package torch

  1. Overview
  2. Docs
package torch
Legend:
Page
Library
Module
Module type
Parameter
Class
Class type
Source

Module Torch_core.Wrapper_generatedSource

Sourcemodule C : sig ... end
Sourceval to_tensor_list : unit Ctypes.ptr Ctypes.ptr -> unit Ctypes.ptr list
Sourceval abs_ : C.TensorG.t -> C.TensorG.t
Sourceval abs_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval acos_ : C.TensorG.t -> C.TensorG.t
Sourceval acos_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval adaptive_avg_pool1d : C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval adaptive_avg_pool2d : C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval adaptive_avg_pool2d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval adaptive_avg_pool3d : C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval adaptive_avg_pool3d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval adaptive_avg_pool3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval adaptive_avg_pool3d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval adaptive_max_pool1d : C.TensorG.t -> output_size:int list -> C.TensorG.t * C.TensorG.t
Sourceval adaptive_max_pool2d : C.TensorG.t -> output_size:int list -> C.TensorG.t * C.TensorG.t
Sourceval adaptive_max_pool2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t
Sourceval adaptive_max_pool2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t
Sourceval adaptive_max_pool2d_out : out:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> output_size:int list -> C.TensorG.t * C.TensorG.t
Sourceval adaptive_max_pool3d : C.TensorG.t -> output_size:int list -> C.TensorG.t * C.TensorG.t
Sourceval adaptive_max_pool3d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t
Sourceval adaptive_max_pool3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t
Sourceval adaptive_max_pool3d_out : out:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> output_size:int list -> C.TensorG.t * C.TensorG.t
Sourceval add_ : C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval addbmm : C.TensorG.t -> batch1:C.TensorG.t -> batch2:C.TensorG.t -> C.TensorG.t
Sourceval addbmm_ : C.TensorG.t -> batch1:C.TensorG.t -> batch2:C.TensorG.t -> C.TensorG.t
Sourceval addbmm_out : out:C.TensorG.t -> C.TensorG.t -> batch1:C.TensorG.t -> batch2:C.TensorG.t -> C.TensorG.t
Sourceval addcdiv : C.TensorG.t -> tensor1:C.TensorG.t -> tensor2:C.TensorG.t -> C.TensorG.t
Sourceval addcdiv_ : C.TensorG.t -> tensor1:C.TensorG.t -> tensor2:C.TensorG.t -> C.TensorG.t
Sourceval addcdiv_out : out:C.TensorG.t -> C.TensorG.t -> tensor1:C.TensorG.t -> tensor2:C.TensorG.t -> C.TensorG.t
Sourceval addcmul : C.TensorG.t -> tensor1:C.TensorG.t -> tensor2:C.TensorG.t -> C.TensorG.t
Sourceval addcmul_ : C.TensorG.t -> tensor1:C.TensorG.t -> tensor2:C.TensorG.t -> C.TensorG.t
Sourceval addcmul_out : out:C.TensorG.t -> C.TensorG.t -> tensor1:C.TensorG.t -> tensor2:C.TensorG.t -> C.TensorG.t
Sourceval addmm : C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval addmm_ : C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval addmm_out : out:C.TensorG.t -> C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval addmv_ : C.TensorG.t -> mat:C.TensorG.t -> vec:C.TensorG.t -> C.TensorG.t
Sourceval addmv_out : out:C.TensorG.t -> C.TensorG.t -> mat:C.TensorG.t -> vec:C.TensorG.t -> C.TensorG.t
Sourceval addr_ : C.TensorG.t -> vec1:C.TensorG.t -> vec2:C.TensorG.t -> C.TensorG.t
Sourceval addr_out : out:C.TensorG.t -> C.TensorG.t -> vec1:C.TensorG.t -> vec2:C.TensorG.t -> C.TensorG.t
Sourceval affine_grid_generator : theta:C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval affine_grid_generator_backward : grad:C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval all1 : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval all_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval alpha_dropout : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval alpha_dropout_ : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval any1 : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval any_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval arange : end_:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval arange1 : start:C.TensorG.scalar -> end_:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval arange2 : start:C.TensorG.scalar -> end_:C.TensorG.scalar -> step:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval arange_out : out:C.TensorG.t -> end_:C.TensorG.scalar -> C.TensorG.t
Sourceval arange_out1 : out:C.TensorG.t -> start:C.TensorG.scalar -> end_:C.TensorG.scalar -> C.TensorG.t
Sourceval argmax : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval argmin : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval argsort : C.TensorG.t -> dim:int -> descending:bool -> C.TensorG.t
Sourceval as_strided : C.TensorG.t -> size:int list -> stride:int list -> storage_offset:int -> C.TensorG.t
Sourceval as_strided_ : C.TensorG.t -> size:int list -> stride:int list -> storage_offset:int -> C.TensorG.t
Sourceval asin_ : C.TensorG.t -> C.TensorG.t
Sourceval asin_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval atan2_ : C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval atan_ : C.TensorG.t -> C.TensorG.t
Sourceval atan_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval avg_pool1d : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool2d : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool2d_out : out:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool3d : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool3d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval avg_pool3d_out : out:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
Sourceval baddbmm : C.TensorG.t -> batch1:C.TensorG.t -> batch2:C.TensorG.t -> C.TensorG.t
Sourceval baddbmm_ : C.TensorG.t -> batch1:C.TensorG.t -> batch2:C.TensorG.t -> C.TensorG.t
Sourceval baddbmm_out : out:C.TensorG.t -> C.TensorG.t -> batch1:C.TensorG.t -> batch2:C.TensorG.t -> C.TensorG.t
Sourceval bartlett_window : window_length:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval bartlett_window1 : window_length:int -> periodic:bool -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval batch_norm : C.TensorG.t -> weight:C.TensorG.t option -> bias:C.TensorG.t option -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> training:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> C.TensorG.t
Sourceval batch_norm_backward_elemt : grad_out:C.TensorG.t -> C.TensorG.t -> mean:C.TensorG.t -> invstd:C.TensorG.t -> weight:C.TensorG.t option -> mean_dy:C.TensorG.t -> mean_dy_xmu:C.TensorG.t -> C.TensorG.t
Sourceval batch_norm_backward_reduce : grad_out:C.TensorG.t -> C.TensorG.t -> mean:C.TensorG.t -> invstd:C.TensorG.t -> input_g:bool -> weight_g:bool -> bias_g:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval batch_norm_elemt : C.TensorG.t -> weight:C.TensorG.t option -> bias:C.TensorG.t option -> mean:C.TensorG.t -> invstd:C.TensorG.t -> eps:float -> C.TensorG.t
Sourceval batch_norm_gather_stats : C.TensorG.t -> mean:C.TensorG.t -> invstd:C.TensorG.t -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> momentum:float -> eps:float -> count:int -> C.TensorG.t * C.TensorG.t
Sourceval batch_norm_stats : C.TensorG.t -> eps:float -> C.TensorG.t * C.TensorG.t
Sourceval batch_norm_update_stats : C.TensorG.t -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> momentum:float -> C.TensorG.t * C.TensorG.t
Sourceval bernoulli : C.TensorG.t -> C.TensorG.t
Sourceval bernoulli1 : C.TensorG.t -> p:float -> C.TensorG.t
Sourceval bernoulli_ : C.TensorG.t -> p:C.TensorG.t -> C.TensorG.t
Sourceval bernoulli_1 : C.TensorG.t -> p:float -> C.TensorG.t
Sourceval bernoulli_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval bilinear : input1:C.TensorG.t -> input2:C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> C.TensorG.t
Sourceval binary_cross_entropy : C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> C.TensorG.t
Sourceval binary_cross_entropy_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval binary_cross_entropy_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval binary_cross_entropy_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> C.TensorG.t
Sourceval binary_cross_entropy_with_logits : C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> pos_weight:C.TensorG.t option -> reduction:int -> C.TensorG.t
Sourceval binary_cross_entropy_with_logits_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> pos_weight:C.TensorG.t option -> reduction:int -> C.TensorG.t
Sourceval bincount : C.TensorG.t -> weights:C.TensorG.t option -> minlength:int -> C.TensorG.t
Sourceval blackman_window : window_length:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval blackman_window1 : window_length:int -> periodic:bool -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval bmm_out : out:C.TensorG.t -> C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval broadcast_tensors : C.TensorG.t list -> unit Ctypes.ptr list
Sourceval cartesian_prod : C.TensorG.t list -> C.TensorG.t
Sourceval cat : C.TensorG.t list -> dim:int -> C.TensorG.t
Sourceval cat_out : out:C.TensorG.t -> C.TensorG.t list -> dim:int -> C.TensorG.t
Sourceval cauchy_ : C.TensorG.t -> median:float -> sigma:float -> C.TensorG.t
Sourceval cdist : x1:C.TensorG.t -> x2:C.TensorG.t -> p:float -> C.TensorG.t
Sourceval ceil_ : C.TensorG.t -> C.TensorG.t
Sourceval ceil_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval celu_ : C.TensorG.t -> C.TensorG.t
Sourceval chain_matmul : matrices:C.TensorG.t list -> C.TensorG.t
Sourceval cholesky : C.TensorG.t -> upper:bool -> C.TensorG.t
Sourceval cholesky_inverse : C.TensorG.t -> upper:bool -> C.TensorG.t
Sourceval cholesky_inverse_out : out:C.TensorG.t -> C.TensorG.t -> upper:bool -> C.TensorG.t
Sourceval cholesky_out : out:C.TensorG.t -> C.TensorG.t -> upper:bool -> C.TensorG.t
Sourceval cholesky_solve : C.TensorG.t -> input2:C.TensorG.t -> upper:bool -> C.TensorG.t
Sourceval cholesky_solve_out : out:C.TensorG.t -> C.TensorG.t -> input2:C.TensorG.t -> upper:bool -> C.TensorG.t
Sourceval chunk : C.TensorG.t -> chunks:int -> dim:int -> unit Ctypes.ptr list
Sourceval clamp_ : C.TensorG.t -> min:C.TensorG.scalar -> max:C.TensorG.scalar -> C.TensorG.t
Sourceval clamp_max_ : C.TensorG.t -> max:C.TensorG.scalar -> C.TensorG.t
Sourceval clamp_max_out : out:C.TensorG.t -> C.TensorG.t -> max:C.TensorG.scalar -> C.TensorG.t
Sourceval clamp_min_ : C.TensorG.t -> min:C.TensorG.scalar -> C.TensorG.t
Sourceval clamp_min_out : out:C.TensorG.t -> C.TensorG.t -> min:C.TensorG.scalar -> C.TensorG.t
Sourceval coalesce : C.TensorG.t -> C.TensorG.t
Sourceval combinations : C.TensorG.t -> r:int -> with_replacement:bool -> C.TensorG.t
Sourceval constant_pad_nd : C.TensorG.t -> pad:int list -> C.TensorG.t
Sourceval contiguous : C.TensorG.t -> C.TensorG.t
Sourceval conv1d : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
Sourceval conv2d : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
Sourceval conv3d : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
Sourceval conv_tbc : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t -> pad:int -> C.TensorG.t
Sourceval conv_tbc_backward : C.TensorG.t -> C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t -> pad:int -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval conv_transpose1d : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> C.TensorG.t
Sourceval conv_transpose2d : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> C.TensorG.t
Sourceval conv_transpose3d : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> C.TensorG.t
Sourceval convolution : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> C.TensorG.t
Sourceval copy_sparse_to_sparse_ : C.TensorG.t -> src:C.TensorG.t -> non_blocking:bool -> C.TensorG.t
Sourceval cos_ : C.TensorG.t -> C.TensorG.t
Sourceval cos_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval cosh_ : C.TensorG.t -> C.TensorG.t
Sourceval cosh_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval cosine_embedding_loss : input1:C.TensorG.t -> input2:C.TensorG.t -> target:C.TensorG.t -> margin:float -> reduction:int -> C.TensorG.t
Sourceval cosine_similarity : x1:C.TensorG.t -> x2:C.TensorG.t -> dim:int -> eps:float -> C.TensorG.t
Sourceval cross : C.TensorG.t -> C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval cross_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval ctc_loss : log_probs:C.TensorG.t -> targets:C.TensorG.t -> input_lengths:int list -> target_lengths:int list -> blank:int -> reduction:int -> zero_infinity:bool -> C.TensorG.t
Sourceval ctc_loss1 : log_probs:C.TensorG.t -> targets:C.TensorG.t -> input_lengths:C.TensorG.t -> target_lengths:C.TensorG.t -> blank:int -> reduction:int -> zero_infinity:bool -> C.TensorG.t
Sourceval cudnn_affine_grid_generator : theta:C.TensorG.t -> n:int -> c:int -> h:int -> w:int -> C.TensorG.t
Sourceval cudnn_affine_grid_generator_backward : grad:C.TensorG.t -> n:int -> c:int -> h:int -> w:int -> C.TensorG.t
Sourceval cudnn_batch_norm : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval cudnn_batch_norm_backward : C.TensorG.t -> grad_output:C.TensorG.t -> weight:C.TensorG.t -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> save_mean:C.TensorG.t option -> save_var:C.TensorG.t option -> epsilon:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval cudnn_convolution : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval cudnn_convolution_backward_bias : grad_output:C.TensorG.t -> C.TensorG.t
Sourceval cudnn_convolution_backward_input : self_size:int list -> grad_output:C.TensorG.t -> weight:C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval cudnn_convolution_backward_weight : weight_size:int list -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval cudnn_convolution_transpose : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval cudnn_convolution_transpose_backward_bias : grad_output:C.TensorG.t -> C.TensorG.t
Sourceval cudnn_convolution_transpose_backward_input : grad_output:C.TensorG.t -> weight:C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval cudnn_convolution_transpose_backward_weight : weight_size:int list -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval cudnn_grid_sampler : C.TensorG.t -> grid:C.TensorG.t -> C.TensorG.t
Sourceval cudnn_grid_sampler_backward : C.TensorG.t -> grid:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t * C.TensorG.t
Sourceval cumprod : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval cumprod1 : C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval cumprod_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval cumprod_out1 : out:C.TensorG.t -> C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval cumsum : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval cumsum1 : C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval cumsum_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval cumsum_out1 : out:C.TensorG.t -> C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval dequantize : C.TensorG.t -> C.TensorG.t
Sourceval detach_ : C.TensorG.t -> C.TensorG.t
Sourceval diag : C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval diag_embed : C.TensorG.t -> offset:int -> dim1:int -> dim2:int -> C.TensorG.t
Sourceval diag_out : out:C.TensorG.t -> C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval diagflat : C.TensorG.t -> offset:int -> C.TensorG.t
Sourceval diagonal : C.TensorG.t -> offset:int -> dim1:int -> dim2:int -> C.TensorG.t
Sourceval digamma_ : C.TensorG.t -> C.TensorG.t
Sourceval digamma_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval div_ : C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval dropout : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval dropout_ : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval eig : C.TensorG.t -> eigenvectors:bool -> C.TensorG.t * C.TensorG.t
Sourceval eig_out : e:C.TensorG.t -> v:C.TensorG.t -> C.TensorG.t -> eigenvectors:bool -> C.TensorG.t * C.TensorG.t
Sourceval elu_ : C.TensorG.t -> C.TensorG.t
Sourceval elu_backward : grad_output:C.TensorG.t -> alpha:C.TensorG.scalar -> scale:C.TensorG.scalar -> input_scale:C.TensorG.scalar -> output:C.TensorG.t -> C.TensorG.t
Sourceval elu_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> alpha:C.TensorG.scalar -> scale:C.TensorG.scalar -> input_scale:C.TensorG.scalar -> output:C.TensorG.t -> C.TensorG.t
Sourceval elu_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval embedding : weight:C.TensorG.t -> indices:C.TensorG.t -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> C.TensorG.t
Sourceval embedding_backward : grad:C.TensorG.t -> indices:C.TensorG.t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> C.TensorG.t
Sourceval embedding_bag : weight:C.TensorG.t -> indices:C.TensorG.t -> offsets:C.TensorG.t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:C.TensorG.t option -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval embedding_dense_backward : grad_output:C.TensorG.t -> indices:C.TensorG.t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> C.TensorG.t
Sourceval embedding_renorm_ : C.TensorG.t -> indices:C.TensorG.t -> max_norm:float -> norm_type:float -> C.TensorG.t
Sourceval embedding_sparse_backward : grad:C.TensorG.t -> indices:C.TensorG.t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> C.TensorG.t
Sourceval empty : size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval empty_like : C.TensorG.t -> C.TensorG.t
Sourceval empty_like1 : C.TensorG.t -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval empty_out : out:C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval empty_strided : size:int list -> stride:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval eq_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval erf_ : C.TensorG.t -> C.TensorG.t
Sourceval erf_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval erfc_ : C.TensorG.t -> C.TensorG.t
Sourceval erfc_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval erfinv_ : C.TensorG.t -> C.TensorG.t
Sourceval erfinv_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval exp_ : C.TensorG.t -> C.TensorG.t
Sourceval exp_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval expand : C.TensorG.t -> size:int list -> implicit:bool -> C.TensorG.t
Sourceval expm1_ : C.TensorG.t -> C.TensorG.t
Sourceval expm1_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval exponential_ : C.TensorG.t -> lambd:float -> C.TensorG.t
Sourceval eye : n:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval eye1 : n:int -> m:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval eye_out : out:C.TensorG.t -> n:int -> C.TensorG.t
Sourceval eye_out1 : out:C.TensorG.t -> n:int -> m:int -> C.TensorG.t
Sourceval fbgemm_linear_int8_weight : C.TensorG.t -> weight:C.TensorG.t -> packed:C.TensorG.t -> col_offsets:C.TensorG.t -> weight_scale:C.TensorG.scalar -> weight_zero_point:C.TensorG.scalar -> bias:C.TensorG.t -> C.TensorG.t
Sourceval fbgemm_pack_quantized_matrix : C.TensorG.t -> k:int -> n:int -> C.TensorG.t
Sourceval feature_alpha_dropout : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval feature_alpha_dropout_ : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval feature_dropout : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval feature_dropout_ : C.TensorG.t -> p:float -> train:bool -> C.TensorG.t
Sourceval fft : C.TensorG.t -> signal_ndim:int -> normalized:bool -> C.TensorG.t
Sourceval fill_ : C.TensorG.t -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval fill_1 : C.TensorG.t -> value:C.TensorG.t -> C.TensorG.t
Sourceval flatten : C.TensorG.t -> start_dim:int -> end_dim:int -> C.TensorG.t
Sourceval flip : C.TensorG.t -> dims:int list -> C.TensorG.t
Sourceval floor_ : C.TensorG.t -> C.TensorG.t
Sourceval floor_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval fmod_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval frac_ : C.TensorG.t -> C.TensorG.t
Sourceval frac_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval fractional_max_pool2d : C.TensorG.t -> kernel_size:int list -> output_size:int list -> random_samples:C.TensorG.t -> C.TensorG.t * C.TensorG.t
Sourceval fractional_max_pool2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> output_size:int list -> indices:C.TensorG.t -> C.TensorG.t
Sourceval fractional_max_pool2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> output_size:int list -> indices:C.TensorG.t -> C.TensorG.t
Sourceval fractional_max_pool2d_out : output:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> output_size:int list -> random_samples:C.TensorG.t -> C.TensorG.t * C.TensorG.t
Sourceval fractional_max_pool3d : C.TensorG.t -> kernel_size:int list -> output_size:int list -> random_samples:C.TensorG.t -> C.TensorG.t * C.TensorG.t
Sourceval fractional_max_pool3d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> output_size:int list -> indices:C.TensorG.t -> C.TensorG.t
Sourceval fractional_max_pool3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> output_size:int list -> indices:C.TensorG.t -> C.TensorG.t
Sourceval fractional_max_pool3d_out : output:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> output_size:int list -> random_samples:C.TensorG.t -> C.TensorG.t * C.TensorG.t
Sourceval frobenius_norm : C.TensorG.t -> C.TensorG.t
Sourceval frobenius_norm1 : C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval frobenius_norm_out : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval full : size:int list -> fill_value:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval full_like : C.TensorG.t -> fill_value:C.TensorG.scalar -> C.TensorG.t
Sourceval full_like1 : C.TensorG.t -> fill_value:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval full_out : out:C.TensorG.t -> size:int list -> fill_value:C.TensorG.scalar -> C.TensorG.t
Sourceval gather : C.TensorG.t -> dim:int -> index:C.TensorG.t -> sparse_grad:bool -> C.TensorG.t
Sourceval gather_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> index:C.TensorG.t -> sparse_grad:bool -> C.TensorG.t
Sourceval ge_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval geometric_ : C.TensorG.t -> p:float -> C.TensorG.t
Sourceval ger_out : out:C.TensorG.t -> C.TensorG.t -> vec2:C.TensorG.t -> C.TensorG.t
Sourceval glu : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval glu_backward : grad_output:C.TensorG.t -> C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval glu_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval glu_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval grid_sampler : C.TensorG.t -> grid:C.TensorG.t -> interpolation_mode:int -> padding_mode:int -> C.TensorG.t
Sourceval grid_sampler_2d : C.TensorG.t -> grid:C.TensorG.t -> interpolation_mode:int -> padding_mode:int -> C.TensorG.t
Sourceval grid_sampler_2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> grid:C.TensorG.t -> interpolation_mode:int -> padding_mode:int -> C.TensorG.t * C.TensorG.t
Sourceval grid_sampler_3d : C.TensorG.t -> grid:C.TensorG.t -> interpolation_mode:int -> padding_mode:int -> C.TensorG.t
Sourceval grid_sampler_3d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> grid:C.TensorG.t -> interpolation_mode:int -> padding_mode:int -> C.TensorG.t * C.TensorG.t
Sourceval group_norm : C.TensorG.t -> num_groups:int -> weight:C.TensorG.t option -> bias:C.TensorG.t option -> eps:float -> cudnn_enabled:bool -> C.TensorG.t
Sourceval gru : C.TensorG.t -> hx:C.TensorG.t -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> C.TensorG.t * C.TensorG.t
Sourceval gru1 : data:C.TensorG.t -> batch_sizes:C.TensorG.t -> hx:C.TensorG.t -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> C.TensorG.t * C.TensorG.t
Sourceval gru_cell : C.TensorG.t -> hx:C.TensorG.t -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t option -> b_hh:C.TensorG.t option -> C.TensorG.t
Sourceval gt_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval hamming_window : window_length:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval hamming_window1 : window_length:int -> periodic:bool -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval hamming_window2 : window_length:int -> periodic:bool -> alpha:float -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval hamming_window3 : window_length:int -> periodic:bool -> alpha:float -> beta:float -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval hann_window : window_length:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval hann_window1 : window_length:int -> periodic:bool -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval hardshrink : C.TensorG.t -> C.TensorG.t
Sourceval hardshrink_backward : grad_out:C.TensorG.t -> C.TensorG.t -> lambd:C.TensorG.scalar -> C.TensorG.t
Sourceval hardtanh : C.TensorG.t -> C.TensorG.t
Sourceval hardtanh_ : C.TensorG.t -> C.TensorG.t
Sourceval hardtanh_backward : grad_output:C.TensorG.t -> C.TensorG.t -> min_val:C.TensorG.scalar -> max_val:C.TensorG.scalar -> C.TensorG.t
Sourceval hardtanh_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> min_val:C.TensorG.scalar -> max_val:C.TensorG.scalar -> C.TensorG.t
Sourceval hardtanh_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval hinge_embedding_loss : C.TensorG.t -> target:C.TensorG.t -> margin:float -> reduction:int -> C.TensorG.t
Sourceval histc : C.TensorG.t -> bins:int -> C.TensorG.t
Sourceval histc_out : out:C.TensorG.t -> C.TensorG.t -> bins:int -> C.TensorG.t
Sourceval hspmm : mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval hspmm_out : out:C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval ifft : C.TensorG.t -> signal_ndim:int -> normalized:bool -> C.TensorG.t
Sourceval index : C.TensorG.t -> indices:C.TensorG.t list -> C.TensorG.t
Sourceval index_add : C.TensorG.t -> dim:int -> index:C.TensorG.t -> source:C.TensorG.t -> C.TensorG.t
Sourceval index_add_ : C.TensorG.t -> dim:int -> index:C.TensorG.t -> source:C.TensorG.t -> C.TensorG.t
Sourceval index_copy : C.TensorG.t -> dim:int -> index:C.TensorG.t -> source:C.TensorG.t -> C.TensorG.t
Sourceval index_copy_ : C.TensorG.t -> dim:int -> index:C.TensorG.t -> source:C.TensorG.t -> C.TensorG.t
Sourceval index_fill : C.TensorG.t -> dim:int -> index:C.TensorG.t -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval index_fill1 : C.TensorG.t -> dim:int -> index:C.TensorG.t -> value:C.TensorG.t -> C.TensorG.t
Sourceval index_fill_ : C.TensorG.t -> dim:int -> index:C.TensorG.t -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval index_fill_1 : C.TensorG.t -> dim:int -> index:C.TensorG.t -> value:C.TensorG.t -> C.TensorG.t
Sourceval index_put : C.TensorG.t -> indices:C.TensorG.t list -> values:C.TensorG.t -> accumulate:bool -> C.TensorG.t
Sourceval index_put_ : C.TensorG.t -> indices:C.TensorG.t list -> values:C.TensorG.t -> accumulate:bool -> C.TensorG.t
Sourceval index_select : C.TensorG.t -> dim:int -> index:C.TensorG.t -> C.TensorG.t
Sourceval index_select_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> index:C.TensorG.t -> C.TensorG.t
Sourceval instance_norm : C.TensorG.t -> weight:C.TensorG.t option -> bias:C.TensorG.t option -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> use_input_stats:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> C.TensorG.t
Sourceval int_repr : C.TensorG.t -> C.TensorG.t
Sourceval inverse_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval irfft : C.TensorG.t -> signal_ndim:int -> normalized:bool -> onesided:bool -> signal_sizes:int list -> C.TensorG.t
Sourceval isclose : C.TensorG.t -> C.TensorG.t -> rtol:float -> atol:float -> equal_nan:bool -> C.TensorG.t
Sourceval kl_div : C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval kl_div_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval kthvalue : C.TensorG.t -> k:int -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval kthvalue_out : values:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> k:int -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval l1_loss : C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval l1_loss_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval l1_loss_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval l1_loss_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval layer_norm : C.TensorG.t -> normalized_shape:int list -> weight:C.TensorG.t option -> bias:C.TensorG.t option -> eps:float -> cudnn_enable:bool -> C.TensorG.t
Sourceval le_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval leaky_relu : C.TensorG.t -> C.TensorG.t
Sourceval leaky_relu_ : C.TensorG.t -> C.TensorG.t
Sourceval leaky_relu_backward : grad_output:C.TensorG.t -> C.TensorG.t -> negative_slope:C.TensorG.scalar -> C.TensorG.t
Sourceval leaky_relu_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> negative_slope:C.TensorG.scalar -> C.TensorG.t
Sourceval leaky_relu_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval lerp1 : C.TensorG.t -> end_:C.TensorG.t -> weight:C.TensorG.t -> C.TensorG.t
Sourceval lerp_ : C.TensorG.t -> end_:C.TensorG.t -> weight:C.TensorG.scalar -> C.TensorG.t
Sourceval lerp_1 : C.TensorG.t -> end_:C.TensorG.t -> weight:C.TensorG.t -> C.TensorG.t
Sourceval lerp_out : out:C.TensorG.t -> C.TensorG.t -> end_:C.TensorG.t -> weight:C.TensorG.scalar -> C.TensorG.t
Sourceval lerp_out1 : out:C.TensorG.t -> C.TensorG.t -> end_:C.TensorG.t -> weight:C.TensorG.t -> C.TensorG.t
Sourceval lgamma_ : C.TensorG.t -> C.TensorG.t
Sourceval lgamma_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval linear : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> C.TensorG.t
Sourceval linspace : start:C.TensorG.scalar -> end_:C.TensorG.scalar -> steps:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval linspace_out : out:C.TensorG.t -> start:C.TensorG.scalar -> end_:C.TensorG.scalar -> steps:int -> C.TensorG.t
Sourceval log10_ : C.TensorG.t -> C.TensorG.t
Sourceval log10_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval log1p_ : C.TensorG.t -> C.TensorG.t
Sourceval log1p_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval log2_ : C.TensorG.t -> C.TensorG.t
Sourceval log2_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval log_ : C.TensorG.t -> C.TensorG.t
Sourceval log_normal_ : C.TensorG.t -> mean:float -> std:float -> C.TensorG.t
Sourceval log_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval log_sigmoid : C.TensorG.t -> C.TensorG.t
Sourceval log_sigmoid_backward : grad_output:C.TensorG.t -> C.TensorG.t -> buffer:C.TensorG.t -> C.TensorG.t
Sourceval log_sigmoid_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> buffer:C.TensorG.t -> C.TensorG.t
Sourceval log_sigmoid_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval log_softmax : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval log_softmax1 : C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval logspace : start:C.TensorG.scalar -> end_:C.TensorG.scalar -> steps:int -> base:float -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval logspace_out : out:C.TensorG.t -> start:C.TensorG.scalar -> end_:C.TensorG.scalar -> steps:int -> base:float -> C.TensorG.t
Sourceval logsumexp : C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval logsumexp_out : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval lstm : C.TensorG.t -> hx:C.TensorG.t list -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval lstm1 : data:C.TensorG.t -> batch_sizes:C.TensorG.t -> hx:C.TensorG.t list -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval lstm_cell : C.TensorG.t -> hx:C.TensorG.t list -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t option -> b_hh:C.TensorG.t option -> C.TensorG.t * C.TensorG.t
Sourceval lt_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval lu_solve : C.TensorG.t -> lu_data:C.TensorG.t -> lu_pivots:C.TensorG.t -> C.TensorG.t
Sourceval lu_solve_out : out:C.TensorG.t -> C.TensorG.t -> lu_data:C.TensorG.t -> lu_pivots:C.TensorG.t -> C.TensorG.t
Sourceval margin_ranking_loss : input1:C.TensorG.t -> input2:C.TensorG.t -> target:C.TensorG.t -> margin:float -> reduction:int -> C.TensorG.t
Sourceval masked_fill : C.TensorG.t -> mask:C.TensorG.t -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval masked_fill1 : C.TensorG.t -> mask:C.TensorG.t -> value:C.TensorG.t -> C.TensorG.t
Sourceval masked_fill_ : C.TensorG.t -> mask:C.TensorG.t -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval masked_fill_1 : C.TensorG.t -> mask:C.TensorG.t -> value:C.TensorG.t -> C.TensorG.t
Sourceval masked_scatter : C.TensorG.t -> mask:C.TensorG.t -> source:C.TensorG.t -> C.TensorG.t
Sourceval masked_scatter_ : C.TensorG.t -> mask:C.TensorG.t -> source:C.TensorG.t -> C.TensorG.t
Sourceval masked_select : C.TensorG.t -> mask:C.TensorG.t -> C.TensorG.t
Sourceval masked_select_out : out:C.TensorG.t -> C.TensorG.t -> mask:C.TensorG.t -> C.TensorG.t
Sourceval matrix_power : C.TensorG.t -> n:int -> C.TensorG.t
Sourceval matrix_rank : C.TensorG.t -> symmetric:bool -> C.TensorG.t
Sourceval matrix_rank1 : C.TensorG.t -> tol:float -> symmetric:bool -> C.TensorG.t
Sourceval max2 : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval max_out1 : max:C.TensorG.t -> max_values:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval max_pool1d : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
Sourceval max_pool1d_with_indices : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
Sourceval max_pool2d : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
Sourceval max_pool2d_with_indices : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
Sourceval max_pool2d_with_indices_backward : grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:C.TensorG.t -> C.TensorG.t
Sourceval max_pool2d_with_indices_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:C.TensorG.t -> C.TensorG.t
Sourceval max_pool2d_with_indices_out : output:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
Sourceval max_pool3d : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
Sourceval max_pool3d_with_indices : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
Sourceval max_pool3d_with_indices_backward : grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:C.TensorG.t -> C.TensorG.t
Sourceval max_pool3d_with_indices_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:C.TensorG.t -> C.TensorG.t
Sourceval max_pool3d_with_indices_out : output:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
Sourceval max_unpool2d : C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval max_unpool2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval max_unpool2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval max_unpool2d_out : out:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval max_unpool3d : C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
Sourceval max_unpool3d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
Sourceval max_unpool3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
Sourceval max_unpool3d_out : out:C.TensorG.t -> C.TensorG.t -> indices:C.TensorG.t -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
Sourceval max_values : C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval mean1 : C.TensorG.t -> dtype:Kind.t -> C.TensorG.t
Sourceval mean2 : C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval mean3 : C.TensorG.t -> dim:int list -> dtype:Kind.t -> C.TensorG.t
Sourceval mean4 : C.TensorG.t -> dim:int list -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval mean_out : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval mean_out1 : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> dtype:Kind.t -> C.TensorG.t
Sourceval mean_out2 : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval median1 : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval median_out : values:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval meshgrid : C.TensorG.t list -> unit Ctypes.ptr list
Sourceval min2 : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval min_out1 : min:C.TensorG.t -> min_indices:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval min_values : C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval miopen_batch_norm : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval miopen_batch_norm_backward : C.TensorG.t -> grad_output:C.TensorG.t -> weight:C.TensorG.t -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> save_mean:C.TensorG.t option -> save_var:C.TensorG.t option -> epsilon:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval miopen_convolution : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_convolution_backward_bias : grad_output:C.TensorG.t -> C.TensorG.t
Sourceval miopen_convolution_backward_input : self_size:int list -> grad_output:C.TensorG.t -> weight:C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_convolution_backward_weight : weight_size:int list -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_convolution_transpose : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_convolution_transpose_backward_input : grad_output:C.TensorG.t -> weight:C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_convolution_transpose_backward_weight : weight_size:int list -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_depthwise_convolution : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_depthwise_convolution_backward_input : self_size:int list -> grad_output:C.TensorG.t -> weight:C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval miopen_depthwise_convolution_backward_weight : weight_size:int list -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
Sourceval mkldnn_convolution : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> C.TensorG.t
Sourceval mkldnn_convolution_backward_input : self_size:int list -> grad_output:C.TensorG.t -> weight:C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> C.TensorG.t
Sourceval mkldnn_convolution_backward_weights : weight_size:int list -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> C.TensorG.t * C.TensorG.t
Sourceval mkldnn_linear : C.TensorG.t -> weight:C.TensorG.t -> bias:C.TensorG.t option -> C.TensorG.t
Sourceval mkldnn_max_pool2d : C.TensorG.t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
Sourceval mkldnn_reorder_conv2d_weight : C.TensorG.t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> C.TensorG.t
Sourceval mkldnn_reshape : C.TensorG.t -> shape:int list -> C.TensorG.t
Sourceval mm_out : out:C.TensorG.t -> C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval mode : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval mode_out : values:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
Sourceval mse_loss : C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval mse_loss_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval mse_loss_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval mse_loss_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval mul_ : C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval multi_margin_loss_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> p:C.TensorG.scalar -> margin:C.TensorG.scalar -> weight:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval multi_margin_loss_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> p:C.TensorG.scalar -> margin:C.TensorG.scalar -> weight:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval multilabel_margin_loss : C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval multilabel_margin_loss_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> is_target:C.TensorG.t -> C.TensorG.t
Sourceval multilabel_margin_loss_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> is_target:C.TensorG.t -> C.TensorG.t
Sourceval multilabel_margin_loss_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval multinomial : C.TensorG.t -> num_samples:int -> replacement:bool -> C.TensorG.t
Sourceval multinomial_out : out:C.TensorG.t -> C.TensorG.t -> num_samples:int -> replacement:bool -> C.TensorG.t
Sourceval mvlgamma : C.TensorG.t -> p:int -> C.TensorG.t
Sourceval mvlgamma_ : C.TensorG.t -> p:int -> C.TensorG.t
Sourceval narrow : C.TensorG.t -> dim:int -> start:int -> length:int -> C.TensorG.t
Sourceval narrow_copy : C.TensorG.t -> dim:int -> start:int -> length:int -> C.TensorG.t
Sourceval native_batch_norm : C.TensorG.t -> weight:C.TensorG.t option -> bias:C.TensorG.t option -> running_mean:C.TensorG.t option -> running_var:C.TensorG.t option -> training:bool -> momentum:float -> eps:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval native_norm : C.TensorG.t -> C.TensorG.t
Sourceval ne_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval neg_ : C.TensorG.t -> C.TensorG.t
Sourceval neg_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval nll_loss : C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> C.TensorG.t
Sourceval nll_loss2d : C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> C.TensorG.t
Sourceval nll_loss2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> total_weight:C.TensorG.t -> C.TensorG.t
Sourceval nll_loss2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> total_weight:C.TensorG.t -> C.TensorG.t
Sourceval nll_loss2d_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> C.TensorG.t
Sourceval nll_loss_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> total_weight:C.TensorG.t -> C.TensorG.t
Sourceval nll_loss_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> total_weight:C.TensorG.t -> C.TensorG.t
Sourceval nll_loss_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> weight:C.TensorG.t option -> reduction:int -> ignore_index:int -> C.TensorG.t
Sourceval nonzero_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval norm2 : C.TensorG.t -> p:C.TensorG.scalar -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval norm3 : C.TensorG.t -> p:C.TensorG.scalar -> dim:int list -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval norm_except_dim : v:C.TensorG.t -> pow:int -> dim:int -> C.TensorG.t
Sourceval norm_out : out:C.TensorG.t -> C.TensorG.t -> p:C.TensorG.scalar -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval norm_out1 : out:C.TensorG.t -> C.TensorG.t -> p:C.TensorG.scalar -> dim:int list -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval normal : mean:C.TensorG.t -> std:float -> C.TensorG.t
Sourceval normal1 : mean:float -> std:C.TensorG.t -> C.TensorG.t
Sourceval normal2 : mean:C.TensorG.t -> std:C.TensorG.t -> C.TensorG.t
Sourceval normal_ : C.TensorG.t -> mean:float -> std:float -> C.TensorG.t
Sourceval normal_out : out:C.TensorG.t -> mean:C.TensorG.t -> std:float -> C.TensorG.t
Sourceval normal_out1 : out:C.TensorG.t -> mean:float -> std:C.TensorG.t -> C.TensorG.t
Sourceval normal_out2 : out:C.TensorG.t -> mean:C.TensorG.t -> std:C.TensorG.t -> C.TensorG.t
Sourceval nuclear_norm : C.TensorG.t -> keepdim:bool -> C.TensorG.t
Sourceval nuclear_norm_out : out:C.TensorG.t -> C.TensorG.t -> keepdim:bool -> C.TensorG.t
Sourceval one_hot : C.TensorG.t -> num_classes:int -> C.TensorG.t
Sourceval ones : size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval ones_like : C.TensorG.t -> C.TensorG.t
Sourceval ones_like1 : C.TensorG.t -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval ones_out : out:C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval orgqr : C.TensorG.t -> input2:C.TensorG.t -> C.TensorG.t
Sourceval orgqr_out : out:C.TensorG.t -> C.TensorG.t -> input2:C.TensorG.t -> C.TensorG.t
Sourceval ormqr : C.TensorG.t -> input2:C.TensorG.t -> input3:C.TensorG.t -> left:bool -> transpose:bool -> C.TensorG.t
Sourceval ormqr_out : out:C.TensorG.t -> C.TensorG.t -> input2:C.TensorG.t -> input3:C.TensorG.t -> left:bool -> transpose:bool -> C.TensorG.t
Sourceval pairwise_distance : x1:C.TensorG.t -> x2:C.TensorG.t -> p:float -> eps:float -> keepdim:bool -> C.TensorG.t
Sourceval pdist : C.TensorG.t -> p:float -> C.TensorG.t
Sourceval permute : C.TensorG.t -> dims:int list -> C.TensorG.t
Sourceval pin_memory : C.TensorG.t -> C.TensorG.t
Sourceval pinverse : C.TensorG.t -> rcond:float -> C.TensorG.t
Sourceval pixel_shuffle : C.TensorG.t -> upscale_factor:int -> C.TensorG.t
Sourceval polygamma : n:int -> C.TensorG.t -> C.TensorG.t
Sourceval polygamma_ : C.TensorG.t -> n:int -> C.TensorG.t
Sourceval polygamma_out : out:C.TensorG.t -> n:int -> C.TensorG.t -> C.TensorG.t
Sourceval pow1 : C.TensorG.t -> exponent:C.TensorG.t -> C.TensorG.t
Sourceval pow_ : C.TensorG.t -> exponent:C.TensorG.scalar -> C.TensorG.t
Sourceval pow_1 : C.TensorG.t -> exponent:C.TensorG.t -> C.TensorG.t
Sourceval pow_out : out:C.TensorG.t -> C.TensorG.t -> exponent:C.TensorG.scalar -> C.TensorG.t
Sourceval pow_out1 : out:C.TensorG.t -> C.TensorG.t -> exponent:C.TensorG.t -> C.TensorG.t
Sourceval pow_out2 : out:C.TensorG.t -> C.TensorG.scalar -> exponent:C.TensorG.t -> C.TensorG.t
Sourceval prelu : C.TensorG.t -> weight:C.TensorG.t -> C.TensorG.t
Sourceval prelu_backward : grad_output:C.TensorG.t -> C.TensorG.t -> weight:C.TensorG.t -> C.TensorG.t * C.TensorG.t
Sourceval prod1 : C.TensorG.t -> dtype:Kind.t -> C.TensorG.t
Sourceval prod2 : C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval prod3 : C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval prod4 : C.TensorG.t -> dim:int -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval prod_out : out:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> C.TensorG.t
Sourceval prod_out1 : out:C.TensorG.t -> C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval prod_out2 : out:C.TensorG.t -> C.TensorG.t -> dim:int -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval pstrf : C.TensorG.t -> upper:bool -> C.TensorG.t * C.TensorG.t
Sourceval pstrf_out : u:C.TensorG.t -> pivot:C.TensorG.t -> C.TensorG.t -> upper:bool -> C.TensorG.t * C.TensorG.t
Sourceval put_ : C.TensorG.t -> index:C.TensorG.t -> source:C.TensorG.t -> accumulate:bool -> C.TensorG.t
Sourceval quantize_linear : C.TensorG.t -> scale:float -> zero_point:int -> C.TensorG.t
Sourceval quantized_gru_cell : C.TensorG.t -> hx:C.TensorG.t -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t -> b_hh:C.TensorG.t -> packed_ih:C.TensorG.t -> packed_hh:C.TensorG.t -> col_offsets_ih:C.TensorG.t -> col_offsets_hh:C.TensorG.t -> scale_ih:C.TensorG.scalar -> scale_hh:C.TensorG.scalar -> zero_point_ih:C.TensorG.scalar -> zero_point_hh:C.TensorG.scalar -> C.TensorG.t
Sourceval quantized_lstm : C.TensorG.t -> hx:C.TensorG.t list -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval quantized_lstm_cell : C.TensorG.t -> hx:C.TensorG.t list -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t -> b_hh:C.TensorG.t -> packed_ih:C.TensorG.t -> packed_hh:C.TensorG.t -> col_offsets_ih:C.TensorG.t -> col_offsets_hh:C.TensorG.t -> scale_ih:C.TensorG.scalar -> scale_hh:C.TensorG.scalar -> zero_point_ih:C.TensorG.scalar -> zero_point_hh:C.TensorG.scalar -> C.TensorG.t * C.TensorG.t
Sourceval quantized_rnn_relu_cell : C.TensorG.t -> hx:C.TensorG.t -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t -> b_hh:C.TensorG.t -> packed_ih:C.TensorG.t -> packed_hh:C.TensorG.t -> col_offsets_ih:C.TensorG.t -> col_offsets_hh:C.TensorG.t -> scale_ih:C.TensorG.scalar -> scale_hh:C.TensorG.scalar -> zero_point_ih:C.TensorG.scalar -> zero_point_hh:C.TensorG.scalar -> C.TensorG.t
Sourceval quantized_rnn_tanh_cell : C.TensorG.t -> hx:C.TensorG.t -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t -> b_hh:C.TensorG.t -> packed_ih:C.TensorG.t -> packed_hh:C.TensorG.t -> col_offsets_ih:C.TensorG.t -> col_offsets_hh:C.TensorG.t -> scale_ih:C.TensorG.scalar -> scale_hh:C.TensorG.scalar -> zero_point_ih:C.TensorG.scalar -> zero_point_hh:C.TensorG.scalar -> C.TensorG.t
Sourceval rand : size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval rand_like : C.TensorG.t -> C.TensorG.t
Sourceval rand_like1 : C.TensorG.t -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval rand_out : out:C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval randint : high:int -> size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval randint1 : low:int -> high:int -> size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval randint_like : C.TensorG.t -> high:int -> C.TensorG.t
Sourceval randint_like1 : C.TensorG.t -> low:int -> high:int -> C.TensorG.t
Sourceval randint_like2 : C.TensorG.t -> high:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval randint_like3 : C.TensorG.t -> low:int -> high:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval randint_out : out:C.TensorG.t -> high:int -> size:int list -> C.TensorG.t
Sourceval randint_out1 : out:C.TensorG.t -> low:int -> high:int -> size:int list -> C.TensorG.t
Sourceval randn : size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval randn_like : C.TensorG.t -> C.TensorG.t
Sourceval randn_like1 : C.TensorG.t -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval randn_out : out:C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval random_ : C.TensorG.t -> C.TensorG.t
Sourceval random_1 : C.TensorG.t -> to_:int -> C.TensorG.t
Sourceval random_2 : C.TensorG.t -> from:int -> to_:int -> C.TensorG.t
Sourceval randperm : n:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval randperm_out : out:C.TensorG.t -> n:int -> C.TensorG.t
Sourceval range : start:C.TensorG.scalar -> end_:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval range1 : start:C.TensorG.scalar -> end_:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval range_out : out:C.TensorG.t -> start:C.TensorG.scalar -> end_:C.TensorG.scalar -> C.TensorG.t
Sourceval reciprocal : C.TensorG.t -> C.TensorG.t
Sourceval reciprocal_ : C.TensorG.t -> C.TensorG.t
Sourceval reciprocal_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval reflection_pad1d : C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reflection_pad1d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reflection_pad1d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reflection_pad1d_out : out:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reflection_pad2d : C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reflection_pad2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reflection_pad2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reflection_pad2d_out : out:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval relu_ : C.TensorG.t -> C.TensorG.t
Sourceval remainder_ : C.TensorG.t -> C.TensorG.scalar -> C.TensorG.t
Sourceval remainder_1 : C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval remainder_out1 : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval renorm : C.TensorG.t -> p:C.TensorG.scalar -> dim:int -> maxnorm:C.TensorG.scalar -> C.TensorG.t
Sourceval renorm_ : C.TensorG.t -> p:C.TensorG.scalar -> dim:int -> maxnorm:C.TensorG.scalar -> C.TensorG.t
Sourceval renorm_out : out:C.TensorG.t -> C.TensorG.t -> p:C.TensorG.scalar -> dim:int -> maxnorm:C.TensorG.scalar -> C.TensorG.t
Sourceval repeat : C.TensorG.t -> repeats:int list -> C.TensorG.t
Sourceval repeat_interleave : repeats:C.TensorG.t -> C.TensorG.t
Sourceval repeat_interleave1 : C.TensorG.t -> repeats:C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval repeat_interleave2 : C.TensorG.t -> repeats:int -> dim:int -> C.TensorG.t
Sourceval replication_pad1d : C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad1d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad1d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad1d_out : out:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad2d : C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad2d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad2d_out : out:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad3d : C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad3d_backward : grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval replication_pad3d_out : out:C.TensorG.t -> C.TensorG.t -> padding:int list -> C.TensorG.t
Sourceval reshape : C.TensorG.t -> shape:int list -> C.TensorG.t
Sourceval resize_ : C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval resize_as_ : C.TensorG.t -> the_template:C.TensorG.t -> C.TensorG.t
Sourceval rfft : C.TensorG.t -> signal_ndim:int -> normalized:bool -> onesided:bool -> C.TensorG.t
Sourceval rnn_relu : C.TensorG.t -> hx:C.TensorG.t -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> C.TensorG.t * C.TensorG.t
Sourceval rnn_relu1 : data:C.TensorG.t -> batch_sizes:C.TensorG.t -> hx:C.TensorG.t -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> C.TensorG.t * C.TensorG.t
Sourceval rnn_relu_cell : C.TensorG.t -> hx:C.TensorG.t -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t option -> b_hh:C.TensorG.t option -> C.TensorG.t
Sourceval rnn_tanh : C.TensorG.t -> hx:C.TensorG.t -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> C.TensorG.t * C.TensorG.t
Sourceval rnn_tanh1 : data:C.TensorG.t -> batch_sizes:C.TensorG.t -> hx:C.TensorG.t -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> C.TensorG.t * C.TensorG.t
Sourceval rnn_tanh_cell : C.TensorG.t -> hx:C.TensorG.t -> w_ih:C.TensorG.t -> w_hh:C.TensorG.t -> b_ih:C.TensorG.t option -> b_hh:C.TensorG.t option -> C.TensorG.t
Sourceval roll : C.TensorG.t -> shifts:int list -> dims:int list -> C.TensorG.t
Sourceval rot90 : C.TensorG.t -> k:int -> dims:int list -> C.TensorG.t
Sourceval round_ : C.TensorG.t -> C.TensorG.t
Sourceval round_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval rrelu : C.TensorG.t -> training:bool -> C.TensorG.t
Sourceval rrelu_ : C.TensorG.t -> training:bool -> C.TensorG.t
Sourceval rrelu_with_noise : C.TensorG.t -> noise:C.TensorG.t -> training:bool -> C.TensorG.t
Sourceval rrelu_with_noise_ : C.TensorG.t -> noise:C.TensorG.t -> training:bool -> C.TensorG.t
Sourceval rrelu_with_noise_backward : grad_output:C.TensorG.t -> C.TensorG.t -> noise:C.TensorG.t -> lower:C.TensorG.scalar -> upper:C.TensorG.scalar -> training:bool -> C.TensorG.t
Sourceval rrelu_with_noise_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> noise:C.TensorG.t -> lower:C.TensorG.scalar -> upper:C.TensorG.scalar -> training:bool -> C.TensorG.t
Sourceval rrelu_with_noise_out : out:C.TensorG.t -> C.TensorG.t -> noise:C.TensorG.t -> training:bool -> C.TensorG.t
Sourceval rsqrt_ : C.TensorG.t -> C.TensorG.t
Sourceval rsqrt_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval s_copy_ : C.TensorG.t -> src:C.TensorG.t -> non_blocking:bool -> C.TensorG.t
Sourceval s_native_addmm : C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval s_native_addmm_ : C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval s_native_addmm_out : out:C.TensorG.t -> C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval scalar_tensor : s:C.TensorG.scalar -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval scatter : C.TensorG.t -> dim:int -> index:C.TensorG.t -> src:C.TensorG.t -> C.TensorG.t
Sourceval scatter1 : C.TensorG.t -> dim:int -> index:C.TensorG.t -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval scatter_ : C.TensorG.t -> dim:int -> index:C.TensorG.t -> src:C.TensorG.t -> C.TensorG.t
Sourceval scatter_1 : C.TensorG.t -> dim:int -> index:C.TensorG.t -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval scatter_add : C.TensorG.t -> dim:int -> index:C.TensorG.t -> src:C.TensorG.t -> C.TensorG.t
Sourceval scatter_add_ : C.TensorG.t -> dim:int -> index:C.TensorG.t -> src:C.TensorG.t -> C.TensorG.t
Sourceval select : C.TensorG.t -> dim:int -> index:int -> C.TensorG.t
Sourceval selu_ : C.TensorG.t -> C.TensorG.t
Sourceval set_ : C.TensorG.t -> C.TensorG.t
Sourceval set_1 : C.TensorG.t -> source:C.TensorG.t -> C.TensorG.t
Sourceval set_requires_grad : C.TensorG.t -> r:bool -> C.TensorG.t
Sourceval sigmoid_ : C.TensorG.t -> C.TensorG.t
Sourceval sigmoid_backward : grad_output:C.TensorG.t -> output:C.TensorG.t -> C.TensorG.t
Sourceval sigmoid_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output:C.TensorG.t -> C.TensorG.t
Sourceval sigmoid_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval sign_ : C.TensorG.t -> C.TensorG.t
Sourceval sign_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval sin_ : C.TensorG.t -> C.TensorG.t
Sourceval sin_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval sinh_ : C.TensorG.t -> C.TensorG.t
Sourceval sinh_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval slice : C.TensorG.t -> dim:int -> start:int -> end_:int -> step:int -> C.TensorG.t
Sourceval smooth_l1_loss : C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval smooth_l1_loss_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval smooth_l1_loss_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval smooth_l1_loss_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval soft_margin_loss : C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval soft_margin_loss_backward : grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval soft_margin_loss_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval soft_margin_loss_out : out:C.TensorG.t -> C.TensorG.t -> target:C.TensorG.t -> reduction:int -> C.TensorG.t
Sourceval softmax : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval softmax1 : C.TensorG.t -> dim:int -> dtype:Kind.t -> C.TensorG.t
Sourceval softplus : C.TensorG.t -> C.TensorG.t
Sourceval softplus_backward : grad_output:C.TensorG.t -> C.TensorG.t -> beta:C.TensorG.scalar -> threshold:C.TensorG.scalar -> output:C.TensorG.t -> C.TensorG.t
Sourceval softplus_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> beta:C.TensorG.scalar -> threshold:C.TensorG.scalar -> output:C.TensorG.t -> C.TensorG.t
Sourceval softplus_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval softshrink : C.TensorG.t -> C.TensorG.t
Sourceval softshrink_backward : grad_output:C.TensorG.t -> C.TensorG.t -> lambd:C.TensorG.scalar -> C.TensorG.t
Sourceval softshrink_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> C.TensorG.t -> lambd:C.TensorG.scalar -> C.TensorG.t
Sourceval softshrink_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval solve_out : solution:C.TensorG.t -> lu:C.TensorG.t -> C.TensorG.t -> a:C.TensorG.t -> C.TensorG.t * C.TensorG.t
Sourceval sort : C.TensorG.t -> dim:int -> descending:bool -> C.TensorG.t * C.TensorG.t
Sourceval sort_out : values:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> dim:int -> descending:bool -> C.TensorG.t * C.TensorG.t
Sourceval sparse_coo_tensor : size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval sparse_coo_tensor1 : indices:C.TensorG.t -> values:C.TensorG.t -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval sparse_coo_tensor2 : indices:C.TensorG.t -> values:C.TensorG.t -> size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval sparse_resize_ : C.TensorG.t -> size:int list -> sparse_dim:int -> dense_dim:int -> C.TensorG.t
Sourceval sparse_resize_and_clear_ : C.TensorG.t -> size:int list -> sparse_dim:int -> dense_dim:int -> C.TensorG.t
Sourceval split : C.TensorG.t -> split_size:int -> dim:int -> unit Ctypes.ptr list
Sourceval split_with_sizes : C.TensorG.t -> split_sizes:int list -> dim:int -> unit Ctypes.ptr list
Sourceval sqrt_ : C.TensorG.t -> C.TensorG.t
Sourceval sqrt_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval squeeze1 : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval squeeze_ : C.TensorG.t -> C.TensorG.t
Sourceval squeeze_1 : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval sspaddmm : C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval sspaddmm_out : out:C.TensorG.t -> C.TensorG.t -> mat1:C.TensorG.t -> mat2:C.TensorG.t -> C.TensorG.t
Sourceval stack : C.TensorG.t list -> dim:int -> C.TensorG.t
Sourceval stack_out : out:C.TensorG.t -> C.TensorG.t list -> dim:int -> C.TensorG.t
Sourceval std : C.TensorG.t -> unbiased:bool -> C.TensorG.t
Sourceval std1 : C.TensorG.t -> dim:int list -> unbiased:bool -> keepdim:bool -> C.TensorG.t
Sourceval std_out : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> unbiased:bool -> keepdim:bool -> C.TensorG.t
Sourceval stft : C.TensorG.t -> n_fft:int -> hop_length:int -> win_length:int -> window:C.TensorG.t option -> normalized:bool -> onesided:bool -> C.TensorG.t
Sourceval sub_ : C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval sum1 : C.TensorG.t -> dtype:Kind.t -> C.TensorG.t
Sourceval sum2 : C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval sum3 : C.TensorG.t -> dim:int list -> dtype:Kind.t -> C.TensorG.t
Sourceval sum4 : C.TensorG.t -> dim:int list -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval sum_out : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> keepdim:bool -> C.TensorG.t
Sourceval sum_out1 : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> dtype:Kind.t -> C.TensorG.t
Sourceval sum_out2 : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> keepdim:bool -> dtype:Kind.t -> C.TensorG.t
Sourceval sum_to_size : C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval svd : C.TensorG.t -> some:bool -> compute_uv:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval svd_out : u:C.TensorG.t -> s:C.TensorG.t -> v:C.TensorG.t -> C.TensorG.t -> some:bool -> compute_uv:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval symeig : C.TensorG.t -> eigenvectors:bool -> upper:bool -> C.TensorG.t * C.TensorG.t
Sourceval symeig_out : e:C.TensorG.t -> v:C.TensorG.t -> C.TensorG.t -> eigenvectors:bool -> upper:bool -> C.TensorG.t * C.TensorG.t
Sourceval t_ : C.TensorG.t -> C.TensorG.t
Sourceval take_out : out:C.TensorG.t -> C.TensorG.t -> index:C.TensorG.t -> C.TensorG.t
Sourceval tan_ : C.TensorG.t -> C.TensorG.t
Sourceval tan_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval tanh_ : C.TensorG.t -> C.TensorG.t
Sourceval tanh_backward : grad_output:C.TensorG.t -> output:C.TensorG.t -> C.TensorG.t
Sourceval tanh_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output:C.TensorG.t -> C.TensorG.t
Sourceval tanh_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval tensordot : C.TensorG.t -> C.TensorG.t -> dims_self:int list -> dims_other:int list -> C.TensorG.t
Sourceval threshold : C.TensorG.t -> threshold:C.TensorG.scalar -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval threshold_ : C.TensorG.t -> threshold:C.TensorG.scalar -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval threshold_backward : grad_output:C.TensorG.t -> C.TensorG.t -> threshold:C.TensorG.scalar -> C.TensorG.t
Sourceval threshold_out : out:C.TensorG.t -> C.TensorG.t -> threshold:C.TensorG.scalar -> value:C.TensorG.scalar -> C.TensorG.t
Sourceval to_ : C.TensorG.t -> device:Device.t -> C.TensorG.t
Sourceval to1 : C.TensorG.t -> options:(Kind.t * Device.t) -> non_blocking:bool -> copy:bool -> C.TensorG.t
Sourceval to2 : C.TensorG.t -> dtype:Kind.t -> non_blocking:bool -> copy:bool -> C.TensorG.t
Sourceval to3 : C.TensorG.t -> C.TensorG.t -> non_blocking:bool -> copy:bool -> C.TensorG.t
Sourceval to4 : C.TensorG.t -> device:Device.t -> dtype:Kind.t -> non_blocking:bool -> copy:bool -> C.TensorG.t
Sourceval to_dense : C.TensorG.t -> C.TensorG.t
Sourceval to_dense_backward : grad:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval to_mkldnn : C.TensorG.t -> C.TensorG.t
Sourceval to_mkldnn_backward : grad:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval to_sparse : C.TensorG.t -> C.TensorG.t
Sourceval to_sparse1 : C.TensorG.t -> sparse_dim:int -> C.TensorG.t
Sourceval topk : C.TensorG.t -> k:int -> dim:int -> largest:bool -> sorted:bool -> C.TensorG.t * C.TensorG.t
Sourceval topk_out : values:C.TensorG.t -> indices:C.TensorG.t -> C.TensorG.t -> k:int -> dim:int -> largest:bool -> sorted:bool -> C.TensorG.t * C.TensorG.t
Sourceval totype : C.TensorG.t -> scalar_type:Kind.t -> C.TensorG.t
Sourceval transpose : C.TensorG.t -> dim0:int -> dim1:int -> C.TensorG.t
Sourceval transpose_ : C.TensorG.t -> dim0:int -> dim1:int -> C.TensorG.t
Sourceval triangular_solve : C.TensorG.t -> a:C.TensorG.t -> upper:bool -> transpose:bool -> unitriangular:bool -> C.TensorG.t * C.TensorG.t
Sourceval triangular_solve_out : x:C.TensorG.t -> m:C.TensorG.t -> C.TensorG.t -> a:C.TensorG.t -> upper:bool -> transpose:bool -> unitriangular:bool -> C.TensorG.t * C.TensorG.t
Sourceval tril : C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval tril_ : C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval tril_indices : row:int -> col:int -> offset:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval tril_out : out:C.TensorG.t -> C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval triplet_margin_loss : anchor:C.TensorG.t -> positive:C.TensorG.t -> negative:C.TensorG.t -> margin:float -> p:float -> eps:float -> swap:bool -> reduction:int -> C.TensorG.t
Sourceval triu : C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval triu_ : C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval triu_indices : row:int -> col:int -> offset:int -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval triu_out : out:C.TensorG.t -> C.TensorG.t -> diagonal:int -> C.TensorG.t
Sourceval trunc_ : C.TensorG.t -> C.TensorG.t
Sourceval trunc_out : out:C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval unbind : C.TensorG.t -> dim:int -> unit Ctypes.ptr list
Sourceval unfold : C.TensorG.t -> dimension:int -> size:int -> step:int -> C.TensorG.t
Sourceval uniform_ : C.TensorG.t -> from:float -> to_:float -> C.TensorG.t
Sourceval unique_consecutive : C.TensorG.t -> return_inverse:bool -> return_counts:bool -> dim:int -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval unique_dim : C.TensorG.t -> dim:int -> sorted:bool -> return_inverse:bool -> return_counts:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval unique_dim_consecutive : C.TensorG.t -> dim:int -> return_inverse:bool -> return_counts:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
Sourceval unsqueeze : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval unsqueeze_ : C.TensorG.t -> dim:int -> C.TensorG.t
Sourceval upsample_bicubic2d : C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_bicubic2d_backward : grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_bicubic2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_bicubic2d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_bilinear2d : C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_bilinear2d_backward : grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_bilinear2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_bilinear2d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_linear1d : C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_linear1d_backward : grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_linear1d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_linear1d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_nearest1d : C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval upsample_nearest1d_backward : grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> C.TensorG.t
Sourceval upsample_nearest1d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> C.TensorG.t
Sourceval upsample_nearest1d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval upsample_nearest2d : C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval upsample_nearest2d_backward : grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> C.TensorG.t
Sourceval upsample_nearest2d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> C.TensorG.t
Sourceval upsample_nearest2d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval upsample_nearest3d : C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval upsample_nearest3d_backward : grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> C.TensorG.t
Sourceval upsample_nearest3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> C.TensorG.t
Sourceval upsample_nearest3d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> C.TensorG.t
Sourceval upsample_trilinear3d : C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_trilinear3d_backward : grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_trilinear3d_backward_out : grad_input:C.TensorG.t -> grad_output:C.TensorG.t -> output_size:int list -> input_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval upsample_trilinear3d_out : out:C.TensorG.t -> C.TensorG.t -> output_size:int list -> align_corners:bool -> C.TensorG.t
Sourceval var : C.TensorG.t -> unbiased:bool -> C.TensorG.t
Sourceval var1 : C.TensorG.t -> dim:int list -> unbiased:bool -> keepdim:bool -> C.TensorG.t
Sourceval var_out : out:C.TensorG.t -> C.TensorG.t -> dim:int list -> unbiased:bool -> keepdim:bool -> C.TensorG.t
Sourceval view : C.TensorG.t -> size:int list -> C.TensorG.t
Sourceval where : condition:C.TensorG.t -> C.TensorG.t -> C.TensorG.t -> C.TensorG.t
Sourceval zero_ : C.TensorG.t -> C.TensorG.t
Sourceval zeros : size:int list -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval zeros_like : C.TensorG.t -> C.TensorG.t
Sourceval zeros_like1 : C.TensorG.t -> options:(Kind.t * Device.t) -> C.TensorG.t
Sourceval zeros_out : out:C.TensorG.t -> size:int list -> C.TensorG.t
OCaml

Innovation. Community. Security.

GitHub Discord Twitter Peertube RSS
About
Changelog Releases Industrial Users Academic Users Why OCaml
Ecosystem
Packages Community Events OCaml Planet Jobs
Resources
Install OCaml Get Started Platform Tools Language Manual Standard Library API Books Exercises Papers OCaml Playground Logo
Policies
Carbon Footprint Governance Privacy Code of Conduct