| Converts to array | as_array |
| Computes the sum of gradients of given tensors w.r.t. graph leaves. | autograd_backward |
| Records operation history and defines formulas for differentiating ops. | autograd_function |
| Computes and returns the sum of gradients of outputs w.r.t. the inputs. | autograd_grad |
| Set grad mode | autograd_set_grad_mode |
| Class representing the context. | AutogradContext |
| CuDNN is available | backends_cudnn_is_available |
| CuDNN version | backends_cudnn_version |
| MKL is available | backends_mkl_is_available |
| MKLDNN is available | backends_mkldnn_is_available |
| MPS is available | backends_mps_is_available |
| OpenMP is available | backends_openmp_is_available |
| Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: | broadcast_all |
| Clone a torch module. | clone_module |
| Abstract base class for constraints. | Constraint |
| Contrib sort vertices | contrib_sort_vertices |
| Flush the CPU memory cache | cpu_cache_flush |
| Creates a gradient scaler | cuda_amp_grad_scaler |
| Returns the index of a currently selected device. | cuda_current_device |
| Returns the number of GPUs available. | cuda_device_count |
| Save CUDA Memory State Snapshot to File | cuda_dump_memory_snapshot |
| Empty cache | cuda_empty_cache |
| Returns the major and minor CUDA capability of 'device' | cuda_get_device_capability |
| Returns a bool indicating if CUDA is currently available. | cuda_is_available |
| Capture CUDA Memory State Snapshot | cuda_memory_snapshot |
| Returns a dictionary of CUDA memory allocator statistics for a given device. | cuda_memory_stats cuda_memory_summary |
| Enable Recording of Memory Allocation Stack Traces | cuda_record_memory_history |
| Returns the CUDA runtime version | cuda_runtime_version |
| Waits for all kernels in all streams on a CUDA device to complete. | cuda_synchronize |
| Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. | dataloader |
| Creates an iterator from a DataLoader | dataloader_make_iter |
| Get the next element of a dataloader iterator | dataloader_next |
| Helper function to create an function that generates R6 instances of class 'dataset' | dataset |
| Dataset Subset | dataset_subset |
| 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_bernoulli |
| Creates a categorical distribution parameterized by either 'probs' or 'logits' (but not both). | distr_categorical |
| Creates a Chi2 distribution parameterized by shape parameter 'df'. This is exactly equivalent to 'distr_gamma(alpha=0.5*df, beta=0.5)' | distr_chi2 |
| Creates a Gamma distribution parameterized by shape 'concentration' and 'rate'. | distr_gamma |
| Mixture of components in the same family | distr_mixture_same_family |
| Gaussian distribution | distr_multivariate_normal |
| Creates a normal (also called Gaussian) distribution parameterized by 'loc' and 'scale'. | distr_normal |
| Creates a Poisson distribution parameterized by 'rate', the rate parameter. | distr_poisson |
| Generic R6 class representing distributions | Distribution |
| Enumerate an iterator | enumerate |
| Enumerate an iterator | enumerate.dataloader |
| Install Torch from files | get_install_libs_url install_torch_from_file |
| Install Torch | install_torch |
| Torch Installation Situation Report | install_torch_sitrep |
| Checks if the object is a dataloader | is_dataloader |
| Checks if the object is a nn_buffer | is_nn_buffer |
| Checks if the object is an nn_module | is_nn_module |
| Checks if an object is a nn_parameter | is_nn_parameter |
| Checks if the object is a torch optimizer | is_optimizer |
| Checks if object is a device | is_torch_device |
| Check if object is a torch data type | is_torch_dtype |
| Check if an object is a torch layout. | is_torch_layout |
| Check if an object is a memory format | is_torch_memory_format |
| Checks if an object is a QScheme | is_torch_qscheme |
| Checks if a tensor is undefined | is_undefined_tensor |
| Creates an iterable dataset | iterable_dataset |
| Compile TorchScript code into a graph | jit_compile |
| Loads a 'script_function' or 'script_module' previously saved with 'jit_save' | jit_load |
| Enable idiomatic access to JIT operators from R. | jit_ops |
| Saves a 'script_function' to a path | jit_save |
| Saves a 'script_function' or 'script_module' in bytecode form, to be loaded on a mobile device | jit_save_for_mobile |
| Adds the 'jit_scalar' class to the input | jit_scalar |
| Serialize a Script Module | jit_serialize |
| Trace a function and return an executable 'script_function'. | jit_trace |
| Trace a module | jit_trace_module |
| Adds the 'jit_tuple' class to the input | jit_tuple |
| Unserialize a Script Module | jit_unserialize |
| Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. | linalg_cholesky |
| Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. | linalg_cholesky_ex |
| Computes the condition number of a matrix with respect to a matrix norm. | linalg_cond |
| Computes the determinant of a square matrix. | linalg_det |
| Computes the eigenvalue decomposition of a square matrix if it exists. | linalg_eig |
| Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. | linalg_eigh |
| Computes the eigenvalues of a square matrix. | linalg_eigvals |
| Computes the eigenvalues of a complex Hermitian or real symmetric matrix. | linalg_eigvalsh |
| Computes the first 'n' columns of a product of Householder matrices. | linalg_householder_product |
| Computes the inverse of a square matrix if it exists. | linalg_inv |
| Computes the inverse of a square matrix if it is invertible. | linalg_inv_ex |
| Computes a solution to the least squares problem of a system of linear equations. | linalg_lstsq |
| Computes a matrix norm. | linalg_matrix_norm |
| Computes the 'n'-th power of a square matrix for an integer 'n'. | linalg_matrix_power |
| Computes the numerical rank of a matrix. | linalg_matrix_rank |
| Efficiently multiplies two or more matrices | linalg_multi_dot |
| Computes a vector or matrix norm. | linalg_norm |
| Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. | linalg_pinv |
| Computes the QR decomposition of a matrix. | linalg_qr |
| Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. | linalg_slogdet |
| Computes the solution of a square system of linear equations with a unique solution. | linalg_solve |
| Triangular solve | linalg_solve_triangular |
| Computes the singular value decomposition (SVD) of a matrix. | linalg_svd |
| Computes the singular values of a matrix. | linalg_svdvals |
| Computes the multiplicative inverse of 'torch_tensordot()' | linalg_tensorinv |
| Computes the solution 'X' to the system 'torch_tensordot(A, X) = B'. | linalg_tensorsolve |
| Computes a vector norm. | linalg_vector_norm |
| Load a state dict file | load_state_dict |
| Autocast context manager | local_autocast set_autocast unset_autocast with_autocast |
| Device contexts | local_device with_device |
| Set the learning rate of each parameter group using a cosine annealing schedule | lr_cosine_annealing |
| 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_lambda |
| 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_multiplicative |
| Once cycle learning rate | lr_one_cycle |
| Reduce learning rate on plateau | lr_reduce_on_plateau |
| Creates learning rate schedulers | lr_scheduler |
| Step learning rate decay | lr_step |
| Applies a 1D adaptive average pooling over an input signal composed of several input planes. | nn_adaptive_avg_pool1d |
| Applies a 2D adaptive average pooling over an input signal composed of several input planes. | nn_adaptive_avg_pool2d |
| Applies a 3D adaptive average pooling over an input signal composed of several input planes. | nn_adaptive_avg_pool3d |
| AdaptiveLogSoftmaxWithLoss module | nn_adaptive_log_softmax_with_loss |
| Applies a 1D adaptive max pooling over an input signal composed of several input planes. | nn_adaptive_max_pool1d |
| Applies a 2D adaptive max pooling over an input signal composed of several input planes. | nn_adaptive_max_pool2d |
| Applies a 3D adaptive max pooling over an input signal composed of several input planes. | nn_adaptive_max_pool3d |
| AUM loss | nn_aum_loss |
| Applies a 1D average pooling over an input signal composed of several input planes. | nn_avg_pool1d |
| Applies a 2D average pooling over an input signal composed of several input planes. | nn_avg_pool2d |
| Applies a 3D average pooling over an input signal composed of several input planes. | nn_avg_pool3d |
| BatchNorm1D module | nn_batch_norm1d |
| BatchNorm2D | nn_batch_norm2d |
| BatchNorm3D | nn_batch_norm3d |
| Binary cross entropy loss | nn_bce_loss |
| BCE with logits loss | nn_bce_with_logits_loss |
| Bilinear module | nn_bilinear |
| Creates a nn_buffer | nn_buffer |
| CELU module | nn_celu |
| Sparsemax activation | nn_contrib_sparsemax |
| ConvTranspose1D | nn_conv_transpose1d |
| ConvTranpose2D module | nn_conv_transpose2d |
| ConvTranpose3D module | nn_conv_transpose3d |
| Conv1D module | nn_conv1d |
| Conv2D module | nn_conv2d |
| Conv3D module | nn_conv3d |
| Cosine embedding loss | nn_cosine_embedding_loss |
| CrossEntropyLoss module | nn_cross_entropy_loss |
| The Connectionist Temporal Classification loss. | nn_ctc_loss |
| Dropout module | nn_dropout |
| Dropout2D module | nn_dropout2d |
| Dropout3D module | nn_dropout3d |
| ELU module | nn_elu |
| Embedding module | nn_embedding |
| Embedding bag module | nn_embedding_bag |
| Flattens a contiguous range of dims into a tensor. | nn_flatten |
| Applies a 2D fractional max pooling over an input signal composed of several input planes. | nn_fractional_max_pool2d |
| Applies a 3D fractional max pooling over an input signal composed of several input planes. | nn_fractional_max_pool3d |
| GELU module | nn_gelu |
| GLU module | nn_glu |
| Group normalization | nn_group_norm |
| Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. | nn_gru |
| Hardshwink module | nn_hardshrink |
| Hardsigmoid module | nn_hardsigmoid |
| Hardswish module | nn_hardswish |
| Hardtanh module | nn_hardtanh |
| Hinge embedding loss | nn_hinge_embedding_loss |
| Identity module | nn_identity |
| Calculate gain | nn_init_calculate_gain |
| Constant initialization | nn_init_constant_ |
| Dirac initialization | nn_init_dirac_ |
| Eye initialization | nn_init_eye_ |
| Kaiming normal initialization | nn_init_kaiming_normal_ |
| Kaiming uniform initialization | nn_init_kaiming_uniform_ |
| Normal initialization | nn_init_normal_ |
| Ones initialization | nn_init_ones_ |
| Orthogonal initialization | nn_init_orthogonal_ |
| Sparse initialization | nn_init_sparse_ |
| Truncated normal initialization | nn_init_trunc_normal_ |
| Uniform initialization | nn_init_uniform_ |
| Xavier normal initialization | nn_init_xavier_normal_ |
| Xavier uniform initialization | nn_init_xavier_uniform_ |
| Zeros initialization | nn_init_zeros_ |
| Kullback-Leibler divergence loss | nn_kl_div_loss |
| L1 loss | nn_l1_loss |
| Layer normalization | nn_layer_norm |
| LeakyReLU module | nn_leaky_relu |
| Linear module | nn_linear |
| LogSigmoid module | nn_log_sigmoid |
| LogSoftmax module | nn_log_softmax |
| Applies a 1D power-average pooling over an input signal composed of several input planes. | nn_lp_pool1d |
| Applies a 2D power-average pooling over an input signal composed of several input planes. | nn_lp_pool2d |
| Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. | nn_lstm |
| Margin ranking loss | nn_margin_ranking_loss |
| MaxPool1D module | nn_max_pool1d |
| MaxPool2D module | nn_max_pool2d |
| Applies a 3D max pooling over an input signal composed of several input planes. | nn_max_pool3d |
| Computes a partial inverse of 'MaxPool1d'. | nn_max_unpool1d |
| Computes a partial inverse of 'MaxPool2d'. | nn_max_unpool2d |
| Computes a partial inverse of 'MaxPool3d'. | nn_max_unpool3d |
| Base class for all neural network modules. | nn_module |
| Container that allows named values | nn_module_dict |
| Holds submodules in a list. | nn_module_list |
| MSE loss | nn_mse_loss |
| Multi margin loss | nn_multi_margin_loss |
| MultiHead attention | nn_multihead_attention |
| Multilabel margin loss | nn_multilabel_margin_loss |
| Multi label soft margin loss | nn_multilabel_soft_margin_loss |
| Nll loss | nn_nll_loss |
| Pairwise distance | nn_pairwise_distance |
| Creates an 'nn_parameter' | nn_parameter |
| Poisson NLL loss | nn_poisson_nll_loss |
| PReLU module | nn_prelu |
| Prune top layer(s) of a network | nn_prune_head |
| ReLU module | nn_relu |
| ReLu6 module | nn_relu6 |
| RNN module | nn_rnn |
| RReLU module | nn_rrelu |
| SELU module | nn_selu |
| A sequential container | nn_sequential |
| Sigmoid module | nn_sigmoid |
| Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. | nn_silu |
| Smooth L1 loss | nn_smooth_l1_loss |
| Soft margin loss | nn_soft_margin_loss |
| Softmax module | nn_softmax |
| Softmax2d module | nn_softmax2d |
| Softmin | nn_softmin |
| Softplus module | nn_softplus |
| Softshrink module | nn_softshrink |
| Softsign module | nn_softsign |
| Tanh module | nn_tanh |
| Tanhshrink module | nn_tanhshrink |
| Threshold module | nn_threshold |
| Transformer Encoder Module (R torch) | nn_transformer_encoder |
| Transformer Encoder Layer Module (R torch) | nn_transformer_encoder_layer |
| Triplet margin loss | nn_triplet_margin_loss |
| Triplet margin with distance loss | nn_triplet_margin_with_distance_loss |
| Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. | nn_unflatten |
| Upsample module | nn_upsample |
| Clips gradient norm of an iterable of parameters. | nn_utils_clip_grad_norm_ |
| Clips gradient of an iterable of parameters at specified value. | nn_utils_clip_grad_value_ |
| Packs a Tensor containing padded sequences of variable length. | nn_utils_rnn_pack_padded_sequence |
| Packs a list of variable length Tensors | nn_utils_rnn_pack_sequence |
| Pads a packed batch of variable length sequences. | nn_utils_rnn_pad_packed_sequence |
| Pad a list of variable length Tensors with 'padding_value' | nn_utils_rnn_pad_sequence |
| nn_utils_weight_norm | nn_utils_weight_norm |
| Adaptive_avg_pool1d | nnf_adaptive_avg_pool1d |
| Adaptive_avg_pool2d | nnf_adaptive_avg_pool2d |
| Adaptive_avg_pool3d | nnf_adaptive_avg_pool3d |
| Adaptive_max_pool1d | nnf_adaptive_max_pool1d |
| Adaptive_max_pool2d | nnf_adaptive_max_pool2d |
| Adaptive_max_pool3d | nnf_adaptive_max_pool3d |
| Affine_grid | nnf_affine_grid |
| Alpha_dropout | nnf_alpha_dropout |
| Area under the Min(FPR, FNR) (AUM) | nnf_area_under_min_fpr_fnr |
| Avg_pool1d | nnf_avg_pool1d |
| Avg_pool2d | nnf_avg_pool2d |
| Avg_pool3d | nnf_avg_pool3d |
| Batch_norm | nnf_batch_norm |
| Bilinear | nnf_bilinear |
| Binary_cross_entropy | nnf_binary_cross_entropy |
| Binary_cross_entropy_with_logits | nnf_binary_cross_entropy_with_logits |
| Celu | nnf_celu nnf_celu_ |
| Sparsemax | nnf_contrib_sparsemax |
| Conv_tbc | nnf_conv_tbc |
| Conv_transpose1d | nnf_conv_transpose1d |
| Conv_transpose2d | nnf_conv_transpose2d |
| Conv_transpose3d | nnf_conv_transpose3d |
| Conv1d | nnf_conv1d |
| Conv2d | nnf_conv2d |
| Conv3d | nnf_conv3d |
| Cosine_embedding_loss | nnf_cosine_embedding_loss |
| Cosine_similarity | nnf_cosine_similarity |
| Cross_entropy | nnf_cross_entropy |
| Ctc_loss | nnf_ctc_loss |
| Dropout | nnf_dropout |
| Dropout2d | nnf_dropout2d |
| Dropout3d | nnf_dropout3d |
| Elu | nnf_elu nnf_elu_ |
| Embedding | nnf_embedding |
| Embedding_bag | nnf_embedding_bag |
| Fold | nnf_fold |
| Fractional_max_pool2d | nnf_fractional_max_pool2d |
| Fractional_max_pool3d | nnf_fractional_max_pool3d |
| Gelu | nnf_gelu |
| Glu | nnf_glu |
| Grid_sample | nnf_grid_sample |
| Group_norm | nnf_group_norm |
| Gumbel_softmax | nnf_gumbel_softmax |
| Hardshrink | nnf_hardshrink |
| Hardsigmoid | nnf_hardsigmoid |
| Hardswish | nnf_hardswish |
| Hardtanh | nnf_hardtanh nnf_hardtanh_ |
| Hinge_embedding_loss | nnf_hinge_embedding_loss |
| Instance_norm | nnf_instance_norm |
| Interpolate | nnf_interpolate |
| Kl_div | nnf_kl_div |
| L1_loss | nnf_l1_loss |
| Layer_norm | nnf_layer_norm |
| Leaky_relu | nnf_leaky_relu |
| Linear | nnf_linear |
| Local_response_norm | nnf_local_response_norm |
| Log_softmax | nnf_log_softmax |
| Logsigmoid | nnf_logsigmoid |
| Lp_pool1d | nnf_lp_pool1d |
| Lp_pool2d | nnf_lp_pool2d |
| Margin_ranking_loss | nnf_margin_ranking_loss |
| Max_pool1d | nnf_max_pool1d |
| Max_pool2d | nnf_max_pool2d |
| Max_pool3d | nnf_max_pool3d |
| Max_unpool1d | nnf_max_unpool1d |
| Max_unpool2d | nnf_max_unpool2d |
| Max_unpool3d | nnf_max_unpool3d |
| Mse_loss | nnf_mse_loss |
| Multi head attention forward | nnf_multi_head_attention_forward |
| Multi_margin_loss | nnf_multi_margin_loss |
| Multilabel_margin_loss | nnf_multilabel_margin_loss |
| Multilabel_soft_margin_loss | nnf_multilabel_soft_margin_loss |
| Nll_loss | nnf_nll_loss |
| Normalize | nnf_normalize |
| One_hot | nnf_one_hot |
| Pad | nnf_pad |
| Pairwise_distance | nnf_pairwise_distance |
| Pdist | nnf_pdist |
| Pixel_shuffle | nnf_pixel_shuffle |
| Poisson_nll_loss | nnf_poisson_nll_loss |
| Prelu | nnf_prelu |
| Relu | nnf_relu nnf_relu_ |
| Relu6 | nnf_relu6 |
| Rrelu | nnf_rrelu nnf_rrelu_ |
| Selu | nnf_selu nnf_selu_ |
| Sigmoid | nnf_sigmoid |
| Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See 'nn_silu()' for more information. | nnf_silu |
| Smooth_l1_loss | nnf_smooth_l1_loss |
| Soft_margin_loss | nnf_soft_margin_loss |
| Softmax | nnf_softmax |
| Softmin | nnf_softmin |
| Softplus | nnf_softplus |
| Softshrink | nnf_softshrink |
| Softsign | nnf_softsign |
| Tanhshrink | nnf_tanhshrink |
| Threshold | nnf_threshold nnf_threshold_ |
| Triplet_margin_loss | nnf_triplet_margin_loss |
| Triplet margin with distance loss | nnf_triplet_margin_with_distance_loss |
| Unfold | nnf_unfold |
| Adadelta optimizer | optim_adadelta |
| Adagrad optimizer | optim_adagrad |
| Implements Adam algorithm. | optim_adam |
| Implements AdamW algorithm | optim_adamw |
| Averaged Stochastic Gradient Descent optimizer | optim_asgd |
| LibTorch implementation of Adagrad | optim_ignite_adagrad |
| LibTorch implementation of Adam | optim_ignite_adam |
| LibTorch implementation of AdamW | optim_ignite_adamw |
| LibTorch implementation of RMSprop | optim_ignite_rmsprop |
| LibTorch implementation of SGD | optim_ignite_sgd |
| LBFGS optimizer | optim_lbfgs |
| Dummy value indicating a required value. | optim_required |
| RMSprop optimizer | optim_rmsprop |
| Implements the resilient backpropagation algorithm. | optim_rprop |
| SGD optimizer | optim_sgd |
| Creates a custom optimizer | optimizer |
| Abstract Base Class for LibTorch Optimizers | optimizer_ignite |
| Abstract Base Class for LibTorch Optimizers | OptimizerIgnite |
| Creates a new Sampler | sampler |
| Configure the CPU memory allocator | set_cpu_allocator_config |
| Dataset wrapping tensors. | tensor_dataset |
| Number of threads | threads torch_get_num_interop_threads torch_get_num_threads torch_set_num_interop_threads torch_set_num_threads |
| Abs | torch_abs |
| Absolute | torch_absolute |
| Acos | torch_acos |
| Acosh | torch_acosh |
| Adaptive_avg_pool1d | torch_adaptive_avg_pool1d |
| Add | torch_add |
| Addbmm | torch_addbmm |
| Addcdiv | torch_addcdiv |
| Addcmul | torch_addcmul |
| Addmm | torch_addmm |
| Addmv | torch_addmv |
| Addr | torch_addr |
| Allclose | torch_allclose |
| Amax | torch_amax |
| Amin | torch_amin |
| Angle | torch_angle |
| Arange | torch_arange |
| Arccos | torch_arccos |
| Arccosh | torch_arccosh |
| Arcsin | torch_arcsin |
| Arcsinh | torch_arcsinh |
| Arctan | torch_arctan |
| Arctanh | torch_arctanh |
| Argmax | torch_argmax |
| Argmin | torch_argmin |
| Argsort | torch_argsort |
| As_strided | torch_as_strided |
| Asin | torch_asin |
| Asinh | torch_asinh |
| Atan | torch_atan |
| Atan2 | torch_atan2 |
| Atanh | torch_atanh |
| Atleast_1d | torch_atleast_1d |
| Atleast_2d | torch_atleast_2d |
| Atleast_3d | torch_atleast_3d |
| Avg_pool1d | torch_avg_pool1d |
| Baddbmm | torch_baddbmm |
| Bartlett_window | torch_bartlett_window |
| Bernoulli | torch_bernoulli |
| Bincount | torch_bincount |
| Bitwise_and | torch_bitwise_and |
| Bitwise_not | torch_bitwise_not |
| Bitwise_or | torch_bitwise_or |
| Bitwise_xor | torch_bitwise_xor |
| Blackman_window | torch_blackman_window |
| Block_diag | torch_block_diag |
| Bmm | torch_bmm |
| Broadcast_tensors | torch_broadcast_tensors |
| Bucketize | torch_bucketize |
| Can_cast | torch_can_cast |
| Cartesian_prod | torch_cartesian_prod |
| Cat | torch_cat |
| Cdist | torch_cdist |
| Ceil | torch_ceil |
| Celu | torch_celu |
| Celu_ | torch_celu_ |
| Chain_matmul | torch_chain_matmul |
| Channel_shuffle | torch_channel_shuffle |
| Cholesky | torch_cholesky |
| Cholesky_inverse | torch_cholesky_inverse |
| Cholesky_solve | torch_cholesky_solve |
| Chunk | torch_chunk |
| Clamp | torch_clamp |
| Clip | torch_clip |
| Clone | torch_clone |
| Combinations | torch_combinations |
| Complex | torch_complex |
| Conj | torch_conj |
| Conv_tbc | torch_conv_tbc |
| Conv_transpose1d | torch_conv_transpose1d |
| Conv_transpose2d | torch_conv_transpose2d |
| Conv_transpose3d | torch_conv_transpose3d |
| Conv1d | torch_conv1d |
| Conv2d | torch_conv2d |
| Conv3d | torch_conv3d |
| Cos | torch_cos |
| Cosh | torch_cosh |
| Cosine_similarity | torch_cosine_similarity |
| Count_nonzero | torch_count_nonzero |
| Cross | torch_cross |
| Cummax | torch_cummax |
| Cummin | torch_cummin |
| Cumprod | torch_cumprod |
| Cumsum | torch_cumsum |
| Deg2rad | torch_deg2rad |
| Dequantize | torch_dequantize |
| Det | torch_det |
| Create a Device object | torch_device |
| Diag | torch_diag |
| Diag_embed | torch_diag_embed |
| Diagflat | torch_diagflat |
| Diagonal | torch_diagonal |
| Computes the n-th forward difference along the given dimension. | torch_diff |
| Digamma | torch_digamma |
| Dist | torch_dist |
| Div | torch_div |
| Divide | torch_divide |
| Dot | torch_dot |
| Dstack | torch_dstack |
| Torch data types | 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 |
| Eig | torch_eig |
| Einsum | torch_einsum |
| Empty | torch_empty |
| Empty_like | torch_empty_like |
| Empty_strided | torch_empty_strided |
| Eq | torch_eq |
| Equal | torch_equal |
| Erf | torch_erf |
| Erfc | torch_erfc |
| Erfinv | torch_erfinv |
| Exp | torch_exp |
| Exp2 | torch_exp2 |
| Expm1 | torch_expm1 |
| Eye | torch_eye |
| Fft | torch_fft_fft |
| fftfreq | torch_fft_fftfreq |
| Ifft | torch_fft_ifft |
| Irfft | torch_fft_irfft |
| Rfft | torch_fft_rfft |
| Floating point type info | torch_finfo |
| Fix | torch_fix |
| Flatten | torch_flatten |
| Flip | torch_flip |
| Fliplr | torch_fliplr |
| Flipud | torch_flipud |
| Floor | torch_floor |
| Floor_divide | torch_floor_divide |
| Fmod | torch_fmod |
| Frac | torch_frac |
| Full | torch_full |
| Full_like | torch_full_like |
| Gather | torch_gather |
| Gcd | torch_gcd |
| Ge | torch_ge |
| Create a Generator object | torch_generator |
| Geqrf | torch_geqrf |
| Ger | torch_ger |
| RNG state management | cuda_get_rng_state cuda_set_rng_state torch_get_rng_state torch_set_rng_state |
| Greater | torch_greater |
| Greater_equal | torch_greater_equal |
| Gt | torch_gt |
| Hamming_window | torch_hamming_window |
| Hann_window | torch_hann_window |
| Heaviside | torch_heaviside |
| Histc | torch_histc |
| Hstack | torch_hstack |
| Hypot | torch_hypot |
| I0 | torch_i0 |
| Integer type info | torch_iinfo |
| Imag | torch_imag |
| Index torch tensors | torch_index |
| Modify values selected by 'indices'. | torch_index_put |
| In-place version of 'torch_index_put'. | torch_index_put_ |
| Index_select | torch_index_select |
| A simple exported version of install_path Returns the torch installation path. | torch_install_path |
| Inverse | torch_inverse |
| Is_complex | torch_is_complex |
| Is_floating_point | torch_is_floating_point |
| Verifies if torch is installed | torch_is_installed |
| Is_nonzero | torch_is_nonzero |
| Isclose | torch_isclose |
| Isfinite | torch_isfinite |
| Isinf | torch_isinf |
| Isnan | torch_isnan |
| Isneginf | torch_isneginf |
| Isposinf | torch_isposinf |
| Isreal | torch_isreal |
| Istft | torch_istft |
| Kaiser_window | torch_kaiser_window |
| Kronecker product | torch_kron |
| Kthvalue | torch_kthvalue |
| Creates the corresponding layout | torch_layout torch_sparse_coo torch_strided |
| Lcm | torch_lcm |
| Ldexp | torch_ldexp |
| Le | torch_le |
| Lerp | torch_lerp |
| Less | torch_less |
| Less_equal | torch_less_equal |
| Lgamma | torch_lgamma |
| Linspace | torch_linspace |
| Loads a saved object | torch_load |
| Log | torch_log |
| Log10 | torch_log10 |
| Log1p | torch_log1p |
| Log2 | torch_log2 |
| Logaddexp | torch_logaddexp |
| Logaddexp2 | torch_logaddexp2 |
| Logcumsumexp | torch_logcumsumexp |
| Logdet | torch_logdet |
| Logical_and | torch_logical_and |
| Logical_not | torch_logical_not |
| Logical_or | torch_logical_or |
| Logical_xor | torch_logical_xor |
| Logit | torch_logit |
| Logspace | torch_logspace |
| Logsumexp | torch_logsumexp |
| Lstsq | torch_lstsq |
| Lt | torch_lt |
| LU | torch_lu |
| Lu_solve | torch_lu_solve |
| Lu_unpack | torch_lu_unpack |
| Sets the seed for generating random numbers. | local_torch_manual_seed torch_manual_seed with_torch_manual_seed |
| Masked_select | torch_masked_select |
| Matmul | torch_matmul |
| Matrix_exp | torch_matrix_exp |
| Matrix_power | torch_matrix_power |
| Matrix_rank | torch_matrix_rank |
| Max | torch_max |
| Maximum | torch_maximum |
| Mean | torch_mean |
| Median | torch_median |
| Memory format | torch_channels_last_format torch_contiguous_format torch_memory_format torch_preserve_format |
| Meshgrid | torch_meshgrid |
| Min | torch_min |
| Minimum | torch_minimum |
| Mm | torch_mm |
| Mode | torch_mode |
| Movedim | torch_movedim |
| Mul | torch_mul |
| Multinomial | torch_multinomial |
| Multiply | torch_multiply |
| Mv | torch_mv |
| Mvlgamma | torch_mvlgamma |
| Nanquantile | torch_nanquantile |
| Nansum | torch_nansum |
| Narrow | torch_narrow |
| Ne | torch_ne |
| Neg | torch_neg |
| Negative | torch_negative |
| Nextafter | torch_nextafter |
| Nonzero | torch_nonzero |
| Norm | torch_norm |
| Normal | torch_normal |
| Not_equal | torch_not_equal |
| Ones | torch_ones |
| Ones_like | torch_ones_like |
| Orgqr | torch_orgqr |
| Ormqr | torch_ormqr |
| Outer | torch_outer |
| Pdist | torch_pdist |
| Pinverse | torch_pinverse |
| Pixel_shuffle | torch_pixel_shuffle |
| Poisson | torch_poisson |
| Polar | torch_polar |
| Polygamma | torch_polygamma |
| Pow | torch_pow |
| Prod | torch_prod |
| Promote_types | torch_promote_types |
| Qr | torch_qr |
| Creates the corresponding Scheme object | torch_per_channel_affine torch_per_channel_symmetric torch_per_tensor_affine torch_per_tensor_symmetric torch_qscheme |
| Quantile | torch_quantile |
| Quantize_per_channel | torch_quantize_per_channel |
| Quantize_per_tensor | torch_quantize_per_tensor |
| Rad2deg | torch_rad2deg |
| Rand | torch_rand |
| Rand_like | torch_rand_like |
| Randint | torch_randint |
| Randint_like | torch_randint_like |
| Randn | torch_randn |
| Randn_like | torch_randn_like |
| Randperm | torch_randperm |
| Range | torch_range |
| Real | torch_real |
| Reciprocal | torch_reciprocal |
| Creates the reduction objet | torch_reduction torch_reduction_mean torch_reduction_none torch_reduction_sum |
| Relu | torch_relu |
| Relu_ | torch_relu_ |
| Remainder | torch_remainder |
| Renorm | torch_renorm |
| Repeat_interleave | torch_repeat_interleave |
| Reshape | torch_reshape |
| Result_type | torch_result_type |
| Roll | torch_roll |
| Rot90 | torch_rot90 |
| Round | torch_round |
| Rrelu_ | torch_rrelu_ |
| Rsqrt | torch_rsqrt |
| Saves an object to a disk file. | torch_save |
| Scalar tensor | torch_scalar_tensor |
| Scaled Dot Product Attention | torch_scaled_dot_product_attention |
| Searchsorted | torch_searchsorted |
| Selu | torch_selu |
| Selu_ | torch_selu_ |
| Serialize a torch object returning a raw object | torch_serialize |
| Gets and sets the default floating point dtype. | torch_get_default_dtype torch_set_default_dtype |
| Sgn | torch_sgn |
| Sigmoid | torch_sigmoid |
| Sign | torch_sign |
| Signbit | torch_signbit |
| Sin | torch_sin |
| Sinh | torch_sinh |
| Slogdet | torch_slogdet |
| Sort | torch_sort |
| Sparse_coo_tensor | torch_sparse_coo_tensor |
| Sparse_sampled_addmm | torch_sparse_sampled_addmm |
| Split | torch_split |
| Sqrt | torch_sqrt |
| Square | torch_square |
| Squeeze | torch_squeeze |
| Stack | torch_stack |
| Std | torch_std |
| Std_mean | torch_std_mean |
| Stft | torch_stft |
| Sub | torch_sub |
| Subtract | torch_subtract |
| Sum | torch_sum |
| Svd | torch_svd |
| T | torch_t |
| Take | torch_take |
| Selects values from input at the 1-dimensional indices from indices along the given dim. | torch_take_along_dim |
| Tan | torch_tan |
| Tanh | torch_tanh |
| Converts R objects to a torch tensor | torch_tensor |
| Creates a tensor from a buffer of memory | buffer_from_torch_tensor torch_tensor_from_buffer |
| Tensordot | torch_tensordot |
| Threshold_ | torch_threshold_ |
| Topk | torch_topk |
| Trace | torch_trace |
| Transpose | torch_transpose |
| Trapz | torch_trapz |
| Triangular_solve | torch_triangular_solve |
| Tril | torch_tril |
| Tril_indices | torch_tril_indices |
| Triu | torch_triu |
| Triu_indices | torch_triu_indices |
| TRUE_divide | torch_true_divide |
| Trunc | torch_trunc |
| Unbind | torch_unbind |
| Unique_consecutive | torch_unique_consecutive |
| Unsafe_chunk | torch_unsafe_chunk |
| Unsafe_split | torch_unsafe_split |
| Unsqueeze | torch_unsqueeze |
| Vander | torch_vander |
| Var | torch_var |
| Var_mean | torch_var_mean |
| Vdot | torch_vdot |
| View_as_complex | torch_view_as_complex |
| View_as_real | torch_view_as_real |
| Vstack | torch_vstack |
| Where | torch_where |
| Zeros | torch_zeros |
| Zeros_like | torch_zeros_like |
| Context-manager that enable anomaly detection for the autograd engine. | with_detect_anomaly |
| Enable grad | local_enable_grad with_enable_grad |
| Temporarily modify gradient recording. | local_no_grad with_no_grad |
