Man pages for torch
Tensors and Neural Networks with 'GPU' Acceleration
| as_array | Converts to array |
| autograd_backward | Computes the sum of gradients of given tensors w.r.t. graph... |
| AutogradContext | Class representing the context. |
| autograd_function | Records operation history and defines formulas for... |
| autograd_grad | Computes and returns the sum of gradients of outputs w.r.t.... |
| autograd_set_grad_mode | Set grad mode |
| 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... |
| call_torch_function | Call a (Potentially Unexported) Torch Function |
| 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_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... |
| cuda_record_memory_history | Enable Recording of Memory Allocation Stack Traces |
| cuda_runtime_version | Returns the CUDA runtime version |
| cuda_synchronize | Waits for all kernels in all streams on a CUDA device to... |
| dataloader | Data loader. Combines a dataset and a sampler, and provides... |
| 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... |
| dataset_subset | Dataset Subset |
| default_dtype | Gets and sets the default floating point dtype. |
| distr_bernoulli | Creates a Bernoulli distribution parameterized by 'probs' or... |
| distr_categorical | Creates a categorical distribution parameterized by either... |
| distr_chi2 | Creates a Chi2 distribution parameterized by shape parameter... |
| distr_gamma | Creates a Gamma distribution parameterized by shape... |
| Distribution | Generic R6 class representing distributions |
| 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... |
| distr_poisson | Creates a Poisson distribution parameterized by 'rate', the... |
| enumerate | Enumerate an iterator |
| enumerate.dataloader | Enumerate an iterator |
| 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 |
| jit_compile | Compile TorchScript code into a graph |
| jit_load | Loads a 'script_function' or 'script_module' previously saved... |
| 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... |
| 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 |
| linalg_cholesky_ex | Computes the Cholesky decomposition of a complex Hermitian or... |
| linalg_det | Computes the determinant of a square matrix. |
| linalg_inv_ex | Computes the inverse of a square matrix if it is invertible. |
| linalg_matrix_norm | Computes a matrix norm. |
| linalg_matrix_power | Computes the 'n'-th power of a square matrix for an integer... |
| linalg_norm | Computes a vector or matrix norm. |
| linalg_pinv | Computes the pseudoinverse (Moore-Penrose inverse) of a... |
| linalg_slogdet | Computes the sign and natural logarithm of the absolute value... |
| linalg_solve | Computes the solution of a square system of linear equations... |
| linalg_solve_triangular | Triangular solve |
| 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,... |
| linalg_vector_norm | Computes a vector norm. |
| load_state_dict | Load a state dict file |
| local_autocast | Autocast context manager |
| local_device | Device contexts |
| lr_cosine_annealing | Set the learning rate of each parameter group using a cosine... |
| lr_lambda | Sets the learning rate of each parameter group to the initial... |
| lr_multiplicative | Multiply the learning rate of each parameter group by the... |
| 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 |
| nn_adaptive_avg_pool1d | Applies a 1D adaptive average pooling over an input signal... |
| nn_adaptive_avg_pool2d | Applies a 2D adaptive average pooling over an input signal... |
| nn_adaptive_avg_pool3d | Applies a 3D adaptive average pooling over an input signal... |
| nn_adaptive_log_softmax_with_loss | AdaptiveLogSoftmaxWithLoss module |
| nn_adaptive_max_pool1d | Applies a 1D adaptive max pooling over an input signal... |
| nn_adaptive_max_pool2d | Applies a 2D adaptive max pooling over an input signal... |
| nn_adaptive_max_pool3d | Applies a 3D adaptive max pooling over an input signal... |
| nn_aum_loss | AUM loss |
| nn_avg_pool1d | Applies a 1D average pooling over an input signal composed of... |
| nn_avg_pool2d | Applies a 2D average pooling over an input signal composed of... |
| nn_avg_pool3d | Applies a 3D average pooling over an input signal composed of... |
| 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 |
| 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_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_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_hinge_embedding_loss | Hinge_embedding_loss |
| nnf_instance_norm | Instance_norm |
| nnf_interpolate | Interpolate |
| nnf_kl_div | Kl_div |
| nnf_l1_loss | L1_loss |
| nn_flatten | Flattens a contiguous range of dims into a tensor. |
| 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_multi_head_attention_forward | Multi head attention forward |
| nnf_multilabel_margin_loss | Multilabel_margin_loss |
| nnf_multilabel_soft_margin_loss | Multilabel_soft_margin_loss |
| 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 |
| nn_fractional_max_pool2d | Applies a 2D fractional max pooling over an input signal... |
| nn_fractional_max_pool3d | Applies a 3D fractional max pooling over an input signal... |
| nnf_relu | Relu |
| nnf_relu6 | Relu6 |
| nnf_rrelu | Rrelu |
| nnf_selu | Selu |
| nnf_sigmoid | Sigmoid |
| nnf_silu | Applies the Sigmoid Linear Unit (SiLU) function,... |
| nnf_smooth_l1_loss | Smooth_l1_loss |
| nnf_soft_margin_loss | Soft_margin_loss |
| nnf_softmax | Softmax |
| nnf_softmin | Softmin |
| nnf_softplus | Softplus |
| nnf_softshrink | Softshrink |
| nnf_softsign | Softsign |
| nnf_tanhshrink | Tanhshrink |
| nnf_threshold | Threshold |
| nnf_triplet_margin_loss | Triplet_margin_loss |
| nnf_triplet_margin_with_distance_loss | Triplet margin with distance loss |
| nnf_unfold | Unfold |
| 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... |
| 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... |
| nn_lp_pool2d | Applies a 2D power-average pooling over an input signal... |
| nn_lstm | Applies a multi-layer long short-term memory (LSTM) RNN to an... |
| 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... |
| 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,... |
| nn_smooth_l1_loss | Smooth L1 loss |
| nn_soft_margin_loss | Soft margin 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_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... |
| 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... |
| nn_utils_rnn_pack_padded_sequence | Packs a Tensor containing padded sequences of variable... |
| 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 |
| 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 |
| optimizer | Creates a custom optimizer |
| optimizer_ignite | Abstract Base Class for LibTorch Optimizers |
| OptimizerIgnite | Abstract Base Class for LibTorch Optimizers |
| 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 |
| pipe | Pipe operator |
| reexports | Re-exporting the as_iterator function. |
| sampler | Creates a new Sampler |
| slc | Creates a slice |
| 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_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_broadcast_tensors | Broadcast_tensors |
| torch_bucketize | Bucketize |
| torch_can_cast | Can_cast |
| torch_cartesian_prod | Cartesian_prod |
| torch_cat | Cat |
| torch_cdist | Cdist |
| torch_ceil | Ceil |
| torch_celu | Celu |
| torch_celu_ | Celu_ |
| torch_chain_matmul | Chain_matmul |
| 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_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_diag_embed | Diag_embed |
| torch_diagflat | Diagflat |
| torch_diagonal | Diagonal |
| torch_diff | Computes the n-th forward difference along the given... |
| torch_digamma | Digamma |
| torch_dist | Dist |
| torch_div | Div |
| torch_divide | Divide |
| torch_dot | Dot |
| 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_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_rng_state | RNG state management |
| torch_greater | Greater |
| torch_greater_equal | Greater_equal |
| torch_gt | Gt |
| 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... |
| torch_inverse | Inverse |
| torch_isclose | Isclose |
| torch_is_complex | Is_complex |
| torch_isfinite | Isfinite |
| torch_is_floating_point | Is_floating_point |
| torch_isinf | Isinf |
| torch_is_installed | Verifies if torch is installed |
| torch_isnan | Isnan |
| torch_isneginf | Isneginf |
| torch_is_nonzero | Is_nonzero |
| torch_isposinf | Isposinf |
| torch_isreal | Isreal |
| torch_istft | Istft |
| 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_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_pinverse | Pinverse |
| torch_pixel_shuffle | Pixel_shuffle |
| torch_poisson | Poisson |
| torch_polar | Polar |
| torch_polygamma | Polygamma |
| torch_pow | Pow |
| torch_prod | Prod |
| torch_promote_types | Promote_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_rad2deg | Rad2deg |
| torch_rand | Rand |
| torch_randint | Randint |
| torch_randint_like | Randint_like |
| torch_rand_like | Rand_like |
| torch_randn | Randn |
| torch_randn_like | Randn_like |
| torch_randperm | Randperm |
| torch_range | Range |
| torch_real | Real |
| torch_reciprocal | Reciprocal |
| torch_reduction | 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_sgn | Sgn |
| torch_sigmoid | Sigmoid |
| torch_sign | Sign |
| torch_signbit | Signbit |
| torch_sin | Sin |
| torch_sinh | Sinh |
| torch_slogdet | Slogdet |
| torch_sort | Sort |
| 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_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... |
| 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_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 |
| with_detect_anomaly | Context-manager that enable anomaly detection for the... |
| with_enable_grad | Enable grad |
| with_no_grad | Temporarily modify gradient recording. |
