# Tensors and Neural Networks with 'GPU' Acceleration

## Documentation for package ‘torch’ version 0.16.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A B C D E G I J L N O S T U W

## -- A --

as_array | Converts to array
AutogradContext | Class representing the context.
autograd_backward | Computes the sum of gradients of given tensors w.r.t. graph leaves.
autograd_function | Records operation history and defines formulas for differentiating ops.
autograd_grad | Computes and returns the sum of gradients of outputs w.r.t. the inputs.
autograd_set_grad_mode | Set grad mode

## -- B --

backends_cudnn_is_available | CuDNN is available
backends_cudnn_version | CuDNN version
backends_mkldnn_is_available | MKLDNN is available
backends_mkl_is_available | MKL is available
backends_mps_is_available | MPS is available
backends_openmp_is_available | OpenMP is available
broadcast_all | Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules:
buffer_from_torch_tensor | Creates a tensor from a buffer of memory

## -- C --

clone_module | Clone a torch module.
Constraint | Abstract base class for constraints.
contrib_sort_vertices | Contrib sort vertices
cuda_amp_grad_scaler | Creates a gradient scaler
cuda_current_device | Returns the index of a currently selected device.
cuda_device_count | Returns the number of GPUs available.
cuda_dump_memory_snapshot | Save CUDA Memory State Snapshot to File
cuda_empty_cache | Empty cache
cuda_get_device_capability | Returns the major and minor CUDA capability of 'device'
cuda_get_rng_state | RNG state management
cuda_is_available | Returns a bool indicating if CUDA is currently available.
cuda_memory_snapshot | Capture CUDA Memory State Snapshot
cuda_memory_stats | Returns a dictionary of CUDA memory allocator statistics for a given device.
cuda_memory_summary | Returns a dictionary of CUDA memory allocator statistics for a given device.
cuda_record_memory_history | Enable Recording of Memory Allocation Stack Traces
cuda_runtime_version | Returns the CUDA runtime version
cuda_set_rng_state | RNG state management
cuda_synchronize | Waits for all kernels in all streams on a CUDA device to complete.

## -- D --

dataloader | Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.
dataloader_make_iter | Creates an iterator from a DataLoader
dataloader_next | Get the next element of a dataloader iterator
dataset | Helper function to create an function that generates R6 instances of class 'dataset'
dataset_subset | Dataset Subset
Distribution | Generic R6 class representing distributions
distr_bernoulli | 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'.
distr_categorical | Creates a categorical distribution parameterized by either 'probs' or 'logits' (but not both).
distr_chi2 | Creates a Chi2 distribution parameterized by shape parameter 'df'. This is exactly equivalent to 'distr_gamma(alpha=0.5*df, beta=0.5)'
distr_gamma | Creates a Gamma distribution parameterized by shape 'concentration' and 'rate'.
distr_mixture_same_family | Mixture of components in the same family
distr_multivariate_normal | Gaussian distribution
distr_normal | Creates a normal (also called Gaussian) distribution parameterized by 'loc' and 'scale'.
distr_poisson | Creates a Poisson distribution parameterized by 'rate', the rate parameter.

## -- E --

enumerate | Enumerate an iterator
enumerate.dataloader | Enumerate an iterator

## -- G --

get_install_libs_url | Install Torch from files

## -- I --

install_torch | Install Torch
install_torch_from_file | Install Torch from files
is_dataloader | Checks if the object is a dataloader
is_nn_buffer | Checks if the object is a nn_buffer
is_nn_module | Checks if the object is an nn_module
is_nn_parameter | Checks if an object is a nn_parameter
is_optimizer | Checks if the object is a torch optimizer
is_torch_device | Checks if object is a device
is_torch_dtype | Check if object is a torch data type
is_torch_layout | Check if an object is a torch layout.
is_torch_memory_format | Check if an object is a memory format
is_torch_qscheme | Checks if an object is a QScheme
is_undefined_tensor | Checks if a tensor is undefined
iterable_dataset | Creates an iterable dataset

## -- J --

jit_compile | Compile TorchScript code into a graph
jit_load | Loads a 'script_function' or 'script_module' previously saved with 'jit_save'
jit_ops | Enable idiomatic access to JIT operators from R.
jit_save | Saves a 'script_function' to a path
jit_save_for_mobile | Saves a 'script_function' or 'script_module' in bytecode form, to be loaded on a mobile device
jit_scalar | Adds the 'jit_scalar' class to the input
jit_serialize | Serialize a Script Module
jit_trace | Trace a function and return an executable 'script_function'.
jit_trace_module | Trace a module
jit_tuple | Adds the 'jit_tuple' class to the input
jit_unserialize | Unserialize a Script Module

## -- L --

linalg_cholesky | Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.
linalg_cholesky_ex | Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.
linalg_cond | Computes the condition number of a matrix with respect to a matrix norm.
linalg_det | Computes the determinant of a square matrix.
linalg_eig | Computes the eigenvalue decomposition of a square matrix if it exists.
linalg_eigh | Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.
linalg_eigvals | Computes the eigenvalues of a square matrix.
linalg_eigvalsh | Computes the eigenvalues of a complex Hermitian or real symmetric matrix.
linalg_householder_product | Computes the first 'n' columns of a product of Householder matrices.
linalg_inv | Computes the inverse of a square matrix if it exists.
linalg_inv_ex | Computes the inverse of a square matrix if it is invertible.
linalg_lstsq | Computes a solution to the least squares problem of a system of linear equations.
linalg_matrix_norm | Computes a matrix norm.
linalg_matrix_power | Computes the 'n'-th power of a square matrix for an integer 'n'.
linalg_matrix_rank | Computes the numerical rank of a matrix.
linalg_multi_dot | Efficiently multiplies two or more matrices
linalg_norm | Computes a vector or matrix norm.
linalg_pinv | Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.
linalg_qr | Computes the QR decomposition of a matrix.
linalg_slogdet | Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.
linalg_solve | Computes the solution of a square system of linear equations with a unique solution.
linalg_solve_triangular | Triangular solve
linalg_svd | Computes the singular value decomposition (SVD) of a matrix.
linalg_svdvals | Computes the singular values of a matrix.
linalg_tensorinv | Computes the multiplicative inverse of 'torch_tensordot()'
linalg_tensorsolve | Computes the solution 'X' to the system 'torch_tensordot(A, X) = B'.
linalg_vector_norm | Computes a vector norm.
load_state_dict | Load a state dict file
local_autocast | Autocast context manager
local_device | Device contexts
local_enable_grad | Enable grad
local_no_grad | Temporarily modify gradient recording.
local_torch_manual_seed | Sets the seed for generating random numbers.
lr_cosine_annealing | Set the learning rate of each parameter group using a cosine annealing schedule
lr_lambda | 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.
lr_multiplicative | 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.
lr_one_cycle | Once cycle learning rate
lr_reduce_on_plateau | Reduce learning rate on plateau
lr_scheduler | Creates learning rate schedulers
lr_step | Step learning rate decay

## -- N --

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_area_under_min_fpr_fnr | Area under the Min(FPR, FNR) (AUM)
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 | Celu
nnf_celu_ | Celu
nnf_contrib_sparsemax | Sparsemax
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 | 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 | 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_logsigmoid | Logsigmoid
nnf_log_softmax | Log_softmax
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_multilabel_margin_loss | Multilabel_margin_loss
nnf_multilabel_soft_margin_loss | Multilabel_soft_margin_loss
nnf_multi_head_attention_forward | Multi head attention forward
nnf_multi_margin_loss | Multi_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 | Relu
nnf_relu6 | Relu6
nnf_relu_ | Relu
nnf_rrelu | Rrelu
nnf_rrelu_ | Rrelu
nnf_selu | Selu
nnf_selu_ | Selu
nnf_sigmoid | Sigmoid
nnf_silu | Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See 'nn_silu()' for more information.
nnf_smooth_l1_loss | Smooth_l1_loss
nnf_softmax | Softmax
nnf_softmin | Softmin
nnf_softplus | Softplus
nnf_softshrink | Softshrink
nnf_softsign | Softsign
nnf_soft_margin_loss | Soft_margin_loss
nnf_tanhshrink | Tanhshrink
nnf_threshold | Threshold
nnf_threshold_ | Threshold
nnf_triplet_margin_loss | Triplet_margin_loss
nnf_triplet_margin_with_distance_loss | Triplet margin with distance loss
nnf_unfold | Unfold
nn_adaptive_avg_pool1d | Applies a 1D adaptive average pooling over an input signal composed of several input planes.
nn_adaptive_avg_pool2d | Applies a 2D adaptive average pooling over an input signal composed of several input planes.
nn_adaptive_avg_pool3d | Applies a 3D adaptive average pooling over an input signal composed of several input planes.
nn_adaptive_log_softmax_with_loss | AdaptiveLogSoftmaxWithLoss module
nn_adaptive_max_pool1d | Applies a 1D adaptive max pooling over an input signal composed of several input planes.
nn_adaptive_max_pool2d | Applies a 2D adaptive max pooling over an input signal composed of several input planes.
nn_adaptive_max_pool3d | Applies a 3D adaptive max pooling over an input signal composed of several input planes.
nn_aum_loss | AUM loss
nn_avg_pool1d | Applies a 1D average pooling over an input signal composed of several input planes.
nn_avg_pool2d | Applies a 2D average pooling over an input signal composed of several input planes.
nn_avg_pool3d | Applies a 3D average pooling over an input signal composed of several input planes.
nn_batch_norm1d | BatchNorm1D module
nn_batch_norm2d | BatchNorm2D
nn_batch_norm3d | BatchNorm3D
nn_bce_loss | Binary cross entropy loss
nn_bce_with_logits_loss | BCE with logits loss
nn_bilinear | Bilinear module
nn_buffer | Creates a nn_buffer
nn_celu | CELU module
nn_contrib_sparsemax | Sparsemax activation
nn_conv1d | Conv1D module
nn_conv2d | Conv2D module
nn_conv3d | Conv3D module
nn_conv_transpose1d | ConvTranspose1D
nn_conv_transpose2d | ConvTranpose2D module
nn_conv_transpose3d | ConvTranpose3D module
nn_cosine_embedding_loss | Cosine embedding loss
nn_cross_entropy_loss | CrossEntropyLoss module
nn_ctc_loss | The Connectionist Temporal Classification loss.
nn_dropout | Dropout module
nn_dropout2d | Dropout2D module
nn_dropout3d | Dropout3D module
nn_elu | ELU module
nn_embedding | Embedding module
nn_embedding_bag | Embedding bag module
nn_flatten | Flattens a contiguous range of dims into a tensor.
nn_fractional_max_pool2d | Applies a 2D fractional max pooling over an input signal composed of several input planes.
nn_fractional_max_pool3d | Applies a 3D fractional max pooling over an input signal composed of several input planes.
nn_gelu | GELU module
nn_glu | GLU module
nn_group_norm | Group normalization
nn_gru | Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
nn_hardshrink | Hardshwink module
nn_hardsigmoid | Hardsigmoid module
nn_hardswish | Hardswish module
nn_hardtanh | Hardtanh module
nn_hinge_embedding_loss | Hinge embedding loss
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_kl_div_loss | Kullback-Leibler divergence loss
nn_l1_loss | L1 loss
nn_layer_norm | Layer normalization
nn_leaky_relu | LeakyReLU module
nn_linear | Linear module
nn_log_sigmoid | LogSigmoid module
nn_log_softmax | LogSoftmax module
nn_lp_pool1d | Applies a 1D power-average pooling over an input signal composed of several input planes.
nn_lp_pool2d | Applies a 2D power-average pooling over an input signal composed of several input planes.
nn_lstm | Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.
nn_margin_ranking_loss | Margin ranking loss
nn_max_pool1d | MaxPool1D module
nn_max_pool2d | MaxPool2D module
nn_max_pool3d | Applies a 3D max pooling over an input signal composed of several input planes.
nn_max_unpool1d | Computes a partial inverse of 'MaxPool1d'.
nn_max_unpool2d | Computes a partial inverse of 'MaxPool2d'.
nn_max_unpool3d | Computes a partial inverse of 'MaxPool3d'.
nn_module | Base class for all neural network modules.
nn_module_dict | Container that allows named values
nn_module_list | Holds submodules in a list.
nn_mse_loss | MSE loss
nn_multihead_attention | MultiHead attention
nn_multilabel_margin_loss | Multilabel margin loss
nn_multilabel_soft_margin_loss | Multi label soft margin loss
nn_multi_margin_loss | Multi margin loss
nn_nll_loss | Nll loss
nn_pairwise_distance | Pairwise distance
nn_parameter | Creates an 'nn_parameter'
nn_poisson_nll_loss | Poisson NLL loss
nn_prelu | PReLU module
nn_prune_head | Prune top layer(s) of a network
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_silu | Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.
nn_smooth_l1_loss | Smooth L1 loss
nn_softmax | Softmax module
nn_softmax2d | Softmax2d module
nn_softmin | Softmin
nn_softplus | Softplus module
nn_softshrink | Softshrink module
nn_softsign | Softsign module
nn_soft_margin_loss | Soft margin loss
nn_tanh | Tanh module
nn_tanhshrink | Tanhshrink module
nn_threshold | Threshold module
nn_transformer_encoder | Transformer Encoder Module (R torch)
nn_transformer_encoder_layer | Transformer Encoder Layer Module (R torch)
nn_triplet_margin_loss | Triplet margin loss
nn_triplet_margin_with_distance_loss | Triplet margin with distance loss
nn_unflatten | Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential.
nn_upsample | Upsample module
nn_utils_clip_grad_norm_ | Clips gradient norm of an iterable of parameters.
nn_utils_clip_grad_value_ | Clips gradient of an iterable of parameters at specified value.
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'
nn_utils_weight_norm | nn_utils_weight_norm

## -- O --

optimizer | Creates a custom optimizer
OptimizerIgnite | Abstract Base Class for LibTorch Optimizers
optimizer_ignite | Abstract Base Class for LibTorch Optimizers
optim_adadelta | Adadelta optimizer
optim_adagrad | Adagrad optimizer
optim_adam | Implements Adam algorithm.
optim_adamw | Implements AdamW algorithm
optim_asgd | Averaged Stochastic Gradient Descent optimizer
optim_ignite_adagrad | LibTorch implementation of Adagrad
optim_ignite_adam | LibTorch implementation of Adam
optim_ignite_adamw | LibTorch implementation of AdamW
optim_ignite_rmsprop | LibTorch implementation of RMSprop
optim_ignite_sgd | LibTorch implementation of SGD
optim_lbfgs | LBFGS optimizer
optim_required | Dummy value indicating a required value.
optim_rmsprop | RMSprop optimizer
optim_rprop | Implements the resilient backpropagation algorithm.
optim_sgd | SGD optimizer

## -- S --

sampler | Creates a new Sampler
set_autocast | Autocast context manager

## -- T --

tensor_dataset | Dataset wrapping tensors.
threads | Number of threads
torch_abs | Abs
torch_absolute | Absolute
torch_acos | Acos
torch_acosh | Acosh
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_amax | Amax
torch_amin | Amin
torch_angle | Angle
torch_arange | Arange
torch_arccos | Arccos
torch_arccosh | Arccosh
torch_arcsin | Arcsin
torch_arcsinh | Arcsinh
torch_arctan | Arctan
torch_arctanh | Arctanh
torch_argmax | Argmax
torch_argmin | Argmin
torch_argsort | Argsort
torch_asin | Asin
torch_asinh | Asinh
torch_as_strided | As_strided
torch_atan | Atan
torch_atan2 | Atan2
torch_atanh | Atanh
torch_atleast_1d | Atleast_1d
torch_atleast_2d | Atleast_2d
torch_atleast_3d | Atleast_3d
torch_avg_pool1d | Avg_pool1d
torch_baddbmm | Baddbmm
torch_bartlett_window | Bartlett_window
torch_bernoulli | Bernoulli
torch_bfloat16 | Torch data types
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_block_diag | Block_diag
torch_bmm | Bmm
torch_bool | Torch data types
torch_broadcast_tensors | Broadcast_tensors
torch_bucketize | Bucketize
torch_can_cast | Can_cast
torch_cartesian_prod | Cartesian_prod
torch_cat | Cat
torch_cdist | Cdist
torch_cdouble | Torch data types
torch_ceil | Ceil
torch_celu | Celu
torch_celu_ | Celu_
torch_cfloat | Torch data types
torch_cfloat128 | Torch data types
torch_cfloat32 | Torch data types
torch_cfloat64 | Torch data types
torch_chain_matmul | Chain_matmul
torch_chalf | Torch data types
torch_channels_last_format | Memory format
torch_channel_shuffle | Channel_shuffle
torch_cholesky | Cholesky
torch_cholesky_inverse | Cholesky_inverse
torch_cholesky_solve | Cholesky_solve
torch_chunk | Chunk
torch_clamp | Clamp
torch_clip | Clip
torch_clone | Clone
torch_combinations | Combinations
torch_complex | Complex
torch_conj | Conj
torch_contiguous_format | Memory format
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_count_nonzero | Count_nonzero
torch_cross | Cross
torch_cummax | Cummax
torch_cummin | Cummin
torch_cumprod | Cumprod
torch_cumsum | Cumsum
torch_deg2rad | Deg2rad
torch_dequantize | Dequantize
torch_det | Det
torch_device | Create a Device object
torch_diag | Diag
torch_diagflat | Diagflat
torch_diagonal | Diagonal
torch_diag_embed | Diag_embed
torch_diff | Computes the n-th forward difference along the given dimension.
torch_digamma | Digamma
torch_dist | Dist
torch_div | Div
torch_divide | Divide
torch_dot | Dot
torch_double | Torch data types
torch_dstack | Dstack
torch_dtype | 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_exp2 | Exp2
torch_expm1 | Expm1
torch_eye | Eye
torch_fft_fft | Fft
torch_fft_fftfreq | fftfreq
torch_fft_ifft | Ifft
torch_fft_irfft | Irfft
torch_fft_rfft | Rfft
torch_finfo | Floating point type info
torch_fix | Fix
torch_flatten | Flatten
torch_flip | Flip
torch_fliplr | Fliplr
torch_flipud | Flipud
torch_float | Torch data types
torch_float16 | Torch data types
torch_float32 | Torch data types
torch_float64 | Torch data types
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_gcd | Gcd
torch_ge | Ge
torch_generator | Create a Generator object
torch_geqrf | Geqrf
torch_ger | Ger
torch_get_default_dtype | Gets and sets the default floating point dtype.
torch_get_num_interop_threads | Number of threads
torch_get_num_threads | Number of threads
torch_get_rng_state | RNG state management
torch_greater | Greater
torch_greater_equal | Greater_equal
torch_gt | Gt
torch_half | Torch data types
torch_hamming_window | Hamming_window
torch_hann_window | Hann_window
torch_heaviside | Heaviside
torch_histc | Histc
torch_hstack | Hstack
torch_hypot | Hypot
torch_i0 | I0
torch_iinfo | Integer type info
torch_imag | Imag
torch_index | Index torch tensors
torch_index_put | Modify values selected by 'indices'.
torch_index_put_ | In-place version of 'torch_index_put'.
torch_index_select | Index_select
torch_install_path | A simple exported version of install_path Returns the torch installation path.
torch_int | Torch data types
torch_int16 | Torch data types
torch_int32 | Torch data types
torch_int64 | Torch data types
torch_int8 | Torch data types
torch_inverse | Inverse
torch_isclose | Isclose
torch_isfinite | Isfinite
torch_isinf | Isinf
torch_isnan | Isnan
torch_isneginf | Isneginf
torch_isposinf | Isposinf
torch_isreal | Isreal
torch_istft | Istft
torch_is_complex | Is_complex
torch_is_floating_point | Is_floating_point
torch_is_installed | Verifies if torch is installed
torch_is_nonzero | Is_nonzero
torch_kaiser_window | Kaiser_window
torch_kron | Kronecker product
torch_kthvalue | Kthvalue
torch_layout | Creates the corresponding layout
torch_lcm | Lcm
torch_le | Le
torch_lerp | Lerp
torch_less | Less
torch_less_equal | Less_equal
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_logaddexp | Logaddexp
torch_logaddexp2 | Logaddexp2
torch_logcumsumexp | Logcumsumexp
torch_logdet | Logdet
torch_logical_and | Logical_and
torch_logical_not | Logical_not
torch_logical_or | Logical_or
torch_logical_xor | Logical_xor
torch_logit | Logit
torch_logspace | Logspace
torch_logsumexp | Logsumexp
torch_long | Torch data types
torch_lstsq | Lstsq
torch_lt | Lt
torch_lu | LU
torch_lu_solve | Lu_solve
torch_lu_unpack | Lu_unpack
torch_manual_seed | Sets the seed for generating random numbers.
torch_masked_select | Masked_select
torch_matmul | Matmul
torch_matrix_exp | Matrix_exp
torch_matrix_power | Matrix_power
torch_matrix_rank | Matrix_rank
torch_max | Max
torch_maximum | Maximum
torch_mean | Mean
torch_median | Median
torch_memory_format | Memory format
torch_meshgrid | Meshgrid
torch_min | Min
torch_minimum | Minimum
torch_mm | Mm
torch_mode | Mode
torch_movedim | Movedim
torch_mul | Mul
torch_multinomial | Multinomial
torch_multiply | Multiply
torch_mv | Mv
torch_mvlgamma | Mvlgamma
torch_nanquantile | Nanquantile
torch_nansum | Nansum
torch_narrow | Narrow
torch_ne | Ne
torch_neg | Neg
torch_negative | Negative
torch_nextafter | Nextafter
torch_nonzero | Nonzero
torch_norm | Norm
torch_normal | Normal
torch_not_equal | Not_equal
torch_ones | Ones
torch_ones_like | Ones_like
torch_orgqr | Orgqr
torch_ormqr | Ormqr
torch_outer | Outer
torch_pdist | Pdist
torch_per_channel_affine | Creates the corresponding Scheme object
torch_per_channel_symmetric | Creates the corresponding Scheme object
torch_per_tensor_affine | Creates the corresponding Scheme object
torch_per_tensor_symmetric | Creates the corresponding Scheme object
torch_pinverse | Pinverse
torch_pixel_shuffle | Pixel_shuffle
torch_poisson | Poisson
torch_polar | Polar
torch_polygamma | Polygamma
torch_pow | Pow
torch_preserve_format | Memory format
torch_prod | Prod
torch_promote_types | Promote_types
torch_qint32 | Torch data types
torch_qint8 | Torch data types
torch_qr | Qr
torch_qscheme | Creates the corresponding Scheme object
torch_quantile | Quantile
torch_quantize_per_channel | Quantize_per_channel
torch_quantize_per_tensor | Quantize_per_tensor
torch_quint8 | Torch data types
torch_rad2deg | Rad2deg
torch_rand | Rand
torch_randint | Randint
torch_randint_like | Randint_like
torch_randn | Randn
torch_randn_like | Randn_like
torch_randperm | Randperm
torch_rand_like | Rand_like
torch_range | Range
torch_real | Real
torch_reciprocal | Reciprocal
torch_reduction | Creates the reduction objet
torch_reduction_mean | Creates the reduction objet
torch_reduction_none | Creates the reduction objet
torch_reduction_sum | Creates the reduction objet
torch_relu | Relu
torch_relu_ | Relu_
torch_remainder | Remainder
torch_renorm | Renorm
torch_repeat_interleave | Repeat_interleave
torch_reshape | Reshape
torch_result_type | Result_type
torch_roll | Roll
torch_rot90 | Rot90
torch_round | Round
torch_rrelu_ | Rrelu_
torch_rsqrt | Rsqrt
torch_save | Saves an object to a disk file.
torch_scalar_tensor | Scalar tensor
torch_searchsorted | Searchsorted
torch_selu | Selu
torch_selu_ | Selu_
torch_serialize | Serialize a torch object returning a raw object
torch_set_default_dtype | Gets and sets the default floating point dtype.
torch_set_num_interop_threads | Number of threads
torch_set_num_threads | Number of threads
torch_set_rng_state | RNG state management
torch_sgn | Sgn
torch_short | Torch data types
torch_sigmoid | Sigmoid
torch_sign | Sign
torch_signbit | Signbit
torch_sin | Sin
torch_sinh | Sinh
torch_slogdet | Slogdet
torch_sort | Sort
torch_sparse_coo | Creates the corresponding layout
torch_sparse_coo_tensor | Sparse_coo_tensor
torch_split | Split
torch_sqrt | Sqrt
torch_square | Square
torch_squeeze | Squeeze
torch_stack | Stack
torch_std | Std
torch_std_mean | Std_mean
torch_stft | Stft
torch_strided | Creates the corresponding layout
torch_sub | Sub
torch_subtract | Subtract
torch_sum | Sum
torch_svd | Svd
torch_t | T
torch_take | Take
torch_take_along_dim | Selects values from input at the 1-dimensional indices from indices along the given dim.
torch_tan | Tan
torch_tanh | Tanh
torch_tensor | Converts R objects to a torch tensor
torch_tensordot | Tensordot
torch_tensor_from_buffer | Creates a tensor from a buffer of memory
torch_threshold_ | Threshold_
torch_topk | Topk
torch_trace | Trace
torch_transpose | Transpose
torch_trapz | Trapz
torch_triangular_solve | Triangular_solve
torch_tril | Tril
torch_tril_indices | Tril_indices
torch_triu | Triu
torch_triu_indices | Triu_indices
torch_true_divide | TRUE_divide
torch_trunc | Trunc
torch_uint8 | Torch data types
torch_unbind | Unbind
torch_unique_consecutive | Unique_consecutive
torch_unsafe_chunk | Unsafe_chunk
torch_unsafe_split | Unsafe_split
torch_unsqueeze | Unsqueeze
torch_vander | Vander
torch_var | Var
torch_var_mean | Var_mean
torch_vdot | Vdot
torch_view_as_complex | View_as_complex
torch_view_as_real | View_as_real
torch_vstack | Vstack
torch_where | Where
torch_zeros | Zeros
torch_zeros_like | Zeros_like

## -- U --

unset_autocast | Autocast context manager

## -- W --

with_autocast | Autocast context manager
with_detect_anomaly | Context-manager that enable anomaly detection for the autograd engine.
with_device | Device contexts
with_enable_grad | Enable grad
with_no_grad | Temporarily modify gradient recording.
with_torch_manual_seed | Sets the seed for generating random numbers.