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__tensor_ : 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__tensor_ : 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_pool3d : t -> output_size:int list -> t
val _adaptive_avg_pool3d_backward : grad_output:t -> t -> 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 _aminmax : t -> t * t
val _aminmax_dim : t -> dim:int -> keepdim:bool -> 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 _baddbmm_mkl_ : t -> batch1:t -> batch2:t -> 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 _cat : t list -> dim:int -> t
val _cat_out : out:t -> t list -> dim:int -> t
val _cdist_backward : grad:t -> x1:t -> x2:t -> p:float -> cdist:t -> t
val _cholesky_solve_helper : t -> a:t -> upper:bool -> t
val _coalesce : t -> t
val _coalesced_ : 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_physical : 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_backward : grad_input:t -> grad_weight:t -> grad_output:t -> t -> weight:t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> t * 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 _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_nogroup : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> t
val _copy_from : t -> dst:t -> non_blocking:bool -> t
val _copy_from_and_resize : t -> dst:t -> 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 _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_init_dropout_state : dropout:float -> train:bool -> dropout_seed:int -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> 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 _det_lu_based_helper : t -> t * t * t
val _det_lu_based_helper_backward_helper : det_grad:t -> det:t -> t -> lu:t -> pivs:t -> t
val _dim_arange : like:t -> dim:int -> t
val _dirichlet_grad : x:t -> alpha:t -> total:t -> 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_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_per_sample_weights_backward : 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_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 _euclidean_dist : 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_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_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 _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 _fused_dropout : 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 _fw_primal : 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 _index_copy_ : t -> dim:int -> index:t -> source:t -> t
val _index_put_impl_ : t -> indices:t option list -> values:t -> accumulate:bool -> unsafe:bool -> t
val _indices : t -> t
val _inverse_helper : t -> t
val _linalg_inv_out_helper_ : t -> infos_lu:t -> infos_getri:t -> t
val _linalg_qr_helper : t -> mode:string -> 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 -> t -> t
val _log_softmax_backward_data_out : out:t -> grad_output:t -> output:t -> dim:int -> t -> 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 _lu_with_info : t -> pivot:bool -> check_errors:bool -> t * t * t
val _make_dual : 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_tensor_quantized_tensor : t -> scale:float -> zero_point:int -> t
val _masked_scale : t -> mask:t -> scale:float -> t
val _mkldnn_reshape : t -> shape:int list -> t
val _mkldnn_transpose : t -> dim0:int -> dim1:int -> t
val _mkldnn_transpose_ : t -> dim0:int -> dim1:int -> t
val _neg_view : t -> t
val _nnpack_spatial_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> t
val _nnpack_spatial_convolution_backward_input : t -> grad_output:t -> weight:t -> padding:int list -> t
val _nnpack_spatial_convolution_backward_weight : t -> weightsize:int list -> grad_output:t -> padding:int list -> t
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 _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 _pin_memory : 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_from_tensor : t -> shape:t -> t
val _rowwise_prune : weight:t -> mask:t -> compressed_indices_dtype:Torch_core.Kind.packed -> t * t
val _s_where : condition:t -> t -> t -> t
val _sample_dirichlet : t -> t
val _saturate_weight_to_fp16 : weight:t -> t
val _segment_reduce_backward : grad:t -> output:t -> data:t -> reduce:string -> lengths:t option -> axis:int -> t
val _shape_as_tensor : 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 -> t -> t
val _softmax_backward_data_out : grad_input:t -> grad_output:t -> output:t -> dim:int -> t -> t
val _softmax_out : out:t -> t -> dim:int -> half_to_float:bool -> t
val _solve_helper : t -> a:t -> t * t
val _sparse_addmm : t -> sparse:t -> dense: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_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_int : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
val _sparse_mask_helper : 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_int : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
val _sparse_sparse_matmul : t -> t -> t
val _sparse_sum : t -> t
val _sparse_sum_backward : 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_dtype : t -> dtype:Torch_core.Kind.packed -> 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 _svd_helper : t -> some:bool -> compute_uv:bool -> t * t * t
val _symeig_helper : 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_optional_filled_intlist : values:t -> addends:int list -> t
val _test_optional_intlist : values:t -> addends:int list -> t
val _test_serialization_subcmul : t -> t -> t
val _test_string_default : dummy:t -> a:string -> b:string -> 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_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 _to_copy : t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> non_blocking:bool -> t
val _to_cpu : t list -> t list
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 _unique : t -> sorted:bool -> return_inverse:bool -> t * t
val _unique2 : t -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
val _unpack_dual : dual:t -> level:int -> t * t
val _unsafe_view : t -> size:int list -> t
val _values : t -> t
val _weight_norm : v:t -> g:t -> dim:int -> t
val _weight_norm_cuda_interface : v:t -> g:t -> dim:int -> t * t
val _weight_norm_cuda_interface_backward : grad_w:t -> saved_v:t -> saved_g:t -> saved_norms:t -> dim:int -> t * t
val _weight_norm_differentiable_backward : grad_w:t -> saved_v:t -> saved_g:t -> saved_norms: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 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 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 alias : 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 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 -> keepdim:bool -> t * t
val aminmax_out : min:t -> max:t -> t -> dim:int -> 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_out : out:t -> end_:'a Torch_core.Wrapper.Scalar.t -> t
val arange_start_out : out:t -> start:'a Torch_core.Wrapper.Scalar.t -> end_:'a Torch_core.Wrapper.Scalar.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 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 -> keepdim:bool -> t
val argmin : t -> dim:int -> keepdim:bool -> t
val argmin_out : out:t -> t -> dim:int -> keepdim:bool -> t
val argsort : t -> dim:int -> descending:bool -> t
val as_strided : t -> size:int list -> stride:int list -> storage_offset:int -> t
val as_strided_ : t -> size:int list -> stride:int list -> storage_offset:int -> 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 -> 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 -> 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 -> 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 -> 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 -> 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 -> 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 -> 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_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> 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_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_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_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_stats : t -> eps:float -> t * t
val batch_norm_update_stats : 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_out : out:t -> t -> t
val bernoulli_p : t -> p:float -> 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_backward : grad_output: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 binomial : 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_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_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_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_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_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_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val block_diag : 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_tensor_out : out:t -> t -> boundaries:t -> out_int32:bool -> right:bool -> t
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 cdist : x1:t -> x2:t -> p:float -> compute_mode:int -> 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 chain_matmul : matrices:t list -> t
val chain_matmul_out : out:t -> matrices:t list -> t
val channel_shuffle : 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 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_backward : grad_output:t -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val col2im_backward_grad_input : grad_input:t -> grad_output:t -> 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 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 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 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_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 -> dilation:int list -> t * t * 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_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_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 copy_sparse_to_sparse_ : 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 cov : t -> correction:int -> fweights:t option -> aweights:t option -> t
val cross : t -> t -> dim:int -> 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 -> t
val crow_indices : 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_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_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_backward_input : self_size:int list -> grad_output: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_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_deprecated : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_deprecated2 : t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic: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_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_backward_input : grad_output: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_transpose_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_transpose_deprecated : 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 cudnn_convolution_transpose_deprecated2 : t -> weight:t -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_grid_sampler : t -> grid:t -> t
val cudnn_grid_sampler_backward : t -> grid:t -> grad_output:t -> t * t
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 dequantize : t -> t
val dequantize_tensors : t list -> t list
val det : t -> t
val detach : t -> t
val detach_ : 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_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 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 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_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 eig : t -> eigenvectors:bool -> t * t
val eig_e : e:t -> v:t -> t -> eigenvectors:bool -> t * t
val einsum : equation:string -> t list -> 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 -> 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_renorm_ : 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_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_strided : size:int list -> stride:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> 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 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 expm1 : t -> t
val expm1_ : t -> t
val expm1_out : out:t -> t -> t
val exponential_ : 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_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_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 -> dim:int -> norm:string -> t
val fft_fft2 : t -> s:int list -> dim:int list -> norm:string -> t
val fft_fft2_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_fft_out : out:t -> t -> n:int -> 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 -> dim:int list -> norm:string -> t
val fft_fftn_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_fftshift : t -> dim:int list -> t
val fft_hfft : t -> n:int -> dim:int -> norm:string -> t
val fft_hfft_out : out:t -> t -> n:int -> dim:int -> norm:string -> t
val fft_ifft : t -> n:int -> dim:int -> norm:string -> t
val fft_ifft2 : t -> s:int list -> dim:int list -> norm:string -> t
val fft_ifft2_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_ifft_out : out:t -> t -> n:int -> dim:int -> norm:string -> t
val fft_ifftn : t -> s:int list -> dim:int list -> norm:string -> t
val fft_ifftn_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_ifftshift : t -> dim:int list -> t
val fft_ihfft : t -> n:int -> dim:int -> norm:string -> t
val fft_ihfft_out : out:t -> t -> n:int -> dim:int -> norm:string -> t
val fft_irfft : t -> n:int -> dim:int -> norm:string -> t
val fft_irfft2 : t -> s:int list -> dim:int list -> norm:string -> t
val fft_irfft2_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_irfft_out : out:t -> t -> n:int -> dim:int -> norm:string -> t
val fft_irfftn : t -> s:int list -> dim:int list -> norm:string -> t
val fft_irfftn_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_rfft : t -> n:int -> dim:int -> norm:string -> t
val fft_rfft2 : t -> s:int list -> dim:int list -> norm:string -> t
val fft_rfft2_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_rfft_out : out:t -> t -> n:int -> 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 -> dim:int list -> norm:string -> t
val fft_rfftn_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> 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_tensor_ : 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 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 -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> 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_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 -> t
val gelu_backward : grad:t -> t -> t
val gelu_backward_grad_input : grad_input:t -> grad:t -> t -> t
val gelu_out : out:t -> t -> t
val geometric_ : 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_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_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t * t
val grid_sampler_3d : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
val grid_sampler_3d_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t * 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_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 hann_window : window_length:int -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val hann_window_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> 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_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_backward : grad_output:t -> input_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val im2col_backward_grad_input : grad_input:t -> grad_output:t -> input_size:int list -> 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_alpha : t -> dim:int -> index:t -> source:t -> alpha:'a Torch_core.Wrapper.Scalar.t -> t
val index_add_alpha_ : t -> dim:int -> index:t -> source:t -> alpha:'a Torch_core.Wrapper.Scalar.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_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_tensor : t -> dim:int -> index:t -> value:t -> t
val index_fill_int_tensor_ : 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_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 indices : 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 inverse : t -> t
val inverse_out : out:t -> t -> t
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 isnan : 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 -> win_length:int -> window:t option -> center:bool -> normalized:bool -> onesided:bool -> length:int -> 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_periodic : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val kl_div : t -> target:t -> reduction:Torch_core.Reduction.t -> log_target:bool -> t
val kl_div_backward : grad_output:t -> 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 l1_loss_backward : grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> t
val l1_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Torch_core.Reduction.t -> t
val l1_loss_out : out:t -> 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 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_det : t -> t
val linalg_det_out : out:t -> t -> 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 : t -> t
val linalg_inv_ex : t -> check_errors:bool -> t * t
val linalg_inv_ex_inverse : inverse:t -> info:t -> t -> check_errors:bool -> t * t
val linalg_inv_out : out:t -> t -> t
val linalg_lstsq : t -> b:t -> rcond:float -> driver:string -> t * t * t * t
val linalg_lstsq_out : solution:t -> residuals:t -> rank:t -> singular_values:t -> t -> b:t -> rcond:float -> driver:string -> t * t * t * t
val linalg_matmul : t -> t -> t
val linalg_matmul_out : out:t -> 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_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_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 : t -> mode:string -> t * t
val linalg_qr_out : q:t -> r:t -> t -> mode:string -> t * t
val linalg_slogdet : t -> t * t
val linalg_slogdet_out : sign:t -> logabsdet:t -> t -> t * t
val linalg_solve : t -> t -> t
val linalg_solve_out : out:t -> t -> t -> t
val linalg_svd : t -> full_matrices:bool -> t * t * t
val linalg_svd_u : u:t -> s:t -> vh:t -> t -> full_matrices:bool -> t * t * t
val linalg_svdvals : t -> t
val linalg_svdvals_out : out:t -> t -> 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 -> t
val linalg_tensorsolve_out : out:t -> t -> t -> dims:int list -> 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_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 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 -> t
val logit_ : t -> eps:float -> t
val logit_backward : grad_output:t -> t -> eps:float -> t
val logit_backward_grad_input : grad_input:t -> grad_output:t -> t -> eps:float -> t
val logit_out : out:t -> t -> eps:float -> 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 lstsq : t -> a:t -> t * t
val lstsq_x : x:t -> qr:t -> t -> a:t -> t * t
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_tensor : t -> mask:t -> value:t -> t
val masked_fill_tensor_ : 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_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_power : t -> n:int -> t
val matrix_power_out : out:t -> t -> n:int -> t
val matrix_rank : t -> symmetric:bool -> t
val matrix_rank_tol : t -> tol:float -> symmetric:bool -> 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_backward : grad_output:t -> t -> indices:t -> output_size:int list -> t
val max_unpool2d_backward_grad_input : grad_input:t -> grad_output:t -> 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_backward : grad_output:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_unpool3d_backward_grad_input : grad_input:t -> grad_output:t -> 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 -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val mean_out : out:t -> t -> dim:int list -> 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 meshgrid : t list -> t list
val meshgrid_indexing : t list -> indexing:string -> t list
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_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_backward_bias : grad_output:t -> t
val miopen_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> 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_backward_input : grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_transpose_backward_weight : weight_size:int list -> grad_output:t -> t -> 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_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_depthwise_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> 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 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_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
val mkldnn_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> t
val mkldnn_convolution_backward_weights : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> t * 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_weights : grad_output:t -> t -> weight:t -> bias_defined:bool -> t * 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_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_reorder_conv2d_weight : 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 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 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 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 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 -> posinf:float -> neginf:float -> t
val nan_to_num_ : t -> nan:float -> posinf:float -> neginf:float -> t
val nan_to_num_out : out:t -> t -> nan:float -> posinf:float -> neginf:float -> t
val nanmean : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val nanmean_out : out:t -> t -> dim:int list -> 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 nanquantile : t -> q:t -> dim:int -> keepdim:bool -> t
val nanquantile_new : t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_new_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_new_scalar : t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_new_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> t
val nanquantile_scalar : t -> q:float -> dim:int -> keepdim:bool -> t
val nanquantile_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> t
val nansum : t -> dtype:Torch_core.Kind.packed -> t
val nansum_dim_intlist : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val nansum_intlist_out : out:t -> t -> dim:int list -> 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_group_norm : 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_norm : 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 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 new_empty : t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val new_empty_strided : t -> size:int list -> stride:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> 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_ones : t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val new_zeros : t -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> 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_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 normal : out:t -> mean:t -> std:float -> t
val normal_ : t -> mean:float -> std:float -> t
val normal_float_float_out : out:t -> mean:float -> std:float -> size:int list -> t
val normal_float_tensor_out : out:t -> mean:float -> std:t -> t
val normal_tensor_tensor_out : out:t -> mean:t -> std:t -> 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_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 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 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_unshuffle : 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 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 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 put : t -> index:t -> source:t -> accumulate:bool -> t
val put_ : t -> index:t -> source:t -> accumulate:bool -> t
val q_per_channel_scales : t -> t
val q_per_channel_zero_points : t -> t
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 -> keepdim:bool -> t
val quantile_new : t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_new_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_new_scalar : t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_new_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> t
val quantile_scalar : t -> q:float -> dim:int -> keepdim:bool -> t
val quantile_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> t
val quantize_per_channel : 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_tensor_qparams : 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 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_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_pool2d : 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_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_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_out : out:t -> size:int list -> t
val random_ : t -> t
val random_from_ : t -> from:int -> to_:int -> t
val random_to_ : 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 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 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_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 -> t
val repeat_interleave_self_int : t -> repeats:int -> dim:int -> output_size:int -> t
val repeat_interleave_self_tensor : t -> repeats:t -> dim:int -> output_size:int -> 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_as_ : t -> the_template:t -> t
val resize_as_sparse_ : t -> the_template:t -> t
val resolve_conj : t -> t
val resolve_neg : t -> t
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 rot90 : t -> k:int -> dims:int list -> t
val round : t -> t
val round_ : t -> t
val round_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_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 scalar_tensor : s:'a Torch_core.Wrapper.Scalar.t -> options:(Torch_core.Kind.packed * Torch_core.Device.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 -> t
val searchsorted_scalar : sorted_sequence:t -> 'a Torch_core.Wrapper.Scalar.t -> out_int32:bool -> right:bool -> t
val searchsorted_tensor_out : out:t -> sorted_sequence:t -> t -> out_int32:bool -> right:bool -> t
val segment_reduce : data:t -> reduce:string -> lengths:t option -> indices: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 selu : t -> t
val selu_ : t -> t
val set_ : t -> t
val set_requires_grad : t -> r:bool -> t
val set_source_tensor_ : t -> source:t -> 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 -> end_:int -> step:int -> t
val slice_backward : grad_output:t -> input_sizes:int list -> dim:int -> start:int -> end_:int -> step:int -> t
val slogdet : 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_dilated3d : 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 softplus : t -> t
val softplus_backward : grad_output:t -> t -> beta:'a Torch_core.Wrapper.Scalar.t -> threshold:'a Torch_core.Wrapper.Scalar.t -> output: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 -> output: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 solve : t -> a:t -> t * t
val solve_solution : solution:t -> lu:t -> t -> a: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_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_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_mask : t -> mask:t -> 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 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_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_log1p : t -> t
val special_log1p_out : out:t -> t -> t
val special_log_softmax : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
val special_logit : t -> eps:float -> t
val special_logit_out : out:t -> t -> eps:float -> 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_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 -> t
val special_round_out : out:t -> t -> t
val special_sinc : t -> t
val special_sinc_out : out:t -> 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_with_sizes : t -> split_sizes:int list -> dim:int -> t list
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_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 -> correction:int -> keepdim:bool -> t
val std_correction_out : out:t -> t -> dim:int list -> correction:int -> keepdim:bool -> t
val std_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val std_mean : t -> unbiased:bool -> t * t
val std_mean_correction : t -> dim:int list -> correction:int -> keepdim:bool -> t * t
val std_mean_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t * t
val std_out : out:t -> t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val stft : t -> n_fft:int -> hop_length:int -> win_length:int -> window:t option -> normalized:bool -> onesided:bool -> return_complex:bool -> t
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 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 -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val sum_intlist_out : out:t -> t -> dim:int list -> keepdim:bool -> 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 take : t -> index:t -> t
val take_along_dim : t -> indices:t -> dim:int -> t
val take_along_dim_out : out:t -> t -> indices:t -> dim:int -> 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_other : t -> t -> non_blocking:bool -> copy:bool -> t
val to_sparse : t -> t
val to_sparse_sparse_dim : 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 transpose : t -> dim0:int -> dim1:int -> t
val transpose_ : 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_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_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 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 uniform_ : t -> from:float -> to_:float -> t
val unique_consecutive : t -> return_inverse:bool -> return_counts:bool -> dim:int -> 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 unsafe_chunk : t -> chunks:int -> dim:int -> t list
val unsafe_split : t -> split_size:int -> dim:int -> t list
val unsafe_split_with_sizes : t -> split_sizes:int list -> dim:int -> t list
val unsqueeze : t -> dim:int -> t
val unsqueeze_ : t -> dim:int -> t
val upsample_bicubic2d : t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bicubic2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> 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 -> scales_w:float -> t
val upsample_bicubic2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bilinear2d : t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bilinear2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> 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 -> scales_w:float -> t
val upsample_bilinear2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_linear1d : t -> output_size:int list -> align_corners:bool -> scales:float -> t
val upsample_linear1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales:float -> 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 -> t
val upsample_linear1d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales:float -> t
val upsample_nearest1d : t -> output_size:int list -> scales:float -> t
val upsample_nearest1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales:float -> t
val upsample_nearest1d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales:float -> t
val upsample_nearest1d_out : out:t -> t -> output_size:int list -> scales:float -> t
val upsample_nearest2d : t -> output_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest2d_out : out:t -> t -> output_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d : t -> output_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d_out : out:t -> t -> output_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_trilinear3d : t -> output_size:int list -> align_corners:bool -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_trilinear3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_d:float -> scales_h:float -> scales_w:float -> 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 -> scales_h:float -> scales_w:float -> t
val upsample_trilinear3d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_d:float -> scales_h:float -> scales_w:float -> t
val value_selecting_reduction_backward : grad:t -> dim:int -> indices:t -> sizes:int list -> keepdim:bool -> t
val values : t -> t
val vander : x:t -> n:int -> increasing:bool -> t
val var : t -> unbiased:bool -> t
val var_correction : t -> dim:int list -> correction:int -> keepdim:bool -> t
val var_correction_out : out:t -> t -> dim:int list -> correction:int -> keepdim:bool -> t
val var_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val var_mean : t -> unbiased:bool -> t * t
val var_mean_correction : t -> dim:int list -> correction:int -> keepdim:bool -> t * t
val var_mean_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t * t
val var_out : out:t -> t -> dim:int list -> 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_real : t -> 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 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 zeros_like : t -> t
val zeros_out : out:t -> size:int list -> t
val new_tensor : unit -> t
val int_vec : ?kind:[ `int | `int16 | `int64 | `int8 | `uint8 ] -> int list -> t
val shape : t -> int list
val size : t -> int list
val shape1_exn : t -> int
val shape2_exn : t -> int * int
val shape3_exn : t -> int * int * int
val shape4_exn : t -> int * int * int * int
val requires_grad : t -> bool
val grad_set_enabled : bool -> bool
val get : t -> int -> t
val select : t -> dim:int -> index:int -> t
val float_value : t -> float
val int_value : t -> int
val float_get : t -> int list -> float
val int_get : t -> int list -> int
val float_set : t -> int list -> float -> unit
val int_set : t -> int list -> int -> unit
val fill_float : t -> float -> unit
val fill_int : t -> int -> unit
val backward : ?keep_graph:bool -> ?create_graph:bool -> t -> unit
val run_backward : ?keep_graph:bool -> ?create_graph:bool -> t list -> t list -> t list
val print : t -> unit
val to_string : t -> line_size:int -> string
val sum : t -> t
val mean : t -> t
val argmax : ?dim:int -> ?keepdim:bool -> t -> t
val defined : t -> bool
val device : t -> Torch_core.Device.t
val copy_ : t -> src:t -> 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, Stdlib.Bigarray.c_layout) Stdlib.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, Stdlib.Bigarray.c_layout) Stdlib.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) Stdlib.Bigarray.kind -> ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.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 : Stdlib.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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