouble", "torch_ceil", "torch_celu", "torch_celu_", "torch_cfloat", "torch_cfloat128", "torch_cfloat32", "torch_cfloat64", "torch_chain_matmul", "torch_chalf", "torch_channel_shuffle", "torch_channels_last_format", "torch_cholesky", "torch_cholesky_inverse", "torch_cholesky_solve", "torch_chunk", "torch_clamp", "torch_clip", "torch_clone", "torch_combinations", "torch_complex", "torch_conj", "torch_contiguous_format", "torch_conv_tbc", "torch_conv_transpose1d", "torch_conv_transpose2d", "torch_conv_transpose3d", "torch_conv1d", "torch_conv2d", "torch_conv3d", "torch_cos", "torch_cosh", "torch_cosine_similarity", "torch_count_nonzero", "torch_cross", "torch_cummax", "torch_cummin", "torch_cumprod", "torch_cumsum", "torch_deg2rad", "torch_dequantize", "torch_det", "torch_device", "torch_diag", "torch_diag_embed", "torch_diagflat", "torch_diagonal", "torch_diff", "torch_digamma", "torch_dist", "torch_div", "torch_divide", "torch_dot", "torch_double", "torch_dstack", "torch_einsum", "torch_empty", "torch_empty_like", "torch_empty_strided", "torch_eq", "torch_equal", "torch_erf", "torch_erfc", "torch_erfinv", "torch_exp", "torch_exp2", "torch_expm1", "torch_eye", "torch_fft_fft", "torch_fft_fftfreq", "torch_fft_ifft", "torch_fft_irfft", "torch_fft_rfft", "torch_finfo", "torch_fix", "torch_flatten", "torch_flip", "torch_fliplr", "torch_flipud", "torch_float", "torch_float16", "torch_float32", "torch_float64", "torch_float8_e4m3fn", "torch_float8_e5m2", "torch_floor", "torch_floor_divide", "torch_fmod", "torch_frac", "torch_full", "torch_full_like", "torch_gather", "torch_gcd", "torch_ge", "torch_generator", "torch_geqrf", "torch_ger", "torch_get_default_dtype", "torch_get_num_interop_threads", "torch_get_num_threads", "torch_get_rng_state", "torch_greater", "torch_greater_equal", "torch_gt", "torch_half", "torch_hamming_window", "torch_hann_window", "torch_heaviside", "torch_histc", "torch_hstack", "torch_hypot", "torch_i0", "torch_iinfo", "torch_imag", "torch_index", "torch_index_put", "torch_index_put_", "torch_index_select", "torch_install_path", "torch_int", "torch_int16", "torch_int32", "torch_int64", "torch_int8", "torch_inverse", "torch_is_complex", "torch_is_floating_point", "torch_is_installed", "torch_is_nonzero", "torch_isclose", "torch_isfinite", "torch_isinf", "torch_isnan", "torch_isneginf", "torch_isposinf", "torch_isreal", "torch_istft", "torch_kaiser_window", "torch_kron", "torch_kthvalue", "torch_lcm", "torch_ldexp", "torch_le", "torch_lerp", "torch_less", "torch_less_equal", "torch_lgamma", "torch_linspace", "torch_load", "torch_log", "torch_log10", "torch_log1p", "torch_log2", "torch_logaddexp", "torch_logaddexp2", "torch_logcumsumexp", "torch_logdet", "torch_logical_and", "torch_logical_not", "torch_logical_or", "torch_logical_xor", "torch_logit", "torch_logspace", "torch_logsumexp", "torch_long", "torch_lt", "torch_lu", "torch_lu_solve", "torch_lu_unpack", "torch_manual_seed", "torch_masked_select", "torch_matmul", "torch_matrix_exp", "torch_matrix_power", "torch_max", "torch_maximum", "torch_mean", "torch_median", "torch_meshgrid", "torch_min", "torch_minimum", "torch_mm", "torch_mode", "torch_movedim", "torch_mul", "torch_multinomial", "torch_multiply", "torch_mv", "torch_mvlgamma", "torch_nanquantile", "torch_nansum", "torch_narrow", "torch_ne", "torch_neg", "torch_negative", "torch_nextafter", "torch_nonzero", "torch_norm", "torch_normal", "torch_not_equal", "torch_ones", "torch_ones_like", "torch_orgqr", "torch_ormqr", "torch_outer", "torch_pdist", "torch_per_channel_affine", "torch_per_channel_symmetric", "torch_per_tensor_affine", "torch_per_tensor_symmetric", "torch_pinverse", "torch_pixel_shuffle", "torch_poisson", "torch_polar", "torch_polygamma", "torch_pow", "torch_preserve_format", "torch_prod", "torch_promote_types", "torch_qint32", "torch_qint8", "torch_qr", "torch_quantile", "torch_quantize_per_channel", "torch_quantize_per_tensor", "torch_quint8", "torch_rad2deg", "torch_rand", "torch_rand_like", "torch_randint", "torch_randint_like", "torch_randn", "torch_randn_like", "torch_randperm", "torch_range", "torch_real", "torch_reciprocal", "torch_reduction_mean", "torch_reduction_none", "torch_reduction_sum", "torch_relu", "torch_relu_", "torch_remainder", "torch_renorm", "torch_repeat_interleave", "torch_reshape", "torch_result_type", "torch_roll", "torch_rot90", "torch_round", "torch_rrelu_", "torch_rsqrt", "torch_save", "torch_scalar_tensor", "torch_scaled_dot_product_attention", "torch_searchsorted", "torch_selu", "torch_selu_", "torch_serialize", "torch_set_default_dtype", "torch_set_num_interop_threads", "torch_set_num_threads", "torch_set_rng_state", "torch_sgn", "torch_short", "torch_sigmoid", "torch_sign", "torch_signbit", "torch_sin", "torch_sinh", "torch_slogdet", "torch_sort", "torch_sparse_coo", "torch_sparse_coo_tensor", "torch_sparse_sampled_addmm", "torch_split", "torch_sqrt", "torch_square", "torch_squeeze", "torch_stack", "torch_std", "torch_std_mean", "torch_stft", "torch_strided", "torch_sub", "torch_subtract", "torch_sum", "torch_svd", "torch_t", "torch_take", "torch_take_along_dim", "torch_tan", "torch_tanh", "torch_tensor", "torch_tensor_from_buffer", "torch_tensordot", "torch_threshold_", "torch_topk", "torch_trace", "torch_transpose", "torch_trapz", "torch_triangular_solve", "torch_tril", "torch_tril_indices", "torch_triu", "torch_triu_indices", "torch_true_divide", "torch_trunc", "torch_uint8", "torch_unbind", "torch_unique_consecutive", "torch_unsafe_chunk", "torch_unsafe_split", "torch_unsqueeze", "torch_vander", "torch_var", "torch_var_mean", "torch_vdot", "torch_view_as_complex", "torch_view_as_real", "torch_vstack", "torch_where", "torch_zeros", "torch_zeros_like", "unset_autocast", "with_autocast", "with_detect_anomaly", "with_device", "with_enable_grad", "with_no_grad", "with_torch_manual_seed", "yield" ], "_help": [ { "page": "as_array", "title": "Converts to array", "topics": [ "as_array" ] }, { "page": "autograd_backward", "title": "Computes the sum of gradients of given tensors w.r.t. graph leaves.", "topics": [ "autograd_backward" ] }, { "page": "autograd_function", "title": "Records operation history and defines formulas for differentiating ops.", "topics": [ "autograd_function" ] }, { "page": "autograd_grad", "title": "Computes and returns the sum of gradients of outputs w.r.t. the inputs.", "topics": [ "autograd_grad" ] }, { "page": "autograd_set_grad_mode", "title": "Set grad mode", "topics": [ "autograd_set_grad_mode" ] }, { "page": "AutogradContext", "title": "Class representing the context.", "topics": [ "AutogradContext" ] }, { "page": "backends_cudnn_is_available", "title": "CuDNN is available", "topics": [ "backends_cudnn_is_available" ] }, { "page": "backends_cudnn_version", "title": "CuDNN version", "topics": [ "backends_cudnn_version" ] }, { "page": "backends_mkl_is_available", "title": "MKL is available", "topics": [ "backends_mkl_is_available" ] }, { "page": "backends_mkldnn_is_available", "title": "MKLDNN is available", "topics": [ "backends_mkldnn_is_available" ] }, { "page": "backends_mps_is_available", "title": "MPS is available", "topics": [ "backends_mps_is_available" ] }, { "page": "backends_openmp_is_available", "title": "OpenMP is available", "topics": [ "backends_openmp_is_available" ] }, { "page": "broadcast_all", "title": "Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules:", "topics": [ "broadcast_all" ] }, { "page": "clone_module", "title": "Clone a torch module.", "topics": [ "clone_module" ] }, { "page": "Constraint", "title": "Abstract base class for constraints.", "topics": [ "Constraint" ] }, { "page": "contrib_sort_vertices", "title": "Contrib sort vertices", "topics": [ "contrib_sort_vertices" ] }, { "page": "cpu_cache_flush", "title": "Flush the CPU memory cache", "topics": [ "cpu_cache_flush" ] }, { "page": "cuda_amp_grad_scaler", "title": "Creates a gradient scaler", "topics": [ "cuda_amp_grad_scaler" ] }, { "page": "cuda_current_device", "title": "Returns the index of a currently selected device.", "topics": [ "cuda_current_device" ] }, { "page": "cuda_device_count", "title": "Returns the number of GPUs available.", "topics": [ "cuda_device_count" ] }, { "page": "cuda_dump_memory_snapshot", "title": "Save CUDA Memory State Snapshot to File", "topics": [ "cuda_dump_memory_snapshot" ] }, { "page": "cuda_empty_cache", "title": "Empty cache", "topics": [ "cuda_empty_cache" ] }, { "page": "cuda_get_device_capability", "title": "Returns the major and minor CUDA capability of 'device'", "topics": [ "cuda_get_device_capability" ] }, { "page": "cuda_is_available", "title": "Returns a bool indicating if CUDA is currently available.", "topics": [ "cuda_is_available" ] }, { "page": "cuda_memory_snapshot", "title": "Capture CUDA Memory State Snapshot", "topics": [ "cuda_memory_snapshot" ] }, { "page": "cuda_memory_stats", "title": "Returns a dictionary of CUDA memory allocator statistics for a given device.", "topics": [ "cuda_memory_stats", "cuda_memory_summary" ] }, { "page": "cuda_record_memory_history", "title": "Enable Recording of Memory Allocation Stack Traces", "topics": [ "cuda_record_memory_history" ] }, { "page": "cuda_runtime_version", "title": "Returns the CUDA runtime version", "topics": [ "cuda_runtime_version" ] }, { "page": "cuda_synchronize", "title": "Waits for all kernels in all streams on a CUDA device to complete.", "topics": [ "cuda_synchronize" ] }, { "page": "dataloader", "title": "Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.", "topics": [ "dataloader" ] }, { "page": "dataloader_make_iter", "title": "Creates an iterator from a DataLoader", "topics": [ "dataloader_make_iter" ] }, { "page": "dataloader_next", "title": "Get the next element of a dataloader iterator", "topics": [ "dataloader_next" ] }, { "page": "dataset", "title": "Helper function to create an function that generates R6 instances of class 'dataset'", "topics": [ "dataset" ] }, { "page": "dataset_subset", "title": "Dataset Subset", "topics": [ "dataset_subset" ] }, { "page": "distr_bernoulli", "title": "Creates a Bernoulli distribution parameterized by 'probs' or 'logits' (but not both). Samples are binary (0 or 1). They take the value '1' with probability 'p' and '0' with probability '1 - p'.", "concept": [ "distributions" ], "topics": [ "distr_bernoulli" ] }, { "page": "distr_categorical", "title": "Creates a categorical distribution parameterized by either 'probs' or 'logits' (but not both).", "topics": [ "distr_categorical" ] }, { "page": "distr_chi2", "title": "Creates a Chi2 distribution parameterized by shape parameter 'df'. This is exactly equivalent to 'distr_gamma(alpha=0.5*df, beta=0.5)'", "concept": [ "distributions" ], "topics": [ "distr_chi2" ] }, { "page": "distr_gamma", "title": "Creates a Gamma distribution parameterized by shape 'concentration' and 'rate'.", "concept": [ "distributions" ], "topics": [ "distr_gamma" ] }, { "page": "distr_mixture_same_family", "title": "Mixture of components in the same family", "topics": [ "distr_mixture_same_family" ] }, { "page": "distr_multivariate_normal", "title": "Gaussian distribution", "concept": [ "distributions" ], "topics": [ "distr_multivariate_normal" ] }, { "page": "distr_normal", "title": "Creates a normal (also called Gaussian) distribution parameterized by 'loc' and 'scale'.", "concept": [ "distributions" ], "topics": [ "distr_normal" ] }, { "page": "distr_poisson", "title": "Creates a Poisson distribution parameterized by 'rate', the rate parameter.", "concept": [ "distributions" ], "topics": [ "distr_poisson" ] }, { "page": "Distribution", "title": "Generic R6 class representing distributions", "topics": [ "Distribution" ] }, { "page": "enumerate", "title": "Enumerate an iterator", "topics": [ "enumerate" ] }, { "page": "enumerate.dataloader", "title": "Enumerate an iterator", "topics": [ "enumerate.dataloader" ] }, { "page": "install_torch_from_file", "title": "Install Torch from files", "topics": [ "get_install_libs_url", "install_torch_from_file" ] }, { "page": "install_torch", "title": "Install Torch", "topics": [ "install_torch" ] }, { "page": "install_torch_sitrep", "title": "Torch Installation Situation Report", "topics": [ "install_torch_sitrep" ] }, { "page": "is_dataloader", "title": "Checks if the object is a dataloader", "topics": [ "is_dataloader" ] }, { "page": "is_nn_buffer", "title": "Checks if the object is a nn_buffer", "topics": [ "is_nn_buffer" ] }, { "page": "is_nn_module", "title": "Checks if the object is an nn_module", "topics": [ "is_nn_module" ] }, { "page": "is_nn_parameter", "title": "Checks if an object is a nn_parameter", "topics": [ "is_nn_parameter" ] }, { "page": "is_optimizer", "title": "Checks if the object is a torch optimizer", "topics": [ "is_optimizer" ] }, { "page": "is_torch_device", "title": "Checks if object is a device", "concept": [ "tensor-attributes" ], "topics": [ "is_torch_device" ] }, { "page": "is_torch_dtype", "title": "Check if object is a torch data type", "concept": [ "tensor-attributes" ], "topics": [ "is_torch_dtype" ] }, { "page": "is_torch_layout", "title": "Check if an object is a torch layout.", "topics": [ "is_torch_layout" ] }, { "page": "is_torch_memory_format", "title": "Check if an object is a memory format", "topics": [ "is_torch_memory_format" ] }, { "page": "is_torch_qscheme", "title": "Checks if an object is a QScheme", "topics": [ "is_torch_qscheme" ] }, { "page": "is_undefined_tensor", "title": "Checks if a tensor is undefined", "topics": [ "is_undefined_tensor" ] }, { "page": "iterable_dataset", "title": "Creates an iterable dataset", "topics": [ "iterable_dataset" ] }, { "page": "jit_compile", "title": "Compile TorchScript code into a graph", "topics": [ "jit_compile" ] }, { "page": "jit_load", "title": "Loads a 'script_function' or 'script_module' previously saved with 'jit_save'", "topics": [ "jit_load" ] }, { "page": "jit_ops", "title": "Enable idiomatic access to JIT operators from R.", "topics": [ "jit_ops" ] }, { "page": "jit_save", "title": "Saves a 'script_function' to a path", "topics": [ "jit_save" ] }, { "page": "jit_save_for_mobile", "title": "Saves a 'script_function' or 'script_module' in bytecode form, to be loaded on a mobile device", "topics": [ "jit_save_for_mobile" ] }, { "page": "jit_scalar", "title": "Adds the 'jit_scalar' class to the input", "topics": [ "jit_scalar" ] }, { "page": "jit_serialize", "title": "Serialize a Script Module", "topics": [ "jit_serialize" ] }, { "page": "jit_trace", "title": "Trace a function and return an executable 'script_function'.", "topics": [ "jit_trace" ] }, { "page": "jit_trace_module", "title": "Trace a module", "topics": [ "jit_trace_module" ] }, { "page": "jit_tuple", "title": "Adds the 'jit_tuple' class to the input", "topics": [ "jit_tuple" ] }, { "page": "jit_unserialize", "title": "Unserialize a Script Module", "topics": [ "jit_unserialize" ] }, { "page": "linalg_cholesky", "title": "Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.", "concept": [ "linalg" ], "topics": [ "linalg_cholesky" ] }, { "page": "linalg_cholesky_ex", "title": "Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.", "concept": [ "linalg" ], "topics": [ "linalg_cholesky_ex" ] }, { "page": "linalg_cond", "title": "Computes the condition number of a matrix with respect to a matrix norm.", "topics": [ "linalg_cond" ] }, { "page": "linalg_det", "title": "Computes the determinant of a square matrix.", "concept": [ "linalg" ], "topics": [ "linalg_det" ] }, { "page": "linalg_eig", "title": "Computes the eigenvalue decomposition of a square matrix if it exists.", "concept": [ "linalg" ], "topics": [ "linalg_eig" ] }, { "page": "linalg_eigh", "title": "Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.", "concept": [ "linalg" ], "topics": [ "linalg_eigh" ] }, { "page": "linalg_eigvals", "title": "Computes the eigenvalues of a square matrix.", "concept": [ "linalg" ], "topics": [ "linalg_eigvals" ] }, { "page": "linalg_eigvalsh", "title": "Computes the eigenvalues of a complex Hermitian or real symmetric matrix.", "concept": [ "linalg" ], "topics": [ "linalg_eigvalsh" ] }, { "page": "linalg_householder_product", "title": "Computes the first 'n' columns of a product of Householder matrices.", "concept": [ "linalg" ], "topics": [ "linalg_householder_product" ] }, { "page": "linalg_inv", "title": "Computes the inverse of a square matrix if it exists.", "concept": [ "linalg" ], "topics": [ "linalg_inv" ] }, { "page": "linalg_inv_ex", "title": "Computes the inverse of a square matrix if it is invertible.", "concept": [ "linalg" ], "topics": [ "linalg_inv_ex" ] }, { "page": "linalg_lstsq", "title": "Computes a solution to the least squares problem of a system of linear equations.", "concept": [ "linalg" ], "topics": [ "linalg_lstsq" ] }, { "page": "linalg_matrix_norm", "title": "Computes a matrix norm.", "concept": [ "linalg" ], "topics": [ "linalg_matrix_norm" ] }, { "page": "linalg_matrix_power", "title": "Computes the 'n'-th power of a square matrix for an integer 'n'.", "concept": [ "linalg" ], "topics": [ "linalg_matrix_power" ] }, { "page": "linalg_matrix_rank", "title": "Computes the numerical rank of a matrix.", "concept": [ "linalg" ], "topics": [ "linalg_matrix_rank" ] }, { "page": "linalg_multi_dot", "title": "Efficiently multiplies two or more matrices", "concept": [ "linalg" ], "topics": [ "linalg_multi_dot" ] }, { "page": "linalg_norm", "title": "Computes a vector or matrix norm.", "concept": [ "linalg" ], "topics": [ "linalg_norm" ] }, { "page": "linalg_pinv", "title": "Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.", "concept": [ "linalg" ], "topics": [ "linalg_pinv" ] }, { "page": "linalg_qr", "title": "Computes the QR decomposition of a matrix.", "concept": [ "linalg" ], "topics": [ "linalg_qr" ] }, { "page": "linalg_slogdet", "title": "Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.", "concept": [ "linalg" ], "topics": [ "linalg_slogdet" ] }, { "page": "linalg_solve", "title": "Computes the solution of a square system of linear equations with a unique solution.", "concept": [ "linalg" ], "topics": [ "linalg_solve" ] }, { "page": "linalg_solve_triangular", "title": "Triangular solve", "concept": [ "linalg" ], "topics": [ "linalg_solve_triangular" ] }, { "page": "linalg_svd", "title": "Computes the singular value decomposition (SVD) of a matrix.", "concept": [ "linalg" ], "topics": [ "linalg_svd" ] }, { "page": "linalg_svdvals", "title": "Computes the singular values of a matrix.", "concept": [ "linalg" ], "topics": [ "linalg_svdvals" ] }, { "page": "linalg_tensorinv", "title": "Computes the multiplicative inverse of 'torch_tensordot()'", "concept": [ "linalg" ], "topics": [ "linalg_tensorinv" ] }, { "page": "linalg_tensorsolve", "title": "Computes the solution 'X' to the system 'torch_tensordot(A, X) = B'.", "concept": [ "linalg" ], "topics": [ "linalg_tensorsolve" ] }, { "page": "linalg_vector_norm", "title": "Computes a vector norm.", "concept": [ "linalg" ], "topics": [ "linalg_vector_norm" ] }, { "page": "load_state_dict", "title": "Load a state dict file", "concept": [ "serialization" ], "topics": [ "load_state_dict" ] }, { "page": "local_autocast", "title": "Autocast context manager", "topics": [ "local_autocast", "set_autocast", "unset_autocast", "with_autocast" ] }, { "page": "local_device", "title": "Device contexts", "topics": [ "local_device", "with_device" ] }, { "page": "lr_cosine_annealing", "title": "Set the learning rate of each parameter group using a cosine annealing schedule", "topics": [ "lr_cosine_annealing" ] }, { "page": "lr_lambda", "title": "Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.", "topics": [ "lr_lambda" ] }, { "page": "lr_multiplicative", "title": "Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr.", "topics": [ "lr_multiplicative" ] }, { "page": "lr_one_cycle", "title": "Once cycle learning rate", "topics": [ "lr_one_cycle" ] }, { "page": "lr_reduce_on_plateau", "title": "Reduce learning rate on plateau", "topics": [ "lr_reduce_on_plateau" ] }, { "page": "lr_scheduler", "title": "Creates learning rate schedulers", "topics": [ "lr_scheduler" ] }, { "page": "lr_step", "title": "Step learning rate decay", "topics": [ "lr_step" ] }, { "page": "nn_adaptive_avg_pool1d", "title": "Applies a 1D adaptive average pooling over an input signal composed of several input planes.", "topics": [ "nn_adaptive_avg_pool1d" ] }, { "page": "nn_adaptive_avg_pool2d", "title": "Applies a 2D adaptive average pooling over an input signal composed of several input planes.", "topics": [ "nn_adaptive_avg_pool2d" ] }, { "page": "nn_adaptive_avg_pool3d", "title": "Applies a 3D adaptive average pooling over an input signal composed of several input planes.", "topics": [ "nn_adaptive_avg_pool3d" ] }, { "page": "nn_adaptive_log_softmax_with_loss", "title": "AdaptiveLogSoftmaxWithLoss module", "topics": [ "nn_adaptive_log_softmax_with_loss" ] }, { "page": "nn_adaptive_max_pool1d", "title": "Applies a 1D adaptive max pooling over an input signal composed of several input planes.", "topics": [ "nn_adaptive_max_pool1d" ] }, { "page": "nn_adaptive_max_pool2d", "title": "Applies a 2D adaptive max pooling over an input signal composed of several input planes.", "topics": [ "nn_adaptive_max_pool2d" ] }, { "page": "nn_adaptive_max_pool3d", "title": "Applies a 3D adaptive max pooling over an input signal composed of several input planes.", "topics": [ "nn_adaptive_max_pool3d" ] }, { "page": "nn_aum_loss", "title": "AUM loss", "topics": [ "nn_aum_loss" ] }, { "page": "nn_avg_pool1d", "title": "Applies a 1D average pooling over an input signal composed of several input planes.", "topics": [ "nn_avg_pool1d" ] }, { "page": "nn_avg_pool2d", "title": "Applies a 2D average pooling over an input signal composed of several input planes.", "topics": [ "nn_avg_pool2d" ] }, { "page": "nn_avg_pool3d", "title": "Applies a 3D average pooling over an input signal composed of several input planes.", "topics": [ "nn_avg_pool3d" ] }, { "page": "nn_batch_norm1d", "title": "BatchNorm1D module", "topics": [ "nn_batch_norm1d" ] }, { "page": "nn_batch_norm2d", "title": "BatchNorm2D", "topics": [ "nn_batch_norm2d" ] }, { "page": "nn_batch_norm3d", "title": "BatchNorm3D", "topics": [ "nn_batch_norm3d" ] }, { "page": "nn_bce_loss", "title": "Binary cross entropy loss", "topics": [ "nn_bce_loss" ] }, { "page": "nn_bce_with_logits_loss", "title": "BCE with logits loss", "topics": [ "nn_bce_with_logits_loss" ] }, { "page": "nn_bilinear", "title": "Bilinear module", "topics": [ "nn_bilinear" ] }, { "page": "nn_buffer", "title": "Creates a nn_buffer", "topics": [ "nn_buffer" ] }, { "page": "nn_celu", "title": "CELU module", "topics": [ "nn_celu" ] }, { "page": "nn_contrib_sparsemax", "title": "Sparsemax activation", "topics": [ "nn_contrib_sparsemax" ] }, { "page": "nn_conv_transpose1d", "title": "ConvTranspose1D", "topics": [ "nn_conv_transpose1d" ] }, { "page": "nn_conv_transpose2d", "title": "ConvTranpose2D module", "topics": [ "nn_conv_transpose2d" ] }, { "page": "nn_conv_transpose3d", "title": "ConvTranpose3D module", "topics": [ "nn_conv_transpose3d" ] }, { "page": "nn_conv1d", "title": "Conv1D module", "topics": [ "nn_conv1d" ] }, { "page": "nn_conv2d", "title": "Conv2D module", "topics": [ "nn_conv2d" ] }, { "page": "nn_conv3d", "title": "Conv3D module", "topics": [ "nn_conv3d" ] }, { "page": "nn_cosine_embedding_loss", "title": "Cosine embedding loss", "topics": [ "nn_cosine_embedding_loss" ] }, { "page": "nn_cross_entropy_loss", "title": "CrossEntropyLoss module", "topics": [ "nn_cross_entropy_loss" ] }, { "page": "nn_ctc_loss", "title": "The Connectionist Temporal Classification loss.", "topics": [ "nn_ctc_loss" ] }, { "page": "nn_dropout", "title": "Dropout module", "topics": [ "nn_dropout" ] }, { "page": "nn_dropout2d", "title": "Dropout2D module", "topics": [ "nn_dropout2d" ] }, { "page": "nn_dropout3d", "title": "Dropout3D module", "topics": [ "nn_dropout3d" ] }, { "page": "nn_elu", "title": "ELU module", "topics": [ "nn_elu" ] }, { "page": "nn_embedding", "title": "Embedding module", "topics": [ "nn_embedding" ] }, { "page": "nn_embedding_bag", "title": "Embedding bag module", "topics": [ "nn_embedding_bag" ] }, { "page": "nn_flatten", "title": "Flattens a contiguous range of dims into a tensor.", "topics": [ "nn_flatten" ] }, { "page": "nn_fractional_max_pool2d", "title": "Applies a 2D fractional max pooling over an input signal composed of several input planes.", "topics": [ "nn_fractional_max_pool2d" ] }, { "page": "nn_fractional_max_pool3d", "title": "Applies a 3D fractional max pooling over an input signal composed of several input planes.", "topics": [ "nn_fractional_max_pool3d" ] }, { "page": "nn_gelu", "title": "GELU module", "topics": [ "nn_gelu" ] }, { "page": "nn_glu", "title": "GLU module", "topics": [ "nn_glu" ] }, { "page": "nn_group_norm", "title": "Group normalization", "topics": [ "nn_group_norm" ] }, { "page": "nn_gru", "title": "Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.", "topics": [ "nn_gru" ] }, { "page": "nn_hardshrink", "title": "Hardshwink module", "topics": [ "nn_hardshrink" ] }, { "page": "nn_hardsigmoid", "title": "Hardsigmoid module", "topics": [ "nn_hardsigmoid" ] }, { "page": "nn_hardswish", "title": "Hardswish module", "topics": [ "nn_hardswish" ] }, { "page": "nn_hardtanh", "title": "Hardtanh module", "topics": [ "nn_hardtanh" ] }, { "page": "nn_hinge_embedding_loss", "title": "Hinge embedding loss", "topics": [ "nn_hinge_embedding_loss" ] }, { "page": "nn_identity", "title": "Identity module", "topics": [ "nn_identity" ] }, { "page": "nn_init_calculate_gain", "title": "Calculate gain", "topics": [ "nn_init_calculate_gain" ] }, { "page": "nn_init_constant_", "title": "Constant initialization", "topics": [ "nn_init_constant_" ] }, { "page": "nn_init_dirac_", "title": "Dirac initialization", "topics": [ "nn_init_dirac_" ] }, { "page": "nn_init_eye_", "title": "Eye initialization", "topics": [ "nn_init_eye_" ] }, { "page": "nn_init_kaiming_normal_", "title": "Kaiming normal initialization", "topics": [ "nn_init_kaiming_normal_" ] }, { "page": "nn_init_kaiming_uniform_", "title": "Kaiming uniform initialization", "topics": [ "nn_init_kaiming_uniform_" ] }, { "page": "nn_init_normal_", "title": "Normal initialization", "topics": [ "nn_init_normal_" ] }, { "page": "nn_init_ones_", "title": "Ones initialization", "topics": [ "nn_init_ones_" ] }, { "page": "nn_init_orthogonal_", "title": "Orthogonal initialization", "topics": [ "nn_init_orthogonal_" ] }, { "page": "nn_init_sparse_", "title": "Sparse initialization", "topics": [ "nn_init_sparse_" ] }, { "page": "nn_init_trunc_normal_", "title": "Truncated normal initialization", "topics": [ "nn_init_trunc_normal_" ] }, { "page": "nn_init_uniform_", "title": "Uniform initialization", "topics": [ "nn_init_uniform_" ] }, { "page": "nn_init_xavier_normal_", "title": "Xavier normal initialization", "topics": [ "nn_init_xavier_normal_" ] }, { "page": "nn_init_xavier_uniform_", "title": "Xavier uniform initialization", "topics": [ "nn_init_xavier_uniform_" ] }, { "page": "nn_init_zeros_", "title": "Zeros initialization", "topics": [ "nn_init_zeros_" ] }, { "page": "nn_kl_div_loss", "title": "Kullback-Leibler divergence loss", "topics": [ "nn_kl_div_loss" ] }, { "page": "nn_l1_loss", "title": "L1 loss", "topics": [ "nn_l1_loss" ] }, { "page": "nn_layer_norm", "title": "Layer normalization", "topics": [ "nn_layer_norm" ] }, { "page": "nn_leaky_relu", "title": "LeakyReLU module", "topics": [ "nn_leaky_relu" ] }, { "page": "nn_linear", "title": "Linear module", "topics": [ "nn_linear" ] }, { "page": "nn_log_sigmoid", "title": "LogSigmoid module", "topics": [ "nn_log_sigmoid" ] }, { "page": "nn_log_softmax", "title": "LogSoftmax module", "topics": [ "nn_log_softmax" ] }, { "page": "nn_lp_pool1d", "title": "Applies a 1D power-average pooling over an input signal composed of several input planes.", "topics": [ "nn_lp_pool1d" ] }, { "page": "nn_lp_pool2d", "title": "Applies a 2D power-average pooling over an input signal composed of several input planes.", "topics": [ "nn_lp_pool2d" ] }, { "page": "nn_lstm", "title": "Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.", "topics": [ "nn_lstm" ] }, { "page": "nn_margin_ranking_loss", "title": "Margin ranking loss", "topics": [ "nn_margin_ranking_loss" ] }, { "page": "nn_max_pool1d", "title": "MaxPool1D module", "topics": [ "nn_max_pool1d" ] }, { "page": "nn_max_pool2d", "title": "MaxPool2D module", "topics": [ "nn_max_pool2d" ] }, { "page": "nn_max_pool3d", "title": "Applies a 3D max pooling over an input signal composed of several input planes.", "topics": [ "nn_max_pool3d" ] }, { "page": "nn_max_unpool1d", "title": "Computes a partial inverse of 'MaxPool1d'.", "topics": [ "nn_max_unpool1d" ] }, { "page": "nn_max_unpool2d", "title": "Computes a partial inverse of 'MaxPool2d'.", "topics": [ "nn_max_unpool2d" ] }, { "page": "nn_max_unpool3d", "title": "Computes a partial inverse of 'MaxPool3d'.", "topics": [ "nn_max_unpool3d" ] }, { "page": "nn_module", "title": "Base class for all neural network modules.", "topics": [ "nn_module" ] }, { "page": "nn_module_dict", "title": "Container that allows named values", "topics": [ "nn_module_dict" ] }, { "page": "nn_module_list", "title": "Holds submodules in a list.", "topics": [ "nn_module_list" ] }, { "page": "nn_mse_loss", "title": "MSE loss", "topics": [ "nn_mse_loss" ] }, { "page": "nn_multi_margin_loss", "title": "Multi margin loss", "topics": [ "nn_multi_margin_loss" ] }, { "page": "nn_multihead_attention", "title": "MultiHead attention", "topics": [ "nn_multihead_attention" ] }, { "page": "nn_multilabel_margin_loss", "title": "Multilabel margin loss", "topics": [ "nn_multilabel_margin_loss" ] }, { "page": "nn_multilabel_soft_margin_loss", "title": "Multi label soft margin loss", "topics": [ "nn_multilabel_soft_margin_loss" ] }, { "page": "nn_nll_loss", "title": "Nll loss", "topics": [ "nn_nll_loss" ] }, { "page": "nn_pairwise_distance", "title": "Pairwise distance", "topics": [ "nn_pairwise_distance" ] }, { "page": "nn_parameter", "title": "Creates an 'nn_parameter'", "topics": [ "nn_parameter" ] }, { "page": "nn_poisson_nll_loss", "title": "Poisson NLL loss", "topics": [ "nn_poisson_nll_loss" ] }, { "page": "nn_prelu", "title": "PReLU module", "topics": [ "nn_prelu" ] }, { "page": "nn_prune_head", "title": "Prune top layer(s) of a network", "topics": [ "nn_prune_head" ] }, { "page": "nn_relu", "title": "ReLU module", "topics": [ "nn_relu" ] }, { "page": "nn_relu6", "title": "ReLu6 module", "topics": [ "nn_relu6" ] }, { "page": "nn_rnn", "title": "RNN module", "topics": [ "nn_rnn" ] }, { "page": "nn_rrelu", "title": "RReLU module", "topics": [ "nn_rrelu" ] }, { "page": "nn_selu", "title": "SELU module", "topics": [ "nn_selu" ] }, { "page": "nn_sequential", "title": "A sequential container", "topics": [ "nn_sequential" ] }, { "page": "nn_sigmoid", "title": "Sigmoid module", "topics": [ "nn_sigmoid" ] }, { "page": "nn_silu", "title": "Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.", "topics": [ "nn_silu" ] }, { "page": "nn_smooth_l1_loss", "title": "Smooth L1 loss", "topics": [ "nn_smooth_l1_loss" ] }, { "page": "nn_soft_margin_loss", "title": "Soft margin loss", "topics": [ "nn_soft_margin_loss" ] }, { "page": "nn_softmax", "title": "Softmax module", "topics": [ "nn_softmax" ] }, { "page": "nn_softmax2d", "title": "Softmax2d module", "topics": [ "nn_softmax2d" ] }, { "page": "nn_softmin", "title": "Softmin", "topics": [ "nn_softmin" ] }, { "page": "nn_softplus", "title": "Softplus module", "topics": [ "nn_softplus" ] }, { "page": "nn_softshrink", "title": "Softshrink module", "topics": [ "nn_softshrink" ] }, { "page": "nn_softsign", "title": "Softsign module", "topics": [ "nn_softsign" ] }, { "page": "nn_tanh", "title": "Tanh module", "topics": [ "nn_tanh" ] }, { "page": "nn_tanhshrink", "title": "Tanhshrink module", "topics": [ "nn_tanhshrink" ] }, { "page": "nn_threshold", "title": "Threshold module", "topics": [ "nn_threshold" ] }, { "page": "nn_transformer_encoder", "title": "Transformer Encoder Module (R torch)", "topics": [ "nn_transformer_encoder" ] }, { "page": "nn_transformer_encoder_layer", "title": "Transformer Encoder Layer Module (R torch)", "topics": [ "nn_transformer_encoder_layer" ] }, { "page": "nn_triplet_margin_loss", "title": "Triplet margin loss", "topics": [ "nn_triplet_margin_loss" ] }, { "page": "nn_triplet_margin_with_distance_loss", "title": "Triplet margin with distance loss", "topics": [ "nn_triplet_margin_with_distance_loss" ] }, { "page": "nn_unflatten", "title": "Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential.", "topics": [ "nn_unflatten" ] }, { "page": "nn_upsample", "title": "Upsample module", "topics": [ "nn_upsample" ] }, { "page": "nn_utils_clip_grad_norm_", "title": "Clips gradient norm of an iterable of parameters.", "topics": [ "nn_utils_clip_grad_norm_" ] }, { "page": "nn_utils_clip_grad_value_", "title": "Clips gradient of an iterable of parameters at specified value.", "topics": [ "nn_utils_clip_grad_value_" ] }, { "page": "nn_utils_rnn_pack_padded_sequence", "title": "Packs a Tensor containing padded sequences of variable length.", "topics": [ "nn_utils_rnn_pack_padded_sequence" ] }, { "page": "nn_utils_rnn_pack_sequence", "title": "Packs a list of variable length Tensors", "topics": [ "nn_utils_rnn_pack_sequence" ] }, { "page": "nn_utils_rnn_pad_packed_sequence", "title": "Pads a packed batch of variable length sequences.", "topics": [ "nn_utils_rnn_pad_packed_sequence" ] }, { "page": "nn_utils_rnn_pad_sequence", "title": "Pad a list of variable length Tensors with 'padding_value'", "topics": [ "nn_utils_rnn_pad_sequence" ] }, { "page": "nn_utils_weight_norm", "title": "nn_utils_weight_norm", "topics": [ "nn_utils_weight_norm" ] }, { "page": "nnf_adaptive_avg_pool1d", "title": "Adaptive_avg_pool1d", "topics": [ "nnf_adaptive_avg_pool1d" ] }, { "page": "nnf_adaptive_avg_pool2d", "title": "Adaptive_avg_pool2d", "topics": [ "nnf_adaptive_avg_pool2d" ] }, { "page": "nnf_adaptive_avg_pool3d", "title": "Adaptive_avg_pool3d", "topics": [ "nnf_adaptive_avg_pool3d" ] }, { "page": "nnf_adaptive_max_pool1d", "title": "Adaptive_max_pool1d", "topics": [ "nnf_adaptive_max_pool1d" ] }, { "page": "nnf_adaptive_max_pool2d", "title": "Adaptive_max_pool2d", "topics": [ "nnf_adaptive_max_pool2d" ] }, { "page": "nnf_adaptive_max_pool3d", "title": "Adaptive_max_pool3d", "topics": [ "nnf_adaptive_max_pool3d" ] }, { "page": "nnf_affine_grid", "title": "Affine_grid", "topics": [ "nnf_affine_grid" ] }, { "page": "nnf_alpha_dropout", "title": "Alpha_dropout", "topics": [ "nnf_alpha_dropout" ] }, { "page": "nnf_area_under_min_fpr_fnr", "title": "Area under the Min(FPR, FNR) (AUM)", "topics": [ "nnf_area_under_min_fpr_fnr" ] }, { "page": "nnf_avg_pool1d", "title": "Avg_pool1d", "topics": [ "nnf_avg_pool1d" ] }, { "page": "nnf_avg_pool2d", "title": "Avg_pool2d", "topics": [ "nnf_avg_pool2d" ] }, { "page": "nnf_avg_pool3d", "title": "Avg_pool3d", "topics": [ "nnf_avg_pool3d" ] }, { "page": "nnf_batch_norm", "title": "Batch_norm", "topics": [ "nnf_batch_norm" ] }, { "page": "nnf_bilinear", "title": "Bilinear", "topics": [ "nnf_bilinear" ] }, { "page": "nnf_binary_cross_entropy", "title": "Binary_cross_entropy", "topics": [ "nnf_binary_cross_entropy" ] }, { "page": "nnf_binary_cross_entropy_with_logits", "title": "Binary_cross_entropy_with_logits", "topics": [ "nnf_binary_cross_entropy_with_logits" ] }, { "page": "nnf_celu", "title": "Celu", "topics": [ "nnf_celu", "nnf_celu_" ] }, { "page": "nnf_contrib_sparsemax", "title": "Sparsemax", "topics": [ "nnf_contrib_sparsemax" ] }, { "page": "nnf_conv_tbc", "title": "Conv_tbc", "topics": [ "nnf_conv_tbc" ] }, { "page": "nnf_conv_transpose1d", "title": "Conv_transpose1d", "topics": [ "nnf_conv_transpose1d" ] }, { "page": "nnf_conv_transpose2d", "title": "Conv_transpose2d", "topics": [ "nnf_conv_transpose2d" ] }, { "page": "nnf_conv_transpose3d", "title": "Conv_transpose3d", "topics": [ "nnf_conv_transpose3d" ] }, { "page": "nnf_conv1d", "title": "Conv1d", "topics": [ "nnf_conv1d" ] }, { "page": "nnf_conv2d", "title": "Conv2d", "topics": [ "nnf_conv2d" ] }, { "page": "nnf_conv3d", "title": "Conv3d", "topics": [ "nnf_conv3d" ] }, { "page": "nnf_cosine_embedding_loss", "title": "Cosine_embedding_loss", "topics": [ "nnf_cosine_embedding_loss" ] }, { "page": "nnf_cosine_similarity", "title": "Cosine_similarity", "topics": [ "nnf_cosine_similarity" ] }, { "page": "nnf_cross_entropy", "title": "Cross_entropy", "topics": [ "nnf_cross_entropy" ] }, { "page": "nnf_ctc_loss", "title": "Ctc_loss", "topics": [ "nnf_ctc_loss" ] }, { "page": "nnf_dropout", "title": "Dropout", "topics": [ "nnf_dropout" ] }, { "page": "nnf_dropout2d", "title": "Dropout2d", "topics": [ "nnf_dropout2d" ] }, { "page": "nnf_dropout3d", "title": "Dropout3d", "topics": [ "nnf_dropout3d" ] }, { "page": "nnf_elu", "title": "Elu", "topics": [ "nnf_elu", "nnf_elu_" ] }, { "page": "nnf_embedding", "title": "Embedding", "topics": [ "nnf_embedding" ] }, { "page": "nnf_embedding_bag", "title": "Embedding_bag", "topics": [ "nnf_embedding_bag" ] }, { "page": "nnf_fold", "title": "Fold", "topics": [ "nnf_fold" ] }, { "page": "nnf_fractional_max_pool2d", "title": "Fractional_max_pool2d", "topics": [ "nnf_fractional_max_pool2d" ] }, { "page": "nnf_fractional_max_pool3d", "title": "Fractional_max_pool3d", "topics": [ "nnf_fractional_max_pool3d" ] }, { "page": "nnf_gelu", "title": "Gelu", "topics": [ "nnf_gelu" ] }, { "page": "nnf_glu", "title": "Glu", "topics": [ "nnf_glu" ] }, { "page": "nnf_grid_sample", "title": "Grid_sample", "topics": [ "nnf_grid_sample" ] }, { "page": "nnf_group_norm", "title": "Group_norm", "topics": [ "nnf_group_norm" ] }, { "page": "nnf_gumbel_softmax", "title": "Gumbel_softmax", "topics": [ "nnf_gumbel_softmax" ] }, { "page": "nnf_hardshrink", "title": "Hardshrink", "topics": [ "nnf_hardshrink" ] }, { "page": "nnf_hardsigmoid", "title": "Hardsigmoid", "topics": [ "nnf_hardsigmoid" ] }, { "page": "nnf_hardswish", "title": "Hardswish", "topics": [ "nnf_hardswish" ] }, { "page": "nnf_hardtanh", "title": "Hardtanh", "topics": [ "nnf_hardtanh", "nnf_hardtanh_" ] }, { "page": "nnf_hinge_embedding_loss", "title": "Hinge_embedding_loss", "topics": [ "nnf_hinge_embedding_loss" ] }, { "page": "nnf_instance_norm", "title": "Instance_norm", "topics": [ "nnf_instance_norm" ] }, { "page": "nnf_interpolate", "title": "Interpolate", "topics": [ "nnf_interpolate" ] }, { "page": "nnf_kl_div", "title": "Kl_div", "topics": [ "nnf_kl_div" ] }, { "page": "nnf_l1_loss", "title": "L1_loss", "topics": [ "nnf_l1_loss" ] }, { "page": "nnf_layer_norm", "title": "Layer_norm", "topics": [ "nnf_layer_norm" ] }, { "page": "nnf_leaky_relu", "title": "Leaky_relu", "topics": [ "nnf_leaky_relu" ] }, { "page": "nnf_linear", "title": "Linear", "topics": [ "nnf_linear" ] }, { "page": "nnf_local_response_norm", "title": "Local_response_norm", "topics": [ "nnf_local_response_norm" ] }, { "page": "nnf_log_softmax", "title": "Log_softmax", "topics": [ "nnf_log_softmax" ] }, { "page": "nnf_logsigmoid", "title": "Logsigmoid", "topics": [ "nnf_logsigmoid" ] }, { "page": "nnf_lp_pool1d", "title": "Lp_pool1d", "topics": [ "nnf_lp_pool1d" ] }, { "page": "nnf_lp_pool2d", "title": "Lp_pool2d", "topics": [ "nnf_lp_pool2d" ] }, { "page": "nnf_margin_ranking_loss", "title": "Margin_ranking_loss", "topics": [ "nnf_margin_ranking_loss" ] }, { "page": "nnf_max_pool1d", "title": "Max_pool1d", "topics": [ "nnf_max_pool1d" ] }, { "page": "nnf_max_pool2d", "title": "Max_pool2d", "topics": [ "nnf_max_pool2d" ] }, { "page": "nnf_max_pool3d", "title": "Max_pool3d", "topics": [ "nnf_max_pool3d" ] }, { "page": "nnf_max_unpool1d", "title": "Max_unpool1d", "topics": [ "nnf_max_unpool1d" ] }, { "page": "nnf_max_unpool2d", "title": "Max_unpool2d", "topics": [ "nnf_max_unpool2d" ] }, { "page": "nnf_max_unpool3d", "title": "Max_unpool3d", "topics": [ "nnf_max_unpool3d" ] }, { "page": "nnf_mse_loss", "title": "Mse_loss", "topics": [ "nnf_mse_loss" ] }, { "page": "nnf_multi_head_attention_forward", "title": "Multi head attention forward", "topics": [ "nnf_multi_head_attention_forward" ] }, { "page": "nnf_multi_margin_loss", "title": "Multi_margin_loss", "topics": [ "nnf_multi_margin_loss" ] }, { "page": "nnf_multilabel_margin_loss", "title": "Multilabel_margin_loss", "topics": [ "nnf_multilabel_margin_loss" ] }, { "page": "nnf_multilabel_soft_margin_loss", "title": "Multilabel_soft_margin_loss", "topics": [ "nnf_multilabel_soft_margin_loss" ] }, { "page": "nnf_nll_loss", "title": "Nll_loss", "topics": [ "nnf_nll_loss" ] }, { "page": "nnf_normalize", "title": "Normalize", "topics": [ "nnf_normalize" ] }, { "page": "nnf_one_hot", "title": "One_hot", "topics": [ "nnf_one_hot" ] }, { "page": "nnf_pad", "title": "Pad", "topics": [ "nnf_pad" ] }, { "page": "nnf_pairwise_distance", "title": "Pairwise_distance", "topics": [ "nnf_pairwise_distance" ] }, { "page": "nnf_pdist", "title": "Pdist", "topics": [ "nnf_pdist" ] }, { "page": "nnf_pixel_shuffle", "title": "Pixel_shuffle", "topics": [ "nnf_pixel_shuffle" ] }, { "page": "nnf_poisson_nll_loss", "title": "Poisson_nll_loss", "topics": [ "nnf_poisson_nll_loss" ] }, { "page": "nnf_prelu", "title": "Prelu", "topics": [ "nnf_prelu" ] }, { "page": "nnf_relu", "title": "Relu", "topics": [ "nnf_relu", "nnf_relu_" ] }, { "page": "nnf_relu6", "title": "Relu6", "topics": [ "nnf_relu6" ] }, { "page": "nnf_rrelu", "title": "Rrelu", "topics": [ "nnf_rrelu", "nnf_rrelu_" ] }, { "page": "nnf_selu", "title": "Selu", "topics": [ "nnf_selu", "nnf_selu_" ] }, { "page": "nnf_sigmoid", "title": "Sigmoid", "topics": [ "nnf_sigmoid" ] }, { "page": "nnf_silu", "title": "Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See 'nn_silu()' for more information.", "topics": [ "nnf_silu" ] }, { "page": "nnf_smooth_l1_loss", "title": "Smooth_l1_loss", "topics": [ "nnf_smooth_l1_loss" ] }, { "page": "nnf_soft_margin_loss", "title": "Soft_margin_loss", "topics": [ "nnf_soft_margin_loss" ] }, { "page": "nnf_softmax", "title": "Softmax", "topics": [ "nnf_softmax" ] }, { "page": "nnf_softmin", "title": "Softmin", "topics": [ "nnf_softmin" ] }, { "page": "nnf_softplus", "title": "Softplus", "topics": [ "nnf_softplus" ] }, { "page": "nnf_softshrink", "title": "Softshrink", "topics": [ "nnf_softshrink" ] }, { "page": "nnf_softsign", "title": "Softsign", "topics": [ "nnf_softsign" ] }, { "page": "nnf_tanhshrink", "title": "Tanhshrink", "topics": [ "nnf_tanhshrink" ] }, { "page": "nnf_threshold", "title": "Threshold", "topics": [ "nnf_threshold", "nnf_threshold_" ] }, { "page": "nnf_triplet_margin_loss", "title": "Triplet_margin_loss", "topics": [ "nnf_triplet_margin_loss" ] }, { "page": "nnf_triplet_margin_with_distance_loss", "title": "Triplet margin with distance loss", "topics": [ "nnf_triplet_margin_with_distance_loss" ] }, { "page": "nnf_unfold", "title": "Unfold", "topics": [ "nnf_unfold" ] }, { "page": "optim_adadelta", "title": "Adadelta optimizer", "topics": [ "optim_adadelta" ] }, { "page": "optim_adagrad", "title": "Adagrad optimizer", "topics": [ "optim_adagrad" ] }, { "page": "optim_adam", "title": "Implements Adam algorithm.", "topics": [ "optim_adam" ] }, { "page": "optim_adamw", "title": "Implements AdamW algorithm", "topics": [ "optim_adamw" ] }, { "page": "optim_asgd", "title": "Averaged Stochastic Gradient Descent optimizer", "topics": [ "optim_asgd" ] }, { "page": "optim_ignite_adagrad", "title": "LibTorch implementation of Adagrad", "topics": [ "optim_ignite_adagrad" ] }, { "page": "optim_ignite_adam", "title": "LibTorch implementation of Adam", "topics": [ "optim_ignite_adam" ] }, { "page": "optim_ignite_adamw", "title": "LibTorch implementation of AdamW", "topics": [ "optim_ignite_adamw" ] }, { "page": "optim_ignite_rmsprop", "title": "LibTorch implementation of RMSprop", "topics": [ "optim_ignite_rmsprop" ] }, { "page": "optim_ignite_sgd", "title": "LibTorch implementation of SGD", "topics": [ "optim_ignite_sgd" ] }, { "page": "optim_lbfgs", "title": "LBFGS optimizer", "topics": [ "optim_lbfgs" ] }, { "page": "optim_required", "title": "Dummy value indicating a required value.", "topics": [ "optim_required" ] }, { "page": "optim_rmsprop", "title": "RMSprop optimizer", "topics": [ "optim_rmsprop" ] }, { "page": "optim_rprop", "title": "Implements the resilient backpropagation algorithm.", "topics": [ "optim_rprop" ] }, { "page": "optim_sgd", "title": "SGD optimizer", "topics": [ "optim_sgd" ] }, { "page": "optimizer", "title": "Creates a custom optimizer", "topics": [ "optimizer" ] }, { "page": "optimizer_ignite", "title": "Abstract Base Class for LibTorch Optimizers", "topics": [ "optimizer_ignite" ] }, { "page": "OptimizerIgnite", "title": "Abstract Base Class for LibTorch Optimizers", "topics": [ "OptimizerIgnite" ] }, { "page": "sampler", "title": "Creates a new Sampler", "topics": [ "sampler" ] }, { "page": "set_cpu_allocator_config", "title": "Configure the CPU memory allocator", "topics": [ "set_cpu_allocator_config" ] }, { "page": "tensor_dataset", "title": "Dataset wrapping tensors.", "topics": [ "tensor_dataset" ] }, { "page": "threads", "title": "Number of threads", "topics": [ "threads", "torch_get_num_interop_threads", "torch_get_num_threads", "torch_set_num_interop_threads", "torch_set_num_threads" ] }, { "page": "torch_abs", "title": "Abs", "topics": [ "torch_abs" ] }, { "page": "torch_absolute", "title": "Absolute", "topics": [ "torch_absolute" ] }, { "page": "torch_acos", "title": "Acos", "topics": [ "torch_acos" ] }, { "page": "torch_acosh", "title": "Acosh", "topics": [ "torch_acosh" ] }, { "page": "torch_adaptive_avg_pool1d", "title": "Adaptive_avg_pool1d", "topics": [ "torch_adaptive_avg_pool1d" ] }, { "page": "torch_add", "title": "Add", "topics": [ "torch_add" ] }, { "page": "torch_addbmm", "title": "Addbmm", "topics": [ "torch_addbmm" ] }, { "page": "torch_addcdiv", "title": "Addcdiv", "topics": [ "torch_addcdiv" ] }, { "page": "torch_addcmul", "title": "Addcmul", "topics": [ "torch_addcmul" ] }, { "page": "torch_addmm", "title": "Addmm", "topics": [ "torch_addmm" ] }, { "page": "torch_addmv", "title": "Addmv", "topics": [ "torch_addmv" ] }, { "page": "torch_addr", "title": "Addr", "topics": [ "torch_addr" ] }, { "page": "torch_allclose", "title": "Allclose", "topics": [ "torch_allclose" ] }, { "page": "torch_amax", "title": "Amax", "topics": [ "torch_amax" ] }, { "page": "torch_amin", "title": "Amin", "topics": [ "torch_amin" ] }, { "page": "torch_angle", "title": "Angle", "topics": [ "torch_angle" ] }, { "page": "torch_arange", "title": "Arange", "topics": [ "torch_arange" ] }, { "page": "torch_arccos", "title": "Arccos", "topics": [ "torch_arccos" ] }, { "page": "torch_arccosh", "title": "Arccosh", "topics": [ "torch_arccosh" ] }, { "page": "torch_arcsin", "title": "Arcsin", "topics": [ "torch_arcsin" ] }, { "page": "torch_arcsinh", "title": "Arcsinh", "topics": [ "torch_arcsinh" ] }, { "page": "torch_arctan", "title": "Arctan", "topics": [ "torch_arctan" ] }, { "page": "torch_arctanh", "title": "Arctanh", "topics": [ "torch_arctanh" ] }, { "page": "torch_argmax", "title": "Argmax", "topics": [ "torch_argmax" ] }, { "page": "torch_argmin", "title": "Argmin", "topics": [ "torch_argmin" ] }, { "page": "torch_argsort", "title": "Argsort", "topics": [ "torch_argsort" ] }, { "page": "torch_as_strided", "title": "As_strided", "topics": [ "torch_as_strided" ] }, { "page": "torch_asin", "title": "Asin", "topics": [ "torch_asin" ] }, { "page": "torch_asinh", "title": "Asinh", "topics": [ "torch_asinh" ] }, { "page": "torch_atan", "title": "Atan", "topics": [ "torch_atan" ] }, { "page": "torch_atan2", "title": "Atan2", "topics": [ "torch_atan2" ] }, { "page": "torch_atanh", "title": "Atanh", "topics": [ "torch_atanh" ] }, { "page": "torch_atleast_1d", "title": "Atleast_1d", "topics": [ "torch_atleast_1d" ] }, { "page": "torch_atleast_2d", "title": "Atleast_2d", "topics": [ "torch_atleast_2d" ] }, { "page": "torch_atleast_3d", "title": "Atleast_3d", "topics": [ "torch_atleast_3d" ] }, { "page": "torch_avg_pool1d", "title": "Avg_pool1d", "topics": [ "torch_avg_pool1d" ] }, { "page": "torch_baddbmm", "title": "Baddbmm", "topics": [ "torch_baddbmm" ] }, { "page": "torch_bartlett_window", "title": "Bartlett_window", "topics": [ "torch_bartlett_window" ] }, { "page": "torch_bernoulli", "title": "Bernoulli", "topics": [ "torch_bernoulli" ] }, { "page": "torch_bincount", "title": "Bincount", "topics": [ "torch_bincount" ] }, { "page": "torch_bitwise_and", "title": "Bitwise_and", "topics": [ "torch_bitwise_and" ] }, { "page": "torch_bitwise_not", "title": "Bitwise_not", "topics": [ "torch_bitwise_not" ] }, { "page": "torch_bitwise_or", "title": "Bitwise_or", "topics": [ "torch_bitwise_or" ] }, { "page": "torch_bitwise_xor", "title": "Bitwise_xor", "topics": [ "torch_bitwise_xor" ] }, { "page": "torch_blackman_window", "title": "Blackman_window", "topics": [ "torch_blackman_window" ] }, { "page": "torch_block_diag", "title": "Block_diag", "topics": [ "torch_block_diag" ] }, { "page": "torch_bmm", "title": "Bmm", "topics": [ "torch_bmm" ] }, { "page": "torch_broadcast_tensors", "title": "Broadcast_tensors", "topics": [ "torch_broadcast_tensors" ] }, { "page": "torch_bucketize", "title": "Bucketize", "topics": [ "torch_bucketize" ] }, { "page": "torch_can_cast", "title": "Can_cast", "topics": [ "torch_can_cast" ] }, { "page": "torch_cartesian_prod", "title": "Cartesian_prod", "topics": [ "torch_cartesian_prod" ] }, { "page": "torch_cat", "title": "Cat", "topics": [ "torch_cat" ] }, { "page": "torch_cdist", "title": "Cdist", "topics": [ "torch_cdist" ] }, { "page": "torch_ceil", "title": "Ceil", "topics": [ "torch_ceil" ] }, { "page": "torch_celu", "title": "Celu", "topics": [ "torch_celu" ] }, { "page": "torch_celu_", "title": "Celu_", "topics": [ "torch_celu_" ] }, { "page": "torch_chain_matmul", "title": "Chain_matmul", "topics": [ "torch_chain_matmul" ] }, { "page": "torch_channel_shuffle", "title": "Channel_shuffle", "topics": [ "torch_channel_shuffle" ] }, { "page": "torch_cholesky", "title": "Cholesky", "topics": [ "torch_cholesky" ] }, { "page": "torch_cholesky_inverse", "title": "Cholesky_inverse", "topics": [ "torch_cholesky_inverse" ] }, { "page": "torch_cholesky_solve", "title": "Cholesky_solve", "topics": [ "torch_cholesky_solve" ] }, { "page": "torch_chunk", "title": "Chunk", "topics": [ "torch_chunk" ] }, { "page": "torch_clamp", "title": "Clamp", "topics": [ "torch_clamp" ] }, { "page": "torch_clip", "title": "Clip", "topics": [ "torch_clip" ] }, { "page": "torch_clone", "title": "Clone", "topics": [ "torch_clone" ] }, { "page": "torch_combinations", "title": "Combinations", "topics": [ "torch_combinations" ] }, { "page": "torch_complex", "title": "Complex", "topics": [ "torch_complex" ] }, { "page": "torch_conj", "title": "Conj", "topics": [ "torch_conj" ] }, { "page": "torch_conv_tbc", "title": "Conv_tbc", "topics": [ "torch_conv_tbc" ] }, { "page": "torch_conv_transpose1d", "title": "Conv_transpose1d", "topics": [ "torch_conv_transpose1d" ] }, { "page": "torch_conv_transpose2d", "title": "Conv_transpose2d", "topics": [ "torch_conv_transpose2d" ] }, { "page": "torch_conv_transpose3d", "title": "Conv_transpose3d", "topics": [ "torch_conv_transpose3d" ] }, { "page": "torch_conv1d", "title": "Conv1d", "topics": [ "torch_conv1d" ] }, { "page": "torch_conv2d", "title": "Conv2d", "topics": [ "torch_conv2d" ] }, { "page": "torch_conv3d", "title": "Conv3d", "topics": [ "torch_conv3d" ] }, { "page": "torch_cos", "title": "Cos", "topics": [ "torch_cos" ] }, { "page": "torch_cosh", "title": "Cosh", "topics": [ "torch_cosh" ] }, { "page": "torch_cosine_similarity", "title": "Cosine_similarity", "topics": [ "torch_cosine_similarity" ] }, { "page": "torch_count_nonzero", "title": "Count_nonzero", "topics": [ "torch_count_nonzero" ] }, { "page": "torch_cross", "title": "Cross", "topics": [ "torch_cross" ] }, { "page": "torch_cummax", "title": "Cummax", "topics": [ "torch_cummax" ] }, { "page": "torch_cummin", "title": "Cummin", "topics": [ "torch_cummin" ] }, { "page": "torch_cumprod", "title": "Cumprod", "topics": [ "torch_cumprod" ] }, { "page": "torch_cumsum", "title": "Cumsum", "topics": [ "torch_cumsum" ] }, { "page": "torch_deg2rad", "title": "Deg2rad", "topics": [ "torch_deg2rad" ] }, { "page": "torch_dequantize", "title": "Dequantize", "topics": [ "torch_dequantize" ] }, { "page": "torch_det", "title": "Det", "topics": [ "torch_det" ] }, { "page": "torch_device", "title": "Create a Device object", "concept": [ "tensor-attributtes" ], "topics": [ "torch_device" ] }, { "page": "torch_diag", "title": "Diag", "topics": [ "torch_diag" ] }, { "page": "torch_diag_embed", "title": "Diag_embed", "topics": [ "torch_diag_embed" ] }, { "page": "torch_diagflat", "title": "Diagflat", "topics": [ "torch_diagflat" ] }, { "page": "torch_diagonal", "title": "Diagonal", "topics": [ "torch_diagonal" ] }, { "page": "torch_diff", "title": "Computes the n-th forward difference along the given dimension.", "topics": [ "torch_diff" ] }, { "page": "torch_digamma", "title": "Digamma", "topics": [ "torch_digamma" ] }, { "page": "torch_dist", "title": "Dist", "topics": [ "torch_dist" ] }, { "page": "torch_div", "title": "Div", "topics": [ "torch_div" ] }, { "page": "torch_divide", "title": "Divide", "topics": [ "torch_divide" ] }, { "page": "torch_dot", "title": "Dot", "topics": [ "torch_dot" ] }, { "page": "torch_dstack", "title": "Dstack", "topics": [ "torch_dstack" ] }, { "page": "torch_dtype", "title": "Torch data types", "concept": [ "tensor-attributes" ], "topics": [ "torch_bfloat16", "torch_bool", "torch_cdouble", "torch_cfloat", "torch_cfloat128", "torch_cfloat32", "torch_cfloat64", "torch_chalf", "torch_double", "torch_dtype", "torch_float", "torch_float16", "torch_float32", "torch_float64", "torch_float8_e4m3fn", "torch_float8_e5m2", "torch_half", "torch_int", "torch_int16", "torch_int32", "torch_int64", "torch_int8", "torch_long", "torch_qint32", "torch_qint8", "torch_quint8", "torch_short", "torch_uint8" ] }, { "page": "torch_eig", "title": "Eig", "topics": [ "torch_eig" ] }, { "page": "torch_einsum", "title": "Einsum", "topics": [ "torch_einsum" ] }, { "page": "torch_empty", "title": "Empty", "topics": [ "torch_empty" ] }, { "page": "torch_empty_like", "title": "Empty_like", "topics": [ "torch_empty_like" ] }, { "page": "torch_empty_strided", "title": "Empty_strided", "topics": [ "torch_empty_strided" ] }, { "page": "torch_eq", "title": "Eq", "topics": [ "torch_eq" ] }, { "page": "torch_equal", "title": "Equal", "topics": [ "torch_equal" ] }, { "page": "torch_erf", "title": "Erf", "topics": [ "torch_erf" ] }, { "page": "torch_erfc", "title": "Erfc", "topics": [ "torch_erfc" ] }, { "page": "torch_erfinv", "title": "Erfinv", "topics": [ "torch_erfinv" ] }, { "page": "torch_exp", "title": "Exp", "topics": [ "torch_exp" ] }, { "page": "torch_exp2", "title": "Exp2", "topics": [ "torch_exp2" ] }, { "page": "torch_expm1", "title": "Expm1", "topics": [ "torch_expm1" ] }, { "page": "torch_eye", "title": "Eye", "topics": [ "torch_eye" ] }, { "page": "torch_fft_fft", "title": "Fft", "topics": [ "torch_fft_fft" ] }, { "page": "torch_fft_fftfreq", "title": "fftfreq", "topics": [ "torch_fft_fftfreq" ] }, { "page": "torch_fft_ifft", "title": "Ifft", "topics": [ "torch_fft_ifft" ] }, { "page": "torch_fft_irfft", "title": "Irfft", "topics": [ "torch_fft_irfft" ] }, { "page": "torch_fft_rfft", "title": "Rfft", "topics": [ "torch_fft_rfft" ] }, { "page": "torch_finfo", "title": "Floating point type info", "concept": [ "tensor-attributes" ], "topics": [ "torch_finfo" ] }, { "page": "torch_fix", "title": "Fix", "topics": [ "torch_fix" ] }, { "page": "torch_flatten", "title": "Flatten", "topics": [ "torch_flatten" ] }, { "page": "torch_flip", "title": "Flip", "topics": [ "torch_flip" ] }, { "page": "torch_fliplr", "title": "Fliplr", "topics": [ "torch_fliplr" ] }, { "page": "torch_flipud", "title": "Flipud", "topics": [ "torch_flipud" ] }, { "page": "torch_floor", "title": "Floor", "topics": [ "torch_floor" ] }, { "page": "torch_floor_divide", "title": "Floor_divide", "topics": [ "torch_floor_divide" ] }, { "page": "torch_fmod", "title": "Fmod", "topics": [ "torch_fmod" ] }, { "page": "torch_frac", "title": "Frac", "topics": [ "torch_frac" ] }, { "page": "torch_full", "title": "Full", "topics": [ "torch_full" ] }, { "page": "torch_full_like", "title": "Full_like", "topics": [ "torch_full_like" ] }, { "page": "torch_gather", "title": "Gather", "topics": [ "torch_gather" ] }, { "page": "torch_gcd", "title": "Gcd", "topics": [ "torch_gcd" ] }, { "page": "torch_ge", "title": "Ge", "topics": [ "torch_ge" ] }, { "page": "torch_generator", "title": "Create a Generator object", "topics": [ "torch_generator" ] }, { "page": "torch_geqrf", "title": "Geqrf", "topics": [ "torch_geqrf" ] }, { "page": "torch_ger", "title": "Ger", "topics": [ "torch_ger" ] }, { "page": "torch_get_rng_state", "title": "RNG state management", "topics": [ "cuda_get_rng_state", "cuda_set_rng_state", "torch_get_rng_state", "torch_set_rng_state" ] }, { "page": "torch_greater", "title": "Greater", "topics": [ "torch_greater" ] }, { "page": "torch_greater_equal", "title": "Greater_equal", "topics": [ "torch_greater_equal" ] }, { "page": "torch_gt", "title": "Gt", "topics": [ "torch_gt" ] }, { "page": "torch_hamming_window", "title": "Hamming_window", "topics": [ "torch_hamming_window" ] }, { "page": "torch_hann_window", "title": "Hann_window", "topics": [ "torch_hann_window" ] }, { "page": "torch_heaviside", "title": "Heaviside", "topics": [ "torch_heaviside" ] }, { "page": "torch_histc", "title": "Histc", "topics": [ "torch_histc" ] }, { "page": "torch_hstack", "title": "Hstack", "topics": [ "torch_hstack" ] }, { "page": "torch_hypot", "title": "Hypot", "topics": [ "torch_hypot" ] }, { "page": "torch_i0", "title": "I0", "topics": [ "torch_i0" ] }, { "page": "torch_iinfo", "title": "Integer type info", "concept": [ "tensor-attributes" ], "topics": [ "torch_iinfo" ] }, { "page": "torch_imag", "title": "Imag", "topics": [ "torch_imag" ] }, { "page": "torch_index", "title": "Index torch tensors", "topics": [ "torch_index" ] }, { "page": "torch_index_put", "title": "Modify values selected by 'indices'.", "topics": [ "torch_index_put" ] }, { "page": "torch_index_put_", "title": "In-place version of 'torch_index_put'.", "topics": [ "torch_index_put_" ] }, { "page": "torch_index_select", "title": "Index_select", "topics": [ "torch_index_select" ] }, { "page": "torch_install_path", "title": "A simple exported version of install_path Returns the torch installation path.", "topics": [ "torch_install_path" ] }, { "page": "torch_inverse", "title": "Inverse", "topics": [ "torch_inverse" ] }, { "page": "torch_is_complex", "title": "Is_complex", "topics": [ "torch_is_complex" ] }, { "page": "torch_is_floating_point", "title": "Is_floating_point", "topics": [ "torch_is_floating_point" ] }, { "page": "torch_is_installed", "title": "Verifies if torch is installed", "topics": [ "torch_is_installed" ] }, { "page": "torch_is_nonzero", "title": "Is_nonzero", "topics": [ "torch_is_nonzero" ] }, { "page": "torch_isclose", "title": "Isclose", "topics": [ "torch_isclose" ] }, { "page": "torch_isfinite", "title": "Isfinite", "topics": [ "torch_isfinite" ] }, { "page": "torch_isinf", "title": "Isinf", "topics": [ "torch_isinf" ] }, { "page": "torch_isnan", "title": "Isnan", "topics": [ "torch_isnan" ] }, { "page": "torch_isneginf", "title": "Isneginf", "topics": [ "torch_isneginf" ] }, { "page": "torch_isposinf", "title": "Isposinf", "topics": [ "torch_isposinf" ] }, { "page": "torch_isreal", "title": "Isreal", "topics": [ "torch_isreal" ] }, { "page": "torch_istft", "title": "Istft", "topics": [ "torch_istft" ] }, { "page": "torch_kaiser_window", "title": "Kaiser_window", "topics": [ "torch_kaiser_window" ] }, { "page": "torch_kron", "title": "Kronecker product", "topics": [ "torch_kron" ] }, { "page": "torch_kthvalue", "title": "Kthvalue", "topics": [ "torch_kthvalue" ] }, { "page": "torch_layout", "title": "Creates the corresponding layout", "topics": [ "torch_layout", "torch_sparse_coo", "torch_strided" ] }, { "page": "torch_lcm", "title": "Lcm", "topics": [ "torch_lcm" ] }, { "page": "torch_ldexp", "title": "Ldexp", "topics": [ "torch_ldexp" ] }, { "page": "torch_le", "title": "Le", "topics": [ "torch_le" ] }, { "page": "torch_lerp", "title": "Lerp", "topics": [ "torch_lerp" ] }, { "page": "torch_less", "title": "Less", "topics": [ "torch_less" ] }, { "page": "torch_less_equal", "title": "Less_equal", "topics": [ "torch_less_equal" ] }, { "page": "torch_lgamma", "title": "Lgamma", "topics": [ "torch_lgamma" ] }, { "page": "torch_linspace", "title": "Linspace", "topics": [ "torch_linspace" ] }, { "page": "torch_load", "title": "Loads a saved object", "concept": [ "serialization", "torch_save" ], "topics": [ "torch_load" ] }, { "page": "torch_log", "title": "Log", "topics": [ "torch_log" ] }, { "page": "torch_log10", "title": "Log10", "topics": [ "torch_log10" ] }, { "page": "torch_log1p", "title": "Log1p", "topics": [ "torch_log1p" ] }, { "page": "torch_log2", "title": "Log2", "topics": [ "torch_log2" ] }, { "page": "torch_logaddexp", "title": "Logaddexp", "topics": [ "torch_logaddexp" ] }, { "page": "torch_logaddexp2", "title": "Logaddexp2", "topics": [ "torch_logaddexp2" ] }, { "page": "torch_logcumsumexp", "title": "Logcumsumexp", "topics": [ "torch_logcumsumexp" ] }, { "page": "torch_logdet", "title": "Logdet", "topics": [ "torch_logdet" ] }, { "page": "torch_logical_and", "title": "Logical_and", "topics": [ "torch_logical_and" ] }, { "page": "torch_logical_not", "title": "Logical_not", "topics": [ "torch_logical_not" ] }, { "page": "torch_logical_or", "title": "Logical_or", "topics": [ "torch_logical_or" ] }, { "page": "torch_logical_xor", "title": "Logical_xor", "topics": [ "torch_logical_xor" ] }, { "page": "torch_logit", "title": "Logit", "topics": [ "torch_logit" ] }, { "page": "torch_logspace", "title": "Logspace", "topics": [ "torch_logspace" ] }, { "page": "torch_logsumexp", "title": "Logsumexp", "topics": [ "torch_logsumexp" ] }, { "page": "torch_lstsq", "title": "Lstsq", "topics": [ "torch_lstsq" ] }, { "page": "torch_lt", "title": "Lt", "topics": [ "torch_lt" ] }, { "page": "torch_lu", "title": "LU", "topics": [ "torch_lu" ] }, { "page": "torch_lu_solve", "title": "Lu_solve", "topics": [ "torch_lu_solve" ] }, { "page": "torch_lu_unpack", "title": "Lu_unpack", "topics": [ "torch_lu_unpack" ] }, { "page": "torch_manual_seed", "title": "Sets the seed for generating random numbers.", "topics": [ "local_torch_manual_seed", "torch_manual_seed", "with_torch_manual_seed" ] }, { "page": "torch_masked_select", "title": "Masked_select", "topics": [ "torch_masked_select" ] }, { "page": "torch_matmul", "title": "Matmul", "topics": [ "torch_matmul" ] }, { "page": "torch_matrix_exp", "title": "Matrix_exp", "topics": [ "torch_matrix_exp" ] }, { "page": "torch_matrix_power", "title": "Matrix_power", "topics": [ "torch_matrix_power" ] }, { "page": "torch_matrix_rank", "title": "Matrix_rank", "topics": [ "torch_matrix_rank" ] }, { "page": "torch_max", "title": "Max", "topics": [ "torch_max" ] }, { "page": "torch_maximum", "title": "Maximum", "topics": [ "torch_maximum" ] }, { "page": "torch_mean", "title": "Mean", "topics": [ "torch_mean" ] }, { "page": "torch_median", "title": "Median", "topics": [ "torch_median" ] }, { "page": "torch_memory_format", "title": "Memory format", "topics": [ "torch_channels_last_format", "torch_contiguous_format", "torch_memory_format", "torch_preserve_format" ] }, { "page": "torch_meshgrid", "title": "Meshgrid", "topics": [ "torch_meshgrid" ] }, { "page": "torch_min", "title": "Min", "topics": [ "torch_min" ] }, { "page": "torch_minimum", "title": "Minimum", "topics": [ "torch_minimum" ] }, { "page": "torch_mm", "title": "Mm", "topics": [ "torch_mm" ] }, { "page": "torch_mode", "title": "Mode", "topics": [ "torch_mode" ] }, { "page": "torch_movedim", "title": "Movedim", "topics": [ "torch_movedim" ] }, { "page": "torch_mul", "title": "Mul", "topics": [ "torch_mul" ] }, { "page": "torch_multinomial", "title": "Multinomial", "topics": [ "torch_multinomial" ] }, { "page": "torch_multiply", "title": "Multiply", "topics": [ "torch_multiply" ] }, { "page": "torch_mv", "title": "Mv", "topics": [ "torch_mv" ] }, { "page": "torch_mvlgamma", "title": "Mvlgamma", "topics": [ "torch_mvlgamma" ] }, { "page": "torch_nanquantile", "title": "Nanquantile", "topics": [ "torch_nanquantile" ] }, { "page": "torch_nansum", "title": 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