package torch

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

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

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

val 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.

val set_int1 : t -> int -> int -> unit

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

val get_float2 : t -> int -> int -> float

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

val get_float1 : t -> int -> float

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

val get_int2 : t -> int -> int -> int

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

val get_int1 : t -> int -> int

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

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

Gets an integer element from an arbitrary dimension tensor.

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

Sets an integer element on an arbitrary dimension tensor.

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

Gets a float element from an arbitrary dimension tensor.

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

Sets a float element on an arbitrary dimension tensor.

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

Gets an integer element from a single dimension tensor.

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

Sets an integer element on a single dimension tensor.

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

Gets a float element from a single dimension tensor.

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

Sets a float element on a single dimension tensor.

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

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

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

Pointwise addition.

val (-) : t -> t -> t

Pointwise substraction.

val (*) : t -> t -> t

Pointwise multiplication.

val (/) : t -> t -> t

Pointwise division.

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

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

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

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

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

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

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

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

val (~-) : t -> t

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

val (=) : t -> t -> t

Pointwise equality.

val eq : t -> t -> bool

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

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

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

val f : float -> t

f v returns a scalar tensor with value v.

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

Creates a tensor with value 0.

val ones : create

Creates a tensor with value 1.

val rand : create

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

val randn : create

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

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

Creates a tensor from a list of float values.

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

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

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

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

val type_ : t -> Torch_core.Kind.packed

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

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

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

val 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.

val 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.

val 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.

val 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.

val to_float0_exn : t -> float

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

val to_float1_exn : t -> float array

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

val to_float2_exn : t -> float array array

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

val to_float3_exn : t -> float array array array

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

val 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.

val 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.

val 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.

val 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.

val to_int0_exn : t -> int

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

val to_int1_exn : t -> int array

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

val to_int2_exn : t -> int array array

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

val to_int3_exn : t -> int array array array

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

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

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

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

of_float1 v creates a single dimension tensor with values vs.

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

of_float2 v creates a two dimension tensor with values vs.

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

of_float3 v creates a three dimension tensor with values vs.

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

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

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

of_int1 v creates a single dimension tensor with values vs.

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

of_int2 v creates a two dimension tensor with values vs.

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

of_int3 v creates a three dimension tensor with values vs.

val conv2d : ?padding:(int * int) -> ?dilation:(int * int) -> ?groups:int -> t -> t -> t option -> stride:(int * int) -> t
val conv_transpose2d : ?output_padding:(int * int) -> ?padding:(int * int) -> ?dilation:(int * int) -> ?groups:int -> t -> t -> t option -> stride:(int * int) -> t
val max_pool2d : ?padding:(int * int) -> ?dilation:(int * int) -> ?ceil_mode:bool -> ?stride:(int * int) -> t -> ksize:(int * int) -> t
val avg_pool2d : ?padding:(int * int) -> ?count_include_pad:bool -> ?ceil_mode:bool -> ?stride:(int * int) -> ?divisor_override:int -> t -> ksize:(int * int) -> t
val const_batch_norm : ?momentum:float -> ?eps:float -> t -> t
val 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.

val 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.

val 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.

val cross_entropy_for_logits : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
val 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.

val nll_loss : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
val 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.

val 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.

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

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

val copy : t -> t

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

val shape_str : t -> string

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

val 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.

val minimum : t -> t

minimum t returns the minimum element of tensor t.

val maximum : t -> t

maximum t returns the maximum element of tensor t.

val 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].

val 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.

val scale : t -> float -> t

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

val 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.

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