package torch
Legend:
Library
Module
Module type
Parameter
Class
Class type
module C : sig ... end
val to_tensor_list : unit Ctypes.ptr Ctypes.ptr -> unit Ctypes.ptr list
val __and__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __and__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __iand__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __iand__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __ilshift__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __ilshift__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __ior__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __ior__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __irshift__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __irshift__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __ixor__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __ixor__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __lshift__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __lshift__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __or__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __or__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __rshift__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __rshift__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __xor__ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val __xor__tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _adaptive_avg_pool2d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val _adaptive_avg_pool2d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _adaptive_avg_pool3d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val _adaptive_avg_pool3d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _add_batch_dim : unit Ctypes.ptr -> batch_dim:int -> level:int -> C.TensorG.t
val _add_relu : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _add_relu_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _add_relu_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _add_relu_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _add_relu_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _aminmax : unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val _aminmax_dim : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val _amp_update_scale_ : unit Ctypes.ptr -> growth_tracker:unit Ctypes.ptr -> found_inf:unit Ctypes.ptr -> scale_growth_factor:float -> scale_backoff_factor:float -> growth_interval:int -> C.TensorG.t
val _baddbmm_mkl_ : unit Ctypes.ptr -> batch1:unit Ctypes.ptr -> batch2:unit Ctypes.ptr -> C.TensorG.t
val _cast_byte : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cast_char : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cast_double : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cast_float : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cast_half : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cast_int : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cast_long : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cast_short : unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _cat : C.TensorG.t list -> dim:int -> C.TensorG.t
val _cat_out : out:unit Ctypes.ptr -> C.TensorG.t list -> dim:int -> C.TensorG.t
val _cdist_backward : grad:unit Ctypes.ptr -> x1:unit Ctypes.ptr -> x2:unit Ctypes.ptr -> p:float -> cdist:unit Ctypes.ptr -> C.TensorG.t
val _cholesky_solve_helper : unit Ctypes.ptr -> a:unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val _coalesce : unit Ctypes.ptr -> C.TensorG.t
val _coalesced_ : unit Ctypes.ptr -> coalesced:bool -> C.TensorG.t
val _compute_linear_combination : unit Ctypes.ptr -> coefficients:unit Ctypes.ptr -> C.TensorG.t
val _compute_linear_combination_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> coefficients:unit Ctypes.ptr -> C.TensorG.t
val _conj : unit Ctypes.ptr -> C.TensorG.t
val _conj_physical : unit Ctypes.ptr -> C.TensorG.t
val _conv_depthwise2d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> kernel_size:int list -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> C.TensorG.t
val _conv_depthwise2d_backward : grad_input:unit Ctypes.ptr -> grad_weight:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> weight:unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> C.TensorG.t * C.TensorG.t
val _conv_depthwise2d_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> weight:unit Ctypes.ptr -> kernel_size:int list -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> C.TensorG.t
val _convert_indices_from_coo_to_csr : unit Ctypes.ptr -> size:int -> out_int32:bool -> C.TensorG.t
val _convert_indices_from_coo_to_csr_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> size:int -> out_int32:bool -> C.TensorG.t
val _convolution : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> benchmark:bool -> deterministic:bool -> cudnn_enabled:bool -> allow_tf32:bool -> C.TensorG.t
val _convolution_deprecated : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> benchmark:bool -> deterministic:bool -> cudnn_enabled:bool -> C.TensorG.t
val _convolution_mode : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> C.TensorG.t
val _convolution_nogroup : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> C.TensorG.t
val _copy_from : unit Ctypes.ptr -> dst:unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val _copy_from_and_resize : unit Ctypes.ptr -> dst:unit Ctypes.ptr -> C.TensorG.t
val _ctc_loss : log_probs:unit Ctypes.ptr -> targets:unit Ctypes.ptr -> input_lengths:int list -> target_lengths:int list -> blank:int -> zero_infinity:bool -> C.TensorG.t * C.TensorG.t
val _ctc_loss_backward : grad:unit Ctypes.ptr -> log_probs:unit Ctypes.ptr -> targets:unit Ctypes.ptr -> input_lengths:int list -> target_lengths:int list -> neg_log_likelihood:unit Ctypes.ptr -> log_alpha:unit Ctypes.ptr -> blank:int -> zero_infinity:bool -> C.TensorG.t
val _cudnn_ctc_loss : log_probs:unit Ctypes.ptr -> targets:unit Ctypes.ptr -> input_lengths:int list -> target_lengths:int list -> blank:int -> deterministic:bool -> zero_infinity:bool -> C.TensorG.t * C.TensorG.t
val _cudnn_init_dropout_state : dropout:float -> train:bool -> dropout_seed:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val _cudnn_rnn : unit Ctypes.ptr -> weight:C.TensorG.t list -> weight_stride0:int -> weight_buf:unit Ctypes.ptr option -> hx:unit Ctypes.ptr -> cx:unit Ctypes.ptr option -> mode:int -> hidden_size:int -> proj_size:int -> num_layers:int -> batch_first:bool -> dropout:float -> train:bool -> bidirectional:bool -> batch_sizes:int list -> dropout_state:unit Ctypes.ptr option -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val _cudnn_rnn_flatten_weight : weight_arr:C.TensorG.t list -> weight_stride0:int -> input_size:int -> mode:int -> hidden_size:int -> proj_size:int -> num_layers:int -> batch_first:bool -> bidirectional:bool -> C.TensorG.t
val _det_lu_based_helper : unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val _det_lu_based_helper_backward_helper : det_grad:unit Ctypes.ptr -> det:unit Ctypes.ptr -> unit Ctypes.ptr -> lu:unit Ctypes.ptr -> pivs:unit Ctypes.ptr -> C.TensorG.t
val _dim_arange : like:unit Ctypes.ptr -> dim:int -> C.TensorG.t
val _dirichlet_grad : x:unit Ctypes.ptr -> alpha:unit Ctypes.ptr -> total:unit Ctypes.ptr -> C.TensorG.t
val _embedding_bag : weight:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offsets:unit Ctypes.ptr -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:unit Ctypes.ptr option -> include_last_offset:bool -> padding_idx:int -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val _embedding_bag_backward : grad:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offsets:unit Ctypes.ptr -> offset2bag:unit Ctypes.ptr -> bag_size:unit Ctypes.ptr -> maximum_indices:unit Ctypes.ptr -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:unit Ctypes.ptr option -> padding_idx:int -> C.TensorG.t
val _embedding_bag_dense_backward : grad:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offset2bag:unit Ctypes.ptr -> bag_size:unit Ctypes.ptr -> maximum_indices:unit Ctypes.ptr -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> per_sample_weights:unit Ctypes.ptr option -> padding_idx:int -> C.TensorG.t
val _embedding_bag_forward_only : weight:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offsets:unit Ctypes.ptr -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:unit Ctypes.ptr option -> include_last_offset:bool -> padding_idx:int -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val _embedding_bag_per_sample_weights_backward : grad:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offsets:unit Ctypes.ptr -> offset2bag:unit Ctypes.ptr -> mode:int -> padding_idx:int -> C.TensorG.t
val _embedding_bag_sparse_backward : grad:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offsets:unit Ctypes.ptr -> offset2bag:unit Ctypes.ptr -> bag_size:unit Ctypes.ptr -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> per_sample_weights:unit Ctypes.ptr option -> padding_idx:int -> C.TensorG.t
val _empty_affine_quantized : size:int list -> options:(Kind.packed * Device.t) -> scale:float -> zero_point:int -> C.TensorG.t
val _empty_per_channel_affine_quantized : size:int list -> scales:unit Ctypes.ptr -> zero_points:unit Ctypes.ptr -> axis:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val _euclidean_dist : x1:unit Ctypes.ptr -> x2:unit Ctypes.ptr -> C.TensorG.t
val _fake_quantize_learnable_per_channel_affine : unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> axis:int -> quant_min:int -> quant_max:int -> grad_factor:float -> C.TensorG.t
val _fake_quantize_learnable_per_channel_affine_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> axis:int -> quant_min:int -> quant_max:int -> grad_factor:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val _fake_quantize_learnable_per_tensor_affine : unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> quant_min:int -> quant_max:int -> grad_factor:float -> C.TensorG.t
val _fake_quantize_learnable_per_tensor_affine_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> quant_min:int -> quant_max:int -> grad_factor:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val _fake_quantize_per_tensor_affine_cachemask_tensor_qparams : unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> fake_quant_enabled:unit Ctypes.ptr -> quant_min:int -> quant_max:int -> C.TensorG.t * C.TensorG.t
val _fft_c2c : unit Ctypes.ptr -> dim:int list -> normalization:int -> forward:bool -> C.TensorG.t
val _fft_c2c_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> normalization:int -> forward:bool -> C.TensorG.t
val _fft_c2r : unit Ctypes.ptr -> dim:int list -> normalization:int -> last_dim_size:int -> C.TensorG.t
val _fft_c2r_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> normalization:int -> last_dim_size:int -> C.TensorG.t
val _fft_r2c : unit Ctypes.ptr -> dim:int list -> normalization:int -> onesided:bool -> C.TensorG.t
val _fft_r2c_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> normalization:int -> onesided:bool -> C.TensorG.t
val _fused_dropout : unit Ctypes.ptr -> p:float -> C.TensorG.t * C.TensorG.t
val _fused_moving_avg_obs_fq_helper : unit Ctypes.ptr -> observer_on:unit Ctypes.ptr -> fake_quant_on:unit Ctypes.ptr -> running_min:unit Ctypes.ptr -> running_max:unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> C.TensorG.t * C.TensorG.t
val _fw_primal : unit Ctypes.ptr -> level:int -> C.TensorG.t
val _gather_sparse_backward : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> grad:unit Ctypes.ptr -> C.TensorG.t
val _grid_sampler_2d_cpu_fallback : unit Ctypes.ptr -> grid:unit Ctypes.ptr -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> C.TensorG.t
val _grid_sampler_2d_cpu_fallback_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> grid:unit Ctypes.ptr -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> C.TensorG.t * C.TensorG.t
val _index_copy_ : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> source:unit Ctypes.ptr -> C.TensorG.t
val _index_put_impl_ : unit Ctypes.ptr -> indices:unit Ctypes.ptr option list -> values:unit Ctypes.ptr -> accumulate:bool -> unsafe:bool -> C.TensorG.t
val _indices : unit Ctypes.ptr -> C.TensorG.t
val _inverse_helper : unit Ctypes.ptr -> C.TensorG.t
val _linalg_inv_out_helper_ : unit Ctypes.ptr -> infos_lu:unit Ctypes.ptr -> infos_getri:unit Ctypes.ptr -> C.TensorG.t
val _linalg_qr_helper : unit Ctypes.ptr -> mode:string -> C.TensorG.t * C.TensorG.t
val _log_softmax : unit Ctypes.ptr -> dim:int -> half_to_float:bool -> C.TensorG.t
val _log_softmax_backward_data : grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> dim:int -> unit Ctypes.ptr -> C.TensorG.t
val _log_softmax_backward_data_out : out:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> dim:int -> unit Ctypes.ptr -> C.TensorG.t
val _log_softmax_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> half_to_float:bool -> C.TensorG.t
val _logcumsumexp : unit Ctypes.ptr -> dim:int -> C.TensorG.t
val _logcumsumexp_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t
val _lu_with_info : unit Ctypes.ptr -> pivot:bool -> check_errors:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val _make_dual : primal:unit Ctypes.ptr -> tangent:unit Ctypes.ptr -> level:int -> C.TensorG.t
val _make_per_channel_quantized_tensor : unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> axis:int -> C.TensorG.t
val _make_per_tensor_quantized_tensor : unit Ctypes.ptr -> scale:float -> zero_point:int -> C.TensorG.t
val _masked_scale : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> scale:float -> C.TensorG.t
val _mkldnn_reshape : unit Ctypes.ptr -> shape:int list -> C.TensorG.t
val _mkldnn_transpose : unit Ctypes.ptr -> dim0:int -> dim1:int -> C.TensorG.t
val _mkldnn_transpose_ : unit Ctypes.ptr -> dim0:int -> dim1:int -> C.TensorG.t
val _neg_view : unit Ctypes.ptr -> C.TensorG.t
val _nnpack_spatial_convolution : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> padding:int list -> stride:int list -> C.TensorG.t
val _nnpack_spatial_convolution_backward_input : unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> C.TensorG.t
val _nnpack_spatial_convolution_backward_weight : unit Ctypes.ptr -> weightsize:int list -> grad_output:unit Ctypes.ptr -> padding:int list -> C.TensorG.t
val _pack_padded_sequence : unit Ctypes.ptr -> lengths:unit Ctypes.ptr -> batch_first:bool -> C.TensorG.t * C.TensorG.t
val _pack_padded_sequence_backward : grad:unit Ctypes.ptr -> input_size:int list -> batch_sizes:unit Ctypes.ptr -> batch_first:bool -> C.TensorG.t
val _pad_packed_sequence : data:unit Ctypes.ptr -> batch_sizes:unit Ctypes.ptr -> batch_first:bool -> padding_value:unit Ctypes.ptr -> total_length:int -> C.TensorG.t * C.TensorG.t
val _pdist_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> p:float -> pdist:unit Ctypes.ptr -> C.TensorG.t
val _pin_memory : unit Ctypes.ptr -> device:Device.t -> C.TensorG.t
val _remove_batch_dim : unit Ctypes.ptr -> level:int -> batch_size:int -> out_dim:int -> C.TensorG.t
val _reshape_alias : unit Ctypes.ptr -> size:int list -> stride:int list -> C.TensorG.t
val _reshape_from_tensor : unit Ctypes.ptr -> shape:unit Ctypes.ptr -> C.TensorG.t
val _rowwise_prune : weight:unit Ctypes.ptr -> mask:unit Ctypes.ptr -> compressed_indices_dtype:Kind.packed -> C.TensorG.t * C.TensorG.t
val _s_where : condition:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _sample_dirichlet : unit Ctypes.ptr -> C.TensorG.t
val _saturate_weight_to_fp16 : weight:unit Ctypes.ptr -> C.TensorG.t
val _segment_reduce_backward : grad:unit Ctypes.ptr -> output:unit Ctypes.ptr -> data:unit Ctypes.ptr -> reduce:string -> lengths:unit Ctypes.ptr option -> axis:int -> C.TensorG.t
val _shape_as_tensor : unit Ctypes.ptr -> C.TensorG.t
val _sobol_engine_draw : quasi:unit Ctypes.ptr -> n:int -> sobolstate:unit Ctypes.ptr -> dimension:int -> num_generated:int -> dtype:Kind.packed -> C.TensorG.t * C.TensorG.t
val _sobol_engine_ff_ : unit Ctypes.ptr -> n:int -> sobolstate:unit Ctypes.ptr -> dimension:int -> num_generated:int -> C.TensorG.t
val _sobol_engine_initialize_state_ : unit Ctypes.ptr -> dimension:int -> C.TensorG.t
val _sobol_engine_scramble_ : unit Ctypes.ptr -> ltm:unit Ctypes.ptr -> dimension:int -> C.TensorG.t
val _softmax : unit Ctypes.ptr -> dim:int -> half_to_float:bool -> C.TensorG.t
val _softmax_backward_data : grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> dim:int -> unit Ctypes.ptr -> C.TensorG.t
val _softmax_backward_data_out : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> dim:int -> unit Ctypes.ptr -> C.TensorG.t
val _softmax_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> half_to_float:bool -> C.TensorG.t
val _solve_helper : unit Ctypes.ptr -> a:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val _sparse_addmm : unit Ctypes.ptr -> sparse:unit Ctypes.ptr -> dense:unit Ctypes.ptr -> C.TensorG.t
val _sparse_coo_tensor_unsafe : indices:unit Ctypes.ptr -> values:unit Ctypes.ptr -> size:int list -> options:(Kind.packed * Device.t) -> C.TensorG.t
val _sparse_coo_tensor_with_dims : sparse_dim:int -> dense_dim:int -> size:int list -> options:(Kind.packed * Device.t) -> C.TensorG.t
val _sparse_coo_tensor_with_dims_and_tensors : sparse_dim:int -> dense_dim:int -> size:int list -> indices:unit Ctypes.ptr -> values:unit Ctypes.ptr -> options:(Kind.packed * Device.t) -> C.TensorG.t
val _sparse_csr_tensor_unsafe : crow_indices:unit Ctypes.ptr -> col_indices:unit Ctypes.ptr -> values:unit Ctypes.ptr -> size:int list -> options:(Kind.packed * Device.t) -> C.TensorG.t
val _sparse_log_softmax : unit Ctypes.ptr -> dim:int -> half_to_float:bool -> C.TensorG.t
val _sparse_log_softmax_backward_data : grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> dim:int -> unit Ctypes.ptr -> C.TensorG.t
val _sparse_log_softmax_int : unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val _sparse_mask_helper : tr:unit Ctypes.ptr -> mask_indices:unit Ctypes.ptr -> C.TensorG.t
val _sparse_mm : sparse:unit Ctypes.ptr -> dense:unit Ctypes.ptr -> C.TensorG.t
val _sparse_softmax : unit Ctypes.ptr -> dim:int -> half_to_float:bool -> C.TensorG.t
val _sparse_softmax_backward_data : grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> dim:int -> unit Ctypes.ptr -> C.TensorG.t
val _sparse_softmax_int : unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val _sparse_sparse_matmul : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _sparse_sum : unit Ctypes.ptr -> C.TensorG.t
val _sparse_sum_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> C.TensorG.t
val _sparse_sum_dim : unit Ctypes.ptr -> dim:int list -> C.TensorG.t
val _sparse_sum_dim_dtype : unit Ctypes.ptr -> dim:int list -> dtype:Kind.packed -> C.TensorG.t
val _sparse_sum_dtype : unit Ctypes.ptr -> dtype:Kind.packed -> C.TensorG.t
val _stack : C.TensorG.t list -> dim:int -> C.TensorG.t
val _stack_out : out:unit Ctypes.ptr -> C.TensorG.t list -> dim:int -> C.TensorG.t
val _standard_gamma : unit Ctypes.ptr -> C.TensorG.t
val _standard_gamma_grad : unit Ctypes.ptr -> output:unit Ctypes.ptr -> C.TensorG.t
val _svd_helper : unit Ctypes.ptr -> some:bool -> compute_uv:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val _symeig_helper : unit Ctypes.ptr -> eigenvectors:bool -> upper:bool -> C.TensorG.t * C.TensorG.t
val _test_ambiguous_defaults : dummy:unit Ctypes.ptr -> a:int -> b:int -> C.TensorG.t
val _test_ambiguous_defaults_b : dummy:unit Ctypes.ptr -> a:int -> b:string -> C.TensorG.t
val _test_optional_filled_intlist : values:unit Ctypes.ptr -> addends:int list -> C.TensorG.t
val _test_optional_intlist : values:unit Ctypes.ptr -> addends:int list -> C.TensorG.t
val _test_serialization_subcmul : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val _test_string_default : dummy:unit Ctypes.ptr -> a:string -> b:string -> C.TensorG.t
val _thnn_differentiable_gru_cell_backward : grad_hy:unit Ctypes.ptr -> input_gates:unit Ctypes.ptr -> hidden_gates:unit Ctypes.ptr -> hx:unit Ctypes.ptr -> input_bias:unit Ctypes.ptr option -> hidden_bias:unit Ctypes.ptr option -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val _thnn_differentiable_lstm_cell_backward : grad_hy:unit Ctypes.ptr option -> grad_cy:unit Ctypes.ptr option -> input_gates:unit Ctypes.ptr -> hidden_gates:unit Ctypes.ptr -> input_bias:unit Ctypes.ptr option -> hidden_bias:unit Ctypes.ptr option -> cx:unit Ctypes.ptr -> cy:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val _thnn_fused_gru_cell : input_gates:unit Ctypes.ptr -> hidden_gates:unit Ctypes.ptr -> hx:unit Ctypes.ptr -> input_bias:unit Ctypes.ptr option -> hidden_bias:unit Ctypes.ptr option -> C.TensorG.t * C.TensorG.t
val _thnn_fused_gru_cell_backward : grad_hy:unit Ctypes.ptr -> workspace:unit Ctypes.ptr -> has_bias:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val _thnn_fused_lstm_cell : input_gates:unit Ctypes.ptr -> hidden_gates:unit Ctypes.ptr -> cx:unit Ctypes.ptr -> input_bias:unit Ctypes.ptr option -> hidden_bias:unit Ctypes.ptr option -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val _thnn_fused_lstm_cell_backward : grad_hy:unit Ctypes.ptr option -> grad_cy:unit Ctypes.ptr option -> cx:unit Ctypes.ptr -> cy:unit Ctypes.ptr -> workspace:unit Ctypes.ptr -> has_bias:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val _to_copy : unit Ctypes.ptr -> options:(Kind.packed * Device.t) -> non_blocking:bool -> C.TensorG.t
val _to_cpu : C.TensorG.t list -> unit Ctypes.ptr list
val _trilinear : i1:unit Ctypes.ptr -> i2:unit Ctypes.ptr -> i3:unit Ctypes.ptr -> expand1:int list -> expand2:int list -> expand3:int list -> sumdim:int list -> unroll_dim:int -> C.TensorG.t
val _unique : unit Ctypes.ptr -> sorted:bool -> return_inverse:bool -> C.TensorG.t * C.TensorG.t
val _unique2 : unit Ctypes.ptr -> sorted:bool -> return_inverse:bool -> return_counts:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val _unpack_dual : dual:unit Ctypes.ptr -> level:int -> C.TensorG.t * C.TensorG.t
val _unsafe_view : unit Ctypes.ptr -> size:int list -> C.TensorG.t
val _values : unit Ctypes.ptr -> C.TensorG.t
val _weight_norm : v:unit Ctypes.ptr -> g:unit Ctypes.ptr -> dim:int -> C.TensorG.t
val _weight_norm_cuda_interface : v:unit Ctypes.ptr -> g:unit Ctypes.ptr -> dim:int -> C.TensorG.t * C.TensorG.t
val _weight_norm_cuda_interface_backward : grad_w:unit Ctypes.ptr -> saved_v:unit Ctypes.ptr -> saved_g:unit Ctypes.ptr -> saved_norms:unit Ctypes.ptr -> dim:int -> C.TensorG.t * C.TensorG.t
val _weight_norm_differentiable_backward : grad_w:unit Ctypes.ptr -> saved_v:unit Ctypes.ptr -> saved_g:unit Ctypes.ptr -> saved_norms:unit Ctypes.ptr -> dim:int -> C.TensorG.t * C.TensorG.t
val abs : unit Ctypes.ptr -> C.TensorG.t
val abs_ : unit Ctypes.ptr -> C.TensorG.t
val abs_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val absolute : unit Ctypes.ptr -> C.TensorG.t
val absolute_ : unit Ctypes.ptr -> C.TensorG.t
val absolute_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val acos : unit Ctypes.ptr -> C.TensorG.t
val acos_ : unit Ctypes.ptr -> C.TensorG.t
val acos_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val acosh : unit Ctypes.ptr -> C.TensorG.t
val acosh_ : unit Ctypes.ptr -> C.TensorG.t
val acosh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val adaptive_avg_pool1d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val adaptive_avg_pool2d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val adaptive_avg_pool2d_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val adaptive_avg_pool3d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val adaptive_avg_pool3d_backward : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val adaptive_avg_pool3d_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val adaptive_max_pool1d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t * C.TensorG.t
val adaptive_max_pool2d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t * C.TensorG.t
val adaptive_max_pool2d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> C.TensorG.t
val adaptive_max_pool2d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> C.TensorG.t
val adaptive_max_pool2d_out : out:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> output_size:int list -> C.TensorG.t * C.TensorG.t
val adaptive_max_pool3d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t * C.TensorG.t
val adaptive_max_pool3d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> C.TensorG.t
val adaptive_max_pool3d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> C.TensorG.t
val adaptive_max_pool3d_out : out:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> output_size:int list -> C.TensorG.t * C.TensorG.t
val add : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val add_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val add_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val add_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val add_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val addbmm : unit Ctypes.ptr -> batch1:unit Ctypes.ptr -> batch2:unit Ctypes.ptr -> C.TensorG.t
val addbmm_ : unit Ctypes.ptr -> batch1:unit Ctypes.ptr -> batch2:unit Ctypes.ptr -> C.TensorG.t
val addbmm_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> batch1:unit Ctypes.ptr -> batch2:unit Ctypes.ptr -> C.TensorG.t
val addcdiv : unit Ctypes.ptr -> tensor1:unit Ctypes.ptr -> tensor2:unit Ctypes.ptr -> C.TensorG.t
val addcdiv_ : unit Ctypes.ptr -> tensor1:unit Ctypes.ptr -> tensor2:unit Ctypes.ptr -> C.TensorG.t
val addcdiv_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> tensor1:unit Ctypes.ptr -> tensor2:unit Ctypes.ptr -> C.TensorG.t
val addcmul : unit Ctypes.ptr -> tensor1:unit Ctypes.ptr -> tensor2:unit Ctypes.ptr -> C.TensorG.t
val addcmul_ : unit Ctypes.ptr -> tensor1:unit Ctypes.ptr -> tensor2:unit Ctypes.ptr -> C.TensorG.t
val addcmul_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> tensor1:unit Ctypes.ptr -> tensor2:unit Ctypes.ptr -> C.TensorG.t
val addmm : unit Ctypes.ptr -> mat1:unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val addmm_ : unit Ctypes.ptr -> mat1:unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val addmm_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> mat1:unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val addmv : unit Ctypes.ptr -> mat:unit Ctypes.ptr -> vec:unit Ctypes.ptr -> C.TensorG.t
val addmv_ : unit Ctypes.ptr -> mat:unit Ctypes.ptr -> vec:unit Ctypes.ptr -> C.TensorG.t
val addmv_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> mat:unit Ctypes.ptr -> vec:unit Ctypes.ptr -> C.TensorG.t
val addr : unit Ctypes.ptr -> vec1:unit Ctypes.ptr -> vec2:unit Ctypes.ptr -> C.TensorG.t
val addr_ : unit Ctypes.ptr -> vec1:unit Ctypes.ptr -> vec2:unit Ctypes.ptr -> C.TensorG.t
val addr_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> vec1:unit Ctypes.ptr -> vec2:unit Ctypes.ptr -> C.TensorG.t
val affine_grid_generator : theta:unit Ctypes.ptr -> size:int list -> align_corners:bool -> C.TensorG.t
val affine_grid_generator_backward : grad:unit Ctypes.ptr -> size:int list -> align_corners:bool -> C.TensorG.t
val alias : unit Ctypes.ptr -> C.TensorG.t
val align_as : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val align_tensors : C.TensorG.t list -> unit Ctypes.ptr list
val all : unit Ctypes.ptr -> C.TensorG.t
val all_all_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val all_dim : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val all_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val alpha_dropout : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val alpha_dropout_ : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val amax : unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val amax_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val amin : unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val amin_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val aminmax : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val aminmax_out : min:unit Ctypes.ptr -> max:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val angle : unit Ctypes.ptr -> C.TensorG.t
val angle_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val any : unit Ctypes.ptr -> C.TensorG.t
val any_all_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val any_dim : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val any_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val arange : end_:unit Ctypes.ptr -> options:(Kind.packed * Device.t) -> C.TensorG.t
val arange_out : out:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> C.TensorG.t
val arange_start : start:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> options:(Kind.packed * Device.t) -> C.TensorG.t
val arange_start_out : out:unit Ctypes.ptr -> start:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> C.TensorG.t
val arange_start_step : start:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> step:unit Ctypes.ptr -> options:(Kind.packed * Device.t) -> C.TensorG.t
val arccos : unit Ctypes.ptr -> C.TensorG.t
val arccos_ : unit Ctypes.ptr -> C.TensorG.t
val arccos_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val arccosh : unit Ctypes.ptr -> C.TensorG.t
val arccosh_ : unit Ctypes.ptr -> C.TensorG.t
val arccosh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val arcsin : unit Ctypes.ptr -> C.TensorG.t
val arcsin_ : unit Ctypes.ptr -> C.TensorG.t
val arcsin_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val arcsinh : unit Ctypes.ptr -> C.TensorG.t
val arcsinh_ : unit Ctypes.ptr -> C.TensorG.t
val arcsinh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val arctan : unit Ctypes.ptr -> C.TensorG.t
val arctan_ : unit Ctypes.ptr -> C.TensorG.t
val arctan_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val arctanh : unit Ctypes.ptr -> C.TensorG.t
val arctanh_ : unit Ctypes.ptr -> C.TensorG.t
val arctanh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val argmax : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val argmax_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val argmin : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val argmin_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t
val argsort : unit Ctypes.ptr -> dim:int -> descending:bool -> C.TensorG.t
val as_strided : unit Ctypes.ptr -> size:int list -> stride:int list -> storage_offset:int -> C.TensorG.t
val as_strided_ : unit Ctypes.ptr -> size:int list -> stride:int list -> storage_offset:int -> C.TensorG.t
val asin : unit Ctypes.ptr -> C.TensorG.t
val asin_ : unit Ctypes.ptr -> C.TensorG.t
val asin_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val asinh : unit Ctypes.ptr -> C.TensorG.t
val asinh_ : unit Ctypes.ptr -> C.TensorG.t
val asinh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val atan : unit Ctypes.ptr -> C.TensorG.t
val atan2 : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val atan2_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val atan2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val atan_ : unit Ctypes.ptr -> C.TensorG.t
val atan_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val atanh : unit Ctypes.ptr -> C.TensorG.t
val atanh_ : unit Ctypes.ptr -> C.TensorG.t
val atanh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val atleast_1d : unit Ctypes.ptr -> C.TensorG.t
val atleast_1d_sequence : C.TensorG.t list -> unit Ctypes.ptr list
val atleast_2d : unit Ctypes.ptr -> C.TensorG.t
val atleast_2d_sequence : C.TensorG.t list -> unit Ctypes.ptr list
val atleast_3d : unit Ctypes.ptr -> C.TensorG.t
val atleast_3d_sequence : C.TensorG.t list -> unit Ctypes.ptr list
val avg_pool1d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> C.TensorG.t
val avg_pool2d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val avg_pool2d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val avg_pool2d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val avg_pool2d_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val avg_pool3d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val avg_pool3d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val avg_pool3d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val avg_pool3d_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> C.TensorG.t
val baddbmm : unit Ctypes.ptr -> batch1:unit Ctypes.ptr -> batch2:unit Ctypes.ptr -> C.TensorG.t
val baddbmm_ : unit Ctypes.ptr -> batch1:unit Ctypes.ptr -> batch2:unit Ctypes.ptr -> C.TensorG.t
val baddbmm_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> batch1:unit Ctypes.ptr -> batch2:unit Ctypes.ptr -> C.TensorG.t
val bartlett_window : window_length:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val bartlett_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> C.TensorG.t
val batch_norm : unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> bias:unit Ctypes.ptr option -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> training:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> C.TensorG.t
val batch_norm_backward_elemt : grad_out:unit Ctypes.ptr -> unit Ctypes.ptr -> mean:unit Ctypes.ptr -> invstd:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> mean_dy:unit Ctypes.ptr -> mean_dy_xmu:unit Ctypes.ptr -> count:unit Ctypes.ptr -> C.TensorG.t
val batch_norm_backward_reduce : grad_out:unit Ctypes.ptr -> unit Ctypes.ptr -> mean:unit Ctypes.ptr -> invstd:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> input_g:bool -> weight_g:bool -> bias_g:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val batch_norm_elemt : unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> bias:unit Ctypes.ptr option -> mean:unit Ctypes.ptr -> invstd:unit Ctypes.ptr -> eps:float -> C.TensorG.t
val batch_norm_elemt_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> bias:unit Ctypes.ptr option -> mean:unit Ctypes.ptr -> invstd:unit Ctypes.ptr -> eps:float -> C.TensorG.t
val batch_norm_gather_stats : unit Ctypes.ptr -> mean:unit Ctypes.ptr -> invstd:unit Ctypes.ptr -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> momentum:float -> eps:float -> count:int -> C.TensorG.t * C.TensorG.t
val batch_norm_gather_stats_with_counts : unit Ctypes.ptr -> mean:unit Ctypes.ptr -> invstd:unit Ctypes.ptr -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> momentum:float -> eps:float -> counts:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val batch_norm_stats : unit Ctypes.ptr -> eps:float -> C.TensorG.t * C.TensorG.t
val batch_norm_update_stats : unit Ctypes.ptr -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> momentum:float -> C.TensorG.t * C.TensorG.t
val bernoulli : unit Ctypes.ptr -> C.TensorG.t
val bernoulli_ : unit Ctypes.ptr -> p:unit Ctypes.ptr -> C.TensorG.t
val bernoulli_float_ : unit Ctypes.ptr -> p:float -> C.TensorG.t
val bernoulli_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bernoulli_p : unit Ctypes.ptr -> p:float -> C.TensorG.t
val bilinear : input1:unit Ctypes.ptr -> input2:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> C.TensorG.t
val binary_cross_entropy : unit Ctypes.ptr -> target:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> reduction:Reduction.t -> C.TensorG.t
val binary_cross_entropy_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> reduction:Reduction.t -> C.TensorG.t
val binary_cross_entropy_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> reduction:Reduction.t -> C.TensorG.t
val binary_cross_entropy_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> reduction:Reduction.t -> C.TensorG.t
val binary_cross_entropy_with_logits : unit Ctypes.ptr -> target:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> pos_weight:unit Ctypes.ptr option -> reduction:Reduction.t -> C.TensorG.t
val binary_cross_entropy_with_logits_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> pos_weight:unit Ctypes.ptr option -> reduction:Reduction.t -> C.TensorG.t
val bincount : unit Ctypes.ptr -> weights:unit Ctypes.ptr option -> minlength:int -> C.TensorG.t
val binomial : count:unit Ctypes.ptr -> prob:unit Ctypes.ptr -> C.TensorG.t
val bitwise_and : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_and_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_and_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_and_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_and_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_and_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_left_shift : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_left_shift_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_left_shift_scalar_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_left_shift_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_left_shift_tensor_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_left_shift_tensor_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_left_shift_tensor_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_not : unit Ctypes.ptr -> C.TensorG.t
val bitwise_not_ : unit Ctypes.ptr -> C.TensorG.t
val bitwise_not_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_or : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_or_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_or_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_or_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_or_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_or_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_right_shift : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_right_shift_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_right_shift_scalar_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_right_shift_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_right_shift_tensor_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_right_shift_tensor_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_right_shift_tensor_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_xor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_xor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_xor_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_xor_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_xor_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val bitwise_xor_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val blackman_window : window_length:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val blackman_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> C.TensorG.t
val block_diag : C.TensorG.t list -> C.TensorG.t
val bmm : unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val bmm_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val broadcast_tensors : C.TensorG.t list -> unit Ctypes.ptr list
val broadcast_to : unit Ctypes.ptr -> size:int list -> C.TensorG.t
val bucketize : unit Ctypes.ptr -> boundaries:unit Ctypes.ptr -> out_int32:bool -> right:bool -> C.TensorG.t
val bucketize_scalar : unit Ctypes.ptr -> boundaries:unit Ctypes.ptr -> out_int32:bool -> right:bool -> C.TensorG.t
val bucketize_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> boundaries:unit Ctypes.ptr -> out_int32:bool -> right:bool -> C.TensorG.t
val cartesian_prod : C.TensorG.t list -> C.TensorG.t
val cat : C.TensorG.t list -> dim:int -> C.TensorG.t
val cat_out : out:unit Ctypes.ptr -> C.TensorG.t list -> dim:int -> C.TensorG.t
val cauchy_ : unit Ctypes.ptr -> median:float -> sigma:float -> C.TensorG.t
val cdist : x1:unit Ctypes.ptr -> x2:unit Ctypes.ptr -> p:float -> compute_mode:int -> C.TensorG.t
val ceil : unit Ctypes.ptr -> C.TensorG.t
val ceil_ : unit Ctypes.ptr -> C.TensorG.t
val ceil_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val celu : unit Ctypes.ptr -> C.TensorG.t
val celu_ : unit Ctypes.ptr -> C.TensorG.t
val chain_matmul : matrices:C.TensorG.t list -> C.TensorG.t
val chain_matmul_out : out:unit Ctypes.ptr -> matrices:C.TensorG.t list -> C.TensorG.t
val channel_shuffle : unit Ctypes.ptr -> groups:int -> C.TensorG.t
val cholesky : unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val cholesky_inverse : unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val cholesky_inverse_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val cholesky_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val cholesky_solve : unit Ctypes.ptr -> input2:unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val cholesky_solve_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> input2:unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val choose_qparams_optimized : unit Ctypes.ptr -> numel:int -> n_bins:int -> ratio:float -> bit_width:int -> C.TensorG.t * C.TensorG.t
val chunk : unit Ctypes.ptr -> chunks:int -> dim:int -> unit Ctypes.ptr list
val clamp : unit Ctypes.ptr -> min:unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_ : unit Ctypes.ptr -> min:unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_max : unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_max_ : unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_max_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_max_tensor : unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_max_tensor_ : unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_max_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_min : unit Ctypes.ptr -> min:unit Ctypes.ptr -> C.TensorG.t
val clamp_min_ : unit Ctypes.ptr -> min:unit Ctypes.ptr -> C.TensorG.t
val clamp_min_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> min:unit Ctypes.ptr -> C.TensorG.t
val clamp_min_tensor : unit Ctypes.ptr -> min:unit Ctypes.ptr -> C.TensorG.t
val clamp_min_tensor_ : unit Ctypes.ptr -> min:unit Ctypes.ptr -> C.TensorG.t
val clamp_min_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> min:unit Ctypes.ptr -> C.TensorG.t
val clamp_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> min:unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clamp_tensor : unit Ctypes.ptr -> min:unit Ctypes.ptr option -> max:unit Ctypes.ptr option -> C.TensorG.t
val clamp_tensor_ : unit Ctypes.ptr -> min:unit Ctypes.ptr option -> max:unit Ctypes.ptr option -> C.TensorG.t
val clamp_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> min:unit Ctypes.ptr option -> max:unit Ctypes.ptr option -> C.TensorG.t
val clip : unit Ctypes.ptr -> min:unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clip_ : unit Ctypes.ptr -> min:unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clip_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> min:unit Ctypes.ptr -> max:unit Ctypes.ptr -> C.TensorG.t
val clip_tensor : unit Ctypes.ptr -> min:unit Ctypes.ptr option -> max:unit Ctypes.ptr option -> C.TensorG.t
val clip_tensor_ : unit Ctypes.ptr -> min:unit Ctypes.ptr option -> max:unit Ctypes.ptr option -> C.TensorG.t
val clip_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> min:unit Ctypes.ptr option -> max:unit Ctypes.ptr option -> C.TensorG.t
val clone : unit Ctypes.ptr -> C.TensorG.t
val coalesce : unit Ctypes.ptr -> C.TensorG.t
val col2im : unit Ctypes.ptr -> output_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val col2im_backward : grad_output:unit Ctypes.ptr -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val col2im_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val col2im_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> output_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val col_indices : unit Ctypes.ptr -> C.TensorG.t
val column_stack : C.TensorG.t list -> C.TensorG.t
val column_stack_out : out:unit Ctypes.ptr -> C.TensorG.t list -> C.TensorG.t
val combinations : unit Ctypes.ptr -> r:int -> with_replacement:bool -> C.TensorG.t
val complex : real:unit Ctypes.ptr -> imag:unit Ctypes.ptr -> C.TensorG.t
val complex_out : out:unit Ctypes.ptr -> real:unit Ctypes.ptr -> imag:unit Ctypes.ptr -> C.TensorG.t
val concat : C.TensorG.t list -> dim:int -> C.TensorG.t
val concat_out : out:unit Ctypes.ptr -> C.TensorG.t list -> dim:int -> C.TensorG.t
val conj : unit Ctypes.ptr -> C.TensorG.t
val conj_physical : unit Ctypes.ptr -> C.TensorG.t
val conj_physical_ : unit Ctypes.ptr -> C.TensorG.t
val conj_physical_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val constant_pad_nd : unit Ctypes.ptr -> pad:int list -> C.TensorG.t
val contiguous : unit Ctypes.ptr -> C.TensorG.t
val conv1d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
val conv1d_padding : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> C.TensorG.t
val conv2d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
val conv2d_padding : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> C.TensorG.t
val conv3d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
val conv3d_padding : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> C.TensorG.t
val conv_depthwise3d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> kernel_size:int list -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> C.TensorG.t
val conv_depthwise3d_backward : grad_input:unit Ctypes.ptr -> grad_weight:unit Ctypes.ptr -> grad_bias:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> weight:unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val conv_tbc : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr -> pad:int -> C.TensorG.t
val conv_tbc_backward : unit Ctypes.ptr -> unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr -> pad:int -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val conv_transpose1d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> C.TensorG.t
val conv_transpose2d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> C.TensorG.t
val conv_transpose3d : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> C.TensorG.t
val convolution : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> C.TensorG.t
val convolution_overrideable : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> C.TensorG.t
val copy_sparse_to_sparse_ : unit Ctypes.ptr -> src:unit Ctypes.ptr -> non_blocking:bool -> C.TensorG.t
val copysign : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val copysign_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val copysign_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val copysign_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val copysign_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val copysign_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val corrcoef : unit Ctypes.ptr -> C.TensorG.t
val cos : unit Ctypes.ptr -> C.TensorG.t
val cos_ : unit Ctypes.ptr -> C.TensorG.t
val cos_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val cosh : unit Ctypes.ptr -> C.TensorG.t
val cosh_ : unit Ctypes.ptr -> C.TensorG.t
val cosh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val cosine_embedding_loss : input1:unit Ctypes.ptr -> input2:unit Ctypes.ptr -> target:unit Ctypes.ptr -> margin:float -> reduction:Reduction.t -> C.TensorG.t
val cosine_similarity : x1:unit Ctypes.ptr -> x2:unit Ctypes.ptr -> dim:int -> eps:float -> C.TensorG.t
val cov : unit Ctypes.ptr -> correction:int -> fweights:unit Ctypes.ptr option -> aweights:unit Ctypes.ptr option -> C.TensorG.t
val cross : unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t
val cross_entropy_loss : unit Ctypes.ptr -> target:unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> reduction:Reduction.t -> ignore_index:int -> label_smoothing:float -> C.TensorG.t
val cross_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t
val crow_indices : unit Ctypes.ptr -> C.TensorG.t
val ctc_loss : log_probs:unit Ctypes.ptr -> targets:unit Ctypes.ptr -> input_lengths:int list -> target_lengths:int list -> blank:int -> reduction:Reduction.t -> zero_infinity:bool -> C.TensorG.t
val ctc_loss_tensor : log_probs:unit Ctypes.ptr -> targets:unit Ctypes.ptr -> input_lengths:unit Ctypes.ptr -> target_lengths:unit Ctypes.ptr -> blank:int -> reduction:Reduction.t -> zero_infinity:bool -> C.TensorG.t
val cudnn_affine_grid_generator : theta:unit Ctypes.ptr -> n:int -> c:int -> h:int -> w:int -> C.TensorG.t
val cudnn_affine_grid_generator_backward : grad:unit Ctypes.ptr -> n:int -> c:int -> h:int -> w:int -> C.TensorG.t
val cudnn_batch_norm : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> training:bool -> exponential_average_factor:float -> epsilon:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val cudnn_batch_norm_backward : unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> save_mean:unit Ctypes.ptr option -> save_var:unit Ctypes.ptr option -> epsilon:float -> reservespace:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val cudnn_convolution : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> C.TensorG.t
val cudnn_convolution_add_relu : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> z:unit Ctypes.ptr -> alpha:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
val cudnn_convolution_backward_input : self_size:int list -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> C.TensorG.t
val cudnn_convolution_backward_weight : weight_size:int list -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> C.TensorG.t
val cudnn_convolution_deprecated : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val cudnn_convolution_deprecated2 : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val cudnn_convolution_relu : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> C.TensorG.t
val cudnn_convolution_transpose : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> C.TensorG.t
val cudnn_convolution_transpose_backward_input : grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> C.TensorG.t
val cudnn_convolution_transpose_backward_weight : weight_size:int list -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> C.TensorG.t
val cudnn_convolution_transpose_deprecated : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val cudnn_convolution_transpose_deprecated2 : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val cudnn_grid_sampler : unit Ctypes.ptr -> grid:unit Ctypes.ptr -> C.TensorG.t
val cudnn_grid_sampler_backward : unit Ctypes.ptr -> grid:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val cummax : unit Ctypes.ptr -> dim:int -> C.TensorG.t * C.TensorG.t
val cummax_out : values:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t * C.TensorG.t
val cummaxmin_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> dim:int -> C.TensorG.t
val cummin : unit Ctypes.ptr -> dim:int -> C.TensorG.t * C.TensorG.t
val cummin_out : values:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t * C.TensorG.t
val cumprod : unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val cumprod_ : unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val cumprod_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> output:unit Ctypes.ptr -> C.TensorG.t
val cumprod_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val cumsum : unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val cumsum_ : unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val cumsum_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val cumulative_trapezoid : y:unit Ctypes.ptr -> dim:int -> C.TensorG.t
val cumulative_trapezoid_x : y:unit Ctypes.ptr -> x:unit Ctypes.ptr -> dim:int -> C.TensorG.t
val data : unit Ctypes.ptr -> C.TensorG.t
val deg2rad : unit Ctypes.ptr -> C.TensorG.t
val deg2rad_ : unit Ctypes.ptr -> C.TensorG.t
val deg2rad_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val dequantize : unit Ctypes.ptr -> C.TensorG.t
val dequantize_tensors : C.TensorG.t list -> unit Ctypes.ptr list
val det : unit Ctypes.ptr -> C.TensorG.t
val detach : unit Ctypes.ptr -> C.TensorG.t
val detach_ : unit Ctypes.ptr -> C.TensorG.t
val diag : unit Ctypes.ptr -> diagonal:int -> C.TensorG.t
val diag_backward : grad:unit Ctypes.ptr -> input_sizes:int list -> diagonal:int -> C.TensorG.t
val diag_embed : unit Ctypes.ptr -> offset:int -> dim1:int -> dim2:int -> C.TensorG.t
val diag_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> diagonal:int -> C.TensorG.t
val diagflat : unit Ctypes.ptr -> offset:int -> C.TensorG.t
val diagonal : unit Ctypes.ptr -> offset:int -> dim1:int -> dim2:int -> C.TensorG.t
val diagonal_backward : grad_output:unit Ctypes.ptr -> input_sizes:int list -> offset:int -> dim1:int -> dim2:int -> C.TensorG.t
val diff : unit Ctypes.ptr -> n:int -> dim:int -> prepend:unit Ctypes.ptr option -> append:unit Ctypes.ptr option -> C.TensorG.t
val diff_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> dim:int -> prepend:unit Ctypes.ptr option -> append:unit Ctypes.ptr option -> C.TensorG.t
val digamma : unit Ctypes.ptr -> C.TensorG.t
val digamma_ : unit Ctypes.ptr -> C.TensorG.t
val digamma_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val dist : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val div : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val div_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val div_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val div_out_mode : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val div_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val div_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val div_scalar_mode : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val div_scalar_mode_ : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val div_tensor_mode : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val div_tensor_mode_ : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val divide : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val divide_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val divide_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val divide_out_mode : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val divide_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val divide_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val divide_scalar_mode : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val divide_scalar_mode_ : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val divide_tensor_mode : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val divide_tensor_mode_ : unit Ctypes.ptr -> unit Ctypes.ptr -> rounding_mode:string -> C.TensorG.t
val dot : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val dot_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val dropout : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val dropout_ : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val dsplit : unit Ctypes.ptr -> sections:int -> unit Ctypes.ptr list
val dsplit_array : unit Ctypes.ptr -> indices:int list -> unit Ctypes.ptr list
val dstack : C.TensorG.t list -> C.TensorG.t
val dstack_out : out:unit Ctypes.ptr -> C.TensorG.t list -> C.TensorG.t
val eig : unit Ctypes.ptr -> eigenvectors:bool -> C.TensorG.t * C.TensorG.t
val eig_e : e:unit Ctypes.ptr -> v:unit Ctypes.ptr -> unit Ctypes.ptr -> eigenvectors:bool -> C.TensorG.t * C.TensorG.t
val einsum : equation:string -> C.TensorG.t list -> C.TensorG.t
val elu : unit Ctypes.ptr -> C.TensorG.t
val elu_ : unit Ctypes.ptr -> C.TensorG.t
val elu_backward : grad_output:unit Ctypes.ptr -> alpha:unit Ctypes.ptr -> scale:unit Ctypes.ptr -> input_scale:unit Ctypes.ptr -> is_result:bool -> self_or_result:unit Ctypes.ptr -> C.TensorG.t
val elu_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> alpha:unit Ctypes.ptr -> scale:unit Ctypes.ptr -> input_scale:unit Ctypes.ptr -> is_result:bool -> self_or_result:unit Ctypes.ptr -> C.TensorG.t
val elu_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val embedding : weight:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> C.TensorG.t
val embedding_backward : grad:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> C.TensorG.t
val embedding_bag : weight:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offsets:unit Ctypes.ptr -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:unit Ctypes.ptr option -> include_last_offset:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val embedding_bag_padding_idx : weight:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> offsets:unit Ctypes.ptr -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:unit Ctypes.ptr option -> include_last_offset:bool -> padding_idx:int -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val embedding_dense_backward : grad_output:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> C.TensorG.t
val embedding_renorm_ : unit Ctypes.ptr -> indices:unit Ctypes.ptr -> max_norm:float -> norm_type:float -> C.TensorG.t
val embedding_sparse_backward : grad:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> C.TensorG.t
val empty : size:int list -> options:(Kind.packed * Device.t) -> C.TensorG.t
val empty_like : unit Ctypes.ptr -> C.TensorG.t
val empty_out : out:unit Ctypes.ptr -> size:int list -> C.TensorG.t
val empty_quantized : size:int list -> qtensor:unit Ctypes.ptr -> options:(Kind.packed * Device.t) -> C.TensorG.t
val empty_strided : size:int list -> stride:int list -> options:(Kind.packed * Device.t) -> C.TensorG.t
val eq : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val eq_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val eq_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val eq_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val eq_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val eq_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val erf : unit Ctypes.ptr -> C.TensorG.t
val erf_ : unit Ctypes.ptr -> C.TensorG.t
val erf_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val erfc : unit Ctypes.ptr -> C.TensorG.t
val erfc_ : unit Ctypes.ptr -> C.TensorG.t
val erfc_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val erfinv : unit Ctypes.ptr -> C.TensorG.t
val erfinv_ : unit Ctypes.ptr -> C.TensorG.t
val erfinv_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val exp : unit Ctypes.ptr -> C.TensorG.t
val exp2 : unit Ctypes.ptr -> C.TensorG.t
val exp2_ : unit Ctypes.ptr -> C.TensorG.t
val exp2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val exp_ : unit Ctypes.ptr -> C.TensorG.t
val exp_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val expand : unit Ctypes.ptr -> size:int list -> implicit:bool -> C.TensorG.t
val expand_as : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val expm1 : unit Ctypes.ptr -> C.TensorG.t
val expm1_ : unit Ctypes.ptr -> C.TensorG.t
val expm1_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val exponential_ : unit Ctypes.ptr -> lambd:float -> C.TensorG.t
val eye : n:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val eye_m : n:int -> m:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val eye_m_out : out:unit Ctypes.ptr -> n:int -> m:int -> C.TensorG.t
val eye_out : out:unit Ctypes.ptr -> n:int -> C.TensorG.t
val fake_quantize_per_channel_affine : unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> axis:int -> quant_min:int -> quant_max:int -> C.TensorG.t
val fake_quantize_per_channel_affine_cachemask : unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> axis:int -> quant_min:int -> quant_max:int -> C.TensorG.t * C.TensorG.t
val fake_quantize_per_channel_affine_cachemask_backward : grad:unit Ctypes.ptr -> mask:unit Ctypes.ptr -> C.TensorG.t
val fake_quantize_per_tensor_affine : unit Ctypes.ptr -> scale:float -> zero_point:int -> quant_min:int -> quant_max:int -> C.TensorG.t
val fake_quantize_per_tensor_affine_cachemask : unit Ctypes.ptr -> scale:float -> zero_point:int -> quant_min:int -> quant_max:int -> C.TensorG.t * C.TensorG.t
val fake_quantize_per_tensor_affine_cachemask_backward : grad:unit Ctypes.ptr -> mask:unit Ctypes.ptr -> C.TensorG.t
val fake_quantize_per_tensor_affine_tensor_qparams : unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> quant_min:int -> quant_max:int -> C.TensorG.t
val fbgemm_linear_fp16_weight : unit Ctypes.ptr -> packed_weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr -> C.TensorG.t
val fbgemm_linear_fp16_weight_fp32_activation : unit Ctypes.ptr -> packed_weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr -> C.TensorG.t
val fbgemm_linear_int8_weight : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> packed:unit Ctypes.ptr -> col_offsets:unit Ctypes.ptr -> weight_scale:unit Ctypes.ptr -> weight_zero_point:unit Ctypes.ptr -> bias:unit Ctypes.ptr -> C.TensorG.t
val fbgemm_linear_int8_weight_fp32_activation : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> packed:unit Ctypes.ptr -> col_offsets:unit Ctypes.ptr -> weight_scale:unit Ctypes.ptr -> weight_zero_point:unit Ctypes.ptr -> bias:unit Ctypes.ptr -> C.TensorG.t
val fbgemm_pack_gemm_matrix_fp16 : unit Ctypes.ptr -> C.TensorG.t
val fbgemm_pack_quantized_matrix : unit Ctypes.ptr -> C.TensorG.t
val fbgemm_pack_quantized_matrix_kn : unit Ctypes.ptr -> k:int -> n:int -> C.TensorG.t
val feature_alpha_dropout : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val feature_alpha_dropout_ : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val feature_dropout : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val feature_dropout_ : unit Ctypes.ptr -> p:float -> train:bool -> C.TensorG.t
val fft_fft : unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_fft2 : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_fft2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_fft_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_fftfreq : n:int -> d:float -> options:(Kind.packed * Device.t) -> C.TensorG.t
val fft_fftfreq_out : out:unit Ctypes.ptr -> n:int -> d:float -> C.TensorG.t
val fft_fftn : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_fftn_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_fftshift : unit Ctypes.ptr -> dim:int list -> C.TensorG.t
val fft_hfft : unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_hfft_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_ifft : unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_ifft2 : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_ifft2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_ifft_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_ifftn : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_ifftn_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_ifftshift : unit Ctypes.ptr -> dim:int list -> C.TensorG.t
val fft_ihfft : unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_ihfft_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_irfft : unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_irfft2 : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_irfft2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_irfft_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_irfftn : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_irfftn_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_rfft : unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_rfft2 : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_rfft2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_rfft_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> dim:int -> norm:string -> C.TensorG.t
val fft_rfftfreq : n:int -> d:float -> options:(Kind.packed * Device.t) -> C.TensorG.t
val fft_rfftfreq_out : out:unit Ctypes.ptr -> n:int -> d:float -> C.TensorG.t
val fft_rfftn : unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fft_rfftn_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> s:int list -> dim:int list -> norm:string -> C.TensorG.t
val fill_ : unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val fill_diagonal_ : unit Ctypes.ptr -> fill_value:unit Ctypes.ptr -> wrap:bool -> C.TensorG.t
val fill_tensor_ : unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val fix : unit Ctypes.ptr -> C.TensorG.t
val fix_ : unit Ctypes.ptr -> C.TensorG.t
val fix_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val flatten : unit Ctypes.ptr -> start_dim:int -> end_dim:int -> C.TensorG.t
val flatten_dense_tensors : C.TensorG.t list -> C.TensorG.t
val flip : unit Ctypes.ptr -> dims:int list -> C.TensorG.t
val fliplr : unit Ctypes.ptr -> C.TensorG.t
val flipud : unit Ctypes.ptr -> C.TensorG.t
val float_power : unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val float_power_ : unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val float_power_scalar : unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val float_power_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val float_power_tensor_ : unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val float_power_tensor_scalar : unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val float_power_tensor_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val float_power_tensor_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> C.TensorG.t
val floor : unit Ctypes.ptr -> C.TensorG.t
val floor_ : unit Ctypes.ptr -> C.TensorG.t
val floor_divide : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val floor_divide_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val floor_divide_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val floor_divide_scalar : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val floor_divide_scalar_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val floor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmax : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmax_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmin : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmin_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmod : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmod_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmod_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmod_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmod_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fmod_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val frac : unit Ctypes.ptr -> C.TensorG.t
val frac_ : unit Ctypes.ptr -> C.TensorG.t
val frac_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val fractional_max_pool2d : unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> random_samples:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val fractional_max_pool2d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> indices:unit Ctypes.ptr -> C.TensorG.t
val fractional_max_pool2d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> indices:unit Ctypes.ptr -> C.TensorG.t
val fractional_max_pool2d_output : output:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> random_samples:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val fractional_max_pool3d : unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> random_samples:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val fractional_max_pool3d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> indices:unit Ctypes.ptr -> C.TensorG.t
val fractional_max_pool3d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> indices:unit Ctypes.ptr -> C.TensorG.t
val fractional_max_pool3d_output : output:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> output_size:int list -> random_samples:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val frexp : unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val frexp_tensor_out : mantissa:unit Ctypes.ptr -> exponent:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val frobenius_norm : unit Ctypes.ptr -> C.TensorG.t
val frobenius_norm_dim : unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val frobenius_norm_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val from_file : filename:string -> shared:bool -> size:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val full : size:int list -> fill_value:unit Ctypes.ptr -> options:(Kind.packed * Device.t) -> C.TensorG.t
val full_like : unit Ctypes.ptr -> fill_value:unit Ctypes.ptr -> C.TensorG.t
val full_out : out:unit Ctypes.ptr -> size:int list -> fill_value:unit Ctypes.ptr -> C.TensorG.t
val fused_moving_avg_obs_fake_quant : unit Ctypes.ptr -> observer_on:unit Ctypes.ptr -> fake_quant_on:unit Ctypes.ptr -> running_min:unit Ctypes.ptr -> running_max:unit Ctypes.ptr -> scale:unit Ctypes.ptr -> zero_point:unit Ctypes.ptr -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> C.TensorG.t
val gather : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> sparse_grad:bool -> C.TensorG.t
val gather_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> sparse_grad:bool -> C.TensorG.t
val gather_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> sparse_grad:bool -> C.TensorG.t
val gcd : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gcd_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gcd_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ge : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ge_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ge_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ge_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ge_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ge_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gelu : unit Ctypes.ptr -> C.TensorG.t
val gelu_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gelu_backward_grad_input : grad_input:unit Ctypes.ptr -> grad:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gelu_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val geometric_ : unit Ctypes.ptr -> p:float -> C.TensorG.t
val geqrf : unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val geqrf_a : a:unit Ctypes.ptr -> tau:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val ger : unit Ctypes.ptr -> vec2:unit Ctypes.ptr -> C.TensorG.t
val ger_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> vec2:unit Ctypes.ptr -> C.TensorG.t
val glu : unit Ctypes.ptr -> dim:int -> C.TensorG.t
val glu_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t
val glu_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t
val glu_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t
val grad : unit Ctypes.ptr -> C.TensorG.t
val greater : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_equal : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_equal_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_equal_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_equal_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_equal_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_equal_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val greater_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val grid_sampler : unit Ctypes.ptr -> grid:unit Ctypes.ptr -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> C.TensorG.t
val grid_sampler_2d : unit Ctypes.ptr -> grid:unit Ctypes.ptr -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> C.TensorG.t
val grid_sampler_2d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> grid:unit Ctypes.ptr -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> C.TensorG.t * C.TensorG.t
val grid_sampler_3d : unit Ctypes.ptr -> grid:unit Ctypes.ptr -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> C.TensorG.t
val grid_sampler_3d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> grid:unit Ctypes.ptr -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> C.TensorG.t * C.TensorG.t
val group_norm : unit Ctypes.ptr -> num_groups:int -> weight:unit Ctypes.ptr option -> bias:unit Ctypes.ptr option -> eps:float -> cudnn_enabled:bool -> C.TensorG.t
val gru : unit Ctypes.ptr -> hx:unit Ctypes.ptr -> 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
val gru_cell : unit Ctypes.ptr -> hx:unit Ctypes.ptr -> w_ih:unit Ctypes.ptr -> w_hh:unit Ctypes.ptr -> b_ih:unit Ctypes.ptr option -> b_hh:unit Ctypes.ptr option -> C.TensorG.t
val gru_data : data:unit Ctypes.ptr -> batch_sizes:unit Ctypes.ptr -> hx:unit Ctypes.ptr -> params:C.TensorG.t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> C.TensorG.t * C.TensorG.t
val gt : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gt_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gt_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gt_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gt_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val gt_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hamming_window : window_length:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val hamming_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> C.TensorG.t
val hamming_window_periodic_alpha : window_length:int -> periodic:bool -> alpha:float -> options:(Kind.packed * Device.t) -> C.TensorG.t
val hamming_window_periodic_alpha_beta : window_length:int -> periodic:bool -> alpha:float -> beta:float -> options:(Kind.packed * Device.t) -> C.TensorG.t
val hann_window : window_length:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val hann_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> C.TensorG.t
val hardshrink : unit Ctypes.ptr -> C.TensorG.t
val hardshrink_backward : grad_out:unit Ctypes.ptr -> unit Ctypes.ptr -> lambd:unit Ctypes.ptr -> C.TensorG.t
val hardshrink_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_out:unit Ctypes.ptr -> unit Ctypes.ptr -> lambd:unit Ctypes.ptr -> C.TensorG.t
val hardshrink_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hardsigmoid : unit Ctypes.ptr -> C.TensorG.t
val hardsigmoid_ : unit Ctypes.ptr -> C.TensorG.t
val hardsigmoid_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hardsigmoid_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hardsigmoid_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hardswish : unit Ctypes.ptr -> C.TensorG.t
val hardswish_ : unit Ctypes.ptr -> C.TensorG.t
val hardswish_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hardswish_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hardtanh : unit Ctypes.ptr -> C.TensorG.t
val hardtanh_ : unit Ctypes.ptr -> C.TensorG.t
val hardtanh_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> min_val:unit Ctypes.ptr -> max_val:unit Ctypes.ptr -> C.TensorG.t
val hardtanh_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> min_val:unit Ctypes.ptr -> max_val:unit Ctypes.ptr -> C.TensorG.t
val hardtanh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val heaviside : unit Ctypes.ptr -> values:unit Ctypes.ptr -> C.TensorG.t
val heaviside_ : unit Ctypes.ptr -> values:unit Ctypes.ptr -> C.TensorG.t
val heaviside_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> values:unit Ctypes.ptr -> C.TensorG.t
val hinge_embedding_loss : unit Ctypes.ptr -> target:unit Ctypes.ptr -> margin:float -> reduction:Reduction.t -> C.TensorG.t
val histc : unit Ctypes.ptr -> bins:int -> C.TensorG.t
val histc_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> bins:int -> C.TensorG.t
val hsplit : unit Ctypes.ptr -> sections:int -> unit Ctypes.ptr list
val hsplit_array : unit Ctypes.ptr -> indices:int list -> unit Ctypes.ptr list
val hspmm : mat1:unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val hspmm_out : out:unit Ctypes.ptr -> mat1:unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val hstack : C.TensorG.t list -> C.TensorG.t
val hstack_out : out:unit Ctypes.ptr -> C.TensorG.t list -> C.TensorG.t
val huber_loss : unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> delta:float -> C.TensorG.t
val huber_loss_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> delta:float -> C.TensorG.t
val huber_loss_backward_out : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> delta:float -> C.TensorG.t
val huber_loss_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> delta:float -> C.TensorG.t
val hypot : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hypot_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val hypot_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val i0 : unit Ctypes.ptr -> C.TensorG.t
val i0_ : unit Ctypes.ptr -> C.TensorG.t
val i0_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val igamma : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val igamma_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val igamma_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val igammac : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val igammac_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val igammac_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val im2col : unit Ctypes.ptr -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val im2col_backward : grad_output:unit Ctypes.ptr -> input_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val im2col_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> input_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val im2col_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> C.TensorG.t
val imag : unit Ctypes.ptr -> C.TensorG.t
val index : unit Ctypes.ptr -> indices:unit Ctypes.ptr option list -> C.TensorG.t
val index_add : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> source:unit Ctypes.ptr -> C.TensorG.t
val index_add_ : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> source:unit Ctypes.ptr -> C.TensorG.t
val index_add_alpha : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> source:unit Ctypes.ptr -> alpha:unit Ctypes.ptr -> C.TensorG.t
val index_add_alpha_ : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> source:unit Ctypes.ptr -> alpha:unit Ctypes.ptr -> C.TensorG.t
val index_copy : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> source:unit Ctypes.ptr -> C.TensorG.t
val index_copy_ : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> source:unit Ctypes.ptr -> C.TensorG.t
val index_fill : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val index_fill_ : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val index_fill_int_tensor : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val index_fill_int_tensor_ : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val index_put : unit Ctypes.ptr -> indices:unit Ctypes.ptr option list -> values:unit Ctypes.ptr -> accumulate:bool -> C.TensorG.t
val index_put_ : unit Ctypes.ptr -> indices:unit Ctypes.ptr option list -> values:unit Ctypes.ptr -> accumulate:bool -> C.TensorG.t
val index_select : unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> C.TensorG.t
val index_select_backward : grad:unit Ctypes.ptr -> self_sizes:int list -> dim:int -> index:unit Ctypes.ptr -> C.TensorG.t
val index_select_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> index:unit Ctypes.ptr -> C.TensorG.t
val indices : unit Ctypes.ptr -> C.TensorG.t
val infinitely_differentiable_gelu_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val inner : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val inner_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val instance_norm : unit Ctypes.ptr -> weight:unit Ctypes.ptr option -> bias:unit Ctypes.ptr option -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> use_input_stats:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> C.TensorG.t
val int_repr : unit Ctypes.ptr -> C.TensorG.t
val inverse : unit Ctypes.ptr -> C.TensorG.t
val inverse_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val isclose : unit Ctypes.ptr -> unit Ctypes.ptr -> rtol:float -> atol:float -> equal_nan:bool -> C.TensorG.t
val isfinite : unit Ctypes.ptr -> C.TensorG.t
val isin : elements:unit Ctypes.ptr -> test_elements:unit Ctypes.ptr -> assume_unique:bool -> invert:bool -> C.TensorG.t
val isin_scalar_tensor : element:unit Ctypes.ptr -> test_elements:unit Ctypes.ptr -> assume_unique:bool -> invert:bool -> C.TensorG.t
val isin_scalar_tensor_out : out:unit Ctypes.ptr -> element:unit Ctypes.ptr -> test_elements:unit Ctypes.ptr -> assume_unique:bool -> invert:bool -> C.TensorG.t
val isin_tensor_scalar : elements:unit Ctypes.ptr -> test_element:unit Ctypes.ptr -> assume_unique:bool -> invert:bool -> C.TensorG.t
val isin_tensor_scalar_out : out:unit Ctypes.ptr -> elements:unit Ctypes.ptr -> test_element:unit Ctypes.ptr -> assume_unique:bool -> invert:bool -> C.TensorG.t
val isin_tensor_tensor_out : out:unit Ctypes.ptr -> elements:unit Ctypes.ptr -> test_elements:unit Ctypes.ptr -> assume_unique:bool -> invert:bool -> C.TensorG.t
val isinf : unit Ctypes.ptr -> C.TensorG.t
val isnan : unit Ctypes.ptr -> C.TensorG.t
val isneginf : unit Ctypes.ptr -> C.TensorG.t
val isneginf_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val isposinf : unit Ctypes.ptr -> C.TensorG.t
val isposinf_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val isreal : unit Ctypes.ptr -> C.TensorG.t
val istft : unit Ctypes.ptr -> n_fft:int -> hop_length:int -> win_length:int -> window:unit Ctypes.ptr option -> center:bool -> normalized:bool -> onesided:bool -> length:int -> return_complex:bool -> C.TensorG.t
val kaiser_window : window_length:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val kaiser_window_beta : window_length:int -> periodic:bool -> beta:float -> options:(Kind.packed * Device.t) -> C.TensorG.t
val kaiser_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> C.TensorG.t
val kl_div : unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> log_target:bool -> C.TensorG.t
val kl_div_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> log_target:bool -> C.TensorG.t
val kron : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val kron_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val kthvalue : unit Ctypes.ptr -> k:int -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val kthvalue_values : values:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> k:int -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val l1_loss : unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val l1_loss_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val l1_loss_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val l1_loss_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val layer_norm : unit Ctypes.ptr -> normalized_shape:int list -> weight:unit Ctypes.ptr option -> bias:unit Ctypes.ptr option -> eps:float -> cudnn_enable:bool -> C.TensorG.t
val lcm : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lcm_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lcm_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ldexp : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ldexp_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val ldexp_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val le : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val le_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val le_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val le_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val le_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val le_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val leaky_relu : unit Ctypes.ptr -> C.TensorG.t
val leaky_relu_ : unit Ctypes.ptr -> C.TensorG.t
val leaky_relu_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> negative_slope:unit Ctypes.ptr -> self_is_result:bool -> C.TensorG.t
val leaky_relu_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> negative_slope:unit Ctypes.ptr -> self_is_result:bool -> C.TensorG.t
val leaky_relu_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lerp : unit Ctypes.ptr -> end_:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> C.TensorG.t
val lerp_ : unit Ctypes.ptr -> end_:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> C.TensorG.t
val lerp_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> end_:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> C.TensorG.t
val lerp_tensor : unit Ctypes.ptr -> end_:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> C.TensorG.t
val lerp_tensor_ : unit Ctypes.ptr -> end_:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> C.TensorG.t
val lerp_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> end_:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> C.TensorG.t
val less : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_equal : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_equal_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_equal_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_equal_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_equal_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_equal_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val less_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lgamma : unit Ctypes.ptr -> C.TensorG.t
val lgamma_ : unit Ctypes.ptr -> C.TensorG.t
val lgamma_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_cholesky : unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val linalg_cholesky_ex : unit Ctypes.ptr -> upper:bool -> check_errors:bool -> C.TensorG.t * C.TensorG.t
val linalg_cholesky_ex_l : l:unit Ctypes.ptr -> info:unit Ctypes.ptr -> unit Ctypes.ptr -> upper:bool -> check_errors:bool -> C.TensorG.t * C.TensorG.t
val linalg_cholesky_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> upper:bool -> C.TensorG.t
val linalg_cond : unit Ctypes.ptr -> p:unit Ctypes.ptr -> C.TensorG.t
val linalg_cond_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> p:unit Ctypes.ptr -> C.TensorG.t
val linalg_cond_p_str : unit Ctypes.ptr -> p:string -> C.TensorG.t
val linalg_cond_p_str_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> p:string -> C.TensorG.t
val linalg_det : unit Ctypes.ptr -> C.TensorG.t
val linalg_det_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_eig : unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val linalg_eig_out : eigenvalues:unit Ctypes.ptr -> eigenvectors:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val linalg_eigh : unit Ctypes.ptr -> uplo:string -> C.TensorG.t * C.TensorG.t
val linalg_eigh_eigvals : eigvals:unit Ctypes.ptr -> eigvecs:unit Ctypes.ptr -> unit Ctypes.ptr -> uplo:string -> C.TensorG.t * C.TensorG.t
val linalg_eigvals : unit Ctypes.ptr -> C.TensorG.t
val linalg_eigvals_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_eigvalsh : unit Ctypes.ptr -> uplo:string -> C.TensorG.t
val linalg_eigvalsh_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> uplo:string -> C.TensorG.t
val linalg_householder_product : unit Ctypes.ptr -> tau:unit Ctypes.ptr -> C.TensorG.t
val linalg_householder_product_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> tau:unit Ctypes.ptr -> C.TensorG.t
val linalg_inv : unit Ctypes.ptr -> C.TensorG.t
val linalg_inv_ex : unit Ctypes.ptr -> check_errors:bool -> C.TensorG.t * C.TensorG.t
val linalg_inv_ex_inverse : inverse:unit Ctypes.ptr -> info:unit Ctypes.ptr -> unit Ctypes.ptr -> check_errors:bool -> C.TensorG.t * C.TensorG.t
val linalg_inv_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_lstsq : unit Ctypes.ptr -> b:unit Ctypes.ptr -> rcond:float -> driver:string -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val linalg_lstsq_out : solution:unit Ctypes.ptr -> residuals:unit Ctypes.ptr -> rank:unit Ctypes.ptr -> singular_values:unit Ctypes.ptr -> unit Ctypes.ptr -> b:unit Ctypes.ptr -> rcond:float -> driver:string -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val linalg_matmul : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_matmul_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_matrix_power : unit Ctypes.ptr -> n:int -> C.TensorG.t
val linalg_matrix_power_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> C.TensorG.t
val linalg_matrix_rank : unit Ctypes.ptr -> tol:float -> hermitian:bool -> C.TensorG.t
val linalg_matrix_rank_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> tol:float -> hermitian:bool -> C.TensorG.t
val linalg_matrix_rank_out_tol_tensor : out:unit Ctypes.ptr -> unit Ctypes.ptr -> tol:unit Ctypes.ptr -> hermitian:bool -> C.TensorG.t
val linalg_matrix_rank_tol_tensor : unit Ctypes.ptr -> tol:unit Ctypes.ptr -> hermitian:bool -> C.TensorG.t
val linalg_multi_dot : C.TensorG.t list -> C.TensorG.t
val linalg_multi_dot_out : out:unit Ctypes.ptr -> C.TensorG.t list -> C.TensorG.t
val linalg_pinv : unit Ctypes.ptr -> rcond:float -> hermitian:bool -> C.TensorG.t
val linalg_pinv_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> rcond:float -> hermitian:bool -> C.TensorG.t
val linalg_pinv_out_rcond_tensor : out:unit Ctypes.ptr -> unit Ctypes.ptr -> rcond:unit Ctypes.ptr -> hermitian:bool -> C.TensorG.t
val linalg_pinv_rcond_tensor : unit Ctypes.ptr -> rcond:unit Ctypes.ptr -> hermitian:bool -> C.TensorG.t
val linalg_qr : unit Ctypes.ptr -> mode:string -> C.TensorG.t * C.TensorG.t
val linalg_qr_out : q:unit Ctypes.ptr -> r:unit Ctypes.ptr -> unit Ctypes.ptr -> mode:string -> C.TensorG.t * C.TensorG.t
val linalg_slogdet : unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val linalg_slogdet_out : sign:unit Ctypes.ptr -> logabsdet:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val linalg_solve : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_solve_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_svd : unit Ctypes.ptr -> full_matrices:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val linalg_svd_u : u:unit Ctypes.ptr -> s:unit Ctypes.ptr -> vh:unit Ctypes.ptr -> unit Ctypes.ptr -> full_matrices:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val linalg_svdvals : unit Ctypes.ptr -> C.TensorG.t
val linalg_svdvals_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val linalg_tensorinv : unit Ctypes.ptr -> ind:int -> C.TensorG.t
val linalg_tensorinv_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> ind:int -> C.TensorG.t
val linalg_tensorsolve : unit Ctypes.ptr -> unit Ctypes.ptr -> dims:int list -> C.TensorG.t
val linalg_tensorsolve_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> dims:int list -> C.TensorG.t
val linear : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> C.TensorG.t
val linear_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> C.TensorG.t
val linspace : start:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> steps:int -> options:(Kind.packed * Device.t) -> C.TensorG.t
val linspace_out : out:unit Ctypes.ptr -> start:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> steps:int -> C.TensorG.t
val log : unit Ctypes.ptr -> C.TensorG.t
val log10 : unit Ctypes.ptr -> C.TensorG.t
val log10_ : unit Ctypes.ptr -> C.TensorG.t
val log10_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val log1p : unit Ctypes.ptr -> C.TensorG.t
val log1p_ : unit Ctypes.ptr -> C.TensorG.t
val log1p_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val log2 : unit Ctypes.ptr -> C.TensorG.t
val log2_ : unit Ctypes.ptr -> C.TensorG.t
val log2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val log_ : unit Ctypes.ptr -> C.TensorG.t
val log_normal_ : unit Ctypes.ptr -> mean:float -> std:float -> C.TensorG.t
val log_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val log_sigmoid : unit Ctypes.ptr -> C.TensorG.t
val log_sigmoid_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> buffer:unit Ctypes.ptr -> C.TensorG.t
val log_sigmoid_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> buffer:unit Ctypes.ptr -> C.TensorG.t
val log_sigmoid_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val log_softmax : unit Ctypes.ptr -> dim:int -> dtype:Kind.packed -> C.TensorG.t
val logaddexp : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logaddexp2 : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logaddexp2_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logaddexp_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logcumsumexp : unit Ctypes.ptr -> dim:int -> C.TensorG.t
val logcumsumexp_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> C.TensorG.t
val logdet : unit Ctypes.ptr -> C.TensorG.t
val logical_and : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_and_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_and_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_not : unit Ctypes.ptr -> C.TensorG.t
val logical_not_ : unit Ctypes.ptr -> C.TensorG.t
val logical_not_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_or : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_or_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_or_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_xor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_xor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logical_xor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val logit : unit Ctypes.ptr -> eps:float -> C.TensorG.t
val logit_ : unit Ctypes.ptr -> eps:float -> C.TensorG.t
val logit_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> eps:float -> C.TensorG.t
val logit_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> eps:float -> C.TensorG.t
val logit_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> eps:float -> C.TensorG.t
val logspace : start:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> steps:int -> base:float -> options:(Kind.packed * Device.t) -> C.TensorG.t
val logspace_out : out:unit Ctypes.ptr -> start:unit Ctypes.ptr -> end_:unit Ctypes.ptr -> steps:int -> base:float -> C.TensorG.t
val logsumexp : unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val logsumexp_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> keepdim:bool -> C.TensorG.t
val lstm : unit Ctypes.ptr -> 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
val lstm_cell : unit Ctypes.ptr -> hx:C.TensorG.t list -> w_ih:unit Ctypes.ptr -> w_hh:unit Ctypes.ptr -> b_ih:unit Ctypes.ptr option -> b_hh:unit Ctypes.ptr option -> C.TensorG.t * C.TensorG.t
val lstm_data : data:unit Ctypes.ptr -> batch_sizes:unit Ctypes.ptr -> 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
val lstsq : unit Ctypes.ptr -> a:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val lstsq_x : x:unit Ctypes.ptr -> qr:unit Ctypes.ptr -> unit Ctypes.ptr -> a:unit Ctypes.ptr -> C.TensorG.t * C.TensorG.t
val lt : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lt_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lt_scalar_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lt_tensor : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lt_tensor_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lt_tensor_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val lu_solve : unit Ctypes.ptr -> lu_data:unit Ctypes.ptr -> lu_pivots:unit Ctypes.ptr -> C.TensorG.t
val lu_solve_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> lu_data:unit Ctypes.ptr -> lu_pivots:unit Ctypes.ptr -> C.TensorG.t
val lu_unpack : lu_data:unit Ctypes.ptr -> lu_pivots:unit Ctypes.ptr -> unpack_data:bool -> unpack_pivots:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val lu_unpack_out : p:unit Ctypes.ptr -> l:unit Ctypes.ptr -> u:unit Ctypes.ptr -> lu_data:unit Ctypes.ptr -> lu_pivots:unit Ctypes.ptr -> unpack_data:bool -> unpack_pivots:bool -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val margin_ranking_loss : input1:unit Ctypes.ptr -> input2:unit Ctypes.ptr -> target:unit Ctypes.ptr -> margin:float -> reduction:Reduction.t -> C.TensorG.t
val masked_fill : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val masked_fill_ : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val masked_fill_tensor : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val masked_fill_tensor_ : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> value:unit Ctypes.ptr -> C.TensorG.t
val masked_scatter : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> source:unit Ctypes.ptr -> C.TensorG.t
val masked_scatter_ : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> source:unit Ctypes.ptr -> C.TensorG.t
val masked_select : unit Ctypes.ptr -> mask:unit Ctypes.ptr -> C.TensorG.t
val masked_select_backward : grad:unit Ctypes.ptr -> unit Ctypes.ptr -> mask:unit Ctypes.ptr -> C.TensorG.t
val masked_select_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> mask:unit Ctypes.ptr -> C.TensorG.t
val matmul : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val matmul_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val matrix_exp : unit Ctypes.ptr -> C.TensorG.t
val matrix_exp_backward : unit Ctypes.ptr -> grad:unit Ctypes.ptr -> C.TensorG.t
val matrix_power : unit Ctypes.ptr -> n:int -> C.TensorG.t
val matrix_power_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> n:int -> C.TensorG.t
val matrix_rank : unit Ctypes.ptr -> symmetric:bool -> C.TensorG.t
val matrix_rank_tol : unit Ctypes.ptr -> tol:float -> symmetric:bool -> C.TensorG.t
val max : unit Ctypes.ptr -> C.TensorG.t
val max_dim : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val max_dim_max : max:unit Ctypes.ptr -> max_values:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val max_other : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val max_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val max_pool1d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
val max_pool1d_with_indices : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
val max_pool2d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
val max_pool2d_with_indices : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
val max_pool2d_with_indices_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:unit Ctypes.ptr -> C.TensorG.t
val max_pool2d_with_indices_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:unit Ctypes.ptr -> C.TensorG.t
val max_pool2d_with_indices_out : out:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
val max_pool3d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
val max_pool3d_with_indices : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
val max_pool3d_with_indices_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:unit Ctypes.ptr -> C.TensorG.t
val max_pool3d_with_indices_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:unit Ctypes.ptr -> C.TensorG.t
val max_pool3d_with_indices_out : out:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t * C.TensorG.t
val max_unpool2d : unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val max_unpool2d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val max_unpool2d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val max_unpool2d_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val max_unpool3d : unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
val max_unpool3d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
val max_unpool3d_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
val max_unpool3d_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> indices:unit Ctypes.ptr -> output_size:int list -> stride:int list -> padding:int list -> C.TensorG.t
val maximum : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val maximum_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mean : unit Ctypes.ptr -> dtype:Kind.packed -> C.TensorG.t
val mean_dim : unit Ctypes.ptr -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> C.TensorG.t
val mean_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> C.TensorG.t
val median : unit Ctypes.ptr -> C.TensorG.t
val median_dim : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val median_dim_values : values:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val meshgrid : C.TensorG.t list -> unit Ctypes.ptr list
val meshgrid_indexing : C.TensorG.t list -> indexing:string -> unit Ctypes.ptr list
val min : unit Ctypes.ptr -> C.TensorG.t
val min_dim : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val min_dim_min : min:unit Ctypes.ptr -> min_indices:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val min_other : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val min_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val minimum : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val minimum_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val miopen_batch_norm : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> training:bool -> exponential_average_factor:float -> epsilon:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val miopen_batch_norm_backward : unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> running_mean:unit Ctypes.ptr option -> running_var:unit Ctypes.ptr option -> save_mean:unit Ctypes.ptr option -> save_var:unit Ctypes.ptr option -> epsilon:float -> C.TensorG.t * C.TensorG.t * C.TensorG.t
val miopen_convolution : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_convolution_backward_bias : grad_output:unit Ctypes.ptr -> C.TensorG.t
val miopen_convolution_backward_input : self_size:int list -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_convolution_backward_weight : weight_size:int list -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_convolution_transpose : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_convolution_transpose_backward_input : grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_convolution_transpose_backward_weight : weight_size:int list -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_depthwise_convolution : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_depthwise_convolution_backward_input : self_size:int list -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_depthwise_convolution_backward_weight : weight_size:int list -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> C.TensorG.t
val miopen_rnn : unit Ctypes.ptr -> weight:C.TensorG.t list -> weight_stride0:int -> hx:unit Ctypes.ptr -> cx:unit Ctypes.ptr option -> mode:int -> hidden_size:int -> num_layers:int -> batch_first:bool -> dropout:float -> train:bool -> bidirectional:bool -> batch_sizes:int list -> dropout_state:unit Ctypes.ptr option -> C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t * C.TensorG.t
val mish : unit Ctypes.ptr -> C.TensorG.t
val mish_ : unit Ctypes.ptr -> C.TensorG.t
val mish_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mish_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mkldnn_adaptive_avg_pool2d : unit Ctypes.ptr -> output_size:int list -> C.TensorG.t
val mkldnn_adaptive_avg_pool2d_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mkldnn_convolution : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> C.TensorG.t
val mkldnn_convolution_backward_input : self_size:int list -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> C.TensorG.t
val mkldnn_convolution_backward_weights : weight_size:int list -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> C.TensorG.t * C.TensorG.t
val mkldnn_linear : unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias:unit Ctypes.ptr option -> C.TensorG.t
val mkldnn_linear_backward_input : input_size:int list -> grad_output:unit Ctypes.ptr -> weight:unit Ctypes.ptr -> C.TensorG.t
val mkldnn_linear_backward_weights : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> weight:unit Ctypes.ptr -> bias_defined:bool -> C.TensorG.t * C.TensorG.t
val mkldnn_max_pool2d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
val mkldnn_max_pool2d_backward : grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
val mkldnn_max_pool3d : unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
val mkldnn_max_pool3d_backward : grad_output:unit Ctypes.ptr -> output:unit Ctypes.ptr -> unit Ctypes.ptr -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> C.TensorG.t
val mkldnn_reorder_conv2d_weight : unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> C.TensorG.t
val mkldnn_reorder_conv3d_weight : unit Ctypes.ptr -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> C.TensorG.t
val mm : unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val mm_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> mat2:unit Ctypes.ptr -> C.TensorG.t
val mode : unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val mode_values : values:unit Ctypes.ptr -> indices:unit Ctypes.ptr -> unit Ctypes.ptr -> dim:int -> keepdim:bool -> C.TensorG.t * C.TensorG.t
val moveaxis : unit Ctypes.ptr -> source:int list -> destination:int list -> C.TensorG.t
val moveaxis_int : unit Ctypes.ptr -> source:int -> destination:int -> C.TensorG.t
val movedim : unit Ctypes.ptr -> source:int list -> destination:int list -> C.TensorG.t
val movedim_int : unit Ctypes.ptr -> source:int -> destination:int -> C.TensorG.t
val mse_loss : unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val mse_loss_backward : grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val mse_loss_backward_grad_input : grad_input:unit Ctypes.ptr -> grad_output:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val mse_loss_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> target:unit Ctypes.ptr -> reduction:Reduction.t -> C.TensorG.t
val msort : unit Ctypes.ptr -> C.TensorG.t
val msort_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mul : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mul_ : unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mul_out : out:unit Ctypes.ptr -> unit Ctypes.ptr -> unit Ctypes.ptr -> C.TensorG.t
val mul_scalar : unit