package torch

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package torch
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Module Torch.TensorSource

include module type of Torch_core.Wrapper.Tensor with type t := t
include Torch_core.Wrapper_generated_intf.S with type t := t and type 'a scalar := 'a Torch_core.Wrapper.Scalar.t
Sourceval __and__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __and__tensor_ : t -> t -> t
Sourceval __iand__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __iand__tensor_ : t -> t -> t
Sourceval __ilshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __ilshift__tensor_ : t -> t -> t
Sourceval __ior__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __ior__tensor_ : t -> t -> t
Sourceval __irshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __irshift__tensor_ : t -> t -> t
Sourceval __ixor__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __ixor__tensor_ : t -> t -> t
Sourceval __lshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __lshift__scalar_out_ : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __lshift__tensor_ : t -> t -> t
Sourceval __lshift__tensor_out_ : out:t -> t -> t -> t
Sourceval __or__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __or__tensor_ : t -> t -> t
Sourceval __rshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __rshift__scalar_out_ : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval __rshift__tensor_ : t -> t -> t
Sourceval __rshift__tensor_out_ : out:t -> t -> t -> t
Sourceval __xor__ : t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval _add_relu_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval _add_relu_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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:Torch_core.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:Torch_core.Kind.packed -> cpu_dtype:Torch_core.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:(Torch_core.Kind.packed * Torch_core.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 _cufft_get_plan_cache_max_size : device_index:int -> int64
Sourceval _cufft_get_plan_cache_size : device_index:int -> int64
Sourceval _debug_has_internal_overlap : t -> int64
Sourceval _dim_arange : like:t -> dim:int -> t
Sourceval _dimi : t -> int64
Sourceval _dimv : t -> int64
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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.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_adam : out:t list -> t list -> grads:t list -> exp_avgs:t list -> exp_avg_sqs:t list -> max_exp_avg_sqs:t list -> state_steps:t list -> lr:float -> beta1:float -> beta2:float -> weight_decay:float -> eps:float -> amsgrad:bool -> maximize:bool -> grad_scale:t option -> found_inf:t option -> unit
Sourceval _fused_adam_ : t list -> grads:t list -> exp_avgs:t list -> exp_avg_sqs:t list -> max_exp_avg_sqs:t list -> state_steps:t list -> lr:float -> beta1:float -> beta2:float -> weight_decay:float -> eps:float -> amsgrad:bool -> maximize:bool -> grad_scale:t option -> found_inf:t option -> unit
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 _has_compatible_shallow_copy_type : t -> from:t -> bool
Sourceval _has_same_storage_numel : t -> t -> bool
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 _is_zerotensor : t -> bool
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:Torch_core.Kind.packed -> t
Sourceval _log_softmax_backward_data_out : out:t -> grad_output:t -> output:t -> dim:int -> input_dtype:Torch_core.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_available : unit -> bool
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 _nnz : t -> int64
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 Torch_core.Wrapper.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:Torch_core.Device.t -> t
Sourceval _pin_memory_out : out:t -> t -> device:Torch_core.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:Torch_core.Device.t -> t
Sourceval _resize_output_ : t -> size:int list -> device:Torch_core.Device.t -> t
Sourceval _resize_output_out : out:t -> t -> size:int list -> device:Torch_core.Device.t -> t
Sourceval _rowwise_prune : weight:t -> mask:t -> compressed_indices_dtype:Torch_core.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 Torch_core.Wrapper.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 Torch_core.Wrapper.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:Torch_core.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:Torch_core.Kind.packed -> t
Sourceval _softmax_backward_data_out : grad_input:t -> grad_output:t -> output:t -> dim:int -> input_dtype:Torch_core.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval _sparse_bsr_tensor_unsafe : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval _sparse_compressed_tensor_unsafe : compressed_indices:t -> plain_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval _sparse_coo_tensor_unsafe : indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval _sparse_coo_tensor_with_dims : sparse_dim:int -> dense_dim:int -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval _sparse_csr_prod : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval _sparse_csr_prod_dim_dtype_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval _sparse_csr_sum : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval _sparse_csr_sum_dim_dtype_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval _sparse_csr_tensor_unsafe : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.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:Torch_core.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:Torch_core.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:Torch_core.Kind.packed -> t
Sourceval _sparse_sum_dim_out : out:t -> t -> dim:int list -> t
Sourceval _sparse_sum_dtype : t -> dtype:Torch_core.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:(Torch_core.Kind.packed * Torch_core.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:Torch_core.Kind.packed -> t
Sourceval _to_dense_out : out:t -> t -> dtype:Torch_core.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 _use_cudnn_ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> bool
Sourceval _use_cudnn_ctc_loss_tensor : log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> blank:int -> bool
Sourceval _use_cudnn_rnn_flatten_weight : unit -> bool
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 _version : t -> int64
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 Torch_core.Wrapper.Scalar.t -> t
Sourceval add_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval add_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 allclose : t -> t -> rtol:float -> atol:float -> equal_nan:bool -> bool
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 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_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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval bartlett_window_out : out:t -> window_length:int -> t
Sourceval bartlett_window_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.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:Torch_core.Reduction.t -> t
Sourceval binary_cross_entropy_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> t
Sourceval binary_cross_entropy_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> t
Sourceval binary_cross_entropy_out : out:t -> t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> t
Sourceval binary_cross_entropy_with_logits : t -> target:t -> weight:t option -> pos_weight:t option -> reduction:Torch_core.Reduction.t -> t
Sourceval binary_cross_entropy_with_logits_out : out:t -> t -> target:t -> weight:t option -> pos_weight:t option -> reduction:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_and_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_and_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_and_scalar_tensor : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval bitwise_and_scalar_tensor_out : out:t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval bitwise_left_shift_scalar_tensor_out : out:t -> 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval bitwise_left_shift_tensor_out : out:t -> t -> t -> t
Sourceval bitwise_left_shift_tensor_scalar : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_left_shift_tensor_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_left_shift_tensor_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_or_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_or_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_or_scalar_tensor : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval bitwise_or_scalar_tensor_out : out:t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval bitwise_right_shift_scalar_tensor_out : out:t -> 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval bitwise_right_shift_tensor_out : out:t -> t -> t -> t
Sourceval bitwise_right_shift_tensor_scalar : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_right_shift_tensor_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_right_shift_tensor_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_xor : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_xor_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_xor_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval bitwise_xor_scalar_tensor : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval bitwise_xor_scalar_tensor_out : out:t -> 'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval blackman_window_out : out:t -> window_length:int -> t
Sourceval blackman_window_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.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 Torch_core.Wrapper.Scalar.t -> boundaries:t -> out_int32:bool -> right:bool -> t
Sourceval bucketize_scalar_out : out:t -> 'a Torch_core.Wrapper.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 can_cast : from:Torch_core.Kind.packed -> to_:Torch_core.Kind.packed -> bool
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 Torch_core.Wrapper.Scalar.t -> max:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval clamp_max : t -> max:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval clamp_max_ : t -> max:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval clamp_max_out : out:t -> t -> max:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval clamp_min_ : t -> min:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval clamp_min_out : out:t -> t -> min:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> max:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> max:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval clip_out : out:t -> t -> min:'a Torch_core.Wrapper.Scalar.t -> max:'a Torch_core.Wrapper.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_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_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_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 Torch_core.Wrapper.Scalar.t -> t
Sourceval copysign_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval copysign_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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:Torch_core.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:Torch_core.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:Torch_core.Reduction.t -> zero_infinity:bool -> t
Sourceval ctc_loss_tensor : log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> blank:int -> reduction:Torch_core.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 Torch_core.Wrapper.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 Torch_core.Wrapper.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 cudnn_is_acceptable : t -> bool
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:Torch_core.Kind.packed -> t
Sourceval cumprod_ : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
Sourceval cumprod_backward : grad:t -> t -> dim:int -> output:t -> t
Sourceval cumprod_out : out:t -> t -> dim:int -> dtype:Torch_core.Kind.packed -> t
Sourceval cumsum : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
Sourceval cumsum_ : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
Sourceval cumsum_out : out:t -> t -> dim:int -> dtype:Torch_core.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 dense_dim : t -> int64
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 Torch_core.Wrapper.Scalar.t -> t
Sourceval div_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval div_scalar_mode : t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
Sourceval div_scalar_mode_ : t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
Sourceval div_scalar_mode_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
Sourceval div_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval divide_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval divide_scalar_mode : t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
Sourceval divide_scalar_mode_ : t -> 'a Torch_core.Wrapper.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 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 Torch_core.Wrapper.Scalar.t -> scale:'a Torch_core.Wrapper.Scalar.t -> input_scale:'a Torch_core.Wrapper.Scalar.t -> is_result:bool -> self_or_result:t -> t
Sourceval elu_backward_grad_input : grad_input:t -> grad_output:t -> alpha:'a Torch_core.Wrapper.Scalar.t -> scale:'a Torch_core.Wrapper.Scalar.t -> input_scale:'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval empty_strided_out : out:t -> size:int list -> stride:int list -> t
Sourceval eq_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval eq_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 equal : t -> t -> bool
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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval eye_m : n:int -> m:int -> options:(Torch_core.Kind.packed * Torch_core.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 Torch_core.Wrapper.Scalar.t -> weight_zero_point:'a Torch_core.Wrapper.Scalar.t -> bias:t -> t
Sourceval fbgemm_linear_int8_weight_fp32_activation : t -> weight:t -> packed:t -> col_offsets:t -> weight_scale:'a Torch_core.Wrapper.Scalar.t -> weight_zero_point:'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval fill_ : t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval fill_diagonal_ : t -> fill_value:'a Torch_core.Wrapper.Scalar.t -> wrap:bool -> t
Sourceval fill_scalar_out : out:t -> t -> value:'a Torch_core.Wrapper.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_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 Torch_core.Wrapper.Scalar.t -> t
Sourceval float_power_scalar : 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
Sourceval float_power_scalar_out : out:t -> 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
Sourceval float_power_tensor_ : t -> exponent:t -> t
Sourceval float_power_tensor_scalar : t -> exponent:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval float_power_tensor_scalar_out : out:t -> t -> exponent:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval floor_divide_scalar_ : t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval fmod_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.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 Torch_core.Wrapper.Scalar.t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval full_like : t -> fill_value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval full_like_out : out:t -> t -> fill_value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval full_out : out:t -> size:int list -> fill_value:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval ge_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval greater_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval greater_equal : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval greater_equal_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval greater_equal_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval gt_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval hamming_window_out : out:t -> window_length:int -> t
Sourceval hamming_window_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval hamming_window_periodic_alpha : window_length:int -> periodic:bool -> alpha:float -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval hamming_window_periodic_alpha_beta : window_length:int -> periodic:bool -> alpha:float -> beta:float -> options:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval hann_window_out : out:t -> window_length:int -> t
Sourceval hann_window_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval hardshrink_backward_grad_input : grad_input:t -> grad_out:t -> t -> lambd:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> max_val:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval hardtanh_backward_grad_input : grad_input:t -> grad_output:t -> t -> min_val:'a Torch_core.Wrapper.Scalar.t -> max_val:'a Torch_core.Wrapper.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:Torch_core.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_backward : grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> delta:float -> t
Sourceval huber_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> delta:float -> t
Sourceval huber_loss_out : out:t -> t -> target:t -> reduction:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval index_fill_ : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval index_fill_int_scalar_out : out:t -> t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.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 is_coalesced : t -> bool
Sourceval is_complex : t -> bool
Sourceval is_conj : t -> bool
Sourceval is_distributed : t -> bool
Sourceval is_floating_point : t -> bool
Sourceval is_inference : t -> bool
Sourceval is_leaf : t -> bool
Sourceval is_neg : t -> bool
Sourceval is_nonzero : t -> bool
Sourceval is_pinned : t -> device:Torch_core.Device.t -> bool
Sourceval is_same_size : t -> t -> bool
Sourceval is_set_to : t -> t -> bool
Sourceval is_signed : t -> bool
Sourceval is_vulkan_available : unit -> bool
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 Torch_core.Wrapper.Scalar.t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_scalar_tensor_out : out:t -> element:'a Torch_core.Wrapper.Scalar.t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_tensor_scalar : elements:t -> test_element:'a Torch_core.Wrapper.Scalar.t -> assume_unique:bool -> invert:bool -> t
Sourceval isin_tensor_scalar_out : out:t -> elements:t -> test_element:'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval kaiser_window_beta : window_length:int -> periodic:bool -> beta:float -> options:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval kaiser_window_periodic_out : out:t -> window_length:int -> periodic:bool -> t
Sourceval kl_div : t -> target:t -> reduction:Torch_core.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:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval le_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> self_is_result:bool -> t
Sourceval leaky_relu_backward_grad_input : grad_input:t -> grad_output:t -> t -> negative_slope:'a Torch_core.Wrapper.Scalar.t -> self_is_result:bool -> t
Sourceval leaky_relu_out : out:t -> t -> t
Sourceval lerp : t -> end_:t -> weight:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval lerp_ : t -> end_:t -> weight:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval lerp_scalar_out : out:t -> t -> end_:t -> weight:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval less_equal : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval less_equal_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval less_equal_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval linalg_cond_out : out:t -> t -> p:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> end_:'a Torch_core.Wrapper.Scalar.t -> steps:int -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval linspace_out : out:t -> start:'a Torch_core.Wrapper.Scalar.t -> end_:'a Torch_core.Wrapper.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:Torch_core.Kind.packed -> t
Sourceval log_softmax_int_out : out:t -> t -> dim:int -> dtype:Torch_core.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 Torch_core.Wrapper.Scalar.t -> end_:'a Torch_core.Wrapper.Scalar.t -> steps:int -> base:float -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval logspace_out : out:t -> start:'a Torch_core.Wrapper.Scalar.t -> end_:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval lt_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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:Torch_core.Reduction.t -> t
Sourceval masked_fill : t -> mask:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval masked_fill_ : t -> mask:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval masked_fill_scalar_out : out:t -> t -> mask:t -> value:'a Torch_core.Wrapper.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_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_out : out:t -> t -> t -> t
Sourceval mean_dim : t -> dim:int list option -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval mean_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Torch_core.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_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 Torch_core.Wrapper.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_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_backward : grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> t
Sourceval mse_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> t
Sourceval mse_loss_out : out:t -> t -> target:t -> reduction:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval mul_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval mul_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval multi_margin_loss_backward : grad_output:t -> t -> target:t -> p:'a Torch_core.Wrapper.Scalar.t -> margin:'a Torch_core.Wrapper.Scalar.t -> weight:t option -> reduction:Torch_core.Reduction.t -> t
Sourceval multi_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> p:'a Torch_core.Wrapper.Scalar.t -> margin:'a Torch_core.Wrapper.Scalar.t -> weight:t option -> reduction:Torch_core.Reduction.t -> t
Sourceval multilabel_margin_loss : t -> target:t -> reduction:Torch_core.Reduction.t -> t
Sourceval multilabel_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> is_target:t -> t
Sourceval multilabel_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> is_target:t -> t
Sourceval multilabel_margin_loss_out : out:t -> t -> target:t -> reduction:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval multiply_scalar_ : t -> 'a Torch_core.Wrapper.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:Torch_core.Kind.packed -> t
Sourceval nanmean_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Torch_core.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:Torch_core.Kind.packed -> t
Sourceval nansum_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Torch_core.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 Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval native_norm_scalaropt_dim_dtype_out : out:t -> t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval ne_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval ne_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.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 Torch_core.Wrapper.Scalar.t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval new_full_out : out:t -> t -> size:int list -> fill_value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval new_ones : t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval new_ones_out : out:t -> t -> size:int list -> t
Sourceval new_zeros : t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.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_loss2d : t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> ignore_index:int -> t
Sourceval nll_loss2d_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Torch_core.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:Torch_core.Reduction.t -> ignore_index:int -> total_weight:t -> t
Sourceval nll_loss2d_out : out:t -> t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> ignore_index:int -> t
Sourceval nll_loss_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Torch_core.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:Torch_core.Reduction.t -> ignore_index:int -> total_weight:t -> t
Sourceval nll_loss_nd : t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> ignore_index:int -> t
Sourceval nll_loss_out : out:t -> t -> target:t -> weight:t option -> reduction:Torch_core.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 Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval norm_except_dim : v:t -> pow:int -> dim:int -> t
Sourceval norm_out : out:t -> t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> t
Sourceval norm_scalar_out : out:t -> t -> t
Sourceval norm_scalaropt_dim : t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> t
Sourceval norm_scalaropt_dim_dtype : t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval norm_scalaropt_dtype : t -> p:'a Torch_core.Wrapper.Scalar.t -> dtype:Torch_core.Kind.packed -> t
Sourceval norm_scalaropt_dtype_out : out:t -> t -> p:'a Torch_core.Wrapper.Scalar.t -> dtype:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval not_equal_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval not_equal_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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_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 output_nr : t -> int64
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:Torch_core.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:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval pow_scalar : 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
Sourceval pow_scalar_out : out:t -> 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
Sourceval pow_tensor_ : t -> exponent:t -> t
Sourceval pow_tensor_scalar : t -> exponent:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval pow_tensor_scalar_out : out:t -> t -> exponent:'a Torch_core.Wrapper.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:Torch_core.Kind.packed -> t
Sourceval prod_dim_int : t -> dim:int -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval prod_int_out : out:t -> t -> dim:int -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval prod_out : out:t -> t -> dtype:Torch_core.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_axis : t -> int64
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 q_scale : t -> float
Sourceval q_zero_point : t -> int64
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:Torch_core.Kind.packed -> t
Sourceval quantize_per_channel_out : out:t -> t -> scales:t -> zero_points:t -> axis:int -> dtype:Torch_core.Kind.packed -> t
Sourceval quantize_per_tensor : t -> scale:float -> zero_point:int -> dtype:Torch_core.Kind.packed -> t
Sourceval quantize_per_tensor_dynamic : t -> dtype:Torch_core.Kind.packed -> reduce_range:bool -> t
Sourceval quantize_per_tensor_dynamic_out : out:t -> t -> dtype:Torch_core.Kind.packed -> reduce_range:bool -> t
Sourceval quantize_per_tensor_out : out:t -> t -> scale:float -> zero_point:int -> dtype:Torch_core.Kind.packed -> t
Sourceval quantize_per_tensor_tensor_qparams : t -> scale:t -> zero_point:t -> dtype:Torch_core.Kind.packed -> t
Sourceval quantize_per_tensor_tensor_qparams_out : out:t -> t -> scale:t -> zero_point:t -> dtype:Torch_core.Kind.packed -> t
Sourceval quantize_per_tensor_tensors : t list -> scales:t -> zero_points:t -> dtype:Torch_core.Kind.packed -> t list
Sourceval quantize_per_tensor_tensors_out : out:t list -> t list -> scales:t -> zero_points:t -> dtype:Torch_core.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 Torch_core.Wrapper.Scalar.t -> scale_hh:'a Torch_core.Wrapper.Scalar.t -> zero_point_ih:'a Torch_core.Wrapper.Scalar.t -> zero_point_hh:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> scale_hh:'a Torch_core.Wrapper.Scalar.t -> zero_point_ih:'a Torch_core.Wrapper.Scalar.t -> zero_point_hh:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> scale_hh:'a Torch_core.Wrapper.Scalar.t -> zero_point_ih:'a Torch_core.Wrapper.Scalar.t -> zero_point_hh:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> scale_hh:'a Torch_core.Wrapper.Scalar.t -> zero_point_ih:'a Torch_core.Wrapper.Scalar.t -> zero_point_hh:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval rad2deg : t -> t
Sourceval rad2deg_ : t -> t
Sourceval rad2deg_out : out:t -> 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:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.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_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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval randperm_out : out:t -> n:int -> t
Sourceval range_out : out:t -> start:'a Torch_core.Wrapper.Scalar.t -> end_:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval range_out_ : out:t -> start:'a Torch_core.Wrapper.Scalar.t -> end_:'a Torch_core.Wrapper.Scalar.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval remainder_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval remainder_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval remainder_scalar_tensor : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval remainder_scalar_tensor_out : out:t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> dim:int -> maxnorm:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval renorm_ : t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int -> maxnorm:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval renorm_out : out:t -> t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int -> maxnorm:'a Torch_core.Wrapper.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 retains_grad : t -> bool
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 Torch_core.Wrapper.Scalar.t -> upper:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> upper:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval rsub_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval rsub_tensor_out : out:t -> t -> t -> t
Sourceval scalar_tensor_out : out:t -> s:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval scatter_value_ : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval scatter_value_out : out:t -> t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval scatter_value_reduce : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> reduce:string -> t
Sourceval scatter_value_reduce_ : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> reduce:string -> t
Sourceval scatter_value_reduce_out : out:t -> t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> out_int32:bool -> right:bool -> side:string -> sorter:t option -> t
Sourceval searchsorted_scalar_out : out:t -> sorted_sequence:t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.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 Torch_core.Wrapper.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:Torch_core.Reduction.t -> beta:float -> t
Sourceval smooth_l1_loss_backward : grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> beta:float -> t
Sourceval smooth_l1_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> beta:float -> t
Sourceval smooth_l1_loss_out : out:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> beta:float -> t
Sourceval soft_margin_loss : t -> target:t -> reduction:Torch_core.Reduction.t -> t
Sourceval soft_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> t
Sourceval soft_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> t
Sourceval soft_margin_loss_out : out:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> t
Sourceval softmax : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
Sourceval softmax_int_out : out:t -> t -> dim:int -> dtype:Torch_core.Kind.packed -> t
Sourceval softplus : t -> t
Sourceval softplus_backward : grad_output:t -> t -> beta:'a Torch_core.Wrapper.Scalar.t -> threshold:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval softplus_backward_grad_input : grad_input:t -> grad_output:t -> t -> beta:'a Torch_core.Wrapper.Scalar.t -> threshold:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval softplus_out : out:t -> t -> t
Sourceval softshrink : t -> t
Sourceval softshrink_backward : grad_output:t -> t -> lambd:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval softshrink_backward_grad_input : grad_input:t -> grad_output:t -> t -> lambd:'a Torch_core.Wrapper.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_bsc_tensor_ccol_row_value_size : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_bsr_tensor : crow_indices:t -> col_indices:t -> values:t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_bsr_tensor_crow_col_value_size : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_compressed_tensor : compressed_indices:t -> plain_indices:t -> values:t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_compressed_tensor_comp_plain_value_size : compressed_indices:t -> plain_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_coo_tensor : size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_coo_tensor_indices : indices:t -> values:t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_coo_tensor_indices_size : indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.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:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_csc_tensor_ccol_row_value_size : ccol_indices:t -> row_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_csr_tensor : crow_indices:t -> col_indices:t -> values:t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_csr_tensor_crow_col_value_size : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
Sourceval sparse_dim : t -> int64
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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_chebyshev_polynomial_t_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_t_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_chebyshev_polynomial_u_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_u_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_chebyshev_polynomial_v_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_v_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_chebyshev_polynomial_w_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_chebyshev_polynomial_w_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_hermite_polynomial_h_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_hermite_polynomial_h_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_hermite_polynomial_he_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_hermite_polynomial_he_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_laguerre_polynomial_l_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_laguerre_polynomial_l_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_legendre_polynomial_p_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_legendre_polynomial_p_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_t_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_t_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_u_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_u_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_v_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_v_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_shifted_chebyshev_polynomial_w_n_scalar_out : out:t -> x:t -> n:'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> n:t -> t
Sourceval special_shifted_chebyshev_polynomial_w_x_scalar_out : out:t -> x:'a Torch_core.Wrapper.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:Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval special_xlog1py_other_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval special_xlog1py_out : out:t -> t -> t -> t
Sourceval special_xlog1py_self_scalar : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval special_xlog1py_self_scalar_out : out:t -> 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval special_xlogy : t -> t -> t
Sourceval special_xlogy_other_scalar : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval special_xlogy_other_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval special_xlogy_out : out:t -> t -> t -> t
Sourceval special_xlogy_self_scalar : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval special_xlogy_self_scalar_out : out:t -> 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval special_zeta : t -> t -> t
Sourceval special_zeta_other_scalar : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval special_zeta_other_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval special_zeta_out : out:t -> t -> t -> t
Sourceval special_zeta_self_scalar : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval special_zeta_self_scalar_out : out:t -> 'a Torch_core.Wrapper.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 stride : t -> dim:int -> int64
Sourceval sub : t -> t -> t
Sourceval sub_ : t -> t -> t
Sourceval sub_out : out:t -> t -> t -> t
Sourceval sub_scalar : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval sub_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval sub_scalar_out : out:t -> t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval subtract_scalar_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval sum_dim_intlist : t -> dim:int list option -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval sum_intlist_out : out:t -> t -> dim:int list option -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
Sourceval sum_out : out:t -> t -> dtype:Torch_core.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 Torch_core.Wrapper.Scalar.t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval threshold_ : t -> threshold:'a Torch_core.Wrapper.Scalar.t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval threshold_backward : grad_output:t -> t -> threshold:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval threshold_backward_grad_input : grad_input:t -> grad_output:t -> t -> threshold:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval threshold_out : out:t -> t -> threshold:'a Torch_core.Wrapper.Scalar.t -> value:'a Torch_core.Wrapper.Scalar.t -> t
Sourceval tile : t -> dims:int list -> t
Sourceval to_ : t -> device:Torch_core.Device.t -> t
Sourceval to_dense : t -> dtype:Torch_core.Kind.packed -> t
Sourceval to_dense_backward : grad:t -> t -> t
Sourceval to_dtype : t -> dtype:Torch_core.Kind.packed -> non_blocking:bool -> copy:bool -> t
Sourceval to_dtype_layout : t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> non_blocking:bool -> copy:bool -> t
Sourceval to_mkldnn : t -> dtype:Torch_core.Kind.packed -> t
Sourceval to_mkldnn_backward : grad:t -> t -> t
Sourceval to_mkldnn_out : out:t -> t -> dtype:Torch_core.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:Torch_core.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:(Torch_core.Kind.packed * Torch_core.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:Torch_core.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:(Torch_core.Kind.packed * Torch_core.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval true_divide_scalar_ : t -> 'a Torch_core.Wrapper.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:Torch_core.Kind.packed -> t
Sourceval view_copy_dtype_out : out:t -> t -> dtype:Torch_core.Kind.packed -> t
Sourceval view_copy_out : out:t -> t -> size:int list -> t
Sourceval view_dtype : t -> dtype:Torch_core.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 Torch_core.Wrapper.Scalar.t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval where_scalarother : condition:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval where_scalarself : condition:t -> 'a Torch_core.Wrapper.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 Torch_core.Wrapper.Scalar.t -> t
Sourceval xlogy_outscalar_self : out:t -> 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval xlogy_outtensor : out:t -> t -> t -> t
Sourceval xlogy_scalar_other : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval xlogy_scalar_other_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
Sourceval xlogy_scalar_self : 'a Torch_core.Wrapper.Scalar.t -> t -> t
Sourceval zero : t -> t
Sourceval zero_ : t -> t
Sourceval zero_out : out:t -> 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 : Base.unit -> t
Sourceval int_vec : ?kind:[ `int | `int16 | `int64 | `int8 | `uint8 ] -> Base.int Base.list -> t
Sourceval shape : t -> Base.int Base.list
Sourceval size : t -> Base.int Base.list
Sourceval shape1_exn : t -> Base.int
Sourceval shape2_exn : t -> Base.int * Base.int
Sourceval shape3_exn : t -> Base.int * Base.int * Base.int
Sourceval shape4_exn : t -> Base.int * Base.int * Base.int * Base.int
Sourceval requires_grad : t -> Base.bool
Sourceval grad_set_enabled : Base.bool -> Base.bool
Sourceval get : t -> Base.int -> t
Sourceval select : t -> dim:Base.int -> index:Base.int -> t
Sourceval float_value : t -> Base.float
Sourceval int_value : t -> Base.int
Sourceval float_get : t -> Base.int Base.list -> Base.float
Sourceval int_get : t -> Base.int Base.list -> Base.int
Sourceval float_set : t -> Base.int Base.list -> Base.float -> Base.unit
Sourceval fill_float : t -> Base.float -> Base.unit
Sourceval fill_int : t -> Base.int -> Base.unit
Sourceval backward : ?keep_graph:Base.bool -> ?create_graph:Base.bool -> t -> Base.unit
Sourceval run_backward : ?keep_graph:Base.bool -> ?create_graph:Base.bool -> t Base.list -> t Base.list -> t Base.list
Sourceval print : t -> Base.unit
Sourceval to_string : t -> line_size:Base.int -> Base.string
Sourceval sum : t -> t
Sourceval mean : t -> t
Sourceval argmax : ?dim:Base.int -> ?keepdim:Base.bool -> t -> t
Sourceval defined : t -> Base.bool
Sourceval copy_ : t -> src:t -> Base.unit
Sourceval set_data : t -> src:t -> Base.unit
Sourceval max : t -> t -> t
Sourceval min : t -> t -> t
Sourceval set_float2 : t -> int -> int -> float -> unit

set_float2 t i j v sets the element at index i and j of bidimensional tensor t to v.

Sourceval set_float1 : t -> int -> float -> unit

set_float1 t i v sets the element at index i of single dimension tensor t to v.

Sourceval set_int2 : t -> int -> int -> int -> unit

set_int2 t i j v sets the element at index i and j of bidimensional tensor t to v.

Sourceval set_int1 : t -> int -> int -> unit

set_int1 t i v sets the element at index i of single dimension tensor t to v.

Sourceval get_float2 : t -> int -> int -> float

get_float2 t i j returns the current value from bidimensional tensor t at index i and j.

Sourceval get_float1 : t -> int -> float

get_float1 t i j returns the current value from single dimension tensor t at index i.

Sourceval get_int2 : t -> int -> int -> int

get_int2 t i j returns the current value from bidimensional tensor t at indexex i and j.

Sourceval get_int1 : t -> int -> int

get_int1 t i j returns the current value from single dimension tensor t at index i.

Sourceval (.%{}) : t -> int list -> int

Gets an integer element from an arbitrary dimension tensor.

Sourceval (.%{}<-) : t -> int list -> int -> unit

Sets an integer element on an arbitrary dimension tensor.

Sourceval (.%.{}) : t -> int list -> float

Gets a float element from an arbitrary dimension tensor.

Sourceval (.%.{}<-) : t -> int list -> float -> unit

Sets a float element on an arbitrary dimension tensor.

Sourceval (.%[]) : t -> int -> int

Gets an integer element from a single dimension tensor.

Sourceval (.%[]<-) : t -> int -> int -> unit

Sets an integer element on a single dimension tensor.

Sourceval (.%.[]) : t -> int -> float

Gets a float element from a single dimension tensor.

Sourceval (.%.[]<-) : t -> int -> float -> unit

Sets a float element on a single dimension tensor.

Sourceval no_grad_ : t -> f:(t -> 'a) -> 'a

no_grad_ t ~f runs f on t without tracking gradients for t.

Sourceval no_grad : (unit -> 'a) -> 'a
Sourceval zero_grad : t -> unit
Sourceval (+) : t -> t -> t

Pointwise addition.

Sourceval (-) : t -> t -> t

Pointwise substraction.

Sourceval (*) : t -> t -> t

Pointwise multiplication.

Sourceval (/) : t -> t -> t

Pointwise division.

Sourceval (+=) : t -> t -> unit

t += u modifies t by adding values from u in a pointwise way.

Sourceval (-=) : t -> t -> unit

t -= u modifies t by subtracting values from u in a pointwise way.

Sourceval (*=) : t -> t -> unit

t *= u modifies t by multiplying values from u in a pointwise way.

Sourceval (/=) : t -> t -> unit

t /= u modifies t by dividing values from u in a pointwise way.

Sourceval (~-) : t -> t

~-u returns the opposite of t, i.e. the same as Tensor.(f 0. - t).

Sourceval (=) : t -> t -> t

Pointwise equality.

Sourceval eq : t -> t -> bool

eq t1 t2 returns true if t1 and t2 have the same kind, shape, and all their elements are identical.

Sourceval eq_scalar : t -> _ Scalar.t -> t
Sourceval mm : t -> t -> t

mm t1 t2 returns the dot product or matrix multiplication between t1 and t2.

Sourceval f : float -> t

f v returns a scalar tensor with value v.

Sourcetype create = ?requires_grad:bool -> ?kind:Torch_core.Kind.packed -> ?device:Torch_core.Device.t -> ?scale:float -> int list -> t
Sourceval zeros : create

Creates a tensor with value 0.

Sourceval ones : create

Creates a tensor with value 1.

Sourceval rand : create

Creates a tensor with random values sampled uniformly between 0 and 1.

Sourceval randn : create

Creates a tensor with random values sampled using a standard normal distribution.

Sourceval float_vec : ?kind:[ `double | `float | `half ] -> ?device:Torch_core.Device.t -> float list -> t

Creates a tensor from a list of float values.

Sourceval to_type : t -> type_:Torch_core.Kind.packed -> t

to_type t ~type_ returns a tensor similar to t but converted to kind type_.

Sourceval to_kind : t -> kind:Torch_core.Kind.packed -> t

to_kind t ~kind returns a tensor similar to t but converted to kind kind.

Sourceval type_ : t -> Torch_core.Kind.packed

kind t returns the kind of elements hold in tensor t.

Sourceval to_device : ?device:Torch_core.Device.t -> t -> t

to_device t ~device returns a tensor identical to t but placed on device device.

Sourceval to_float0 : t -> float option

to_float0 t returns the value hold in a scalar (0-dimension) tensor. If the dimension are incorrect, None is returned.

Sourceval to_float1 : t -> float array option

to_float1 t returns the array of values hold in a single dimension tensor. If the dimension are incorrect, None is returned.

Sourceval to_float2 : t -> float array array option

to_float2 t returns the array of values hold in a bidimensional tensor. If the dimension are incorrect, None is returned.

Sourceval to_float3 : t -> float array array array option

to_float3 t returns the array of values hold in a tridimensional tensor. If the dimension are incorrect, None is returned.

Sourceval to_float0_exn : t -> float

to_float0_exn t returns the value hold in a scalar (0-dimension) tensor.

Sourceval to_float1_exn : t -> float array

to_float1_exn t returns the array of values hold in a single dimension tensor.

Sourceval to_float2_exn : t -> float array array

to_float2_exn t returns the array of values hold in a bidimensional tensor.

Sourceval to_float3_exn : t -> float array array array

to_float3_exn t returns the array of values hold in a tridimensional tensor.

Sourceval to_int0 : t -> int option

to_int0 t returns the value hold in a scalar (0-dimension) tensor. If the dimension are incorrect, None is returned.

Sourceval to_int1 : t -> int array option

to_int1 t returns the array of values hold in a single dimension tensor. If the dimension are incorrect, None is returned.

Sourceval to_int2 : t -> int array array option

to_int2 t returns the array of values hold in a bidimensional tensor. If the dimension are incorrect, None is returned.

Sourceval to_int3 : t -> int array array array option

to_int3 t returns the array of values hold in a tridimensional tensor. If the dimension are incorrect, None is returned.

Sourceval to_int0_exn : t -> int

to_int0_exn t returns the value hold in a scalar (0-dimension) tensor.

Sourceval to_int1_exn : t -> int array

to_int1_exn t returns the array of values hold in a single dimension tensor.

Sourceval to_int2_exn : t -> int array array

to_int2_exn t returns the array of values hold in a bidimensional tensor.

Sourceval to_int3_exn : t -> int array array array

to_int3_exn t returns the array of values hold in a tridimensional tensor.

Sourceval of_float0 : ?device:Torch_core.Device.t -> float -> t

of_float0 v creates a scalar (0-dimension) tensor with value v.

Sourceval of_float1 : ?device:Torch_core.Device.t -> float array -> t

of_float1 v creates a single dimension tensor with values vs.

Sourceval of_float2 : ?device:Torch_core.Device.t -> float array array -> t

of_float2 v creates a two dimension tensor with values vs.

Sourceval of_float3 : ?device:Torch_core.Device.t -> float array array array -> t

of_float3 v creates a three dimension tensor with values vs.

Sourceval of_int0 : ?device:Torch_core.Device.t -> int -> t

of_int0 v creates a scalar (0-dimension) tensor with value v.

Sourceval of_int1 : ?device:Torch_core.Device.t -> int array -> t

of_int1 v creates a single dimension tensor with values vs.

Sourceval of_int2 : ?device:Torch_core.Device.t -> int array array -> t

of_int2 v creates a two dimension tensor with values vs.

Sourceval of_int3 : ?device:Torch_core.Device.t -> int array array array -> t

of_int3 v creates a three dimension tensor with values vs.

Sourceval conv2d : ?padding:(int * int) -> ?dilation:(int * int) -> ?groups:int -> t -> t -> t option -> stride:(int * int) -> t
Sourceval conv_transpose2d : ?output_padding:(int * int) -> ?padding:(int * int) -> ?dilation:(int * int) -> ?groups:int -> t -> t -> t option -> stride:(int * int) -> t
Sourceval max_pool2d : ?padding:(int * int) -> ?dilation:(int * int) -> ?ceil_mode:bool -> ?stride:(int * int) -> t -> ksize:(int * int) -> t
Sourceval avg_pool2d : ?padding:(int * int) -> ?count_include_pad:bool -> ?ceil_mode:bool -> ?stride:(int * int) -> ?divisor_override:int -> t -> ksize:(int * int) -> t
Sourceval const_batch_norm : ?momentum:float -> ?eps:float -> t -> t
Sourceval of_bigarray : ?device:Torch_core.Device.t -> ('a, 'b, Bigarray.c_layout) Bigarray.Genarray.t -> t

of_bigarray ba returns a tensor which shape and kind are based on ba and holding the same data.

Sourceval copy_to_bigarray : t -> ('b, 'a, Bigarray.c_layout) Bigarray.Genarray.t -> unit

copy_to_bigarray t ba copies the data from t to ba. The dimensions of ba and its kind of element must match the dimension and kind of t.

Sourceval to_bigarray : t -> kind:('a, 'b) Bigarray.kind -> ('a, 'b, Bigarray.c_layout) Bigarray.Genarray.t

to_bigarray t ~kind converts t to a bigarray using the c layout. kind has to be compatible with the element kind of t.

Sourceval cross_entropy_for_logits : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
Sourceval dropout : t -> p:float -> is_training:bool -> t

dropout t ~p ~is_training applies dropout to t with probability p. If is_training is false, t is returned. If is_training is true, a tensor similar to t is returned except that each element has a probability p to be replaced by 0.

Sourceval nll_loss : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
Sourceval bce_loss : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t

bce_loss t ~targets returns the binary cross entropy loss between t and targets. Elements of t are supposed to represent a probability distribution (according to the last dimension of t), so should be between 0 and 1 and sum to 1.

Sourceval bce_loss_with_logits : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t

bce_loss_with_logits t ~targets returns the binary cross entropy loss between t and targets. Elements of t are logits, a softmax is used in this function to convert them to a probability distribution.

mse_loss t1 t2 returns the square of the difference between t1 and t2. reduction can be used to either keep the whole tensor or reduce it by averaging or summing.

Sourceval mse_loss : ?reduction:Torch_core.Reduction.t -> t -> t -> t
Sourceval huber_loss : ?reduction:Torch_core.Reduction.t -> t -> t -> t
Sourceval pp : Format.formatter -> t -> unit

pp is a pretty-printer for tensors to be used in top-levels such as utop or jupyter.

Sourceval copy : t -> t

copy t returns a new copy of t with the same size and data which does not share storage with t.

Sourceval shape_str : t -> string

shape_str t returns the shape/size of the current tensor as a string. This is useful for pretty printing.

Sourceval print_shape : ?name:string -> t -> unit

print_shape ?name t prints the shape/size of t on stdout. If name is provided, this is also printed.

Sourceval minimum : t -> t

minimum t returns the minimum element of tensor t.

Sourceval maximum : t -> t

maximum t returns the maximum element of tensor t.

Sourceval flatten : t -> t

flatten t returns a flattened version of t, i.e. a single dimension version of the tensor. This is equivalent to Tensor.view t ~size:[-1].

Sourceval squeeze_last : t -> t

squeeze_last t squeezes the last dimension of t, i.e. if this dimension has a size of 1 it is removed.

Sourceval scale : t -> float -> t

scale t f returns the result of multiplying tensor t by f.

Sourceval to_list : t -> t list

to_list t returns the list of tensors extracted from the first dimension. This is the inverse of cat ~dim:0.

Sourceval min_values : t -> dim:int list -> keepdim:bool -> t
Sourceval max_values : t -> dim:int list -> keepdim:bool -> t
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