package torch

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package torch
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Module Wrapper.TensorSource

Sourcetype t
include Wrapper_generated_intf.S with type t := t and type 'a scalar := 'a Scalar.t
Sourceval __and__ : t -> 'a Scalar.t -> t
Sourceval __and__tensor_ : t -> t -> t
Sourceval __iand__ : t -> 'a Scalar.t -> t
Sourceval __iand__tensor_ : t -> t -> t
Sourceval __ilshift__ : t -> 'a Scalar.t -> t
Sourceval __ilshift__tensor_ : t -> t -> t
Sourceval __ior__ : t -> 'a Scalar.t -> t
Sourceval __ior__tensor_ : t -> t -> t
Sourceval __irshift__ : t -> 'a Scalar.t -> t
Sourceval __irshift__tensor_ : t -> t -> t
Sourceval __ixor__ : t -> 'a Scalar.t -> t
Sourceval __ixor__tensor_ : t -> t -> t
Sourceval __lshift__ : t -> 'a Scalar.t -> t
Sourceval __lshift__scalar_out_ : out:t -> t -> 'a Scalar.t -> t
Sourceval __lshift__tensor_ : t -> t -> t
Sourceval __lshift__tensor_out_ : out:t -> t -> t -> t
Sourceval __or__ : t -> 'a Scalar.t -> t
Sourceval __or__tensor_ : t -> t -> t
Sourceval __rshift__ : t -> 'a Scalar.t -> t
Sourceval __rshift__scalar_out_ : out:t -> t -> 'a Scalar.t -> t
Sourceval __rshift__tensor_ : t -> t -> t
Sourceval __rshift__tensor_out_ : out:t -> t -> t -> t
Sourceval __xor__ : t -> 'a Scalar.t -> t
Sourceval __xor__tensor_ : t -> t -> t
Sourceval _adaptive_avg_pool2d : t -> output_size:int list -> t
Sourceval _adaptive_avg_pool2d_backward : grad_output:t -> t -> t
Sourceval _adaptive_avg_pool2d_backward_out : out:t -> grad_output:t -> t -> t
Sourceval _adaptive_avg_pool2d_out : out:t -> t -> output_size:int list -> t
Sourceval _adaptive_avg_pool3d : t -> output_size:int list -> t
Sourceval _adaptive_avg_pool3d_backward : grad_output:t -> t -> t
Sourceval _adaptive_avg_pool3d_backward_out : out:t -> grad_output:t -> t -> t
Sourceval _adaptive_avg_pool3d_out : out:t -> t -> output_size:int list -> t
Sourceval _add_batch_dim : t -> batch_dim:int -> level:int -> t
Sourceval _add_relu : t -> t -> t
Sourceval _add_relu_ : t -> t -> t
Sourceval _add_relu_out : out:t -> t -> t -> t
Sourceval _add_relu_scalar : t -> 'a Scalar.t -> t
Sourceval _add_relu_scalar_ : t -> 'a Scalar.t -> t
Sourceval _add_relu_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval _addmm_activation : t -> mat1:t -> mat2:t -> use_gelu:bool -> t
Sourceval _addmm_activation_out : out:t -> t -> mat1:t -> mat2:t -> use_gelu:bool -> t
Sourceval _aminmax : t -> t * t
Sourceval _aminmax_dim : t -> dim:int -> keepdim:bool -> t * t
Sourceval _aminmax_dim_out : out0:t -> out1:t -> t -> dim:int -> keepdim:bool -> t * t
Sourceval _aminmax_out : out0:t -> out1:t -> t -> t * t
Sourceval _amp_update_scale : t -> growth_tracker:t -> found_inf:t -> scale_growth_factor:float -> scale_backoff_factor:float -> growth_interval:int -> t * t
Sourceval _amp_update_scale_ : t -> growth_tracker:t -> found_inf:t -> scale_growth_factor:float -> scale_backoff_factor:float -> growth_interval:int -> t
Sourceval _amp_update_scale_out : out:t -> t -> growth_tracker:t -> found_inf:t -> scale_growth_factor:float -> scale_backoff_factor:float -> growth_interval:int -> t
Sourceval _assert_tensor_metadata : a:t -> size:int list option -> stride:int list option -> dtype:Kind.packed -> unit
Sourceval _autocast_to_full_precision : t -> cuda_enabled:bool -> cpu_enabled:bool -> t
Sourceval _autocast_to_reduced_precision : t -> cuda_enabled:bool -> cpu_enabled:bool -> cuda_dtype:Kind.packed -> cpu_dtype:Kind.packed -> t
Sourceval _cast_byte : t -> non_blocking:bool -> t
Sourceval _cast_char : t -> non_blocking:bool -> t
Sourceval _cast_double : t -> non_blocking:bool -> t
Sourceval _cast_float : t -> non_blocking:bool -> t
Sourceval _cast_half : t -> non_blocking:bool -> t
Sourceval _cast_int : t -> non_blocking:bool -> t
Sourceval _cast_long : t -> non_blocking:bool -> t
Sourceval _cast_short : t -> non_blocking:bool -> t
Sourceval _cdist_backward : grad:t -> x1:t -> x2:t -> p:float -> cdist:t -> t
Sourceval _cdist_backward_out : out:t -> grad:t -> x1:t -> x2:t -> p:float -> cdist:t -> t
Sourceval _cholesky_solve_helper : t -> a:t -> upper:bool -> t
Sourceval _cholesky_solve_helper_out : out:t -> t -> a:t -> upper:bool -> t
Sourceval _coalesce : t -> t
Sourceval _coalesce_out : out:t -> t -> t
Sourceval _coalesced : t -> coalesced:bool -> t
Sourceval _coalesced_ : t -> coalesced:bool -> t
Sourceval _coalesced_out : out:t -> t -> coalesced:bool -> t
Sourceval _compute_linear_combination : t -> coefficients:t -> t
Sourceval _compute_linear_combination_out : out:t -> t -> coefficients:t -> t
Sourceval _conj : t -> t
Sourceval _conj_copy : t -> t
Sourceval _conj_copy_out : out:t -> t -> t
Sourceval _conj_physical : t -> t
Sourceval _conj_physical_out : out:t -> t -> t
Sourceval _conv_depthwise2d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval _conv_depthwise2d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval _convert_indices_from_coo_to_csr : t -> size:int -> out_int32:bool -> t
Sourceval _convert_indices_from_coo_to_csr_out : out:t -> t -> size:int -> out_int32:bool -> t
Sourceval _convert_indices_from_csr_to_coo : crow_indices:t -> col_indices:t -> out_int32:bool -> transpose:bool -> t
Sourceval _convert_indices_from_csr_to_coo_out : out:t -> crow_indices:t -> col_indices:t -> out_int32:bool -> transpose:bool -> t
Sourceval _convolution : t -> weight:t -> bias:t 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 -> t
Sourceval _convolution_deprecated : t -> weight:t -> bias:t 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 -> t
Sourceval _convolution_mode : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
Sourceval _convolution_out : out:t -> t -> weight:t -> bias:t 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 -> t
Sourceval _copy_from : t -> dst:t -> non_blocking:bool -> t
Sourceval _copy_from_and_resize : t -> dst:t -> t
Sourceval _copy_from_and_resize_out : out:t -> t -> dst:t -> t
Sourceval _copy_from_out : out:t -> t -> dst:t -> non_blocking:bool -> t
Sourceval _ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> zero_infinity:bool -> t * t
Sourceval _ctc_loss_backward : grad:t -> log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> neg_log_likelihood:t -> log_alpha:t -> blank:int -> zero_infinity:bool -> t
Sourceval _ctc_loss_backward_out : out:t -> grad:t -> log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> neg_log_likelihood:t -> log_alpha:t -> blank:int -> zero_infinity:bool -> t
Sourceval _ctc_loss_backward_tensor : grad:t -> log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> neg_log_likelihood:t -> log_alpha:t -> blank:int -> zero_infinity:bool -> t
Sourceval _ctc_loss_out : out0:t -> out1:t -> log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> zero_infinity:bool -> t * t
Sourceval _ctc_loss_tensor : log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> blank:int -> zero_infinity:bool -> t * t
Sourceval _cudnn_ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> deterministic:bool -> zero_infinity:bool -> t * t
Sourceval _cudnn_ctc_loss_out : out0:t -> out1:t -> log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> deterministic:bool -> zero_infinity:bool -> t * t
Sourceval _cudnn_ctc_loss_tensor : log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> blank:int -> deterministic:bool -> zero_infinity:bool -> t * t
Sourceval _cudnn_init_dropout_state : dropout:float -> train:bool -> dropout_seed:int -> options:(Kind.packed * Device.t) -> t
Sourceval _cudnn_init_dropout_state_out : out:t -> dropout:float -> train:bool -> dropout_seed:int -> t
Sourceval _cudnn_rnn : t -> weight:t list -> weight_stride0:int -> weight_buf:t option -> hx:t -> cx:t 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:t option -> t * t * t * t * t
Sourceval _cudnn_rnn_flatten_weight : weight_arr:t list -> weight_stride0:int -> input_size:int -> mode:int -> hidden_size:int -> proj_size:int -> num_layers:int -> batch_first:bool -> bidirectional:bool -> t
Sourceval _cudnn_rnn_flatten_weight_out : out:t -> weight_arr:t list -> weight_stride0:int -> input_size:int -> mode:int -> hidden_size:int -> proj_size:int -> num_layers:int -> batch_first:bool -> bidirectional:bool -> t
Sourceval _cudnn_rnn_out : out0:t -> out1:t -> out2:t -> out3:t -> out4:t -> t -> weight:t list -> weight_stride0:int -> weight_buf:t option -> hx:t -> cx:t 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:t option -> t * t * t * t * t
Sourceval _dim_arange : like:t -> dim:int -> t
Sourceval _dirichlet_grad : x:t -> alpha:t -> total:t -> t
Sourceval _dirichlet_grad_out : out:t -> x:t -> alpha:t -> total:t -> t
Sourceval _efficientzerotensor : size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _efficientzerotensor_out : out:t -> size:int list -> t
Sourceval _embedding_bag : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int -> t * t * t * t
Sourceval _embedding_bag_backward : grad:t -> indices:t -> offsets:t -> offset2bag:t -> bag_size:t -> maximum_indices:t -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> padding_idx:int -> t
Sourceval _embedding_bag_dense_backward : grad:t -> indices:t -> offset2bag:t -> bag_size:t -> maximum_indices:t -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> per_sample_weights:t option -> padding_idx:int -> t
Sourceval _embedding_bag_dense_backward_out : out:t -> grad:t -> indices:t -> offset2bag:t -> bag_size:t -> maximum_indices:t -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> per_sample_weights:t option -> padding_idx:int -> t
Sourceval _embedding_bag_forward_only : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int -> t * t * t * t
Sourceval _embedding_bag_forward_only_out : out0:t -> out1:t -> out2:t -> out3:t -> weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int -> t * t * t * t
Sourceval _embedding_bag_out : out0:t -> out1:t -> out2:t -> out3:t -> weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int -> t * t * t * t
Sourceval _embedding_bag_per_sample_weights_backward : grad:t -> weight:t -> indices:t -> offsets:t -> offset2bag:t -> mode:int -> padding_idx:int -> t
Sourceval _embedding_bag_per_sample_weights_backward_out : out:t -> grad:t -> weight:t -> indices:t -> offsets:t -> offset2bag:t -> mode:int -> padding_idx:int -> t
Sourceval _embedding_bag_sparse_backward : grad:t -> indices:t -> offsets:t -> offset2bag:t -> bag_size:t -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> per_sample_weights:t option -> padding_idx:int -> t
Sourceval _empty_affine_quantized : size:int list -> options:(Kind.packed * Device.t) -> scale:float -> zero_point:int -> t
Sourceval _empty_affine_quantized_out : out:t -> size:int list -> scale:float -> zero_point:int -> t
Sourceval _empty_per_channel_affine_quantized : size:int list -> scales:t -> zero_points:t -> axis:int -> options:(Kind.packed * Device.t) -> t
Sourceval _empty_per_channel_affine_quantized_out : out:t -> size:int list -> scales:t -> zero_points:t -> axis:int -> t
Sourceval _euclidean_dist : x1:t -> x2:t -> t
Sourceval _euclidean_dist_out : out:t -> x1:t -> x2:t -> t
Sourceval _fake_quantize_learnable_per_channel_affine : t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> grad_factor:float -> t
Sourceval _fake_quantize_learnable_per_channel_affine_backward : grad:t -> t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> grad_factor:float -> t * t * t
Sourceval _fake_quantize_learnable_per_channel_affine_out : out:t -> t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> grad_factor:float -> t
Sourceval _fake_quantize_learnable_per_tensor_affine : t -> scale:t -> zero_point:t -> quant_min:int -> quant_max:int -> grad_factor:float -> t
Sourceval _fake_quantize_learnable_per_tensor_affine_backward : grad:t -> t -> scale:t -> zero_point:t -> quant_min:int -> quant_max:int -> grad_factor:float -> t * t * t
Sourceval _fake_quantize_learnable_per_tensor_affine_out : out:t -> t -> scale:t -> zero_point:t -> quant_min:int -> quant_max:int -> grad_factor:float -> t
Sourceval _fake_quantize_per_tensor_affine_cachemask_tensor_qparams : t -> scale:t -> zero_point:t -> fake_quant_enabled:t -> quant_min:int -> quant_max:int -> t * t
Sourceval _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out : out0:t -> out1:t -> t -> scale:t -> zero_point:t -> fake_quant_enabled:t -> quant_min:int -> quant_max:int -> t * t
Sourceval _fft_c2c : t -> dim:int list -> normalization:int -> forward:bool -> t
Sourceval _fft_c2c_out : out:t -> t -> dim:int list -> normalization:int -> forward:bool -> t
Sourceval _fft_c2r : t -> dim:int list -> normalization:int -> last_dim_size:int -> t
Sourceval _fft_c2r_out : out:t -> t -> dim:int list -> normalization:int -> last_dim_size:int -> t
Sourceval _fft_r2c : t -> dim:int list -> normalization:int -> onesided:bool -> t
Sourceval _fft_r2c_out : out:t -> t -> dim:int list -> normalization:int -> onesided:bool -> t
Sourceval _flash_scaled_dot_product_attention : query:t -> key:t -> value:t -> cum_seq_q:t -> cum_seq_k:t -> max_q:int -> max_k:int -> dropout_p:float -> is_causal:bool -> t
Sourceval _foobar : t -> arg1:bool -> arg2:bool -> arg3:bool -> t
Sourceval _foobar_out : out:t -> t -> arg1:bool -> arg2:bool -> arg3:bool -> t
Sourceval _fused_dropout : t -> p:float -> t * t
Sourceval _fused_dropout_out : out0:t -> out1:t -> t -> p:float -> t * t
Sourceval _fused_moving_avg_obs_fq_helper : t -> observer_on:t -> fake_quant_on:t -> running_min:t -> running_max:t -> scale:t -> zero_point:t -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> t * t
Sourceval _fused_moving_avg_obs_fq_helper_functional : t -> observer_on:t -> fake_quant_on:t -> running_min:t -> running_max:t -> scale:t -> zero_point:t -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> t * t * t * t * t * t
Sourceval _fused_moving_avg_obs_fq_helper_out : out0:t -> out1:t -> t -> observer_on:t -> fake_quant_on:t -> running_min:t -> running_max:t -> scale:t -> zero_point:t -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> t * t
Sourceval _fw_primal : t -> level:int -> t
Sourceval _fw_primal_copy : t -> level:int -> t
Sourceval _fw_primal_copy_out : out:t -> t -> level:int -> t
Sourceval _gather_sparse_backward : t -> dim:int -> index:t -> grad:t -> t
Sourceval _grid_sampler_2d_cpu_fallback : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
Sourceval _grid_sampler_2d_cpu_fallback_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t * t
Sourceval _grid_sampler_2d_cpu_fallback_out : out:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
Sourceval _histogramdd_bin_edges : t -> bins:int list -> range:float list -> weight:t option -> density:bool -> t list
Sourceval _histogramdd_bin_edges_out : out:t list -> t -> bins:int list -> range:float list -> weight:t option -> density:bool -> unit
Sourceval _histogramdd_from_bin_cts : t -> bins:int list -> range:float list -> weight:t option -> density:bool -> t
Sourceval _histogramdd_from_bin_cts_out : out:t -> t -> bins:int list -> range:float list -> weight:t option -> density:bool -> t
Sourceval _histogramdd_from_bin_tensors : t -> bins:t list -> weight:t option -> density:bool -> t
Sourceval _histogramdd_from_bin_tensors_out : out:t -> t -> bins:t list -> weight:t option -> density:bool -> t
Sourceval _index_put_impl : t -> indices:t option list -> values:t -> accumulate:bool -> unsafe:bool -> t
Sourceval _index_put_impl_ : t -> indices:t option list -> values:t -> accumulate:bool -> unsafe:bool -> t
Sourceval _index_put_impl_out : out:t -> t -> indices:t option list -> values:t -> accumulate:bool -> unsafe:bool -> t
Sourceval _indices : t -> t
Sourceval _indices_copy : t -> t
Sourceval _indices_copy_out : out:t -> t -> t
Sourceval _linalg_check_errors : info:t -> api_name:string -> is_matrix:bool -> unit
Sourceval _linalg_det : a:t -> t * t * t
Sourceval _linalg_det_result : t -> lu:t -> pivots:t -> a:t -> t * t * t
Sourceval _linalg_eigh : a:t -> uplo:string -> compute_v:bool -> t * t
Sourceval _linalg_eigh_eigenvalues : eigenvalues:t -> eigenvectors:t -> a:t -> uplo:string -> compute_v:bool -> t * t
Sourceval _linalg_slogdet : a:t -> t * t * t * t
Sourceval _linalg_slogdet_sign : sign:t -> logabsdet:t -> lu:t -> pivots:t -> a:t -> t * t * t * t
Sourceval _linalg_solve_ex : a:t -> b:t -> left:bool -> check_errors:bool -> t * t * t * t
Sourceval _linalg_solve_ex_result : t -> lu:t -> pivots:t -> info:t -> a:t -> b:t -> left:bool -> check_errors:bool -> t * t * t * t
Sourceval _linalg_svd : a:t -> full_matrices:bool -> compute_uv:bool -> driver:string -> t * t * t
Sourceval _linalg_svd_u : u:t -> s:t -> vh:t -> a:t -> full_matrices:bool -> compute_uv:bool -> driver:string -> t * t * t
Sourceval _log_softmax : t -> dim:int -> half_to_float:bool -> t
Sourceval _log_softmax_backward_data : grad_output:t -> output:t -> dim:int -> input_dtype:Kind.packed -> t
Sourceval _log_softmax_backward_data_out : out:t -> grad_output:t -> output:t -> dim:int -> input_dtype:Kind.packed -> t
Sourceval _log_softmax_out : out:t -> t -> dim:int -> half_to_float:bool -> t
Sourceval _logcumsumexp : t -> dim:int -> t
Sourceval _logcumsumexp_out : out:t -> t -> dim:int -> t
Sourceval _lstm_mps : t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t * t * t * t
Sourceval _lstm_mps_out : out0:t -> out1:t -> out2:t -> out3:t -> out4:t -> t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t * t * t * t
Sourceval _lu_with_info : t -> pivot:bool -> check_errors:bool -> t * t * t
Sourceval _make_dual : primal:t -> tangent:t -> level:int -> t
Sourceval _make_dual_copy : primal:t -> tangent:t -> level:int -> t
Sourceval _make_dual_copy_out : out:t -> primal:t -> tangent:t -> level:int -> t
Sourceval _make_per_channel_quantized_tensor : t -> scale:t -> zero_point:t -> axis:int -> t
Sourceval _make_per_channel_quantized_tensor_out : out:t -> t -> scale:t -> zero_point:t -> axis:int -> t
Sourceval _make_per_tensor_quantized_tensor : t -> scale:float -> zero_point:int -> t
Sourceval _make_per_tensor_quantized_tensor_out : out:t -> t -> scale:float -> zero_point:int -> t
Sourceval _masked_scale : t -> mask:t -> scale:float -> t
Sourceval _masked_scale_out : out:t -> t -> mask:t -> scale:float -> t
Sourceval _masked_softmax : t -> mask:t -> dim:int option -> mask_type:int option -> t
Sourceval _masked_softmax_backward : grad_output:t -> output:t -> mask:t -> dim:int option -> t
Sourceval _masked_softmax_backward_out : out:t -> grad_output:t -> output:t -> mask:t -> dim:int option -> t
Sourceval _masked_softmax_out : out:t -> t -> mask:t -> dim:int option -> mask_type:int option -> t
Sourceval _mkldnn_reshape : t -> shape:int list -> t
Sourceval _mkldnn_reshape_out : out:t -> t -> shape:int list -> t
Sourceval _mkldnn_transpose : t -> dim0:int -> dim1:int -> t
Sourceval _mkldnn_transpose_ : t -> dim0:int -> dim1:int -> t
Sourceval _mkldnn_transpose_out : out:t -> t -> dim0:int -> dim1:int -> t
Sourceval _mps_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval _mps_convolution_out : out:t -> t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval _mps_convolution_transpose : t -> weight:t -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval _mps_convolution_transpose_out : out:t -> t -> weight:t -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval _mps_max_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval _mps_max_pool2d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval _native_decoder_only_multi_head_attention : query:t -> key:t -> value:t -> embed_dim:int -> num_head:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> mask:t option -> incr_key:t option -> incr_value:t option -> need_weights:bool -> average_attn_weights:bool -> t * t * t * t
Sourceval _native_decoder_only_multi_head_attention_out : out0:t -> out1:t -> out2:t -> out3:t -> query:t -> key:t -> value:t -> embed_dim:int -> num_head:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> mask:t option -> incr_key:t option -> incr_value:t option -> need_weights:bool -> average_attn_weights:bool -> t * t * t * t
Sourceval _native_multi_head_attention : query:t -> key:t -> value:t -> embed_dim:int -> num_head:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> mask:t option -> need_weights:bool -> average_attn_weights:bool -> mask_type:int option -> t * t
Sourceval _native_multi_head_attention_out : out0:t -> out1:t -> query:t -> key:t -> value:t -> embed_dim:int -> num_head:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> mask:t option -> need_weights:bool -> average_attn_weights:bool -> mask_type:int option -> t * t
Sourceval _neg_view : t -> t
Sourceval _neg_view_copy : t -> t
Sourceval _neg_view_copy_out : out:t -> t -> t
Sourceval _nested_from_padded : padded:t -> cpu_nested_shape_example:t -> fuse_transform_0213:bool -> t
Sourceval _nested_from_padded_and_nested_example : padded:t -> nt_example:t -> t
Sourceval _nested_from_padded_and_nested_example_out : out:t -> padded:t -> nt_example:t -> t
Sourceval _nested_from_padded_out : out:t -> padded:t -> cpu_nested_shape_example:t -> fuse_transform_0213:bool -> t
Sourceval _nested_select_backward : grad_output:t -> t -> dim:int -> index:int -> t
Sourceval _nested_sum_backward : grad:t -> t -> dim:int list option -> keepdim:bool -> t
Sourceval _nested_view_from_buffer : t -> nested_size:t -> nested_strides:t -> offsets:int list -> t
Sourceval _nested_view_from_buffer_copy : t -> nested_size:t -> nested_strides:t -> offsets:int list -> t
Sourceval _nested_view_from_buffer_copy_out : out:t -> t -> nested_size:t -> nested_strides:t -> offsets:int list -> t
Sourceval _new_zeros_with_same_feature_meta : t -> t -> self_num_batch_dims:int -> t
Sourceval _new_zeros_with_same_feature_meta_out : out:t -> t -> t -> self_num_batch_dims:int -> t
Sourceval _nnpack_spatial_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> t
Sourceval _nnpack_spatial_convolution_out : out:t -> t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> t
Sourceval _pack_padded_sequence : t -> lengths:t -> batch_first:bool -> t * t
Sourceval _pack_padded_sequence_backward : grad:t -> input_size:int list -> batch_sizes:t -> batch_first:bool -> t
Sourceval _pack_padded_sequence_out : out0:t -> out1:t -> t -> lengths:t -> batch_first:bool -> t * t
Sourceval _pad_circular : t -> pad:int list -> t
Sourceval _pad_enum : t -> pad:int list -> mode:int -> value:float option -> t
Sourceval _pad_packed_sequence : data:t -> batch_sizes:t -> batch_first:bool -> padding_value:'a Scalar.t -> total_length:int -> t * t
Sourceval _pdist_backward : grad:t -> t -> p:float -> pdist:t -> t
Sourceval _pdist_backward_out : out:t -> grad:t -> t -> p:float -> pdist:t -> t
Sourceval _pin_memory : t -> device:Device.t -> t
Sourceval _pin_memory_out : out:t -> t -> device:Device.t -> t
Sourceval _remove_batch_dim : t -> level:int -> batch_size:int -> out_dim:int -> t
Sourceval _reshape_alias : t -> size:int list -> stride:int list -> t
Sourceval _reshape_alias_copy : t -> size:int list -> stride:int list -> t
Sourceval _reshape_alias_copy_out : out:t -> t -> size:int list -> stride:int list -> t
Sourceval _reshape_from_tensor : t -> shape:t -> t
Sourceval _resize_output : t -> size:int list -> device:Device.t -> t
Sourceval _resize_output_ : t -> size:int list -> device:Device.t -> t
Sourceval _resize_output_out : out:t -> t -> size:int list -> device:Device.t -> t
Sourceval _rowwise_prune : weight:t -> mask:t -> compressed_indices_dtype:Kind.packed -> t * t
Sourceval _sample_dirichlet : t -> t
Sourceval _sample_dirichlet_out : out:t -> t -> t
Sourceval _saturate_weight_to_fp16 : weight:t -> t
Sourceval _scaled_dot_product_attention : query:t -> key:t -> value:t -> attn_mask:t option -> dropout_p:float -> need_attn_weights:bool -> is_causal:bool -> t * t
Sourceval _scaled_dot_product_attention_math : query:t -> key:t -> value:t -> attn_mask:t option -> dropout_p:float -> need_attn_weights:bool -> is_causal:bool -> t * t
Sourceval _scatter_reduce : t -> dim:int -> index:t -> src:t -> reduce:string -> include_self:bool -> t
Sourceval _scatter_reduce_ : t -> dim:int -> index:t -> src:t -> reduce:string -> include_self:bool -> t
Sourceval _scatter_reduce_two_out : out:t -> t -> dim:int -> index:t -> src:t -> reduce:string -> include_self:bool -> t
Sourceval _segment_reduce_backward : grad:t -> output:t -> data:t -> reduce:string -> lengths:t option -> offsets:t option -> axis:int -> initial:'a Scalar.t -> t
Sourceval _segment_reduce_backward_out : out:t -> grad:t -> output:t -> data:t -> reduce:string -> lengths:t option -> offsets:t option -> axis:int -> initial:'a Scalar.t -> t
Sourceval _shape_as_tensor : t -> t
Sourceval _slow_conv2d_backward : grad_input:t -> grad_weight:t -> grad_bias:t -> grad_output:t -> t -> weight:t -> kernel_size:int list -> stride:int list -> padding:int list -> t * t * t
Sourceval _sobol_engine_draw : quasi:t -> n:int -> sobolstate:t -> dimension:int -> num_generated:int -> dtype:Kind.packed -> t * t
Sourceval _sobol_engine_ff_ : t -> n:int -> sobolstate:t -> dimension:int -> num_generated:int -> t
Sourceval _sobol_engine_initialize_state_ : t -> dimension:int -> t
Sourceval _sobol_engine_scramble_ : t -> ltm:t -> dimension:int -> t
Sourceval _softmax : t -> dim:int -> half_to_float:bool -> t
Sourceval _softmax_backward_data : grad_output:t -> output:t -> dim:int -> input_dtype:Kind.packed -> t
Sourceval _softmax_backward_data_out : grad_input:t -> grad_output:t -> output:t -> dim:int -> input_dtype:Kind.packed -> t
Sourceval _softmax_out : out:t -> t -> dim:int -> half_to_float:bool -> t
Sourceval _sparse_addmm : t -> mat1:t -> mat2:t -> t
Sourceval _sparse_addmm_out : out:t -> t -> mat1:t -> mat2:t -> t
Sourceval _sparse_broadcast_to : t -> size:int list -> t
Sourceval _sparse_broadcast_to_copy : t -> size:int list -> t
Sourceval _sparse_broadcast_to_copy_out : out:t -> t -> size:int list -> t
Sourceval _sparse_bsc_tensor_unsafe : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_bsr_tensor_unsafe : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_compressed_tensor_unsafe : compressed_indices:t -> plain_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_coo_tensor_unsafe : indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_coo_tensor_with_dims : sparse_dim:int -> dense_dim:int -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_coo_tensor_with_dims_and_tensors : sparse_dim:int -> dense_dim:int -> size:int list -> indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_coo_tensor_with_dims_and_tensors_out : out:t -> sparse_dim:int -> dense_dim:int -> size:int list -> indices:t -> values:t -> t
Sourceval _sparse_coo_tensor_with_dims_out : out:t -> sparse_dim:int -> dense_dim:int -> size:int list -> t
Sourceval _sparse_csc_tensor_unsafe : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_csr_prod : t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval _sparse_csr_prod_dim_dtype_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval _sparse_csr_sum : t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval _sparse_csr_sum_dim_dtype_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval _sparse_csr_tensor_unsafe : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval _sparse_log_softmax : t -> dim:int -> half_to_float:bool -> t
Sourceval _sparse_log_softmax_backward_data : grad_output:t -> output:t -> dim:int -> t -> t
Sourceval _sparse_log_softmax_backward_data_out : out:t -> grad_output:t -> output:t -> dim:int -> t -> t
Sourceval _sparse_log_softmax_int : t -> dim:int -> dtype:Kind.packed -> t
Sourceval _sparse_log_softmax_out : out:t -> t -> dim:int -> half_to_float:bool -> t
Sourceval _sparse_mask_helper : tr:t -> mask_indices:t -> t
Sourceval _sparse_mask_helper_out : out:t -> tr:t -> mask_indices:t -> t
Sourceval _sparse_mm : sparse:t -> dense:t -> t
Sourceval _sparse_softmax : t -> dim:int -> half_to_float:bool -> t
Sourceval _sparse_softmax_backward_data : grad_output:t -> output:t -> dim:int -> t -> t
Sourceval _sparse_softmax_backward_data_out : out:t -> grad_output:t -> output:t -> dim:int -> t -> t
Sourceval _sparse_softmax_int : t -> dim:int -> dtype:Kind.packed -> t
Sourceval _sparse_softmax_out : out:t -> t -> dim:int -> half_to_float:bool -> t
Sourceval _sparse_sparse_matmul : t -> t -> t
Sourceval _sparse_sparse_matmul_out : out:t -> t -> t -> t
Sourceval _sparse_sum : t -> t
Sourceval _sparse_sum_backward : grad:t -> t -> dim:int list -> t
Sourceval _sparse_sum_backward_out : out:t -> grad:t -> t -> dim:int list -> t
Sourceval _sparse_sum_dim : t -> dim:int list -> t
Sourceval _sparse_sum_dim_dtype : t -> dim:int list -> dtype:Kind.packed -> t
Sourceval _sparse_sum_dim_out : out:t -> t -> dim:int list -> t
Sourceval _sparse_sum_dtype : t -> dtype:Kind.packed -> t
Sourceval _spdiags : diagonals:t -> offsets:t -> shape:int list -> t
Sourceval _spdiags_out : out:t -> diagonals:t -> offsets:t -> shape:int list -> t
Sourceval _stack : t list -> dim:int -> t
Sourceval _stack_out : out:t -> t list -> dim:int -> t
Sourceval _standard_gamma : t -> t
Sourceval _standard_gamma_grad : t -> output:t -> t
Sourceval _standard_gamma_grad_out : out:t -> t -> output:t -> t
Sourceval _standard_gamma_out : out:t -> t -> t
Sourceval _symeig_helper : t -> eigenvectors:bool -> upper:bool -> t * t
Sourceval _symeig_helper_out : out0:t -> out1:t -> t -> eigenvectors:bool -> upper:bool -> t * t
Sourceval _test_ambiguous_defaults : dummy:t -> a:int -> b:int -> t
Sourceval _test_ambiguous_defaults_b : dummy:t -> a:int -> b:string -> t
Sourceval _test_autograd_multiple_dispatch : t -> t
Sourceval _test_autograd_multiple_dispatch_fullcoverage_out : out:t -> t -> t
Sourceval _test_autograd_multiple_dispatch_ntonly : t -> b:bool -> t
Sourceval _test_autograd_multiple_dispatch_view : t -> t
Sourceval _test_autograd_multiple_dispatch_view_copy : t -> t
Sourceval _test_autograd_multiple_dispatch_view_copy_out : out:t -> t -> t
Sourceval _test_optional_filled_intlist : values:t -> addends:int list option -> t
Sourceval _test_optional_filled_intlist_out : out:t -> values:t -> addends:int list option -> t
Sourceval _test_optional_floatlist : values:t -> addends:float list -> t
Sourceval _test_optional_floatlist_out : out:t -> values:t -> addends:float list -> t
Sourceval _test_optional_intlist : values:t -> addends:int list option -> t
Sourceval _test_optional_intlist_out : out:t -> values:t -> addends:int list option -> t
Sourceval _test_serialization_subcmul : t -> t -> t
Sourceval _test_string_default : dummy:t -> a:string -> b:string -> t
Sourceval _test_warn_in_autograd : t -> t
Sourceval _test_warn_in_autograd_out : out:t -> t -> t
Sourceval _thnn_differentiable_gru_cell_backward : grad_hy:t -> input_gates:t -> hidden_gates:t -> hx:t -> input_bias:t option -> hidden_bias:t option -> t * t * t * t * t
Sourceval _thnn_differentiable_lstm_cell_backward : grad_hy:t option -> grad_cy:t option -> input_gates:t -> hidden_gates:t -> input_bias:t option -> hidden_bias:t option -> cx:t -> cy:t -> t * t * t * t * t
Sourceval _thnn_fused_gru_cell : input_gates:t -> hidden_gates:t -> hx:t -> input_bias:t option -> hidden_bias:t option -> t * t
Sourceval _thnn_fused_gru_cell_backward : grad_hy:t -> workspace:t -> has_bias:bool -> t * t * t * t * t
Sourceval _thnn_fused_gru_cell_backward_out : out0:t -> out1:t -> out2:t -> out3:t -> out4:t -> grad_hy:t -> workspace:t -> has_bias:bool -> t * t * t * t * t
Sourceval _thnn_fused_gru_cell_out : out0:t -> out1:t -> input_gates:t -> hidden_gates:t -> hx:t -> input_bias:t option -> hidden_bias:t option -> t * t
Sourceval _thnn_fused_lstm_cell : input_gates:t -> hidden_gates:t -> cx:t -> input_bias:t option -> hidden_bias:t option -> t * t * t
Sourceval _thnn_fused_lstm_cell_backward : grad_hy:t option -> grad_cy:t option -> cx:t -> cy:t -> workspace:t -> has_bias:bool -> t * t * t * t * t
Sourceval _thnn_fused_lstm_cell_backward_impl : grad_hy:t option -> grad_cy:t option -> cx:t -> cy:t -> workspace:t -> has_bias:bool -> t * t * t
Sourceval _thnn_fused_lstm_cell_backward_impl_out : out0:t -> out1:t -> out2:t -> grad_hy:t option -> grad_cy:t option -> cx:t -> cy:t -> workspace:t -> has_bias:bool -> t * t * t
Sourceval _thnn_fused_lstm_cell_out : out0:t -> out1:t -> out2:t -> input_gates:t -> hidden_gates:t -> cx:t -> input_bias:t option -> hidden_bias:t option -> t * t * t
Sourceval _to_copy : t -> options:(Kind.packed * Device.t) -> non_blocking:bool -> t
Sourceval _to_copy_out : out:t -> t -> non_blocking:bool -> t
Sourceval _to_cpu : t list -> t list
Sourceval _to_dense : t -> dtype:Kind.packed -> t
Sourceval _to_dense_out : out:t -> t -> dtype:Kind.packed -> t
Sourceval _torch_cuda_cu_linker_symbol_op : t -> t
Sourceval _torch_cuda_cu_linker_symbol_op_out : out:t -> t -> t
Sourceval _transform_bias_rescale_qkv : qkv:t -> qkv_bias:t -> num_heads:int -> t * t * t
Sourceval _transform_bias_rescale_qkv_out : out0:t -> out1:t -> out2:t -> qkv:t -> qkv_bias:t -> num_heads:int -> t * t * t
Sourceval _transformer_decoder_only_layer_fwd : src:t -> embed_dim:int -> num_heads:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> use_gelu:bool -> norm_first:bool -> eps:float -> norm_weight_1:t -> norm_bias_1:t -> norm_weight_2:t -> norm_bias_2:t -> ffn_weight_1:t -> ffn_bias_1:t -> ffn_weight_2:t -> ffn_bias_2:t -> mask:t option -> incr_key:t option -> incr_value:t option -> t * t * t
Sourceval _transformer_decoder_only_layer_fwd_out : out0:t -> out1:t -> out2:t -> src:t -> embed_dim:int -> num_heads:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> use_gelu:bool -> norm_first:bool -> eps:float -> norm_weight_1:t -> norm_bias_1:t -> norm_weight_2:t -> norm_bias_2:t -> ffn_weight_1:t -> ffn_bias_1:t -> ffn_weight_2:t -> ffn_bias_2:t -> mask:t option -> incr_key:t option -> incr_value:t option -> t * t * t
Sourceval _transformer_encoder_layer_fwd : src:t -> embed_dim:int -> num_heads:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> use_gelu:bool -> norm_first:bool -> eps:float -> norm_weight_1:t -> norm_bias_1:t -> norm_weight_2:t -> norm_bias_2:t -> ffn_weight_1:t -> ffn_bias_1:t -> ffn_weight_2:t -> ffn_bias_2:t -> mask:t option -> mask_type:int option -> t
Sourceval _transformer_encoder_layer_fwd_out : out:t -> src:t -> embed_dim:int -> num_heads:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> use_gelu:bool -> norm_first:bool -> eps:float -> norm_weight_1:t -> norm_bias_1:t -> norm_weight_2:t -> norm_bias_2:t -> ffn_weight_1:t -> ffn_bias_1:t -> ffn_weight_2:t -> ffn_bias_2:t -> mask:t option -> mask_type:int option -> t
Sourceval _trilinear : i1:t -> i2:t -> i3:t -> expand1:int list -> expand2:int list -> expand3:int list -> sumdim:int list -> unroll_dim:int -> t
Sourceval _trilinear_out : out:t -> i1:t -> i2:t -> i3:t -> expand1:int list -> expand2:int list -> expand3:int list -> sumdim:int list -> unroll_dim:int -> t
Sourceval _triton_multi_head_attention : query:t -> key:t -> value:t -> embed_dim:int -> num_head:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> mask:t option -> t
Sourceval _triton_multi_head_attention_out : out:t -> query:t -> key:t -> value:t -> embed_dim:int -> num_head:int -> qkv_weight:t -> qkv_bias:t -> proj_weight:t -> proj_bias:t -> mask:t option -> t
Sourceval _triton_scaled_dot_attention : q:t -> k:t -> v:t -> dropout_p:float -> t
Sourceval _triton_scaled_dot_attention_out : out:t -> q:t -> k:t -> v:t -> dropout_p:float -> t
Sourceval _unique : t -> sorted:bool -> return_inverse:bool -> t * t
Sourceval _unique2 : t -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
Sourceval _unique2_out : out0:t -> out1:t -> out2:t -> t -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
Sourceval _unique_out : out0:t -> out1:t -> t -> sorted:bool -> return_inverse:bool -> t * t
Sourceval _unpack_dual : dual:t -> level:int -> t * t
Sourceval _unsafe_view : t -> size:int list -> t
Sourceval _unsafe_view_out : out:t -> t -> size:int list -> t
Sourceval _upsample_bicubic2d_aa : t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bicubic2d_aa_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bicubic2d_aa_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bicubic2d_aa_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_bicubic2d_aa_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_bicubic2d_aa_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bicubic2d_aa_vec : t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_bicubic2d_aa_vec_out : out:t -> t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_bilinear2d_aa : t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bilinear2d_aa_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bilinear2d_aa_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bilinear2d_aa_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_bilinear2d_aa_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_bilinear2d_aa_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_bilinear2d_aa_vec : t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_bilinear2d_aa_vec_out : out:t -> t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact1d : t -> output_size:int list -> scales:float option -> t
Sourceval _upsample_nearest_exact1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales:float option -> t
Sourceval _upsample_nearest_exact1d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales:float option -> t
Sourceval _upsample_nearest_exact1d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact1d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact1d_out : out:t -> t -> output_size:int list -> scales:float option -> t
Sourceval _upsample_nearest_exact1d_vec : t -> output_size:int list option -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact1d_vec_out : out:t -> t -> output_size:int list option -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact2d : t -> output_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact2d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact2d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact2d_out : out:t -> t -> output_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact2d_vec : t -> output_size:int list option -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact2d_vec_out : out:t -> t -> output_size:int list option -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact3d : t -> output_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact3d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact3d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact3d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact3d_out : out:t -> t -> output_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval _upsample_nearest_exact3d_vec : t -> output_size:int list option -> scale_factors:float list -> t
Sourceval _upsample_nearest_exact3d_vec_out : out:t -> t -> output_size:int list option -> scale_factors:float list -> t
Sourceval _validate_compressed_sparse_indices : is_crow:bool -> compressed_idx:t -> plain_idx:t -> cdim:int -> dim:int -> nnz:int -> unit
Sourceval _validate_sparse_bsc_tensor_args : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> unit
Sourceval _validate_sparse_bsr_tensor_args : crow_indices:t -> col_indices:t -> values:t -> size:int list -> unit
Sourceval _validate_sparse_csc_tensor_args : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> unit
Sourceval _values : t -> t
Sourceval _values_copy : t -> t
Sourceval _values_copy_out : out:t -> t -> t
Sourceval _weight_norm : v:t -> g:t -> dim:int -> t
Sourceval _weight_norm_differentiable_backward : grad_w:t -> saved_v:t -> saved_g:t -> saved_norms:t -> dim:int -> t * t
Sourceval _weight_norm_interface : v:t -> g:t -> dim:int -> t * t
Sourceval _weight_norm_interface_backward : grad_w:t -> saved_v:t -> saved_g:t -> saved_norms:t -> dim:int -> t * t
Sourceval _weight_norm_interface_backward_out : out0:t -> out1:t -> grad_w:t -> saved_v:t -> saved_g:t -> saved_norms:t -> dim:int -> t * t
Sourceval _weight_norm_interface_out : out0:t -> out1:t -> v:t -> g:t -> dim:int -> t * t
Sourceval abs : t -> t
Sourceval abs_ : t -> t
Sourceval abs_out : out:t -> t -> t
Sourceval absolute : t -> t
Sourceval absolute_ : t -> t
Sourceval absolute_out : out:t -> t -> t
Sourceval acos : t -> t
Sourceval acos_ : t -> t
Sourceval acos_out : out:t -> t -> t
Sourceval acosh : t -> t
Sourceval acosh_ : t -> t
Sourceval acosh_out : out:t -> t -> t
Sourceval adaptive_avg_pool1d : t -> output_size:int list -> t
Sourceval adaptive_avg_pool2d : t -> output_size:int list -> t
Sourceval adaptive_avg_pool2d_out : out:t -> t -> output_size:int list -> t
Sourceval adaptive_avg_pool3d : t -> output_size:int list -> t
Sourceval adaptive_avg_pool3d_backward : grad_input:t -> grad_output:t -> t -> t
Sourceval adaptive_avg_pool3d_out : out:t -> t -> output_size:int list -> t
Sourceval adaptive_max_pool1d : t -> output_size:int list -> t * t
Sourceval adaptive_max_pool2d : t -> output_size:int list -> t * t
Sourceval adaptive_max_pool2d_backward : grad_output:t -> t -> indices:t -> t
Sourceval adaptive_max_pool2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> indices:t -> t
Sourceval adaptive_max_pool2d_out : out:t -> indices:t -> t -> output_size:int list -> t * t
Sourceval adaptive_max_pool3d : t -> output_size:int list -> t * t
Sourceval adaptive_max_pool3d_backward : grad_output:t -> t -> indices:t -> t
Sourceval adaptive_max_pool3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> indices:t -> t
Sourceval adaptive_max_pool3d_out : out:t -> indices:t -> t -> output_size:int list -> t * t
Sourceval add : t -> t -> t
Sourceval add_ : t -> t -> t
Sourceval add_out : out:t -> t -> t -> t
Sourceval add_scalar : t -> 'a Scalar.t -> t
Sourceval add_scalar_ : t -> 'a Scalar.t -> t
Sourceval add_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval addbmm : t -> batch1:t -> batch2:t -> t
Sourceval addbmm_ : t -> batch1:t -> batch2:t -> t
Sourceval addbmm_out : out:t -> t -> batch1:t -> batch2:t -> t
Sourceval addcdiv : t -> tensor1:t -> tensor2:t -> t
Sourceval addcdiv_ : t -> tensor1:t -> tensor2:t -> t
Sourceval addcdiv_out : out:t -> t -> tensor1:t -> tensor2:t -> t
Sourceval addcmul : t -> tensor1:t -> tensor2:t -> t
Sourceval addcmul_ : t -> tensor1:t -> tensor2:t -> t
Sourceval addcmul_out : out:t -> t -> tensor1:t -> tensor2:t -> t
Sourceval addmm : t -> mat1:t -> mat2:t -> t
Sourceval addmm_ : t -> mat1:t -> mat2:t -> t
Sourceval addmm_out : out:t -> t -> mat1:t -> mat2:t -> t
Sourceval addmv : t -> mat:t -> vec:t -> t
Sourceval addmv_ : t -> mat:t -> vec:t -> t
Sourceval addmv_out : out:t -> t -> mat:t -> vec:t -> t
Sourceval addr : t -> vec1:t -> vec2:t -> t
Sourceval addr_ : t -> vec1:t -> vec2:t -> t
Sourceval addr_out : out:t -> t -> vec1:t -> vec2:t -> t
Sourceval adjoint : t -> t
Sourceval affine_grid_generator : theta:t -> size:int list -> align_corners:bool -> t
Sourceval affine_grid_generator_backward : grad:t -> size:int list -> align_corners:bool -> t
Sourceval affine_grid_generator_out : out:t -> theta:t -> size:int list -> align_corners:bool -> t
Sourceval alias : t -> t
Sourceval alias_copy : t -> t
Sourceval alias_copy_out : out:t -> t -> t
Sourceval align_as : t -> t -> t
Sourceval align_tensors : t list -> t list
Sourceval all : t -> t
Sourceval all_all_out : out:t -> t -> t
Sourceval all_dim : t -> dim:int -> keepdim:bool -> t
Sourceval all_out : out:t -> t -> dim:int -> keepdim:bool -> t
Sourceval alpha_dropout : t -> p:float -> train:bool -> t
Sourceval alpha_dropout_ : t -> p:float -> train:bool -> t
Sourceval amax : t -> dim:int list -> keepdim:bool -> t
Sourceval amax_out : out:t -> t -> dim:int list -> keepdim:bool -> t
Sourceval amin : t -> dim:int list -> keepdim:bool -> t
Sourceval amin_out : out:t -> t -> dim:int list -> keepdim:bool -> t
Sourceval aminmax : t -> dim:int option -> keepdim:bool -> t * t
Sourceval aminmax_out : min:t -> max:t -> t -> dim:int option -> keepdim:bool -> t * t
Sourceval angle : t -> t
Sourceval angle_out : out:t -> t -> t
Sourceval any : t -> t
Sourceval any_all_out : out:t -> t -> t
Sourceval any_dim : t -> dim:int -> keepdim:bool -> t
Sourceval any_out : out:t -> t -> dim:int -> keepdim:bool -> t
Sourceval arange : end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval arange_start : start:'a Scalar.t -> end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval arange_start_step : start:'a Scalar.t -> end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval arccos : t -> t
Sourceval arccos_ : t -> t
Sourceval arccos_out : out:t -> t -> t
Sourceval arccosh : t -> t
Sourceval arccosh_ : t -> t
Sourceval arccosh_out : out:t -> t -> t
Sourceval arcsin : t -> t
Sourceval arcsin_ : t -> t
Sourceval arcsin_out : out:t -> t -> t
Sourceval arcsinh : t -> t
Sourceval arcsinh_ : t -> t
Sourceval arcsinh_out : out:t -> t -> t
Sourceval arctan : t -> t
Sourceval arctan2 : t -> t -> t
Sourceval arctan2_ : t -> t -> t
Sourceval arctan2_out : out:t -> t -> t -> t
Sourceval arctan_ : t -> t
Sourceval arctan_out : out:t -> t -> t
Sourceval arctanh : t -> t
Sourceval arctanh_ : t -> t
Sourceval arctanh_out : out:t -> t -> t
Sourceval argmax_out : out:t -> t -> dim:int option -> keepdim:bool -> t
Sourceval argmin : t -> dim:int option -> keepdim:bool -> t
Sourceval argmin_out : out:t -> t -> dim:int option -> keepdim:bool -> t
Sourceval argsort : t -> dim:int -> descending:bool -> t
Sourceval argsort_stable : t -> stable:bool -> dim:int -> descending:bool -> t
Sourceval argsort_stable_out : out:t -> t -> stable:bool -> dim:int -> descending:bool -> t
Sourceval argwhere : t -> t
Sourceval as_strided : t -> size:int list -> stride:int list -> storage_offset:int option -> t
Sourceval as_strided_ : t -> size:int list -> stride:int list -> storage_offset:int option -> t
Sourceval as_strided_copy : t -> size:int list -> stride:int list -> storage_offset:int option -> t
Sourceval as_strided_copy_out : out:t -> t -> size:int list -> stride:int list -> storage_offset:int option -> t
Sourceval as_strided_scatter : t -> src:t -> size:int list -> stride:int list -> storage_offset:int option -> t
Sourceval as_strided_scatter_out : out:t -> t -> src:t -> size:int list -> stride:int list -> storage_offset:int option -> t
Sourceval asin : t -> t
Sourceval asin_ : t -> t
Sourceval asin_out : out:t -> t -> t
Sourceval asinh : t -> t
Sourceval asinh_ : t -> t
Sourceval asinh_out : out:t -> t -> t
Sourceval atan : t -> t
Sourceval atan2 : t -> t -> t
Sourceval atan2_ : t -> t -> t
Sourceval atan2_out : out:t -> t -> t -> t
Sourceval atan_ : t -> t
Sourceval atan_out : out:t -> t -> t
Sourceval atanh : t -> t
Sourceval atanh_ : t -> t
Sourceval atanh_out : out:t -> t -> t
Sourceval atleast_1d : t -> t
Sourceval atleast_1d_sequence : t list -> t list
Sourceval atleast_2d : t -> t
Sourceval atleast_2d_sequence : t list -> t list
Sourceval atleast_3d : t -> t
Sourceval atleast_3d_sequence : t list -> t list
Sourceval avg_pool1d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
Sourceval avg_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval avg_pool2d_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval avg_pool2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval avg_pool2d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval avg_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval avg_pool3d_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval avg_pool3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval avg_pool3d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int option -> t
Sourceval baddbmm : t -> batch1:t -> batch2:t -> t
Sourceval baddbmm_ : t -> batch1:t -> batch2:t -> t
Sourceval baddbmm_out : out:t -> t -> batch1:t -> batch2:t -> t
Sourceval bartlett_window : window_length:int -> options:(Kind.packed * Device.t) -> t
Sourceval bartlett_window_out : out:t -> window_length:int -> t
Sourceval bartlett_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
Sourceval bartlett_window_periodic_out : out:t -> window_length:int -> periodic:bool -> t
Sourceval batch_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> t
Sourceval batch_norm_backward_elemt : grad_out:t -> t -> mean:t -> invstd:t -> weight:t option -> mean_dy:t -> mean_dy_xmu:t -> count:t -> t
Sourceval batch_norm_backward_elemt_out : out:t -> grad_out:t -> t -> mean:t -> invstd:t -> weight:t option -> mean_dy:t -> mean_dy_xmu:t -> count:t -> t
Sourceval batch_norm_backward_reduce : grad_out:t -> t -> mean:t -> invstd:t -> weight:t option -> input_g:bool -> weight_g:bool -> bias_g:bool -> t * t * t * t
Sourceval batch_norm_backward_reduce_out : out0:t -> out1:t -> out2:t -> out3:t -> grad_out:t -> t -> mean:t -> invstd:t -> weight:t option -> input_g:bool -> weight_g:bool -> bias_g:bool -> t * t * t * t
Sourceval batch_norm_elemt : t -> weight:t option -> bias:t option -> mean:t -> invstd:t -> eps:float -> t
Sourceval batch_norm_elemt_out : out:t -> t -> weight:t option -> bias:t option -> mean:t -> invstd:t -> eps:float -> t
Sourceval batch_norm_gather_stats : t -> mean:t -> invstd:t -> running_mean:t option -> running_var:t option -> momentum:float -> eps:float -> count:int -> t * t
Sourceval batch_norm_gather_stats_out : out0:t -> out1:t -> t -> mean:t -> invstd:t -> running_mean:t option -> running_var:t option -> momentum:float -> eps:float -> count:int -> t * t
Sourceval batch_norm_gather_stats_with_counts : t -> mean:t -> invstd:t -> running_mean:t option -> running_var:t option -> momentum:float -> eps:float -> counts:t -> t * t
Sourceval batch_norm_gather_stats_with_counts_out : out0:t -> out1:t -> t -> mean:t -> invstd:t -> running_mean:t option -> running_var:t option -> momentum:float -> eps:float -> counts:t -> t * t
Sourceval batch_norm_stats : t -> eps:float -> t * t
Sourceval batch_norm_stats_out : out0:t -> out1:t -> t -> eps:float -> t * t
Sourceval batch_norm_update_stats : t -> running_mean:t option -> running_var:t option -> momentum:float -> t * t
Sourceval batch_norm_update_stats_out : out0:t -> out1:t -> t -> running_mean:t option -> running_var:t option -> momentum:float -> t * t
Sourceval bernoulli : t -> t
Sourceval bernoulli_ : t -> p:t -> t
Sourceval bernoulli_float_ : t -> p:float -> t
Sourceval bernoulli_p : t -> p:float -> t
Sourceval bernoulli_tensor : t -> p:t -> t
Sourceval bilinear : input1:t -> input2:t -> weight:t -> bias:t option -> t
Sourceval binary_cross_entropy : t -> target:t -> weight:t option -> reduction:Reduction.t -> t
Sourceval binary_cross_entropy_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> t
Sourceval binary_cross_entropy_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> t
Sourceval binary_cross_entropy_out : out:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> t
Sourceval binary_cross_entropy_with_logits : t -> target:t -> weight:t option -> pos_weight:t option -> reduction:Reduction.t -> t
Sourceval binary_cross_entropy_with_logits_out : out:t -> t -> target:t -> weight:t option -> pos_weight:t option -> reduction:Reduction.t -> t
Sourceval bincount : t -> weights:t option -> minlength:int -> t
Sourceval bincount_out : out:t -> t -> weights:t option -> minlength:int -> t
Sourceval binomial : count:t -> prob:t -> t
Sourceval binomial_out : out:t -> count:t -> prob:t -> t
Sourceval bitwise_and : t -> 'a Scalar.t -> t
Sourceval bitwise_and_ : t -> 'a Scalar.t -> t
Sourceval bitwise_and_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval bitwise_and_scalar_tensor : 'a Scalar.t -> t -> t
Sourceval bitwise_and_scalar_tensor_out : out:t -> 'a Scalar.t -> t -> t
Sourceval bitwise_and_tensor : t -> t -> t
Sourceval bitwise_and_tensor_ : t -> t -> t
Sourceval bitwise_and_tensor_out : out:t -> t -> t -> t
Sourceval bitwise_left_shift : t -> t -> t
Sourceval bitwise_left_shift_ : t -> t -> t
Sourceval bitwise_left_shift_scalar_tensor : 'a Scalar.t -> t -> t
Sourceval bitwise_left_shift_scalar_tensor_out : out:t -> 'a Scalar.t -> t -> t
Sourceval bitwise_left_shift_tensor_out : out:t -> t -> t -> t
Sourceval bitwise_left_shift_tensor_scalar : t -> 'a Scalar.t -> t
Sourceval bitwise_left_shift_tensor_scalar_ : t -> 'a Scalar.t -> t
Sourceval bitwise_left_shift_tensor_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval bitwise_not : t -> t
Sourceval bitwise_not_ : t -> t
Sourceval bitwise_not_out : out:t -> t -> t
Sourceval bitwise_or : t -> 'a Scalar.t -> t
Sourceval bitwise_or_ : t -> 'a Scalar.t -> t
Sourceval bitwise_or_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval bitwise_or_scalar_tensor : 'a Scalar.t -> t -> t
Sourceval bitwise_or_scalar_tensor_out : out:t -> 'a Scalar.t -> t -> t
Sourceval bitwise_or_tensor : t -> t -> t
Sourceval bitwise_or_tensor_ : t -> t -> t
Sourceval bitwise_or_tensor_out : out:t -> t -> t -> t
Sourceval bitwise_right_shift : t -> t -> t
Sourceval bitwise_right_shift_ : t -> t -> t
Sourceval bitwise_right_shift_scalar_tensor : 'a Scalar.t -> t -> t
Sourceval bitwise_right_shift_scalar_tensor_out : out:t -> 'a Scalar.t -> t -> t
Sourceval bitwise_right_shift_tensor_out : out:t -> t -> t -> t
Sourceval bitwise_right_shift_tensor_scalar : t -> 'a Scalar.t -> t
Sourceval bitwise_right_shift_tensor_scalar_ : t -> 'a Scalar.t -> t
Sourceval bitwise_right_shift_tensor_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval bitwise_xor : t -> 'a Scalar.t -> t
Sourceval bitwise_xor_ : t -> 'a Scalar.t -> t
Sourceval bitwise_xor_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval bitwise_xor_scalar_tensor : 'a Scalar.t -> t -> t
Sourceval bitwise_xor_scalar_tensor_out : out:t -> 'a Scalar.t -> t -> t
Sourceval bitwise_xor_tensor : t -> t -> t
Sourceval bitwise_xor_tensor_ : t -> t -> t
Sourceval bitwise_xor_tensor_out : out:t -> t -> t -> t
Sourceval blackman_window : window_length:int -> options:(Kind.packed * Device.t) -> t
Sourceval blackman_window_out : out:t -> window_length:int -> t
Sourceval blackman_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
Sourceval blackman_window_periodic_out : out:t -> window_length:int -> periodic:bool -> t
Sourceval block_diag : t list -> t
Sourceval block_diag_out : out:t -> t list -> t
Sourceval bmm : t -> mat2:t -> t
Sourceval bmm_out : out:t -> t -> mat2:t -> t
Sourceval broadcast_tensors : t list -> t list
Sourceval broadcast_to : t -> size:int list -> t
Sourceval bucketize : t -> boundaries:t -> out_int32:bool -> right:bool -> t
Sourceval bucketize_scalar : 'a Scalar.t -> boundaries:t -> out_int32:bool -> right:bool -> t
Sourceval bucketize_scalar_out : out:t -> 'a Scalar.t -> boundaries:t -> out_int32:bool -> right:bool -> t
Sourceval bucketize_tensor_out : out:t -> t -> boundaries:t -> out_int32:bool -> right:bool -> t
Sourceval cartesian_prod : t list -> t
Sourceval cat : t list -> dim:int -> t
Sourceval cat_out : out:t -> t list -> dim:int -> t
Sourceval cauchy : t -> median:float -> sigma:float -> t
Sourceval cauchy_ : t -> median:float -> sigma:float -> t
Sourceval cauchy_out : out:t -> t -> median:float -> sigma:float -> t
Sourceval ccol_indices : t -> t
Sourceval ccol_indices_copy : t -> t
Sourceval ccol_indices_copy_out : out:t -> t -> t
Sourceval cdist : x1:t -> x2:t -> p:float -> compute_mode:int option -> t
Sourceval ceil : t -> t
Sourceval ceil_ : t -> t
Sourceval ceil_out : out:t -> t -> t
Sourceval celu : t -> t
Sourceval celu_ : t -> t
Sourceval celu_out : out:t -> t -> t
Sourceval chain_matmul : matrices:t list -> t
Sourceval chain_matmul_out : out:t -> matrices:t list -> t
Sourceval chalf : t -> t
Sourceval channel_shuffle : t -> groups:int -> t
Sourceval channel_shuffle_out : out:t -> t -> groups:int -> t
Sourceval cholesky : t -> upper:bool -> t
Sourceval cholesky_inverse : t -> upper:bool -> t
Sourceval cholesky_inverse_out : out:t -> t -> upper:bool -> t
Sourceval cholesky_out : out:t -> t -> upper:bool -> t
Sourceval cholesky_solve : t -> input2:t -> upper:bool -> t
Sourceval cholesky_solve_out : out:t -> t -> input2:t -> upper:bool -> t
Sourceval choose_qparams_optimized : t -> numel:int -> n_bins:int -> ratio:float -> bit_width:int -> t * t
Sourceval chunk : t -> chunks:int -> dim:int -> t list
Sourceval clamp : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
Sourceval clamp_ : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
Sourceval clamp_max : t -> max:'a Scalar.t -> t
Sourceval clamp_max_ : t -> max:'a Scalar.t -> t
Sourceval clamp_max_out : out:t -> t -> max:'a Scalar.t -> t
Sourceval clamp_max_tensor : t -> max:t -> t
Sourceval clamp_max_tensor_ : t -> max:t -> t
Sourceval clamp_max_tensor_out : out:t -> t -> max:t -> t
Sourceval clamp_min : t -> min:'a Scalar.t -> t
Sourceval clamp_min_ : t -> min:'a Scalar.t -> t
Sourceval clamp_min_out : out:t -> t -> min:'a Scalar.t -> t
Sourceval clamp_min_tensor : t -> min:t -> t
Sourceval clamp_min_tensor_ : t -> min:t -> t
Sourceval clamp_min_tensor_out : out:t -> t -> min:t -> t
Sourceval clamp_out : out:t -> t -> min:'a Scalar.t -> max:'a Scalar.t -> t
Sourceval clamp_tensor : t -> min:t option -> max:t option -> t
Sourceval clamp_tensor_ : t -> min:t option -> max:t option -> t
Sourceval clamp_tensor_out : out:t -> t -> min:t option -> max:t option -> t
Sourceval clip : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
Sourceval clip_ : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
Sourceval clip_out : out:t -> t -> min:'a Scalar.t -> max:'a Scalar.t -> t
Sourceval clip_tensor : t -> min:t option -> max:t option -> t
Sourceval clip_tensor_ : t -> min:t option -> max:t option -> t
Sourceval clip_tensor_out : out:t -> t -> min:t option -> max:t option -> t
Sourceval clone : t -> t
Sourceval clone_out : out:t -> t -> t
Sourceval coalesce : t -> t
Sourceval col2im : t -> output_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
Sourceval col2im_out : out:t -> t -> output_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
Sourceval col_indices : t -> t
Sourceval col_indices_copy : t -> t
Sourceval col_indices_copy_out : out:t -> t -> t
Sourceval column_stack : t list -> t
Sourceval column_stack_out : out:t -> t list -> t
Sourceval combinations : t -> r:int -> with_replacement:bool -> t
Sourceval complex : real:t -> imag:t -> t
Sourceval complex_out : out:t -> real:t -> imag:t -> t
Sourceval concat : t list -> dim:int -> t
Sourceval concat_out : out:t -> t list -> dim:int -> t
Sourceval concatenate : t list -> dim:int -> t
Sourceval concatenate_out : out:t -> t list -> dim:int -> t
Sourceval conj : t -> t
Sourceval conj_physical : t -> t
Sourceval conj_physical_ : t -> t
Sourceval conj_physical_out : out:t -> t -> t
Sourceval constant_pad_nd : t -> pad:int list -> t
Sourceval constant_pad_nd_out : out:t -> t -> pad:int list -> t
Sourceval contiguous : t -> t
Sourceval conv1d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval conv1d_padding : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
Sourceval conv2d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval conv2d_padding : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
Sourceval conv3d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval conv3d_padding : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
Sourceval conv_depthwise3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval conv_depthwise3d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval conv_tbc : t -> weight:t -> bias:t -> pad:int -> t
Sourceval conv_tbc_backward : t -> t -> weight:t -> bias:t -> pad:int -> t * t * t
Sourceval conv_tbc_out : out:t -> t -> weight:t -> bias:t -> pad:int -> t
Sourceval conv_transpose1d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
Sourceval conv_transpose2d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
Sourceval conv_transpose3d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
Sourceval convolution : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> t
Sourceval convolution_out : out:t -> t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> t
Sourceval convolution_overrideable : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> t
Sourceval convolution_overrideable_out : out:t -> t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> t
Sourceval copy : t -> src:t -> non_blocking:bool -> t
Sourceval copy_out : out:t -> t -> src:t -> non_blocking:bool -> t
Sourceval copy_sparse_to_sparse : t -> src:t -> non_blocking:bool -> t
Sourceval copy_sparse_to_sparse_ : t -> src:t -> non_blocking:bool -> t
Sourceval copy_sparse_to_sparse_out : out:t -> t -> src:t -> non_blocking:bool -> t
Sourceval copysign : t -> t -> t
Sourceval copysign_ : t -> t -> t
Sourceval copysign_out : out:t -> t -> t -> t
Sourceval copysign_scalar : t -> 'a Scalar.t -> t
Sourceval copysign_scalar_ : t -> 'a Scalar.t -> t
Sourceval copysign_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval corrcoef : t -> t
Sourceval cos : t -> t
Sourceval cos_ : t -> t
Sourceval cos_out : out:t -> t -> t
Sourceval cosh : t -> t
Sourceval cosh_ : t -> t
Sourceval cosh_out : out:t -> t -> t
Sourceval cosine_embedding_loss : input1:t -> input2:t -> target:t -> margin:float -> reduction:Reduction.t -> t
Sourceval cosine_similarity : x1:t -> x2:t -> dim:int -> eps:float -> t
Sourceval count_nonzero : out:t -> t -> dim:int list -> t
Sourceval count_nonzero_out : out:t -> t -> dim:int option -> t
Sourceval cov : t -> correction:int -> fweights:t option -> aweights:t option -> t
Sourceval cross : t -> t -> dim:int option -> t
Sourceval cross_entropy_loss : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> label_smoothing:float -> t
Sourceval cross_out : out:t -> t -> t -> dim:int option -> t
Sourceval crow_indices : t -> t
Sourceval crow_indices_copy : t -> t
Sourceval crow_indices_copy_out : out:t -> t -> t
Sourceval ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> reduction:Reduction.t -> zero_infinity:bool -> t
Sourceval ctc_loss_tensor : log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> blank:int -> reduction:Reduction.t -> zero_infinity:bool -> t
Sourceval cudnn_affine_grid_generator : theta:t -> n:int -> c:int -> h:int -> w:int -> t
Sourceval cudnn_affine_grid_generator_backward : grad:t -> n:int -> c:int -> h:int -> w:int -> t
Sourceval cudnn_affine_grid_generator_backward_out : out:t -> grad:t -> n:int -> c:int -> h:int -> w:int -> t
Sourceval cudnn_affine_grid_generator_out : out:t -> theta:t -> n:int -> c:int -> h:int -> w:int -> t
Sourceval cudnn_batch_norm : t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t * t
Sourceval cudnn_batch_norm_backward : t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> reservespace:t -> t * t * t
Sourceval cudnn_batch_norm_backward_out : out0:t -> out1:t -> out2:t -> t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> reservespace:t -> t * t * t
Sourceval cudnn_batch_norm_out : out0:t -> out1:t -> out2:t -> out3:t -> t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t * t
Sourceval cudnn_convolution : t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
Sourceval cudnn_convolution_add_relu : t -> weight:t -> z:t -> alpha:'a Scalar.t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval cudnn_convolution_add_relu_out : out:t -> t -> weight:t -> z:t -> alpha:'a Scalar.t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval cudnn_convolution_out : out:t -> t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
Sourceval cudnn_convolution_relu : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval cudnn_convolution_relu_out : out:t -> t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval cudnn_convolution_transpose : t -> weight:t -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
Sourceval cudnn_convolution_transpose_out : out:t -> t -> weight:t -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
Sourceval cudnn_grid_sampler : t -> grid:t -> t
Sourceval cudnn_grid_sampler_backward : t -> grid:t -> grad_output:t -> t * t
Sourceval cudnn_grid_sampler_backward_out : out0:t -> out1:t -> t -> grid:t -> grad_output:t -> t * t
Sourceval cudnn_grid_sampler_out : out:t -> t -> grid:t -> t
Sourceval cummax : t -> dim:int -> t * t
Sourceval cummax_out : values:t -> indices:t -> t -> dim:int -> t * t
Sourceval cummaxmin_backward : grad:t -> t -> indices:t -> dim:int -> t
Sourceval cummin : t -> dim:int -> t * t
Sourceval cummin_out : values:t -> indices:t -> t -> dim:int -> t * t
Sourceval cumprod : t -> dim:int -> dtype:Kind.packed -> t
Sourceval cumprod_ : t -> dim:int -> dtype:Kind.packed -> t
Sourceval cumprod_backward : grad:t -> t -> dim:int -> output:t -> t
Sourceval cumprod_out : out:t -> t -> dim:int -> dtype:Kind.packed -> t
Sourceval cumsum : t -> dim:int -> dtype:Kind.packed -> t
Sourceval cumsum_ : t -> dim:int -> dtype:Kind.packed -> t
Sourceval cumsum_out : out:t -> t -> dim:int -> dtype:Kind.packed -> t
Sourceval cumulative_trapezoid : y:t -> dim:int -> t
Sourceval cumulative_trapezoid_x : y:t -> x:t -> dim:int -> t
Sourceval data : t -> t
Sourceval deg2rad : t -> t
Sourceval deg2rad_ : t -> t
Sourceval deg2rad_out : out:t -> t -> t
Sourceval dequantize : t -> t
Sourceval dequantize_self_out : out:t -> t -> t
Sourceval dequantize_tensors : t list -> t list
Sourceval dequantize_tensors_out : out:t list -> t list -> unit
Sourceval det : t -> t
Sourceval detach : t -> t
Sourceval detach_ : t -> t
Sourceval detach_copy : t -> t
Sourceval detach_copy_out : out:t -> t -> t
Sourceval diag : t -> diagonal:int -> t
Sourceval diag_backward : grad:t -> input_sizes:int list -> diagonal:int -> t
Sourceval diag_embed : t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diag_embed_out : out:t -> t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diag_out : out:t -> t -> diagonal:int -> t
Sourceval diagflat : t -> offset:int -> t
Sourceval diagonal : t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diagonal_backward : grad_output:t -> input_sizes:int list -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diagonal_backward_out : out:t -> grad_output:t -> input_sizes:int list -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diagonal_copy : t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diagonal_copy_out : out:t -> t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diagonal_scatter : t -> src:t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diagonal_scatter_out : out:t -> t -> src:t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval diff : t -> n:int -> dim:int -> prepend:t option -> append:t option -> t
Sourceval diff_out : out:t -> t -> n:int -> dim:int -> prepend:t option -> append:t option -> t
Sourceval digamma : t -> t
Sourceval digamma_ : t -> t
Sourceval digamma_out : out:t -> t -> t
Sourceval dist : t -> t -> t
Sourceval dist_out : out:t -> t -> t -> t
Sourceval div : t -> t -> t
Sourceval div_ : t -> t -> t
Sourceval div_out : out:t -> t -> t -> t
Sourceval div_out_mode : out:t -> t -> t -> rounding_mode:string -> t
Sourceval div_scalar : t -> 'a Scalar.t -> t
Sourceval div_scalar_ : t -> 'a Scalar.t -> t
Sourceval div_scalar_mode : t -> 'a Scalar.t -> rounding_mode:string -> t
Sourceval div_scalar_mode_ : t -> 'a Scalar.t -> rounding_mode:string -> t
Sourceval div_scalar_mode_out : out:t -> t -> 'a Scalar.t -> rounding_mode:string -> t
Sourceval div_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval div_tensor_mode : t -> t -> rounding_mode:string -> t
Sourceval div_tensor_mode_ : t -> t -> rounding_mode:string -> t
Sourceval divide : t -> t -> t
Sourceval divide_ : t -> t -> t
Sourceval divide_out : out:t -> t -> t -> t
Sourceval divide_out_mode : out:t -> t -> t -> rounding_mode:string -> t
Sourceval divide_scalar : t -> 'a Scalar.t -> t
Sourceval divide_scalar_ : t -> 'a Scalar.t -> t
Sourceval divide_scalar_mode : t -> 'a Scalar.t -> rounding_mode:string -> t
Sourceval divide_scalar_mode_ : t -> 'a Scalar.t -> rounding_mode:string -> t
Sourceval divide_tensor_mode : t -> t -> rounding_mode:string -> t
Sourceval divide_tensor_mode_ : t -> t -> rounding_mode:string -> t
Sourceval dot : t -> t -> t
Sourceval dot_out : out:t -> t -> t -> t
Sourceval dropout : t -> p:float -> train:bool -> t
Sourceval dropout_ : t -> p:float -> train:bool -> t
Sourceval dsplit : t -> sections:int -> t list
Sourceval dsplit_array : t -> indices:int list -> t list
Sourceval dstack : t list -> t
Sourceval dstack_out : out:t -> t list -> t
Sourceval einsum : equation:string -> t list -> path:int list option -> t
Sourceval elu : t -> t
Sourceval elu_ : t -> t
Sourceval elu_backward : grad_output:t -> alpha:'a Scalar.t -> scale:'a Scalar.t -> input_scale:'a Scalar.t -> is_result:bool -> self_or_result:t -> t
Sourceval elu_backward_grad_input : grad_input:t -> grad_output:t -> alpha:'a Scalar.t -> scale:'a Scalar.t -> input_scale:'a Scalar.t -> is_result:bool -> self_or_result:t -> t
Sourceval elu_out : out:t -> t -> t
Sourceval embedding : weight:t -> indices:t -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> t
Sourceval embedding_backward : grad:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> t
Sourceval embedding_bag : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> t * t * t * t
Sourceval embedding_bag_padding_idx : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int option -> t * t * t * t
Sourceval embedding_dense_backward : grad_output:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> t
Sourceval embedding_dense_backward_out : out:t -> grad_output:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> t
Sourceval embedding_out : out:t -> weight:t -> indices:t -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> t
Sourceval embedding_renorm : t -> indices:t -> max_norm:float -> norm_type:float -> t
Sourceval embedding_renorm_ : t -> indices:t -> max_norm:float -> norm_type:float -> t
Sourceval embedding_renorm_out : out:t -> t -> indices:t -> max_norm:float -> norm_type:float -> t
Sourceval embedding_sparse_backward : grad:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> t
Sourceval empty : size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval empty_like : t -> t
Sourceval empty_like_out : out:t -> t -> t
Sourceval empty_out : out:t -> size:int list -> t
Sourceval empty_quantized : size:int list -> qtensor:t -> options:(Kind.packed * Device.t) -> t
Sourceval empty_quantized_out : out:t -> size:int list -> qtensor:t -> t
Sourceval empty_strided : size:int list -> stride:int list -> options:(Kind.packed * Device.t) -> t
Sourceval empty_strided_out : out:t -> size:int list -> stride:int list -> t
Sourceval eq : t -> 'a Scalar.t -> t
Sourceval eq_ : t -> 'a Scalar.t -> t
Sourceval eq_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval eq_tensor : t -> t -> t
Sourceval eq_tensor_ : t -> t -> t
Sourceval eq_tensor_out : out:t -> t -> t -> t
Sourceval erf : t -> t
Sourceval erf_ : t -> t
Sourceval erf_out : out:t -> t -> t
Sourceval erfc : t -> t
Sourceval erfc_ : t -> t
Sourceval erfc_out : out:t -> t -> t
Sourceval erfinv : t -> t
Sourceval erfinv_ : t -> t
Sourceval erfinv_out : out:t -> t -> t
Sourceval exp : t -> t
Sourceval exp2 : t -> t
Sourceval exp2_ : t -> t
Sourceval exp2_out : out:t -> t -> t
Sourceval exp_ : t -> t
Sourceval exp_out : out:t -> t -> t
Sourceval expand : t -> size:int list -> implicit:bool -> t
Sourceval expand_as : t -> t -> t
Sourceval expand_copy : t -> size:int list -> implicit:bool -> t
Sourceval expand_copy_out : out:t -> t -> size:int list -> implicit:bool -> t
Sourceval expm1 : t -> t
Sourceval expm1_ : t -> t
Sourceval expm1_out : out:t -> t -> t
Sourceval exponential : t -> lambd:float -> t
Sourceval exponential_ : t -> lambd:float -> t
Sourceval exponential_out : out:t -> t -> lambd:float -> t
Sourceval eye : n:int -> options:(Kind.packed * Device.t) -> t
Sourceval eye_m : n:int -> m:int -> options:(Kind.packed * Device.t) -> t
Sourceval eye_m_out : out:t -> n:int -> m:int -> t
Sourceval eye_out : out:t -> n:int -> t
Sourceval fake_quantize_per_channel_affine : t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> t
Sourceval fake_quantize_per_channel_affine_cachemask : t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> t * t
Sourceval fake_quantize_per_channel_affine_cachemask_backward : grad:t -> mask:t -> t
Sourceval fake_quantize_per_channel_affine_cachemask_out : out0:t -> out1:t -> t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> t * t
Sourceval fake_quantize_per_tensor_affine : t -> scale:float -> zero_point:int -> quant_min:int -> quant_max:int -> t
Sourceval fake_quantize_per_tensor_affine_cachemask : t -> scale:float -> zero_point:int -> quant_min:int -> quant_max:int -> t * t
Sourceval fake_quantize_per_tensor_affine_cachemask_backward : grad:t -> mask:t -> t
Sourceval fake_quantize_per_tensor_affine_cachemask_out : out0:t -> out1:t -> t -> scale:float -> zero_point:int -> quant_min:int -> quant_max:int -> t * t
Sourceval fake_quantize_per_tensor_affine_tensor_qparams : t -> scale:t -> zero_point:t -> quant_min:int -> quant_max:int -> t
Sourceval fbgemm_linear_fp16_weight : t -> packed_weight:t -> bias:t -> t
Sourceval fbgemm_linear_fp16_weight_fp32_activation : t -> packed_weight:t -> bias:t -> t
Sourceval fbgemm_linear_int8_weight : t -> weight:t -> packed:t -> col_offsets:t -> weight_scale:'a Scalar.t -> weight_zero_point:'a Scalar.t -> bias:t -> t
Sourceval fbgemm_linear_int8_weight_fp32_activation : t -> weight:t -> packed:t -> col_offsets:t -> weight_scale:'a Scalar.t -> weight_zero_point:'a Scalar.t -> bias:t -> t
Sourceval fbgemm_pack_gemm_matrix_fp16 : t -> t
Sourceval fbgemm_pack_quantized_matrix : t -> t
Sourceval fbgemm_pack_quantized_matrix_kn : t -> k:int -> n:int -> t
Sourceval feature_alpha_dropout : t -> p:float -> train:bool -> t
Sourceval feature_alpha_dropout_ : t -> p:float -> train:bool -> t
Sourceval feature_dropout : t -> p:float -> train:bool -> t
Sourceval feature_dropout_ : t -> p:float -> train:bool -> t
Sourceval fft_fft : t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_fft2 : t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_fft2_out : out:t -> t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_fft_out : out:t -> t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_fftfreq : n:int -> d:float -> options:(Kind.packed * Device.t) -> t
Sourceval fft_fftfreq_out : out:t -> n:int -> d:float -> t
Sourceval fft_fftn : t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_fftn_out : out:t -> t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_fftshift : t -> dim:int list option -> t
Sourceval fft_hfft : t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_hfft2 : t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_hfft2_out : out:t -> t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_hfft_out : out:t -> t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_hfftn : t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_hfftn_out : out:t -> t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_ifft : t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_ifft2 : t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_ifft2_out : out:t -> t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_ifft_out : out:t -> t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_ifftn : t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_ifftn_out : out:t -> t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_ifftshift : t -> dim:int list option -> t
Sourceval fft_ihfft : t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_ihfft2 : t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_ihfft2_out : out:t -> t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_ihfft_out : out:t -> t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_ihfftn : t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_ihfftn_out : out:t -> t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_irfft : t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_irfft2 : t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_irfft2_out : out:t -> t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_irfft_out : out:t -> t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_irfftn : t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_irfftn_out : out:t -> t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_rfft : t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_rfft2 : t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_rfft2_out : out:t -> t -> s:int list option -> dim:int list -> norm:string -> t
Sourceval fft_rfft_out : out:t -> t -> n:int option -> dim:int -> norm:string -> t
Sourceval fft_rfftfreq : n:int -> d:float -> options:(Kind.packed * Device.t) -> t
Sourceval fft_rfftfreq_out : out:t -> n:int -> d:float -> t
Sourceval fft_rfftn : t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fft_rfftn_out : out:t -> t -> s:int list option -> dim:int list option -> norm:string -> t
Sourceval fill : t -> value:'a Scalar.t -> t
Sourceval fill_ : t -> value:'a Scalar.t -> t
Sourceval fill_diagonal_ : t -> fill_value:'a Scalar.t -> wrap:bool -> t
Sourceval fill_scalar_out : out:t -> t -> value:'a Scalar.t -> t
Sourceval fill_tensor : t -> value:t -> t
Sourceval fill_tensor_ : t -> value:t -> t
Sourceval fill_tensor_out : out:t -> t -> value:t -> t
Sourceval fix : t -> t
Sourceval fix_ : t -> t
Sourceval fix_out : out:t -> t -> t
Sourceval flatten : t -> start_dim:int -> end_dim:int -> t
Sourceval flatten_dense_tensors : t list -> t
Sourceval flip : t -> dims:int list -> t
Sourceval flip_out : out:t -> t -> dims:int list -> t
Sourceval fliplr : t -> t
Sourceval flipud : t -> t
Sourceval float_power : t -> exponent:t -> t
Sourceval float_power_ : t -> exponent:'a Scalar.t -> t
Sourceval float_power_scalar : 'a Scalar.t -> exponent:t -> t
Sourceval float_power_scalar_out : out:t -> 'a Scalar.t -> exponent:t -> t
Sourceval float_power_tensor_ : t -> exponent:t -> t
Sourceval float_power_tensor_scalar : t -> exponent:'a Scalar.t -> t
Sourceval float_power_tensor_scalar_out : out:t -> t -> exponent:'a Scalar.t -> t
Sourceval float_power_tensor_tensor_out : out:t -> t -> exponent:t -> t
Sourceval floor : t -> t
Sourceval floor_ : t -> t
Sourceval floor_divide : t -> t -> t
Sourceval floor_divide_ : t -> t -> t
Sourceval floor_divide_out : out:t -> t -> t -> t
Sourceval floor_divide_scalar : t -> 'a Scalar.t -> t
Sourceval floor_divide_scalar_ : t -> 'a Scalar.t -> t
Sourceval floor_out : out:t -> t -> t
Sourceval fmax : t -> t -> t
Sourceval fmax_out : out:t -> t -> t -> t
Sourceval fmin : t -> t -> t
Sourceval fmin_out : out:t -> t -> t -> t
Sourceval fmod : t -> 'a Scalar.t -> t
Sourceval fmod_ : t -> 'a Scalar.t -> t
Sourceval fmod_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval fmod_tensor : t -> t -> t
Sourceval fmod_tensor_ : t -> t -> t
Sourceval fmod_tensor_out : out:t -> t -> t -> t
Sourceval frac : t -> t
Sourceval frac_ : t -> t
Sourceval frac_out : out:t -> t -> t
Sourceval fractional_max_pool2d : t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
Sourceval fractional_max_pool2d_backward : grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
Sourceval fractional_max_pool2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
Sourceval fractional_max_pool2d_output : output:t -> indices:t -> t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
Sourceval fractional_max_pool3d : t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
Sourceval fractional_max_pool3d_backward : grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
Sourceval fractional_max_pool3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
Sourceval fractional_max_pool3d_output : output:t -> indices:t -> t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
Sourceval frexp : t -> t * t
Sourceval frexp_tensor_out : mantissa:t -> exponent:t -> t -> t * t
Sourceval frobenius_norm : t -> t
Sourceval frobenius_norm_dim : t -> dim:int list -> keepdim:bool -> t
Sourceval frobenius_norm_out : out:t -> t -> dim:int list -> keepdim:bool -> t
Sourceval from_file : filename:string -> shared:bool -> size:int option -> options:(Kind.packed * Device.t) -> t
Sourceval from_file_out : out:t -> filename:string -> shared:bool -> size:int option -> t
Sourceval full : size:int list -> fill_value:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval full_like : t -> fill_value:'a Scalar.t -> t
Sourceval full_like_out : out:t -> t -> fill_value:'a Scalar.t -> t
Sourceval full_out : out:t -> size:int list -> fill_value:'a Scalar.t -> t
Sourceval fused_moving_avg_obs_fake_quant : t -> observer_on:t -> fake_quant_on:t -> running_min:t -> running_max:t -> scale:t -> zero_point:t -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> t
Sourceval gather : t -> dim:int -> index:t -> sparse_grad:bool -> t
Sourceval gather_backward : grad:t -> t -> dim:int -> index:t -> sparse_grad:bool -> t
Sourceval gather_out : out:t -> t -> dim:int -> index:t -> sparse_grad:bool -> t
Sourceval gcd : t -> t -> t
Sourceval gcd_ : t -> t -> t
Sourceval gcd_out : out:t -> t -> t -> t
Sourceval ge : t -> 'a Scalar.t -> t
Sourceval ge_ : t -> 'a Scalar.t -> t
Sourceval ge_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval ge_tensor : t -> t -> t
Sourceval ge_tensor_ : t -> t -> t
Sourceval ge_tensor_out : out:t -> t -> t -> t
Sourceval gelu : t -> approximate:string -> t
Sourceval gelu_ : t -> approximate:string -> t
Sourceval gelu_backward : grad_output:t -> t -> approximate:string -> t
Sourceval gelu_backward_grad_input : grad_input:t -> grad_output:t -> t -> approximate:string -> t
Sourceval gelu_out : out:t -> t -> approximate:string -> t
Sourceval geometric : t -> p:float -> t
Sourceval geometric_ : t -> p:float -> t
Sourceval geometric_out : out:t -> t -> p:float -> t
Sourceval geqrf : t -> t * t
Sourceval geqrf_a : a:t -> tau:t -> t -> t * t
Sourceval ger : t -> vec2:t -> t
Sourceval ger_out : out:t -> t -> vec2:t -> t
Sourceval glu : t -> dim:int -> t
Sourceval glu_backward : grad_output:t -> t -> dim:int -> t
Sourceval glu_backward_grad_input : grad_input:t -> grad_output:t -> t -> dim:int -> t
Sourceval glu_backward_jvp : grad_x:t -> grad_glu:t -> x:t -> dgrad_glu:t -> dx:t -> dim:int -> t
Sourceval glu_backward_jvp_out : out:t -> grad_x:t -> grad_glu:t -> x:t -> dgrad_glu:t -> dx:t -> dim:int -> t
Sourceval glu_jvp : glu:t -> x:t -> dx:t -> dim:int -> t
Sourceval glu_jvp_out : out:t -> glu:t -> x:t -> dx:t -> dim:int -> t
Sourceval glu_out : out:t -> t -> dim:int -> t
Sourceval grad : t -> t
Sourceval greater : t -> 'a Scalar.t -> t
Sourceval greater_ : t -> 'a Scalar.t -> t
Sourceval greater_equal : t -> 'a Scalar.t -> t
Sourceval greater_equal_ : t -> 'a Scalar.t -> t
Sourceval greater_equal_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval greater_equal_tensor : t -> t -> t
Sourceval greater_equal_tensor_ : t -> t -> t
Sourceval greater_equal_tensor_out : out:t -> t -> t -> t
Sourceval greater_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval greater_tensor : t -> t -> t
Sourceval greater_tensor_ : t -> t -> t
Sourceval greater_tensor_out : out:t -> t -> t -> t
Sourceval grid_sampler : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
Sourceval grid_sampler_2d : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
Sourceval grid_sampler_2d_out : out:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
Sourceval grid_sampler_3d : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
Sourceval grid_sampler_3d_out : out:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
Sourceval group_norm : t -> num_groups:int -> weight:t option -> bias:t option -> eps:float -> cudnn_enabled:bool -> t
Sourceval gru : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
Sourceval gru_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
Sourceval gru_data : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
Sourceval gt : t -> 'a Scalar.t -> t
Sourceval gt_ : t -> 'a Scalar.t -> t
Sourceval gt_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval gt_tensor : t -> t -> t
Sourceval gt_tensor_ : t -> t -> t
Sourceval gt_tensor_out : out:t -> t -> t -> t
Sourceval hamming_window : window_length:int -> options:(Kind.packed * Device.t) -> t
Sourceval hamming_window_out : out:t -> window_length:int -> t
Sourceval hamming_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
Sourceval hamming_window_periodic_alpha : window_length:int -> periodic:bool -> alpha:float -> options:(Kind.packed * Device.t) -> t
Sourceval hamming_window_periodic_alpha_beta : window_length:int -> periodic:bool -> alpha:float -> beta:float -> options:(Kind.packed * Device.t) -> t
Sourceval hamming_window_periodic_alpha_beta_out : out:t -> window_length:int -> periodic:bool -> alpha:float -> beta:float -> t
Sourceval hamming_window_periodic_alpha_out : out:t -> window_length:int -> periodic:bool -> alpha:float -> t
Sourceval hamming_window_periodic_out : out:t -> window_length:int -> periodic:bool -> t
Sourceval hann_window : window_length:int -> options:(Kind.packed * Device.t) -> t
Sourceval hann_window_out : out:t -> window_length:int -> t
Sourceval hann_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
Sourceval hann_window_periodic_out : out:t -> window_length:int -> periodic:bool -> t
Sourceval hardshrink : t -> t
Sourceval hardshrink_backward : grad_out:t -> t -> lambd:'a Scalar.t -> t
Sourceval hardshrink_backward_grad_input : grad_input:t -> grad_out:t -> t -> lambd:'a Scalar.t -> t
Sourceval hardshrink_out : out:t -> t -> t
Sourceval hardsigmoid : t -> t
Sourceval hardsigmoid_ : t -> t
Sourceval hardsigmoid_backward : grad_output:t -> t -> t
Sourceval hardsigmoid_backward_grad_input : grad_input:t -> grad_output:t -> t -> t
Sourceval hardsigmoid_out : out:t -> t -> t
Sourceval hardswish : t -> t
Sourceval hardswish_ : t -> t
Sourceval hardswish_backward : grad_output:t -> t -> t
Sourceval hardswish_backward_out : out:t -> grad_output:t -> t -> t
Sourceval hardswish_out : out:t -> t -> t
Sourceval hardtanh : t -> t
Sourceval hardtanh_ : t -> t
Sourceval hardtanh_backward : grad_output:t -> t -> min_val:'a Scalar.t -> max_val:'a Scalar.t -> t
Sourceval hardtanh_backward_grad_input : grad_input:t -> grad_output:t -> t -> min_val:'a Scalar.t -> max_val:'a Scalar.t -> t
Sourceval hardtanh_out : out:t -> t -> t
Sourceval heaviside : t -> values:t -> t
Sourceval heaviside_ : t -> values:t -> t
Sourceval heaviside_out : out:t -> t -> values:t -> t
Sourceval hinge_embedding_loss : t -> target:t -> margin:float -> reduction:Reduction.t -> t
Sourceval histc : t -> bins:int -> t
Sourceval histc_out : out:t -> t -> bins:int -> t
Sourceval hsplit : t -> sections:int -> t list
Sourceval hsplit_array : t -> indices:int list -> t list
Sourceval hspmm : mat1:t -> mat2:t -> t
Sourceval hspmm_out : out:t -> mat1:t -> mat2:t -> t
Sourceval hstack : t list -> t
Sourceval hstack_out : out:t -> t list -> t
Sourceval huber_loss : t -> target:t -> reduction:Reduction.t -> delta:float -> t
Sourceval huber_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> delta:float -> t
Sourceval huber_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> delta:float -> t
Sourceval huber_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> delta:float -> t
Sourceval hypot : t -> t -> t
Sourceval hypot_ : t -> t -> t
Sourceval hypot_out : out:t -> t -> t -> t
Sourceval i0 : t -> t
Sourceval i0_ : t -> t
Sourceval i0_out : out:t -> t -> t
Sourceval igamma : t -> t -> t
Sourceval igamma_ : t -> t -> t
Sourceval igamma_out : out:t -> t -> t -> t
Sourceval igammac : t -> t -> t
Sourceval igammac_ : t -> t -> t
Sourceval igammac_out : out:t -> t -> t -> t
Sourceval im2col : t -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
Sourceval im2col_out : out:t -> t -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
Sourceval imag : t -> t
Sourceval index : t -> indices:t option list -> t
Sourceval index_add : t -> dim:int -> index:t -> source:t -> t
Sourceval index_add_ : t -> dim:int -> index:t -> source:t -> t
Sourceval index_add_out : out:t -> t -> dim:int -> index:t -> source:t -> t
Sourceval index_copy : t -> dim:int -> index:t -> source:t -> t
Sourceval index_copy_ : t -> dim:int -> index:t -> source:t -> t
Sourceval index_copy_out : out:t -> t -> dim:int -> index:t -> source:t -> t
Sourceval index_fill : t -> dim:int -> index:t -> value:'a Scalar.t -> t
Sourceval index_fill_ : t -> dim:int -> index:t -> value:'a Scalar.t -> t
Sourceval index_fill_int_scalar_out : out:t -> t -> dim:int -> index:t -> value:'a Scalar.t -> t
Sourceval index_fill_int_tensor : t -> dim:int -> index:t -> value:t -> t
Sourceval index_fill_int_tensor_ : t -> dim:int -> index:t -> value:t -> t
Sourceval index_fill_int_tensor_out : out:t -> t -> dim:int -> index:t -> value:t -> t
Sourceval index_put : t -> indices:t option list -> values:t -> accumulate:bool -> t
Sourceval index_put_ : t -> indices:t option list -> values:t -> accumulate:bool -> t
Sourceval index_put_out : out:t -> t -> indices:t option list -> values:t -> accumulate:bool -> t
Sourceval index_reduce : t -> dim:int -> index:t -> source:t -> reduce:string -> include_self:bool -> t
Sourceval index_reduce_ : t -> dim:int -> index:t -> source:t -> reduce:string -> include_self:bool -> t
Sourceval index_reduce_out : out:t -> t -> dim:int -> index:t -> source:t -> reduce:string -> include_self:bool -> t
Sourceval index_select : t -> dim:int -> index:t -> t
Sourceval index_select_backward : grad:t -> self_sizes:int list -> dim:int -> index:t -> t
Sourceval index_select_out : out:t -> t -> dim:int -> index:t -> t
Sourceval index_tensor_out : out:t -> t -> indices:t option list -> t
Sourceval indices : t -> t
Sourceval indices_copy : t -> t
Sourceval indices_copy_out : out:t -> t -> t
Sourceval infinitely_differentiable_gelu_backward : grad:t -> t -> t
Sourceval inner : t -> t -> t
Sourceval inner_out : out:t -> t -> t -> t
Sourceval instance_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> use_input_stats:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> t
Sourceval int_repr : t -> t
Sourceval int_repr_out : out:t -> t -> t
Sourceval inverse : t -> t
Sourceval inverse_out : out:t -> t -> t
Sourceval isclose : t -> t -> rtol:float -> atol:float -> equal_nan:bool -> t
Sourceval isfinite : t -> t
Sourceval isin : elements:t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_scalar_tensor : element:'a Scalar.t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_scalar_tensor_out : out:t -> element:'a Scalar.t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_tensor_scalar : elements:t -> test_element:'a Scalar.t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_tensor_scalar_out : out:t -> elements:t -> test_element:'a Scalar.t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_tensor_tensor_out : out:t -> elements:t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
Sourceval isinf : t -> t
Sourceval isinf_out : out:t -> t -> t
Sourceval isnan : t -> t
Sourceval isnan_out : out:t -> t -> t
Sourceval isneginf : t -> t
Sourceval isneginf_out : out:t -> t -> t
Sourceval isposinf : t -> t
Sourceval isposinf_out : out:t -> t -> t
Sourceval isreal : t -> t
Sourceval istft : t -> n_fft:int -> hop_length:int option -> win_length:int option -> window:t option -> center:bool -> normalized:bool -> onesided:bool -> length:int option -> return_complex:bool -> t
Sourceval kaiser_window : window_length:int -> options:(Kind.packed * Device.t) -> t
Sourceval kaiser_window_beta : window_length:int -> periodic:bool -> beta:float -> options:(Kind.packed * Device.t) -> t
Sourceval kaiser_window_beta_out : out:t -> window_length:int -> periodic:bool -> beta:float -> t
Sourceval kaiser_window_out : out:t -> window_length:int -> t
Sourceval kaiser_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
Sourceval kaiser_window_periodic_out : out:t -> window_length:int -> periodic:bool -> t
Sourceval kl_div : t -> target:t -> reduction:Reduction.t -> log_target:bool -> t
Sourceval kron : t -> t -> t
Sourceval kron_out : out:t -> t -> t -> t
Sourceval kthvalue : t -> k:int -> dim:int -> keepdim:bool -> t * t
Sourceval kthvalue_values : values:t -> indices:t -> t -> k:int -> dim:int -> keepdim:bool -> t * t
Sourceval l1_loss : t -> target:t -> reduction:Reduction.t -> t
Sourceval layer_norm : t -> normalized_shape:int list -> weight:t option -> bias:t option -> eps:float -> cudnn_enable:bool -> t
Sourceval lcm : t -> t -> t
Sourceval lcm_ : t -> t -> t
Sourceval lcm_out : out:t -> t -> t -> t
Sourceval ldexp : t -> t -> t
Sourceval ldexp_ : t -> t -> t
Sourceval ldexp_out : out:t -> t -> t -> t
Sourceval le : t -> 'a Scalar.t -> t
Sourceval le_ : t -> 'a Scalar.t -> t
Sourceval le_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval le_tensor : t -> t -> t
Sourceval le_tensor_ : t -> t -> t
Sourceval le_tensor_out : out:t -> t -> t -> t
Sourceval leaky_relu : t -> t
Sourceval leaky_relu_ : t -> t
Sourceval leaky_relu_backward : grad_output:t -> t -> negative_slope:'a Scalar.t -> self_is_result:bool -> t
Sourceval leaky_relu_backward_grad_input : grad_input:t -> grad_output:t -> t -> negative_slope:'a Scalar.t -> self_is_result:bool -> t
Sourceval leaky_relu_out : out:t -> t -> t
Sourceval lerp : t -> end_:t -> weight:'a Scalar.t -> t
Sourceval lerp_ : t -> end_:t -> weight:'a Scalar.t -> t
Sourceval lerp_scalar_out : out:t -> t -> end_:t -> weight:'a Scalar.t -> t
Sourceval lerp_tensor : t -> end_:t -> weight:t -> t
Sourceval lerp_tensor_ : t -> end_:t -> weight:t -> t
Sourceval lerp_tensor_out : out:t -> t -> end_:t -> weight:t -> t
Sourceval less : t -> 'a Scalar.t -> t
Sourceval less_ : t -> 'a Scalar.t -> t
Sourceval less_equal : t -> 'a Scalar.t -> t
Sourceval less_equal_ : t -> 'a Scalar.t -> t
Sourceval less_equal_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval less_equal_tensor : t -> t -> t
Sourceval less_equal_tensor_ : t -> t -> t
Sourceval less_equal_tensor_out : out:t -> t -> t -> t
Sourceval less_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval less_tensor : t -> t -> t
Sourceval less_tensor_ : t -> t -> t
Sourceval less_tensor_out : out:t -> t -> t -> t
Sourceval lgamma : t -> t
Sourceval lgamma_ : t -> t
Sourceval lgamma_out : out:t -> t -> t
Sourceval lift : t -> t
Sourceval lift_fresh : t -> t
Sourceval lift_fresh_copy : t -> t
Sourceval lift_fresh_copy_out : out:t -> t -> t
Sourceval lift_out : out:t -> t -> t
Sourceval linalg_cholesky : t -> upper:bool -> t
Sourceval linalg_cholesky_ex : t -> upper:bool -> check_errors:bool -> t * t
Sourceval linalg_cholesky_ex_l : l:t -> info:t -> t -> upper:bool -> check_errors:bool -> t * t
Sourceval linalg_cholesky_out : out:t -> t -> upper:bool -> t
Sourceval linalg_cond : t -> p:'a Scalar.t -> t
Sourceval linalg_cond_out : out:t -> t -> p:'a Scalar.t -> t
Sourceval linalg_cond_p_str : t -> p:string -> t
Sourceval linalg_cond_p_str_out : out:t -> t -> p:string -> t
Sourceval linalg_cross : t -> t -> dim:int -> t
Sourceval linalg_cross_out : out:t -> t -> t -> dim:int -> t
Sourceval linalg_det : a:t -> t
Sourceval linalg_det_out : out:t -> a:t -> t
Sourceval linalg_diagonal : a:t -> offset:int -> dim1:int -> dim2:int -> t
Sourceval linalg_eig : t -> t * t
Sourceval linalg_eig_out : eigenvalues:t -> eigenvectors:t -> t -> t * t
Sourceval linalg_eigh : t -> uplo:string -> t * t
Sourceval linalg_eigh_eigvals : eigvals:t -> eigvecs:t -> t -> uplo:string -> t * t
Sourceval linalg_eigvals : t -> t
Sourceval linalg_eigvals_out : out:t -> t -> t
Sourceval linalg_eigvalsh : t -> uplo:string -> t
Sourceval linalg_eigvalsh_out : out:t -> t -> uplo:string -> t
Sourceval linalg_householder_product : t -> tau:t -> t
Sourceval linalg_householder_product_out : out:t -> t -> tau:t -> t
Sourceval linalg_inv : a:t -> t
Sourceval linalg_inv_ex : a:t -> check_errors:bool -> t * t
Sourceval linalg_inv_ex_inverse : inverse:t -> info:t -> a:t -> check_errors:bool -> t * t
Sourceval linalg_inv_out : out:t -> a:t -> t
Sourceval linalg_ldl_factor : t -> hermitian:bool -> t * t
Sourceval linalg_ldl_factor_ex : t -> hermitian:bool -> check_errors:bool -> t * t * t
Sourceval linalg_ldl_factor_ex_out : ld:t -> pivots:t -> info:t -> t -> hermitian:bool -> check_errors:bool -> t * t * t
Sourceval linalg_ldl_factor_out : ld:t -> pivots:t -> t -> hermitian:bool -> t * t
Sourceval linalg_ldl_solve : ld:t -> pivots:t -> b:t -> hermitian:bool -> t
Sourceval linalg_ldl_solve_out : out:t -> ld:t -> pivots:t -> b:t -> hermitian:bool -> t
Sourceval linalg_lstsq : t -> b:t -> rcond:float option -> driver:string -> t * t * t * t
Sourceval linalg_lstsq_out : solution:t -> residuals:t -> rank:t -> singular_values:t -> t -> b:t -> rcond:float option -> driver:string -> t * t * t * t
Sourceval linalg_lu : a:t -> pivot:bool -> t * t * t
Sourceval linalg_lu_factor : a:t -> pivot:bool -> t * t
Sourceval linalg_lu_factor_ex : a:t -> pivot:bool -> check_errors:bool -> t * t * t
Sourceval linalg_lu_factor_ex_out : lu:t -> pivots:t -> info:t -> a:t -> pivot:bool -> check_errors:bool -> t * t * t
Sourceval linalg_lu_factor_out : lu:t -> pivots:t -> a:t -> pivot:bool -> t * t
Sourceval linalg_lu_out : p:t -> l:t -> u:t -> a:t -> pivot:bool -> t * t * t
Sourceval linalg_lu_solve : lu:t -> pivots:t -> b:t -> left:bool -> adjoint:bool -> t
Sourceval linalg_lu_solve_out : out:t -> lu:t -> pivots:t -> b:t -> left:bool -> adjoint:bool -> t
Sourceval linalg_matmul : t -> t -> t
Sourceval linalg_matmul_out : out:t -> t -> t -> t
Sourceval linalg_matrix_exp : t -> t
Sourceval linalg_matrix_exp_out : out:t -> t -> t
Sourceval linalg_matrix_power : t -> n:int -> t
Sourceval linalg_matrix_power_out : out:t -> t -> n:int -> t
Sourceval linalg_matrix_rank : t -> tol:float -> hermitian:bool -> t
Sourceval linalg_matrix_rank_atol_rtol_float : t -> atol:float option -> rtol:float option -> hermitian:bool -> t
Sourceval linalg_matrix_rank_atol_rtol_float_out : out:t -> t -> atol:float option -> rtol:float option -> hermitian:bool -> t
Sourceval linalg_matrix_rank_atol_rtol_tensor : t -> atol:t option -> rtol:t option -> hermitian:bool -> t
Sourceval linalg_matrix_rank_atol_rtol_tensor_out : out:t -> t -> atol:t option -> rtol:t option -> hermitian:bool -> t
Sourceval linalg_matrix_rank_out : out:t -> t -> tol:float -> hermitian:bool -> t
Sourceval linalg_matrix_rank_out_tol_tensor : out:t -> t -> tol:t -> hermitian:bool -> t
Sourceval linalg_matrix_rank_tol_tensor : t -> tol:t -> hermitian:bool -> t
Sourceval linalg_multi_dot : t list -> t
Sourceval linalg_multi_dot_out : out:t -> t list -> t
Sourceval linalg_pinv : t -> rcond:float -> hermitian:bool -> t
Sourceval linalg_pinv_atol_rtol_float : t -> atol:float option -> rtol:float option -> hermitian:bool -> t
Sourceval linalg_pinv_atol_rtol_float_out : out:t -> t -> atol:float option -> rtol:float option -> hermitian:bool -> t
Sourceval linalg_pinv_atol_rtol_tensor : t -> atol:t option -> rtol:t option -> hermitian:bool -> t
Sourceval linalg_pinv_atol_rtol_tensor_out : out:t -> t -> atol:t option -> rtol:t option -> hermitian:bool -> t
Sourceval linalg_pinv_out : out:t -> t -> rcond:float -> hermitian:bool -> t
Sourceval linalg_pinv_out_rcond_tensor : out:t -> t -> rcond:t -> hermitian:bool -> t
Sourceval linalg_pinv_rcond_tensor : t -> rcond:t -> hermitian:bool -> t
Sourceval linalg_qr : a:t -> mode:string -> t * t
Sourceval linalg_qr_out : q:t -> r:t -> a:t -> mode:string -> t * t
Sourceval linalg_slogdet : a:t -> t * t
Sourceval linalg_slogdet_out : sign:t -> logabsdet:t -> a:t -> t * t
Sourceval linalg_solve : a:t -> b:t -> left:bool -> t
Sourceval linalg_solve_ex : a:t -> b:t -> left:bool -> check_errors:bool -> t * t
Sourceval linalg_solve_ex_out : t -> info:t -> a:t -> b:t -> left:bool -> check_errors:bool -> t * t
Sourceval linalg_solve_out : out:t -> a:t -> b:t -> left:bool -> t
Sourceval linalg_solve_triangular : t -> b:t -> upper:bool -> left:bool -> unitriangular:bool -> t
Sourceval linalg_solve_triangular_out : out:t -> t -> b:t -> upper:bool -> left:bool -> unitriangular:bool -> t
Sourceval linalg_svd : a:t -> full_matrices:bool -> driver:string -> t * t * t
Sourceval linalg_svd_u : u:t -> s:t -> vh:t -> a:t -> full_matrices:bool -> driver:string -> t * t * t
Sourceval linalg_svdvals : a:t -> driver:string -> t
Sourceval linalg_svdvals_out : out:t -> a:t -> driver:string -> t
Sourceval linalg_tensorinv : t -> ind:int -> t
Sourceval linalg_tensorinv_out : out:t -> t -> ind:int -> t
Sourceval linalg_tensorsolve : t -> t -> dims:int list option -> t
Sourceval linalg_tensorsolve_out : out:t -> t -> t -> dims:int list option -> t
Sourceval linalg_vander : x:t -> n:int option -> t
Sourceval linalg_vecdot : x:t -> y:t -> dim:int -> t
Sourceval linalg_vecdot_out : out:t -> x:t -> y:t -> dim:int -> t
Sourceval linear : t -> weight:t -> bias:t option -> t
Sourceval linear_out : out:t -> t -> weight:t -> bias:t option -> t
Sourceval linspace : start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> options:(Kind.packed * Device.t) -> t
Sourceval linspace_out : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> t
Sourceval log : t -> t
Sourceval log10 : t -> t
Sourceval log10_ : t -> t
Sourceval log10_out : out:t -> t -> t
Sourceval log1p : t -> t
Sourceval log1p_ : t -> t
Sourceval log1p_out : out:t -> t -> t
Sourceval log2 : t -> t
Sourceval log2_ : t -> t
Sourceval log2_out : out:t -> t -> t
Sourceval log_ : t -> t
Sourceval log_normal : t -> mean:float -> std:float -> t
Sourceval log_normal_ : t -> mean:float -> std:float -> t
Sourceval log_normal_out : out:t -> t -> mean:float -> std:float -> t
Sourceval log_out : out:t -> t -> t
Sourceval log_sigmoid : t -> t
Sourceval log_sigmoid_backward : grad_output:t -> t -> buffer:t -> t
Sourceval log_sigmoid_backward_grad_input : grad_input:t -> grad_output:t -> t -> buffer:t -> t
Sourceval log_sigmoid_out : out:t -> t -> t
Sourceval log_softmax : t -> dim:int -> dtype:Kind.packed -> t
Sourceval log_softmax_int_out : out:t -> t -> dim:int -> dtype:Kind.packed -> t
Sourceval logaddexp : t -> t -> t
Sourceval logaddexp2 : t -> t -> t
Sourceval logaddexp2_out : out:t -> t -> t -> t
Sourceval logaddexp_out : out:t -> t -> t -> t
Sourceval logcumsumexp : t -> dim:int -> t
Sourceval logcumsumexp_out : out:t -> t -> dim:int -> t
Sourceval logdet : t -> t
Sourceval logical_and : t -> t -> t
Sourceval logical_and_ : t -> t -> t
Sourceval logical_and_out : out:t -> t -> t -> t
Sourceval logical_not : t -> t
Sourceval logical_not_ : t -> t
Sourceval logical_not_out : out:t -> t -> t
Sourceval logical_or : t -> t -> t
Sourceval logical_or_ : t -> t -> t
Sourceval logical_or_out : out:t -> t -> t -> t
Sourceval logical_xor : t -> t -> t
Sourceval logical_xor_ : t -> t -> t
Sourceval logical_xor_out : out:t -> t -> t -> t
Sourceval logit : t -> eps:float option -> t
Sourceval logit_ : t -> eps:float option -> t
Sourceval logit_backward : grad_output:t -> t -> eps:float option -> t
Sourceval logit_backward_grad_input : grad_input:t -> grad_output:t -> t -> eps:float option -> t
Sourceval logit_out : out:t -> t -> eps:float option -> t
Sourceval logspace : start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> base:float -> options:(Kind.packed * Device.t) -> t
Sourceval logspace_out : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> base:float -> t
Sourceval logsumexp : t -> dim:int list -> keepdim:bool -> t
Sourceval logsumexp_out : out:t -> t -> dim:int list -> keepdim:bool -> t
Sourceval lstm : t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t * t
Sourceval lstm_cell : t -> hx:t list -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t * t
Sourceval lstm_data : data:t -> batch_sizes:t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t * t
Sourceval lstm_mps_backward : out0:t -> out1:t list -> out2:t list -> grad_y:t -> grad_hy:t option -> grad_cy:t option -> z_state:t -> cell_state_fwd:t -> t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> unit
Sourceval lt : t -> 'a Scalar.t -> t
Sourceval lt_ : t -> 'a Scalar.t -> t
Sourceval lt_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval lt_tensor : t -> t -> t
Sourceval lt_tensor_ : t -> t -> t
Sourceval lt_tensor_out : out:t -> t -> t -> t
Sourceval lu_solve : t -> lu_data:t -> lu_pivots:t -> t
Sourceval lu_solve_out : out:t -> t -> lu_data:t -> lu_pivots:t -> t
Sourceval lu_unpack : lu_data:t -> lu_pivots:t -> unpack_data:bool -> unpack_pivots:bool -> t * t * t
Sourceval lu_unpack_out : p:t -> l:t -> u:t -> lu_data:t -> lu_pivots:t -> unpack_data:bool -> unpack_pivots:bool -> t * t * t
Sourceval margin_ranking_loss : input1:t -> input2:t -> target:t -> margin:float -> reduction:Reduction.t -> t
Sourceval masked_fill : t -> mask:t -> value:'a Scalar.t -> t
Sourceval masked_fill_ : t -> mask:t -> value:'a Scalar.t -> t
Sourceval masked_fill_scalar_out : out:t -> t -> mask:t -> value:'a Scalar.t -> t
Sourceval masked_fill_tensor : t -> mask:t -> value:t -> t
Sourceval masked_fill_tensor_ : t -> mask:t -> value:t -> t
Sourceval masked_fill_tensor_out : out:t -> t -> mask:t -> value:t -> t
Sourceval masked_scatter : t -> mask:t -> source:t -> t
Sourceval masked_scatter_ : t -> mask:t -> source:t -> t
Sourceval masked_scatter_out : out:t -> t -> mask:t -> source:t -> t
Sourceval masked_select : t -> mask:t -> t
Sourceval masked_select_backward : grad:t -> t -> mask:t -> t
Sourceval masked_select_out : out:t -> t -> mask:t -> t
Sourceval matmul : t -> t -> t
Sourceval matmul_out : out:t -> t -> t -> t
Sourceval matrix_exp : t -> t
Sourceval matrix_exp_backward : t -> grad:t -> t
Sourceval matrix_h : t -> t
Sourceval matrix_power : t -> n:int -> t
Sourceval matrix_power_out : out:t -> t -> n:int -> t
Sourceval max_dim : t -> dim:int -> keepdim:bool -> t * t
Sourceval max_dim_max : max:t -> max_values:t -> t -> dim:int -> keepdim:bool -> t * t
Sourceval max_other : t -> t -> t
Sourceval max_out : out:t -> t -> t -> t
Sourceval max_pool1d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval max_pool1d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
Sourceval max_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval max_pool2d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
Sourceval max_pool2d_with_indices_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
Sourceval max_pool2d_with_indices_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
Sourceval max_pool2d_with_indices_out : out:t -> indices:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
Sourceval max_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval max_pool3d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
Sourceval max_pool3d_with_indices_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
Sourceval max_pool3d_with_indices_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
Sourceval max_pool3d_with_indices_out : out:t -> indices:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
Sourceval max_unpool2d : t -> indices:t -> output_size:int list -> t
Sourceval max_unpool2d_out : out:t -> t -> indices:t -> output_size:int list -> t
Sourceval max_unpool3d : t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
Sourceval max_unpool3d_out : out:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
Sourceval maximum : t -> t -> t
Sourceval maximum_out : out:t -> t -> t -> t
Sourceval mean_dim : t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval mean_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval median : t -> t
Sourceval median_dim : t -> dim:int -> keepdim:bool -> t * t
Sourceval median_dim_values : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
Sourceval median_out : out:t -> t -> t
Sourceval meshgrid : t list -> t list
Sourceval meshgrid_indexing : t list -> indexing:string -> t list
Sourceval mh : t -> t
Sourceval min_dim : t -> dim:int -> keepdim:bool -> t * t
Sourceval min_dim_min : min:t -> min_indices:t -> t -> dim:int -> keepdim:bool -> t * t
Sourceval min_other : t -> t -> t
Sourceval min_out : out:t -> t -> t -> t
Sourceval minimum : t -> t -> t
Sourceval minimum_out : out:t -> t -> t -> t
Sourceval miopen_batch_norm : t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t
Sourceval miopen_batch_norm_backward : t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> t * t * t
Sourceval miopen_batch_norm_backward_out : out0:t -> out1:t -> out2:t -> t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> t * t * t
Sourceval miopen_batch_norm_out : out0:t -> out1:t -> out2:t -> t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t
Sourceval miopen_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
Sourceval miopen_convolution_add_relu : t -> weight:t -> z:t -> alpha:'a Scalar.t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval miopen_convolution_out : out:t -> t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
Sourceval miopen_convolution_relu : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
Sourceval miopen_convolution_transpose : t -> weight:t -> bias:t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
Sourceval miopen_convolution_transpose_out : out:t -> t -> weight:t -> bias:t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
Sourceval miopen_depthwise_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
Sourceval miopen_depthwise_convolution_out : out:t -> t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
Sourceval miopen_rnn : t -> weight:t list -> weight_stride0:int -> hx:t -> cx:t option -> mode:int -> hidden_size:int -> num_layers:int -> batch_first:bool -> dropout:float -> train:bool -> bidirectional:bool -> batch_sizes:int list -> dropout_state:t option -> t * t * t * t * t
Sourceval miopen_rnn_out : out0:t -> out1:t -> out2:t -> out3:t -> out4:t -> t -> weight:t list -> weight_stride0:int -> hx:t -> cx:t option -> mode:int -> hidden_size:int -> num_layers:int -> batch_first:bool -> dropout:float -> train:bool -> bidirectional:bool -> batch_sizes:int list -> dropout_state:t option -> t * t * t * t * t
Sourceval mish : t -> t
Sourceval mish_ : t -> t
Sourceval mish_backward : grad_output:t -> t -> t
Sourceval mish_out : out:t -> t -> t
Sourceval mkldnn_adaptive_avg_pool2d : t -> output_size:int list -> t
Sourceval mkldnn_adaptive_avg_pool2d_backward : grad_output:t -> t -> t
Sourceval mkldnn_adaptive_avg_pool2d_backward_out : out:t -> grad_output:t -> t -> t
Sourceval mkldnn_adaptive_avg_pool2d_out : out:t -> t -> output_size:int list -> t
Sourceval mkldnn_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval mkldnn_convolution_out : out:t -> t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval mkldnn_linear : t -> weight:t -> bias:t option -> t
Sourceval mkldnn_linear_backward_input : input_size:int list -> grad_output:t -> weight:t -> t
Sourceval mkldnn_linear_backward_input_out : out:t -> input_size:int list -> grad_output:t -> weight:t -> t
Sourceval mkldnn_linear_backward_weights : grad_output:t -> t -> weight:t -> bias_defined:bool -> t * t
Sourceval mkldnn_linear_backward_weights_out : out0:t -> out1:t -> grad_output:t -> t -> weight:t -> bias_defined:bool -> t * t
Sourceval mkldnn_linear_out : out:t -> t -> weight:t -> bias:t option -> t
Sourceval mkldnn_max_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_max_pool2d_backward : grad_output:t -> output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_max_pool2d_backward_out : out:t -> grad_output:t -> output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_max_pool2d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_max_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_max_pool3d_backward : grad_output:t -> output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_max_pool3d_backward_out : out:t -> grad_output:t -> output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_max_pool3d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mkldnn_reorder_conv2d_weight : t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval mkldnn_reorder_conv2d_weight_out : out:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval mkldnn_reorder_conv3d_weight : t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval mkldnn_reorder_conv3d_weight_out : out:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
Sourceval mm : t -> mat2:t -> t
Sourceval mm_out : out:t -> t -> mat2:t -> t
Sourceval mode : t -> dim:int -> keepdim:bool -> t * t
Sourceval mode_values : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
Sourceval moveaxis : t -> source:int list -> destination:int list -> t
Sourceval moveaxis_int : t -> source:int -> destination:int -> t
Sourceval movedim : t -> source:int list -> destination:int list -> t
Sourceval movedim_int : t -> source:int -> destination:int -> t
Sourceval mps_max_pool2d_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mps_max_pool2d_backward_out : out:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval mse_loss : t -> target:t -> reduction:Reduction.t -> t
Sourceval mse_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
Sourceval mse_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
Sourceval mse_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> t
Sourceval msort : t -> t
Sourceval msort_out : out:t -> t -> t
Sourceval mt : t -> t
Sourceval mul : t -> t -> t
Sourceval mul_ : t -> t -> t
Sourceval mul_out : out:t -> t -> t -> t
Sourceval mul_scalar : t -> 'a Scalar.t -> t
Sourceval mul_scalar_ : t -> 'a Scalar.t -> t
Sourceval mul_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval multi_margin_loss_backward : grad_output:t -> t -> target:t -> p:'a Scalar.t -> margin:'a Scalar.t -> weight:t option -> reduction:Reduction.t -> t
Sourceval multi_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> p:'a Scalar.t -> margin:'a Scalar.t -> weight:t option -> reduction:Reduction.t -> t
Sourceval multilabel_margin_loss : t -> target:t -> reduction:Reduction.t -> t
Sourceval multilabel_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> is_target:t -> t
Sourceval multilabel_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> is_target:t -> t
Sourceval multilabel_margin_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> t
Sourceval multinomial : t -> num_samples:int -> replacement:bool -> t
Sourceval multinomial_out : out:t -> t -> num_samples:int -> replacement:bool -> t
Sourceval multiply : t -> t -> t
Sourceval multiply_ : t -> t -> t
Sourceval multiply_out : out:t -> t -> t -> t
Sourceval multiply_scalar : t -> 'a Scalar.t -> t
Sourceval multiply_scalar_ : t -> 'a Scalar.t -> t
Sourceval mv : t -> vec:t -> t
Sourceval mv_out : out:t -> t -> vec:t -> t
Sourceval mvlgamma : t -> p:int -> t
Sourceval mvlgamma_ : t -> p:int -> t
Sourceval mvlgamma_out : out:t -> t -> p:int -> t
Sourceval nan_to_num : t -> nan:float option -> posinf:float option -> neginf:float option -> t
Sourceval nan_to_num_ : t -> nan:float option -> posinf:float option -> neginf:float option -> t
Sourceval nan_to_num_out : out:t -> t -> nan:float option -> posinf:float option -> neginf:float option -> t
Sourceval nanmean : t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval nanmean_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval nanmedian : t -> t
Sourceval nanmedian_dim : t -> dim:int -> keepdim:bool -> t * t
Sourceval nanmedian_dim_values : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
Sourceval nanmedian_out : out:t -> t -> t
Sourceval nanquantile : t -> q:t -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval nanquantile_out : out:t -> t -> q:t -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval nanquantile_scalar : t -> q:float -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval nanquantile_scalar_out : out:t -> t -> q:float -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval nansum : t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval nansum_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval narrow : t -> dim:int -> start:int -> length:int -> t
Sourceval narrow_copy : t -> dim:int -> start:int -> length:int -> t
Sourceval narrow_copy_out : out:t -> t -> dim:int -> start:int -> length:int -> t
Sourceval narrow_tensor : t -> dim:int -> start:t -> length:int -> t
Sourceval native_batch_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> t * t * t
Sourceval native_batch_norm_out : out:t -> save_mean:t -> save_invstd:t -> t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> t * t * t
Sourceval native_channel_shuffle : t -> groups:int -> t
Sourceval native_dropout : t -> p:float -> train:bool -> t * t
Sourceval native_dropout_backward : grad_output:t -> mask:t -> scale:float -> t
Sourceval native_dropout_backward_out : out:t -> grad_output:t -> mask:t -> scale:float -> t
Sourceval native_dropout_out : out0:t -> out1:t -> t -> p:float -> train:bool -> t * t
Sourceval native_group_norm : t -> weight:t option -> bias:t option -> n:int -> c:int -> hxw:int -> group:int -> eps:float -> t * t * t
Sourceval native_group_norm_out : out0:t -> out1:t -> out2:t -> t -> weight:t option -> bias:t option -> n:int -> c:int -> hxw:int -> group:int -> eps:float -> t * t * t
Sourceval native_layer_norm : t -> normalized_shape:int list -> weight:t option -> bias:t option -> eps:float -> t * t * t
Sourceval native_layer_norm_out : out0:t -> out1:t -> out2:t -> t -> normalized_shape:int list -> weight:t option -> bias:t option -> eps:float -> t * t * t
Sourceval native_norm : t -> t
Sourceval native_norm_out : out:t -> t -> t
Sourceval native_norm_scalaropt_dim_dtype : t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval native_norm_scalaropt_dim_dtype_out : out:t -> t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval ne : t -> 'a Scalar.t -> t
Sourceval ne_ : t -> 'a Scalar.t -> t
Sourceval ne_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval ne_tensor : t -> t -> t
Sourceval ne_tensor_ : t -> t -> t
Sourceval ne_tensor_out : out:t -> t -> t -> t
Sourceval neg : t -> t
Sourceval neg_ : t -> t
Sourceval neg_out : out:t -> t -> t
Sourceval negative : t -> t
Sourceval negative_ : t -> t
Sourceval negative_out : out:t -> t -> t
Sourceval nested_to_padded_tensor : t -> padding:float -> output_size:int list option -> t
Sourceval new_empty : t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval new_empty_out : out:t -> t -> size:int list -> t
Sourceval new_empty_strided : t -> size:int list -> stride:int list -> options:(Kind.packed * Device.t) -> t
Sourceval new_empty_strided_out : out:t -> t -> size:int list -> stride:int list -> t
Sourceval new_full : t -> size:int list -> fill_value:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval new_full_out : out:t -> t -> size:int list -> fill_value:'a Scalar.t -> t
Sourceval new_ones : t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval new_ones_out : out:t -> t -> size:int list -> t
Sourceval new_zeros : t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval new_zeros_out : out:t -> t -> size:int list -> t
Sourceval nextafter : t -> t -> t
Sourceval nextafter_ : t -> t -> t
Sourceval nextafter_out : out:t -> t -> t -> t
Sourceval nll_loss : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
Sourceval nll_loss2d : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
Sourceval nll_loss2d_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
Sourceval nll_loss2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
Sourceval nll_loss2d_out : out:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
Sourceval nll_loss_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
Sourceval nll_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
Sourceval nll_loss_nd : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
Sourceval nll_loss_out : out:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
Sourceval nonzero : t -> t
Sourceval nonzero_numpy : t -> t list
Sourceval nonzero_out : out:t -> t -> t
Sourceval norm : t -> t
Sourceval norm_dtype_out : out:t -> t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval norm_except_dim : v:t -> pow:int -> dim:int -> t
Sourceval norm_out : out:t -> t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> t
Sourceval norm_scalar_out : out:t -> t -> t
Sourceval norm_scalaropt_dim : t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> t
Sourceval norm_scalaropt_dim_dtype : t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval norm_scalaropt_dtype : t -> p:'a Scalar.t -> dtype:Kind.packed -> t
Sourceval norm_scalaropt_dtype_out : out:t -> t -> p:'a Scalar.t -> dtype:Kind.packed -> t
Sourceval normal_ : t -> mean:float -> std:float -> t
Sourceval normal_functional : t -> mean:float -> std:float -> t
Sourceval not_equal : t -> 'a Scalar.t -> t
Sourceval not_equal_ : t -> 'a Scalar.t -> t
Sourceval not_equal_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval not_equal_tensor : t -> t -> t
Sourceval not_equal_tensor_ : t -> t -> t
Sourceval not_equal_tensor_out : out:t -> t -> t -> t
Sourceval nuclear_norm : t -> keepdim:bool -> t
Sourceval nuclear_norm_dim : t -> dim:int list -> keepdim:bool -> t
Sourceval nuclear_norm_dim_out : out:t -> t -> dim:int list -> keepdim:bool -> t
Sourceval nuclear_norm_out : out:t -> t -> keepdim:bool -> t
Sourceval numpy_t : t -> t
Sourceval one_hot : t -> num_classes:int -> t
Sourceval ones : size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval ones_like : t -> t
Sourceval ones_like_out : out:t -> t -> t
Sourceval ones_out : out:t -> size:int list -> t
Sourceval orgqr : t -> input2:t -> t
Sourceval orgqr_out : out:t -> t -> input2:t -> t
Sourceval ormqr : t -> input2:t -> input3:t -> left:bool -> transpose:bool -> t
Sourceval ormqr_out : out:t -> t -> input2:t -> input3:t -> left:bool -> transpose:bool -> t
Sourceval outer : t -> vec2:t -> t
Sourceval outer_out : out:t -> t -> vec2:t -> t
Sourceval pad : t -> pad:int list -> mode:string -> value:float option -> t
Sourceval pad_sequence : sequences:t list -> batch_first:bool -> padding_value:float -> t
Sourceval pairwise_distance : x1:t -> x2:t -> p:float -> eps:float -> keepdim:bool -> t
Sourceval pdist : t -> p:float -> t
Sourceval permute : t -> dims:int list -> t
Sourceval permute_copy : t -> dims:int list -> t
Sourceval permute_copy_out : out:t -> t -> dims:int list -> t
Sourceval pin_memory : t -> device:Device.t -> t
Sourceval pinverse : t -> rcond:float -> t
Sourceval pixel_shuffle : t -> upscale_factor:int -> t
Sourceval pixel_shuffle_out : out:t -> t -> upscale_factor:int -> t
Sourceval pixel_unshuffle : t -> downscale_factor:int -> t
Sourceval pixel_unshuffle_out : out:t -> t -> downscale_factor:int -> t
Sourceval poisson : t -> t
Sourceval poisson_nll_loss : t -> target:t -> log_input:bool -> full:bool -> eps:float -> reduction:Reduction.t -> t
Sourceval poisson_out : out:t -> t -> t
Sourceval polar : abs:t -> angle:t -> t
Sourceval polar_out : out:t -> abs:t -> angle:t -> t
Sourceval polygamma : n:int -> t -> t
Sourceval polygamma_ : t -> n:int -> t
Sourceval polygamma_out : out:t -> n:int -> t -> t
Sourceval positive : t -> t
Sourceval pow : t -> exponent:t -> t
Sourceval pow_ : t -> exponent:'a Scalar.t -> t
Sourceval pow_scalar : 'a Scalar.t -> exponent:t -> t
Sourceval pow_scalar_out : out:t -> 'a Scalar.t -> exponent:t -> t
Sourceval pow_tensor_ : t -> exponent:t -> t
Sourceval pow_tensor_scalar : t -> exponent:'a Scalar.t -> t
Sourceval pow_tensor_scalar_out : out:t -> t -> exponent:'a Scalar.t -> t
Sourceval pow_tensor_tensor_out : out:t -> t -> exponent:t -> t
Sourceval prelu : t -> weight:t -> t
Sourceval prelu_backward : grad_output:t -> t -> weight:t -> t * t
Sourceval prelu_backward_out : out0:t -> out1:t -> grad_output:t -> t -> weight:t -> t * t
Sourceval prelu_out : out:t -> t -> weight:t -> t
Sourceval prod : t -> dtype:Kind.packed -> t
Sourceval prod_dim_int : t -> dim:int -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval prod_int_out : out:t -> t -> dim:int -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval prod_out : out:t -> t -> dtype:Kind.packed -> t
Sourceval put : t -> index:t -> source:t -> accumulate:bool -> t
Sourceval put_ : t -> index:t -> source:t -> accumulate:bool -> t
Sourceval put_out : out:t -> t -> index:t -> source:t -> accumulate:bool -> t
Sourceval q_per_channel_scales : t -> t
Sourceval q_per_channel_scales_out : out:t -> t -> t
Sourceval q_per_channel_zero_points : t -> t
Sourceval q_per_channel_zero_points_out : out:t -> t -> t
Sourceval qr : t -> some:bool -> t * t
Sourceval qr_q : q:t -> r:t -> t -> some:bool -> t * t
Sourceval quantile : t -> q:t -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval quantile_out : out:t -> t -> q:t -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval quantile_scalar : t -> q:float -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval quantile_scalar_out : out:t -> t -> q:float -> dim:int option -> keepdim:bool -> interpolation:string -> t
Sourceval quantize_per_channel : t -> scales:t -> zero_points:t -> axis:int -> dtype:Kind.packed -> t
Sourceval quantize_per_channel_out : out:t -> t -> scales:t -> zero_points:t -> axis:int -> dtype:Kind.packed -> t
Sourceval quantize_per_tensor : t -> scale:float -> zero_point:int -> dtype:Kind.packed -> t
Sourceval quantize_per_tensor_dynamic : t -> dtype:Kind.packed -> reduce_range:bool -> t
Sourceval quantize_per_tensor_dynamic_out : out:t -> t -> dtype:Kind.packed -> reduce_range:bool -> t
Sourceval quantize_per_tensor_out : out:t -> t -> scale:float -> zero_point:int -> dtype:Kind.packed -> t
Sourceval quantize_per_tensor_tensor_qparams : t -> scale:t -> zero_point:t -> dtype:Kind.packed -> t
Sourceval quantize_per_tensor_tensor_qparams_out : out:t -> t -> scale:t -> zero_point:t -> dtype:Kind.packed -> t
Sourceval quantize_per_tensor_tensors : t list -> scales:t -> zero_points:t -> dtype:Kind.packed -> t list
Sourceval quantize_per_tensor_tensors_out : out:t list -> t list -> scales:t -> zero_points:t -> dtype:Kind.packed -> unit
Sourceval quantized_batch_norm : t -> weight:t option -> bias:t option -> mean:t -> var:t -> eps:float -> output_scale:float -> output_zero_point:int -> t
Sourceval quantized_batch_norm_out : out:t -> t -> weight:t option -> bias:t option -> mean:t -> var:t -> eps:float -> output_scale:float -> output_zero_point:int -> t
Sourceval quantized_gru_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:'a Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a Scalar.t -> t
Sourceval quantized_lstm_cell : t -> hx:t list -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:'a Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a Scalar.t -> t * t
Sourceval quantized_max_pool1d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval quantized_max_pool1d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval quantized_max_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval quantized_max_pool2d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
Sourceval quantized_rnn_relu_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:'a Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a Scalar.t -> t
Sourceval quantized_rnn_tanh_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:'a Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a Scalar.t -> t
Sourceval rad2deg : t -> t
Sourceval rad2deg_ : t -> t
Sourceval rad2deg_out : out:t -> t -> t
Sourceval rand : size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval rand_like : t -> t
Sourceval rand_like_out : out:t -> t -> t
Sourceval rand_out : out:t -> size:int list -> t
Sourceval randint : high:int -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval randint_like : t -> high:int -> t
Sourceval randint_like_low_dtype : t -> low:int -> high:int -> t
Sourceval randint_like_low_dtype_out : out:t -> t -> low:int -> high:int -> t
Sourceval randint_like_out : out:t -> t -> high:int -> t
Sourceval randint_low : low:int -> high:int -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval randint_low_out : out:t -> low:int -> high:int -> size:int list -> t
Sourceval randint_out : out:t -> high:int -> size:int list -> t
Sourceval randn : size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval randn_like : t -> t
Sourceval randn_like_out : out:t -> t -> t
Sourceval randn_out : out:t -> size:int list -> t
Sourceval random : t -> t
Sourceval random_ : t -> t
Sourceval random_from : t -> from:int -> to_:int option -> t
Sourceval random_from_ : t -> from:int -> to_:int option -> t
Sourceval random_from_out : out:t -> t -> from:int -> to_:int option -> t
Sourceval random_out : out:t -> t -> t
Sourceval random_to : t -> to_:int -> t
Sourceval random_to_ : t -> to_:int -> t
Sourceval random_to_out : out:t -> t -> to_:int -> t
Sourceval randperm : n:int -> options:(Kind.packed * Device.t) -> t
Sourceval randperm_out : out:t -> n:int -> t
Sourceval range : start:'a Scalar.t -> end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval range_out : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> t
Sourceval range_out_ : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> t
Sourceval range_step : start:'a Scalar.t -> end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval ravel : t -> t
Sourceval real : t -> t
Sourceval reciprocal : t -> t
Sourceval reciprocal_ : t -> t
Sourceval reciprocal_out : out:t -> t -> t
Sourceval reflection_pad1d : t -> padding:int list -> t
Sourceval reflection_pad1d_backward : grad_output:t -> t -> padding:int list -> t
Sourceval reflection_pad1d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
Sourceval reflection_pad1d_out : out:t -> t -> padding:int list -> t
Sourceval reflection_pad2d : t -> padding:int list -> t
Sourceval reflection_pad2d_backward : grad_output:t -> t -> padding:int list -> t
Sourceval reflection_pad2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
Sourceval reflection_pad2d_out : out:t -> t -> padding:int list -> t
Sourceval reflection_pad3d : t -> padding:int list -> t
Sourceval reflection_pad3d_backward : grad_output:t -> t -> padding:int list -> t
Sourceval reflection_pad3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
Sourceval reflection_pad3d_out : out:t -> t -> padding:int list -> t
Sourceval relu : t -> t
Sourceval relu6 : t -> t
Sourceval relu6_ : t -> t
Sourceval relu_ : t -> t
Sourceval relu_out : out:t -> t -> t
Sourceval remainder : t -> 'a Scalar.t -> t
Sourceval remainder_ : t -> 'a Scalar.t -> t
Sourceval remainder_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval remainder_scalar_tensor : 'a Scalar.t -> t -> t
Sourceval remainder_scalar_tensor_out : out:t -> 'a Scalar.t -> t -> t
Sourceval remainder_tensor : t -> t -> t
Sourceval remainder_tensor_ : t -> t -> t
Sourceval remainder_tensor_out : out:t -> t -> t -> t
Sourceval renorm : t -> p:'a Scalar.t -> dim:int -> maxnorm:'a Scalar.t -> t
Sourceval renorm_ : t -> p:'a Scalar.t -> dim:int -> maxnorm:'a Scalar.t -> t
Sourceval renorm_out : out:t -> t -> p:'a Scalar.t -> dim:int -> maxnorm:'a Scalar.t -> t
Sourceval repeat : t -> repeats:int list -> t
Sourceval repeat_interleave : repeats:t -> output_size:int option -> t
Sourceval repeat_interleave_self_int : t -> repeats:int -> dim:int option -> output_size:int option -> t
Sourceval repeat_interleave_self_tensor : t -> repeats:t -> dim:int option -> output_size:int option -> t
Sourceval repeat_interleave_tensor_out : out:t -> repeats:t -> output_size:int option -> t
Sourceval repeat_out : out:t -> t -> repeats:int list -> t
Sourceval replication_pad1d : t -> padding:int list -> t
Sourceval replication_pad1d_backward : grad_output:t -> t -> padding:int list -> t
Sourceval replication_pad1d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
Sourceval replication_pad1d_out : out:t -> t -> padding:int list -> t
Sourceval replication_pad2d : t -> padding:int list -> t
Sourceval replication_pad2d_backward : grad_output:t -> t -> padding:int list -> t
Sourceval replication_pad2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
Sourceval replication_pad2d_out : out:t -> t -> padding:int list -> t
Sourceval replication_pad3d : t -> padding:int list -> t
Sourceval replication_pad3d_backward : grad_output:t -> t -> padding:int list -> t
Sourceval replication_pad3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
Sourceval replication_pad3d_out : out:t -> t -> padding:int list -> t
Sourceval requires_grad_ : t -> requires_grad:bool -> t
Sourceval reshape : t -> shape:int list -> t
Sourceval reshape_as : t -> t -> t
Sourceval resize : t -> size:int list -> t
Sourceval resize_ : t -> size:int list -> t
Sourceval resize_as : t -> the_template:t -> t
Sourceval resize_as_ : t -> the_template:t -> t
Sourceval resize_as_out : out:t -> t -> the_template:t -> t
Sourceval resize_as_sparse : t -> the_template:t -> t
Sourceval resize_as_sparse_ : t -> the_template:t -> t
Sourceval resize_as_sparse_out : out:t -> t -> the_template:t -> t
Sourceval resize_out : out:t -> t -> size:int list -> t
Sourceval resolve_conj : t -> t
Sourceval resolve_neg : t -> t
Sourceval rnn_relu : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
Sourceval rnn_relu_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
Sourceval rnn_relu_data : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
Sourceval rnn_tanh : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
Sourceval rnn_tanh_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
Sourceval rnn_tanh_data : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
Sourceval roll : t -> shifts:int list -> dims:int list -> t
Sourceval roll_out : out:t -> t -> shifts:int list -> dims:int list -> t
Sourceval rot90 : t -> k:int -> dims:int list -> t
Sourceval rot90_out : out:t -> t -> k:int -> dims:int list -> t
Sourceval round : t -> t
Sourceval round_ : t -> t
Sourceval round_decimals : t -> decimals:int -> t
Sourceval round_decimals_ : t -> decimals:int -> t
Sourceval round_decimals_out : out:t -> t -> decimals:int -> t
Sourceval round_out : out:t -> t -> t
Sourceval row_indices : t -> t
Sourceval row_indices_copy : t -> t
Sourceval row_indices_copy_out : out:t -> t -> t
Sourceval row_stack : t list -> t
Sourceval row_stack_out : out:t -> t list -> t
Sourceval rrelu : t -> training:bool -> t
Sourceval rrelu_ : t -> training:bool -> t
Sourceval rrelu_with_noise : t -> noise:t -> training:bool -> t
Sourceval rrelu_with_noise_ : t -> noise:t -> training:bool -> t
Sourceval rrelu_with_noise_backward : grad_output:t -> t -> noise:t -> lower:'a Scalar.t -> upper:'a Scalar.t -> training:bool -> self_is_result:bool -> t
Sourceval rrelu_with_noise_backward_out : out:t -> grad_output:t -> t -> noise:t -> lower:'a Scalar.t -> upper:'a Scalar.t -> training:bool -> self_is_result:bool -> t
Sourceval rrelu_with_noise_out : out:t -> t -> noise:t -> training:bool -> t
Sourceval rsqrt : t -> t
Sourceval rsqrt_ : t -> t
Sourceval rsqrt_out : out:t -> t -> t
Sourceval rsub : t -> t -> t
Sourceval rsub_scalar : t -> 'a Scalar.t -> t
Sourceval rsub_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval rsub_tensor_out : out:t -> t -> t -> t
Sourceval scalar_tensor : s:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
Sourceval scalar_tensor_out : out:t -> s:'a Scalar.t -> t
Sourceval scatter : t -> dim:int -> index:t -> src:t -> t
Sourceval scatter_ : t -> dim:int -> index:t -> src:t -> t
Sourceval scatter_add : t -> dim:int -> index:t -> src:t -> t
Sourceval scatter_add_ : t -> dim:int -> index:t -> src:t -> t
Sourceval scatter_add_out : out:t -> t -> dim:int -> index:t -> src:t -> t
Sourceval scatter_reduce : t -> dim:int -> index:t -> src:t -> reduce:string -> t
Sourceval scatter_reduce_ : t -> dim:int -> index:t -> src:t -> reduce:string -> t
Sourceval scatter_reduce_out : out:t -> t -> dim:int -> index:t -> src:t -> reduce:string -> t
Sourceval scatter_src_out : out:t -> t -> dim:int -> index:t -> src:t -> t
Sourceval scatter_value : t -> dim:int -> index:t -> value:'a Scalar.t -> t
Sourceval scatter_value_ : t -> dim:int -> index:t -> value:'a Scalar.t -> t
Sourceval scatter_value_out : out:t -> t -> dim:int -> index:t -> value:'a Scalar.t -> t
Sourceval scatter_value_reduce : t -> dim:int -> index:t -> value:'a Scalar.t -> reduce:string -> t
Sourceval scatter_value_reduce_ : t -> dim:int -> index:t -> value:'a Scalar.t -> reduce:string -> t
Sourceval scatter_value_reduce_out : out:t -> t -> dim:int -> index:t -> value:'a Scalar.t -> reduce:string -> t
Sourceval searchsorted : sorted_sequence:t -> t -> out_int32:bool -> right:bool -> side:string -> sorter:t option -> t
Sourceval searchsorted_scalar : sorted_sequence:t -> 'a Scalar.t -> out_int32:bool -> right:bool -> side:string -> sorter:t option -> t
Sourceval searchsorted_scalar_out : out:t -> sorted_sequence:t -> 'a Scalar.t -> out_int32:bool -> right:bool -> side:string -> sorter:t option -> t
Sourceval searchsorted_tensor_out : out:t -> sorted_sequence:t -> t -> out_int32:bool -> right:bool -> side:string -> sorter:t option -> t
Sourceval segment_reduce : data:t -> reduce:string -> lengths:t option -> indices:t option -> offsets:t option -> axis:int -> unsafe:bool -> initial:'a Scalar.t -> t
Sourceval segment_reduce_out : out:t -> data:t -> reduce:string -> lengths:t option -> indices:t option -> offsets:t option -> axis:int -> unsafe:bool -> initial:'a Scalar.t -> t
Sourceval select_backward : grad_output:t -> input_sizes:int list -> dim:int -> index:int -> t
Sourceval select_backward_out : out:t -> grad_output:t -> input_sizes:int list -> dim:int -> index:int -> t
Sourceval select_copy : t -> dim:int -> index:int -> t
Sourceval select_copy_int_out : out:t -> t -> dim:int -> index:int -> t
Sourceval select_scatter : t -> src:t -> dim:int -> index:int -> t
Sourceval select_scatter_out : out:t -> t -> src:t -> dim:int -> index:int -> t
Sourceval selu : t -> t
Sourceval selu_ : t -> t
Sourceval set : t -> t
Sourceval set_ : t -> t
Sourceval set_out : out:t -> t -> t
Sourceval set_requires_grad : t -> r:bool -> t
Sourceval set_source_tensor : t -> source:t -> t
Sourceval set_source_tensor_ : t -> source:t -> t
Sourceval set_source_tensor_out : out:t -> t -> source:t -> t
Sourceval set_source_tensor_storage_offset_ : t -> source:t -> storage_offset:int -> size:int list -> stride:int list -> t
Sourceval sgn : t -> t
Sourceval sgn_ : t -> t
Sourceval sgn_out : out:t -> t -> t
Sourceval sigmoid : t -> t
Sourceval sigmoid_ : t -> t
Sourceval sigmoid_backward : grad_output:t -> output:t -> t
Sourceval sigmoid_backward_grad_input : grad_input:t -> grad_output:t -> output:t -> t
Sourceval sigmoid_out : out:t -> t -> t
Sourceval sign : t -> t
Sourceval sign_ : t -> t
Sourceval sign_out : out:t -> t -> t
Sourceval signbit : t -> t
Sourceval signbit_out : out:t -> t -> t
Sourceval silu : t -> t
Sourceval silu_ : t -> t
Sourceval silu_backward : grad_output:t -> t -> t
Sourceval silu_backward_grad_input : grad_input:t -> grad_output:t -> t -> t
Sourceval silu_out : out:t -> t -> t
Sourceval sin : t -> t
Sourceval sin_ : t -> t
Sourceval sin_out : out:t -> t -> t
Sourceval sinc : t -> t
Sourceval sinc_ : t -> t
Sourceval sinc_out : out:t -> t -> t
Sourceval sinh : t -> t
Sourceval sinh_ : t -> t
Sourceval sinh_out : out:t -> t -> t
Sourceval slice : t -> dim:int -> start:int option -> end_:int option -> step:int -> t
Sourceval slice_backward : grad_output:t -> input_sizes:int list -> dim:int -> start:int -> end_:int -> step:int -> t
Sourceval slice_backward_out : out:t -> grad_output:t -> input_sizes:int list -> dim:int -> start:int -> end_:int -> step:int -> t
Sourceval slice_copy : t -> dim:int -> start:int option -> end_:int option -> step:int -> t
Sourceval slice_copy_tensor_out : out:t -> t -> dim:int -> start:int option -> end_:int option -> step:int -> t
Sourceval slice_scatter : t -> src:t -> dim:int -> start:int option -> end_:int option -> step:int -> t
Sourceval slice_scatter_out : out:t -> t -> src:t -> dim:int -> start:int option -> end_:int option -> step:int -> t
Sourceval slogdet : t -> t * t
Sourceval slogdet_out : sign:t -> logabsdet:t -> t -> t * t
Sourceval slow_conv3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> t
Sourceval slow_conv3d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> t
Sourceval slow_conv_dilated2d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval slow_conv_dilated2d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval slow_conv_dilated3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval slow_conv_dilated3d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
Sourceval slow_conv_transpose2d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
Sourceval slow_conv_transpose2d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
Sourceval slow_conv_transpose3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
Sourceval slow_conv_transpose3d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
Sourceval smm : t -> mat2:t -> t
Sourceval smooth_l1_loss : t -> target:t -> reduction:Reduction.t -> beta:float -> t
Sourceval smooth_l1_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> beta:float -> t
Sourceval smooth_l1_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> beta:float -> t
Sourceval smooth_l1_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> beta:float -> t
Sourceval soft_margin_loss : t -> target:t -> reduction:Reduction.t -> t
Sourceval soft_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
Sourceval soft_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
Sourceval soft_margin_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> t
Sourceval softmax : t -> dim:int -> dtype:Kind.packed -> t
Sourceval softmax_int_out : out:t -> t -> dim:int -> dtype:Kind.packed -> t
Sourceval softplus : t -> t
Sourceval softplus_backward : grad_output:t -> t -> beta:'a Scalar.t -> threshold:'a Scalar.t -> t
Sourceval softplus_backward_grad_input : grad_input:t -> grad_output:t -> t -> beta:'a Scalar.t -> threshold:'a Scalar.t -> t
Sourceval softplus_out : out:t -> t -> t
Sourceval softshrink : t -> t
Sourceval softshrink_backward : grad_output:t -> t -> lambd:'a Scalar.t -> t
Sourceval softshrink_backward_grad_input : grad_input:t -> grad_output:t -> t -> lambd:'a Scalar.t -> t
Sourceval softshrink_out : out:t -> t -> t
Sourceval sort : t -> dim:int -> descending:bool -> t * t
Sourceval sort_stable : t -> stable:bool -> dim:int -> descending:bool -> t * t
Sourceval sort_values : values:t -> indices:t -> t -> dim:int -> descending:bool -> t * t
Sourceval sort_values_stable : values:t -> indices:t -> t -> stable:bool -> dim:int -> descending:bool -> t * t
Sourceval sparse_bsc_tensor : ccol_indices:t -> row_indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_bsc_tensor_ccol_row_value_size : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_bsr_tensor : crow_indices:t -> col_indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_bsr_tensor_crow_col_value_size : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_compressed_tensor : compressed_indices:t -> plain_indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_compressed_tensor_comp_plain_value_size : compressed_indices:t -> plain_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_coo_tensor : size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_coo_tensor_indices : indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_coo_tensor_indices_size : indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_coo_tensor_size_out : out:t -> size:int list -> t
Sourceval sparse_csc_tensor : ccol_indices:t -> row_indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_csc_tensor_ccol_row_value_size : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_csr_tensor : crow_indices:t -> col_indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_csr_tensor_crow_col_value_size : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval sparse_mask : t -> mask:t -> t
Sourceval sparse_mask_out : out:t -> t -> mask:t -> t
Sourceval sparse_resize : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
Sourceval sparse_resize_ : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
Sourceval sparse_resize_and_clear : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
Sourceval sparse_resize_and_clear_ : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
Sourceval sparse_resize_and_clear_out : out:t -> t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
Sourceval sparse_resize_out : out:t -> t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
Sourceval sparse_sampled_addmm : t -> mat1:t -> mat2:t -> t
Sourceval sparse_sampled_addmm_out : out:t -> t -> mat1:t -> mat2:t -> t
Sourceval special_airy_ai : x:t -> t
Sourceval special_airy_ai_out : out:t -> x:t -> t
Sourceval special_bessel_j0 : t -> t
Sourceval special_bessel_j0_out : out:t -> t -> t
Sourceval special_bessel_j1 : t -> t
Sourceval special_bessel_j1_out : out:t -> t -> t
Sourceval special_bessel_y0 : t -> t
Sourceval special_bessel_y0_out : out:t -> t -> t
Sourceval special_bessel_y1 : t -> t
Sourceval special_bessel_y1_out : out:t -> t -> t
Sourceval special_chebyshev_polynomial_t : x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_t_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_t_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_t_out : out:t -> x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_t_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_t_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_u : x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_u_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_u_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_u_out : out:t -> x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_u_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_u_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_v : x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_v_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_v_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_v_out : out:t -> x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_v_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_v_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_w : x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_w_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_w_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_chebyshev_polynomial_w_out : out:t -> x:t -> n:t -> t
Sourceval special_chebyshev_polynomial_w_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_w_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_digamma : t -> t
Sourceval special_digamma_out : out:t -> t -> t
Sourceval special_entr : t -> t
Sourceval special_entr_out : out:t -> t -> t
Sourceval special_erf : t -> t
Sourceval special_erf_out : out:t -> t -> t
Sourceval special_erfc : t -> t
Sourceval special_erfc_out : out:t -> t -> t
Sourceval special_erfcx : t -> t
Sourceval special_erfcx_out : out:t -> t -> t
Sourceval special_erfinv : t -> t
Sourceval special_erfinv_out : out:t -> t -> t
Sourceval special_exp2 : t -> t
Sourceval special_exp2_out : out:t -> t -> t
Sourceval special_expit : t -> t
Sourceval special_expit_out : out:t -> t -> t
Sourceval special_expm1 : t -> t
Sourceval special_expm1_out : out:t -> t -> t
Sourceval special_gammainc : t -> t -> t
Sourceval special_gammainc_out : out:t -> t -> t -> t
Sourceval special_gammaincc : t -> t -> t
Sourceval special_gammaincc_out : out:t -> t -> t -> t
Sourceval special_gammaln : t -> t
Sourceval special_gammaln_out : out:t -> t -> t
Sourceval special_hermite_polynomial_h : x:t -> n:t -> t
Sourceval special_hermite_polynomial_h_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_hermite_polynomial_h_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_hermite_polynomial_h_out : out:t -> x:t -> n:t -> t
Sourceval special_hermite_polynomial_h_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_hermite_polynomial_h_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_hermite_polynomial_he : x:t -> n:t -> t
Sourceval special_hermite_polynomial_he_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_hermite_polynomial_he_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_hermite_polynomial_he_out : out:t -> x:t -> n:t -> t
Sourceval special_hermite_polynomial_he_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_hermite_polynomial_he_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_i0 : t -> t
Sourceval special_i0_out : out:t -> t -> t
Sourceval special_i0e : t -> t
Sourceval special_i0e_out : out:t -> t -> t
Sourceval special_i1 : t -> t
Sourceval special_i1_out : out:t -> t -> t
Sourceval special_i1e : t -> t
Sourceval special_i1e_out : out:t -> t -> t
Sourceval special_laguerre_polynomial_l : x:t -> n:t -> t
Sourceval special_laguerre_polynomial_l_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_laguerre_polynomial_l_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_laguerre_polynomial_l_out : out:t -> x:t -> n:t -> t
Sourceval special_laguerre_polynomial_l_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_laguerre_polynomial_l_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_legendre_polynomial_p : x:t -> n:t -> t
Sourceval special_legendre_polynomial_p_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_legendre_polynomial_p_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_legendre_polynomial_p_out : out:t -> x:t -> n:t -> t
Sourceval special_legendre_polynomial_p_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_legendre_polynomial_p_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_log1p : t -> t
Sourceval special_log1p_out : out:t -> t -> t
Sourceval special_log_ndtr : t -> t
Sourceval special_log_ndtr_out : out:t -> t -> t
Sourceval special_log_softmax : t -> dim:int -> dtype:Kind.packed -> t
Sourceval special_logit : t -> eps:float option -> t
Sourceval special_logit_out : out:t -> t -> eps:float option -> t
Sourceval special_logsumexp : t -> dim:int list -> keepdim:bool -> t
Sourceval special_logsumexp_out : out:t -> t -> dim:int list -> keepdim:bool -> t
Sourceval special_modified_bessel_i0 : t -> t
Sourceval special_modified_bessel_i0_out : out:t -> t -> t
Sourceval special_modified_bessel_i1 : t -> t
Sourceval special_modified_bessel_i1_out : out:t -> t -> t
Sourceval special_modified_bessel_k0 : t -> t
Sourceval special_modified_bessel_k0_out : out:t -> t -> t
Sourceval special_modified_bessel_k1 : t -> t
Sourceval special_modified_bessel_k1_out : out:t -> t -> t
Sourceval special_multigammaln : t -> p:int -> t
Sourceval special_multigammaln_out : out:t -> t -> p:int -> t
Sourceval special_ndtr : t -> t
Sourceval special_ndtr_out : out:t -> t -> t
Sourceval special_ndtri : t -> t
Sourceval special_ndtri_out : out:t -> t -> t
Sourceval special_polygamma : n:int -> t -> t
Sourceval special_polygamma_out : out:t -> n:int -> t -> t
Sourceval special_psi : t -> t
Sourceval special_psi_out : out:t -> t -> t
Sourceval special_round : t -> decimals:int -> t
Sourceval special_round_out : out:t -> t -> decimals:int -> t
Sourceval special_scaled_modified_bessel_k0 : x:t -> t
Sourceval special_scaled_modified_bessel_k0_out : out:t -> x:t -> t
Sourceval special_scaled_modified_bessel_k1 : x:t -> t
Sourceval special_scaled_modified_bessel_k1_out : out:t -> x:t -> t
Sourceval special_shifted_chebyshev_polynomial_t : x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_t_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_t_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_t_out : out:t -> x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_t_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_t_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_u : x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_u_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_u_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_u_out : out:t -> x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_u_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_u_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_v : x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_v_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_v_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_v_out : out:t -> x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_v_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_v_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_w : x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_w_n_scalar : x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_w_n_scalar_out : out:t -> x:t -> n:'a Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_w_out : out:t -> x:t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_w_x_scalar : x:'a Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_w_x_scalar_out : out:t -> x:'a Scalar.t -> n:t -> t
Sourceval special_sinc : t -> t
Sourceval special_sinc_out : out:t -> t -> t
Sourceval special_softmax : t -> dim:int -> dtype:Kind.packed -> t
Sourceval special_spherical_bessel_j0 : x:t -> t
Sourceval special_spherical_bessel_j0_out : out:t -> x:t -> t
Sourceval special_xlog1py : t -> t -> t
Sourceval special_xlog1py_other_scalar : t -> 'a Scalar.t -> t
Sourceval special_xlog1py_other_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval special_xlog1py_out : out:t -> t -> t -> t
Sourceval special_xlog1py_self_scalar : 'a Scalar.t -> t -> t
Sourceval special_xlog1py_self_scalar_out : out:t -> 'a Scalar.t -> t -> t
Sourceval special_xlogy : t -> t -> t
Sourceval special_xlogy_other_scalar : t -> 'a Scalar.t -> t
Sourceval special_xlogy_other_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval special_xlogy_out : out:t -> t -> t -> t
Sourceval special_xlogy_self_scalar : 'a Scalar.t -> t -> t
Sourceval special_xlogy_self_scalar_out : out:t -> 'a Scalar.t -> t -> t
Sourceval special_zeta : t -> t -> t
Sourceval special_zeta_other_scalar : t -> 'a Scalar.t -> t
Sourceval special_zeta_other_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval special_zeta_out : out:t -> t -> t -> t
Sourceval special_zeta_self_scalar : 'a Scalar.t -> t -> t
Sourceval special_zeta_self_scalar_out : out:t -> 'a Scalar.t -> t -> t
Sourceval split : t -> split_size:int -> dim:int -> t list
Sourceval split_copy : t -> split_size:int -> dim:int -> t list
Sourceval split_copy_tensor_out : out:t list -> t -> split_size:int -> dim:int -> unit
Sourceval split_sizes : t -> split_size:int list -> dim:int -> t list
Sourceval split_with_sizes : t -> split_sizes:int list -> dim:int -> t list
Sourceval split_with_sizes_copy : t -> split_sizes:int list -> dim:int -> t list
Sourceval split_with_sizes_copy_out : out:t list -> t -> split_sizes:int list -> dim:int -> unit
Sourceval sqrt : t -> t
Sourceval sqrt_ : t -> t
Sourceval sqrt_out : out:t -> t -> t
Sourceval square : t -> t
Sourceval square_ : t -> t
Sourceval square_out : out:t -> t -> t
Sourceval squeeze : t -> t
Sourceval squeeze_ : t -> t
Sourceval squeeze_copy : t -> t
Sourceval squeeze_copy_dim : t -> dim:int -> t
Sourceval squeeze_copy_dim_out : out:t -> t -> dim:int -> t
Sourceval squeeze_copy_out : out:t -> t -> t
Sourceval squeeze_dim : t -> dim:int -> t
Sourceval squeeze_dim_ : t -> dim:int -> t
Sourceval sspaddmm : t -> mat1:t -> mat2:t -> t
Sourceval sspaddmm_out : out:t -> t -> mat1:t -> mat2:t -> t
Sourceval stack : t list -> dim:int -> t
Sourceval stack_out : out:t -> t list -> dim:int -> t
Sourceval std : t -> unbiased:bool -> t
Sourceval std_correction : t -> dim:int list option -> correction:int option -> keepdim:bool -> t
Sourceval std_correction_out : out:t -> t -> dim:int list option -> correction:int option -> keepdim:bool -> t
Sourceval std_dim : t -> dim:int list option -> unbiased:bool -> keepdim:bool -> t
Sourceval std_mean : t -> unbiased:bool -> t * t
Sourceval std_mean_correction : t -> dim:int list option -> correction:int option -> keepdim:bool -> t * t
Sourceval std_mean_correction_out : out0:t -> out1:t -> t -> dim:int list option -> correction:int option -> keepdim:bool -> t * t
Sourceval std_mean_dim : t -> dim:int list option -> unbiased:bool -> keepdim:bool -> t * t
Sourceval std_out : out:t -> t -> dim:int list option -> unbiased:bool -> keepdim:bool -> t
Sourceval stft : t -> n_fft:int -> hop_length:int option -> win_length:int option -> window:t option -> normalized:bool -> onesided:bool -> return_complex:bool -> t
Sourceval stft_center : t -> n_fft:int -> hop_length:int option -> win_length:int option -> window:t option -> center:bool -> pad_mode:string -> normalized:bool -> onesided:bool -> return_complex:bool -> t
Sourceval sub : t -> t -> t
Sourceval sub_ : t -> t -> t
Sourceval sub_out : out:t -> t -> t -> t
Sourceval sub_scalar : t -> 'a Scalar.t -> t
Sourceval sub_scalar_ : t -> 'a Scalar.t -> t
Sourceval sub_scalar_out : out:t -> t -> 'a Scalar.t -> t
Sourceval subtract : t -> t -> t
Sourceval subtract_ : t -> t -> t
Sourceval subtract_out : out:t -> t -> t -> t
Sourceval subtract_scalar : t -> 'a Scalar.t -> t
Sourceval subtract_scalar_ : t -> 'a Scalar.t -> t
Sourceval sum_dim_intlist : t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval sum_intlist_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Kind.packed -> t
Sourceval sum_out : out:t -> t -> dtype:Kind.packed -> t
Sourceval sum_to_size : t -> size:int list -> t
Sourceval svd : t -> some:bool -> compute_uv:bool -> t * t * t
Sourceval svd_u : u:t -> s:t -> v:t -> t -> some:bool -> compute_uv:bool -> t * t * t
Sourceval swapaxes : t -> axis0:int -> axis1:int -> t
Sourceval swapaxes_ : t -> axis0:int -> axis1:int -> t
Sourceval swapdims : t -> dim0:int -> dim1:int -> t
Sourceval swapdims_ : t -> dim0:int -> dim1:int -> t
Sourceval symeig : t -> eigenvectors:bool -> upper:bool -> t * t
Sourceval symeig_e : e:t -> v:t -> t -> eigenvectors:bool -> upper:bool -> t * t
Sourceval tr : t -> t
Sourceval t_ : t -> t
Sourceval t_copy : t -> t
Sourceval t_copy_out : out:t -> t -> t
Sourceval take : t -> index:t -> t
Sourceval take_along_dim : t -> indices:t -> dim:int option -> t
Sourceval take_along_dim_out : out:t -> t -> indices:t -> dim:int option -> t
Sourceval take_out : out:t -> t -> index:t -> t
Sourceval tan : t -> t
Sourceval tan_ : t -> t
Sourceval tan_out : out:t -> t -> t
Sourceval tanh : t -> t
Sourceval tanh_ : t -> t
Sourceval tanh_backward : grad_output:t -> output:t -> t
Sourceval tanh_backward_grad_input : grad_input:t -> grad_output:t -> output:t -> t
Sourceval tanh_out : out:t -> t -> t
Sourceval tensor_split : t -> sections:int -> dim:int -> t list
Sourceval tensor_split_indices : t -> indices:int list -> dim:int -> t list
Sourceval tensor_split_tensor_indices_or_sections : t -> tensor_indices_or_sections:t -> dim:int -> t list
Sourceval tensordot : t -> t -> dims_self:int list -> dims_other:int list -> t
Sourceval tensordot_out : out:t -> t -> t -> dims_self:int list -> dims_other:int list -> t
Sourceval threshold : t -> threshold:'a Scalar.t -> value:'a Scalar.t -> t
Sourceval threshold_ : t -> threshold:'a Scalar.t -> value:'a Scalar.t -> t
Sourceval threshold_backward : grad_output:t -> t -> threshold:'a Scalar.t -> t
Sourceval threshold_backward_grad_input : grad_input:t -> grad_output:t -> t -> threshold:'a Scalar.t -> t
Sourceval threshold_out : out:t -> t -> threshold:'a Scalar.t -> value:'a Scalar.t -> t
Sourceval tile : t -> dims:int list -> t
Sourceval to_ : t -> device:Device.t -> t
Sourceval to_dense : t -> dtype:Kind.packed -> t
Sourceval to_dense_backward : grad:t -> t -> t
Sourceval to_device : t -> device:Device.t -> dtype:Kind.packed -> non_blocking:bool -> copy:bool -> t
Sourceval to_dtype : t -> dtype:Kind.packed -> non_blocking:bool -> copy:bool -> t
Sourceval to_dtype_layout : t -> options:(Kind.packed * Device.t) -> non_blocking:bool -> copy:bool -> t
Sourceval to_mkldnn : t -> dtype:Kind.packed -> t
Sourceval to_mkldnn_backward : grad:t -> t -> t
Sourceval to_mkldnn_out : out:t -> t -> dtype:Kind.packed -> t
Sourceval to_other : t -> t -> non_blocking:bool -> copy:bool -> t
Sourceval to_padded_tensor : t -> padding:float -> output_size:int list option -> t
Sourceval to_padded_tensor_out : out:t -> t -> padding:float -> output_size:int list option -> t
Sourceval to_sparse : t -> t
Sourceval to_sparse_bsc : t -> blocksize:int list -> t
Sourceval to_sparse_bsc_out : out:t -> t -> blocksize:int list -> t
Sourceval to_sparse_bsr : t -> blocksize:int list -> t
Sourceval to_sparse_bsr_out : out:t -> t -> blocksize:int list -> t
Sourceval to_sparse_csc : t -> t
Sourceval to_sparse_csc_out : out:t -> t -> t
Sourceval to_sparse_csr : t -> t
Sourceval to_sparse_csr_out : out:t -> t -> t
Sourceval to_sparse_out : out:t -> t -> t
Sourceval to_sparse_sparse_dim : t -> sparse_dim:int -> t
Sourceval to_sparse_sparse_dim_out : out:t -> t -> sparse_dim:int -> t
Sourceval topk : t -> k:int -> dim:int -> largest:bool -> sorted:bool -> t * t
Sourceval topk_values : values:t -> indices:t -> t -> k:int -> dim:int -> largest:bool -> sorted:bool -> t * t
Sourceval totype : t -> scalar_type:Kind.packed -> t
Sourceval trace : t -> t
Sourceval trace_backward : grad:t -> sizes:int list -> t
Sourceval trace_out : out:t -> t -> t
Sourceval transpose : t -> dim0:int -> dim1:int -> t
Sourceval transpose_ : t -> dim0:int -> dim1:int -> t
Sourceval transpose_copy : t -> dim0:int -> dim1:int -> t
Sourceval transpose_copy_int_out : out:t -> t -> dim0:int -> dim1:int -> t
Sourceval trapezoid : y:t -> dim:int -> t
Sourceval trapezoid_x : y:t -> x:t -> dim:int -> t
Sourceval trapz : y:t -> x:t -> dim:int -> t
Sourceval trapz_dx : y:t -> dx:float -> dim:int -> t
Sourceval triangular_solve : t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
Sourceval triangular_solve_x : x:t -> m:t -> t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
Sourceval tril : t -> diagonal:int -> t
Sourceval tril_ : t -> diagonal:int -> t
Sourceval tril_indices : row:int -> col:int -> offset:int -> options:(Kind.packed * Device.t) -> t
Sourceval tril_indices_out : out:t -> row:int -> col:int -> offset:int -> t
Sourceval tril_out : out:t -> t -> diagonal:int -> t
Sourceval triplet_margin_loss : anchor:t -> positive:t -> negative:t -> margin:float -> p:float -> eps:float -> swap:bool -> reduction:Reduction.t -> t
Sourceval triu : t -> diagonal:int -> t
Sourceval triu_ : t -> diagonal:int -> t
Sourceval triu_indices : row:int -> col:int -> offset:int -> options:(Kind.packed * Device.t) -> t
Sourceval triu_indices_out : out:t -> row:int -> col:int -> offset:int -> t
Sourceval triu_out : out:t -> t -> diagonal:int -> t
Sourceval true_divide : t -> t -> t
Sourceval true_divide_ : t -> t -> t
Sourceval true_divide_out : out:t -> t -> t -> t
Sourceval true_divide_scalar : t -> 'a Scalar.t -> t
Sourceval true_divide_scalar_ : t -> 'a Scalar.t -> t
Sourceval trunc : t -> t
Sourceval trunc_ : t -> t
Sourceval trunc_out : out:t -> t -> t
Sourceval type_as : t -> t -> t
Sourceval unbind : t -> dim:int -> t list
Sourceval unbind_copy : t -> dim:int -> t list
Sourceval unbind_copy_int_out : out:t list -> t -> dim:int -> unit
Sourceval unflatten : t -> dim:int -> sizes:int list -> t
Sourceval unflatten_dense_tensors : flat:t -> t list -> t list
Sourceval unfold : t -> dimension:int -> size:int -> step:int -> t
Sourceval unfold_backward : grad_in:t -> input_sizes:int list -> dim:int -> size:int -> step:int -> t
Sourceval unfold_backward_out : out:t -> grad_in:t -> input_sizes:int list -> dim:int -> size:int -> step:int -> t
Sourceval unfold_copy : t -> dimension:int -> size:int -> step:int -> t
Sourceval unfold_copy_out : out:t -> t -> dimension:int -> size:int -> step:int -> t
Sourceval uniform : t -> from:float -> to_:float -> t
Sourceval uniform_ : t -> from:float -> to_:float -> t
Sourceval uniform_out : out:t -> t -> from:float -> to_:float -> t
Sourceval unique_consecutive : t -> return_inverse:bool -> return_counts:bool -> dim:int option -> t * t * t
Sourceval unique_consecutive_out : out0:t -> out1:t -> out2:t -> t -> return_inverse:bool -> return_counts:bool -> dim:int option -> t * t * t
Sourceval unique_dim : t -> dim:int -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
Sourceval unique_dim_consecutive : t -> dim:int -> return_inverse:bool -> return_counts:bool -> t * t * t
Sourceval unique_dim_consecutive_out : out0:t -> out1:t -> out2:t -> t -> dim:int -> return_inverse:bool -> return_counts:bool -> t * t * t
Sourceval unique_dim_out : out0:t -> out1:t -> out2:t -> t -> dim:int -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
Sourceval unsafe_chunk : t -> chunks:int -> dim:int -> t list
Sourceval unsafe_split : t -> split_size:int -> dim:int -> t list
Sourceval unsafe_split_tensor_out : out:t list -> t -> split_size:int -> dim:int -> unit
Sourceval unsafe_split_with_sizes : t -> split_sizes:int list -> dim:int -> t list
Sourceval unsafe_split_with_sizes_out : out:t list -> t -> split_sizes:int list -> dim:int -> unit
Sourceval unsqueeze : t -> dim:int -> t
Sourceval unsqueeze_ : t -> dim:int -> t
Sourceval unsqueeze_copy : t -> dim:int -> t
Sourceval unsqueeze_copy_out : out:t -> t -> dim:int -> t
Sourceval upsample_bicubic2d : t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bicubic2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bicubic2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bicubic2d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_bicubic2d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_bicubic2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bicubic2d_vec : t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_bicubic2d_vec_out : out:t -> t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_bilinear2d : t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bilinear2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bilinear2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bilinear2d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_bilinear2d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_bilinear2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_bilinear2d_vec : t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_bilinear2d_vec_out : out:t -> t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_linear1d : t -> output_size:int list -> align_corners:bool -> scales:float option -> t
Sourceval upsample_linear1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales:float option -> t
Sourceval upsample_linear1d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales:float option -> t
Sourceval upsample_linear1d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_linear1d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_linear1d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales:float option -> t
Sourceval upsample_linear1d_vec : t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_linear1d_vec_out : out:t -> t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_nearest1d : t -> output_size:int list -> scales:float option -> t
Sourceval upsample_nearest1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales:float option -> t
Sourceval upsample_nearest1d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales:float option -> t
Sourceval upsample_nearest1d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval upsample_nearest1d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval upsample_nearest1d_out : out:t -> t -> output_size:int list -> scales:float option -> t
Sourceval upsample_nearest1d_vec : t -> output_size:int list option -> scale_factors:float list -> t
Sourceval upsample_nearest1d_vec_out : out:t -> t -> output_size:int list option -> scale_factors:float list -> t
Sourceval upsample_nearest2d : t -> output_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest2d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval upsample_nearest2d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval upsample_nearest2d_out : out:t -> t -> output_size:int list -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest2d_vec : t -> output_size:int list option -> scale_factors:float list -> t
Sourceval upsample_nearest2d_vec_out : out:t -> t -> output_size:int list option -> scale_factors:float list -> t
Sourceval upsample_nearest3d : t -> output_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest3d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest3d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval upsample_nearest3d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> scale_factors:float list -> t
Sourceval upsample_nearest3d_out : out:t -> t -> output_size:int list -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_nearest3d_vec : t -> output_size:int list option -> scale_factors:float list -> t
Sourceval upsample_nearest3d_vec_out : out:t -> t -> output_size:int list option -> scale_factors:float list -> t
Sourceval upsample_trilinear3d : t -> output_size:int list -> align_corners:bool -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_trilinear3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_trilinear3d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_trilinear3d_backward_vec : grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_trilinear3d_backward_vec_out : out:t -> grad_output:t -> output_size:int list option -> input_size:int list -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_trilinear3d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_d:float option -> scales_h:float option -> scales_w:float option -> t
Sourceval upsample_trilinear3d_vec : t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval upsample_trilinear3d_vec_out : out:t -> t -> output_size:int list option -> align_corners:bool -> scale_factors:float list -> t
Sourceval value_selecting_reduction_backward : grad:t -> dim:int -> indices:t -> sizes:int list -> keepdim:bool -> t
Sourceval values : t -> t
Sourceval values_copy : t -> t
Sourceval values_copy_out : out:t -> t -> t
Sourceval vander : x:t -> n:int option -> increasing:bool -> t
Sourceval var : t -> unbiased:bool -> t
Sourceval var_correction : t -> dim:int list option -> correction:int option -> keepdim:bool -> t
Sourceval var_correction_out : out:t -> t -> dim:int list option -> correction:int option -> keepdim:bool -> t
Sourceval var_dim : t -> dim:int list option -> unbiased:bool -> keepdim:bool -> t
Sourceval var_mean : t -> unbiased:bool -> t * t
Sourceval var_mean_correction : t -> dim:int list option -> correction:int option -> keepdim:bool -> t * t
Sourceval var_mean_correction_out : out0:t -> out1:t -> t -> dim:int list option -> correction:int option -> keepdim:bool -> t * t
Sourceval var_mean_dim : t -> dim:int list option -> unbiased:bool -> keepdim:bool -> t * t
Sourceval var_out : out:t -> t -> dim:int list option -> unbiased:bool -> keepdim:bool -> t
Sourceval vdot : t -> t -> t
Sourceval vdot_out : out:t -> t -> t -> t
Sourceval view : t -> size:int list -> t
Sourceval view_as : t -> t -> t
Sourceval view_as_complex : t -> t
Sourceval view_as_complex_copy : t -> t
Sourceval view_as_complex_copy_out : out:t -> t -> t
Sourceval view_as_real : t -> t
Sourceval view_as_real_copy : t -> t
Sourceval view_as_real_copy_out : out:t -> t -> t
Sourceval view_copy : t -> size:int list -> t
Sourceval view_copy_dtype : t -> dtype:Kind.packed -> t
Sourceval view_copy_dtype_out : out:t -> t -> dtype:Kind.packed -> t
Sourceval view_copy_out : out:t -> t -> size:int list -> t
Sourceval view_dtype : t -> dtype:Kind.packed -> t
Sourceval vsplit : t -> sections:int -> t list
Sourceval vsplit_array : t -> indices:int list -> t list
Sourceval vstack : t list -> t
Sourceval vstack_out : out:t -> t list -> t
Sourceval where : condition:t -> t list
Sourceval where_scalar : condition:t -> 'a Scalar.t -> 'a Scalar.t -> t
Sourceval where_scalarother : condition:t -> t -> 'a Scalar.t -> t
Sourceval where_scalarself : condition:t -> 'a Scalar.t -> t -> t
Sourceval where_self : condition:t -> t -> t -> t
Sourceval where_self_out : out:t -> condition:t -> t -> t -> t
Sourceval xlogy : t -> t -> t
Sourceval xlogy_ : t -> t -> t
Sourceval xlogy_outscalar_other : out:t -> t -> 'a Scalar.t -> t
Sourceval xlogy_outscalar_self : out:t -> 'a Scalar.t -> t -> t
Sourceval xlogy_outtensor : out:t -> t -> t -> t
Sourceval xlogy_scalar_other : t -> 'a Scalar.t -> t
Sourceval xlogy_scalar_other_ : t -> 'a Scalar.t -> t
Sourceval xlogy_scalar_self : 'a Scalar.t -> t -> t
Sourceval zero : t -> t
Sourceval zero_ : t -> t
Sourceval zero_out : out:t -> t -> t
Sourceval zeros : size:int list -> options:(Kind.packed * Device.t) -> t
Sourceval zeros_like : t -> t
Sourceval zeros_like_out : out:t -> t -> t
Sourceval zeros_out : out:t -> size:int list -> t
Sourceval new_tensor : unit -> t
Sourceval float_vec : ?kind:[ `double | `float | `half ] -> float list -> t
Sourceval int_vec : ?kind:[ `int | `int16 | `int64 | `int8 | `uint8 ] -> int list -> t
Sourceval of_bigarray : (_, _, Bigarray.c_layout) Bigarray.Genarray.t -> t
Sourceval copy_to_bigarray : t -> (_, _, Bigarray.c_layout) Bigarray.Genarray.t -> unit
Sourceval shape : t -> int list
Sourceval size : t -> int list
Sourceval shape1_exn : t -> int
Sourceval shape2_exn : t -> int * int
Sourceval shape3_exn : t -> int * int * int
Sourceval shape4_exn : t -> int * int * int * int
Sourceval kind : t -> Kind.packed
Sourceval requires_grad : t -> bool
Sourceval grad_set_enabled : bool -> bool
Sourceval get : t -> int -> t
Sourceval select : t -> dim:int -> index:int -> t
Sourceval float_value : t -> float
Sourceval int_value : t -> int
Sourceval float_get : t -> int list -> float
Sourceval int_get : t -> int list -> int
Sourceval float_set : t -> int list -> float -> unit
Sourceval int_set : t -> int list -> int -> unit
Sourceval fill_float : t -> float -> unit
Sourceval fill_int : t -> int -> unit
Sourceval backward : ?keep_graph:bool -> ?create_graph:bool -> t -> unit
Sourceval run_backward : ?keep_graph:bool -> ?create_graph:bool -> t list -> t list -> t list
Sourceval print : t -> unit
Sourceval to_string : t -> line_size:int -> string
Sourceval sum : t -> t
Sourceval mean : t -> t
Sourceval argmax : ?dim:int -> ?keepdim:bool -> t -> t
Sourceval defined : t -> bool
Sourceval device : t -> Device.t
Sourceval copy_ : t -> src:t -> unit
Sourceval max : t -> t -> t
Sourceval min : t -> t -> t