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__1 : t -> t -> t
val __iand__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __iand__1 : t -> t -> t
val __ilshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __ilshift__1 : t -> t -> t
val __ior__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __ior__1 : t -> t -> t
val __irshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __irshift__1 : t -> t -> t
val __ixor__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __ixor__1 : t -> t -> t
val __lshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __lshift__1 : t -> t -> t
val __or__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __or__1 : t -> t -> t
val __rshift__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __rshift__1 : t -> t -> t
val __xor__ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val __xor__1 : t -> t -> t
val _adaptive_avg_pool2d : t -> output_size:int list -> t
val _adaptive_avg_pool2d_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 _addmv_impl_ : t -> self2:t -> mat:t -> vec:t -> t
val _aminmax : t -> t * t
val _aminmax1 : t -> dim:int -> keepdim:bool -> t * t
val _amp_update_scale : growth_tracker:t -> current_scale: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 _bmm : t -> mat2:t -> deterministic:bool -> t
val _bmm_out : out:t -> t -> mat2:t -> deterministic:bool -> 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_helper : t -> upper:bool -> t
val _cholesky_solve_helper : t -> a:t -> upper:bool -> 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 _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 -> t
val _convolution1 : 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_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 _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 _cumprod : t -> dim:int -> t
val _cumprod_out : out:t -> t -> dim:int -> t
val _cumsum : t -> dim:int -> t
val _cumsum_out : out:t -> t -> dim:int -> 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 -> 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 -> t
val _embedding_bag_dense_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 -> per_sample_weights:t option -> 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 -> t * t * t * t
val _embedding_bag_per_sample_weights_backward : grad:t -> weight:t -> indices:t -> offsets:t -> offset2bag:t -> mode: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 -> 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 _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 _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 _linalg_solve_out_helper_ : t -> t -> infos: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 _logcumsumexp : t -> dim:int -> t
val _logcumsumexp_out : out:t -> t -> dim:int -> t
val _lu_solve_helper : t -> lu_data:t -> lu_pivots:t -> t
val _lu_with_info : t -> pivot:bool -> check_errors:bool -> t * t * t
val _make_dual : primal:t -> tangent:t -> level:int -> t
val _make_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 _mode : t -> dim:int -> keepdim:bool -> t * t
val _mode_out : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> 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 _remove_batch_dim : t -> level:int -> batch_size:int -> out_dim:int -> 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 _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 _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_log_softmax : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
val _sparse_log_softmax1 : 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_matrix_mask_helper : tr:t -> mask_indices:t -> t
val _sparse_mm : sparse:t -> dense:t -> t
val _sparse_softmax : t -> dim:int -> dtype:Torch_core.Kind.packed -> t
val _sparse_softmax1 : t -> dim:int -> half_to_float:bool -> t
val _sparse_softmax_backward_data : grad_output:t -> output:t -> dim:int -> t -> t
val _sparse_sparse_matmul : t -> t -> t
val _sparse_sum : t -> t
val _sparse_sum1 : t -> dtype:Torch_core.Kind.packed -> t
val _sparse_sum2 : t -> dim:int list -> t
val _sparse_sum3 : t -> dim:int list -> dtype:Torch_core.Kind.packed -> t
val _sparse_sum_backward : grad:t -> t -> dim:int list -> t
val _stack : t list -> dim:int -> t
val _stack_out : out:t -> t list -> dim:int -> t
val _standard_gamma : t -> t
val _standard_gamma_grad : t -> output:t -> t
val _std : t -> unbiased:bool -> t
val _svd_helper : t -> some:bool -> compute_uv:bool -> t * t * t
val _syevd_helper : t -> compute_eigenvectors:bool -> uplo:string -> 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_defaults1 : 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 _triangular_solve_helper : t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
val _trilinear : i1:t -> i2:t -> i3:t -> expand1:int list -> expand2:int list -> expand3:int list -> sumdim:int list -> unroll_dim:int -> t
val _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 _var : t -> unbiased:bool -> 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_output:t -> t -> t
val adaptive_avg_pool3d_backward_out : 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_out : 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_out : 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 add1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val add_ : t -> t -> t
val add_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val add_out : out:t -> t -> 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 all1 : 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 angle : t -> t
val angle_out : out:t -> t -> t
val any : t -> t
val any1 : 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_out1 : 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_1d1 : t list -> t list
val atleast_2d : t -> t
val atleast_2d1 : t list -> t list
val atleast_3d : t -> t
val atleast_3d1 : 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_out : 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_out : 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_window1 : 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 -> 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 bernoulli1 : t -> p:float -> t
val bernoulli_ : t -> p:t -> t
val bernoulli_1 : t -> p:float -> t
val bernoulli_out : out:t -> t -> t
val bilinear : input1:t -> input2:t -> weight:t -> bias:t option -> t
val binary_cross_entropy : t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> t
val binary_cross_entropy_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Torch_core.Reduction.t -> t
val binary_cross_entropy_backward_out : 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_and1 : t -> t -> t
val bitwise_and_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val bitwise_and_1 : t -> t -> t
val bitwise_and_out : out:t -> t -> t -> t
val bitwise_and_out1 : 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_or1 : t -> t -> t
val bitwise_or_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val bitwise_or_1 : t -> t -> t
val bitwise_or_out : out:t -> t -> t -> t
val bitwise_or_out1 : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val bitwise_xor : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val bitwise_xor1 : t -> t -> t
val bitwise_xor_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val bitwise_xor_1 : t -> t -> t
val bitwise_xor_out : out:t -> t -> t -> t
val bitwise_xor_out1 : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val blackman_window : window_length:int -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val blackman_window1 : 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 bucketize1 : 'a Torch_core.Wrapper.Scalar.t -> boundaries:t -> out_int32:bool -> right:bool -> t
val bucketize_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 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_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_out : out:t -> t -> min:'a Torch_core.Wrapper.Scalar.t -> max:'a Torch_core.Wrapper.Scalar.t -> 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 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_out : 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 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 conj : t -> t
val conj_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 conv3d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> 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 copysign1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val copysign_ : t -> t -> t
val copysign_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val copysign_out : out:t -> t -> 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 cross : t -> t -> dim:int -> t
val cross_out : out:t -> t -> t -> dim:int -> 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_loss1 : 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 -> t
val cudnn_convolution1 : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution2 : 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_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_transpose : 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_convolution_transpose1 : 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_transpose2 : 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_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 -> 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 data : t -> t
val deg2rad : t -> t
val deg2rad_ : t -> t
val deg2rad_out : out:t -> t -> t
val dequantize : t -> t
val dequantize1 : 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: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 div1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val div2 : t -> t -> rounding_mode:string -> t
val div3 : t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
val div_ : t -> t -> t
val div_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val div_2 : t -> t -> rounding_mode:string -> t
val div_3 : t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
val div_out : out:t -> t -> t -> t
val div_out1 : out:t -> t -> t -> rounding_mode:string -> t
val divide : t -> t -> t
val divide1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val divide2 : t -> t -> rounding_mode:string -> t
val divide3 : t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
val divide_ : t -> t -> t
val divide_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val divide_2 : t -> t -> rounding_mode:string -> t
val divide_3 : t -> 'a Torch_core.Wrapper.Scalar.t -> rounding_mode:string -> t
val divide_out : out:t -> t -> t -> t
val divide_out1 : out:t -> 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 dstack : t list -> t
val dstack_out : out:t -> t list -> t
val eig : t -> eigenvectors:bool -> t * t
val eig_out : 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_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_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_meta : size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val empty_out : out:t -> size:int list -> t
val empty_quantized : size:int list -> qtensor:t -> t
val empty_strided : size:int list -> stride:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val eq1 : t -> t -> t
val eq_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val eq_1 : t -> t -> t
val eq_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val eq_out1 : 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 eye1 : n:int -> m:int -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val eye_out : out:t -> n:int -> t
val eye_out1 : out:t -> n:int -> m: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 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_matrix1 : 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_1 : t -> value:t -> t
val fill_diagonal_ : t -> fill_value:'a Torch_core.Wrapper.Scalar.t -> wrap:bool -> t
val fix : t -> t
val fix_ : t -> t
val fix_out : out:t -> t -> 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_power1 : 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
val float_power2 : t -> exponent:'a Torch_core.Wrapper.Scalar.t -> t
val float_power_ : t -> exponent:'a Torch_core.Wrapper.Scalar.t -> t
val float_power_1 : t -> exponent:t -> t
val float_power_out : out:t -> t -> exponent:t -> t
val float_power_out1 : out:t -> 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
val float_power_out2 : out:t -> t -> exponent:'a Torch_core.Wrapper.Scalar.t -> t
val floor : t -> t
val floor_ : t -> t
val floor_divide : t -> t -> t
val floor_divide1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val floor_divide_ : t -> t -> t
val floor_divide_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val floor_divide_out : out:t -> t -> 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 fmod1 : t -> t -> t
val fmod_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val fmod_1 : t -> t -> t
val fmod_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val fmod_out1 : 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_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool2d_out : 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_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool3d_out : output:t -> indices:t -> t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val frobenius_norm : t -> t
val frobenius_norm1 : 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 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 ge1 : t -> t -> t
val ge_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val ge_1 : t -> t -> t
val ge_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val ge_out1 : out:t -> t -> t -> t
val gelu : t -> t
val gelu_backward : grad:t -> t -> t
val geometric_ : t -> p:float -> t
val geqrf : t -> t * t
val geqrf_out : 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_out : 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 greater1 : t -> t -> t
val greater_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val greater_1 : t -> t -> t
val greater_equal : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val greater_equal1 : t -> t -> t
val greater_equal_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val greater_equal_1 : t -> t -> t
val greater_equal_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val greater_equal_out1 : out:t -> t -> t -> t
val greater_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val greater_out1 : 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 gru1 : 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 gru_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
val gt : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val gt1 : t -> t -> t
val gt_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val gt_1 : t -> t -> t
val gt_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val gt_out1 : out:t -> t -> t -> t
val hamming_window : window_length:int -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val hamming_window1 : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val hamming_window2 : window_length:int -> periodic:bool -> alpha:float -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val hamming_window3 : 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_window1 : 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 hardsigmoid : t -> t
val hardsigmoid_ : t -> t
val hardsigmoid_backward : 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_out : 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 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 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_out : 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_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_fill1 : t -> dim:int -> index:t -> value:t -> t
val index_fill_ : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
val index_fill_1 : 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 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_window1 : window_length:int -> periodic:bool -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val kaiser_window2 : window_length:int -> periodic:bool -> beta:float -> 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_out : 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_out : 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 le1 : t -> t -> t
val le_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val le_1 : t -> t -> t
val le_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val le_out1 : 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_out : out:t -> t -> t
val lerp : t -> end_:t -> weight:'a Torch_core.Wrapper.Scalar.t -> t
val lerp1 : t -> end_:t -> weight:t -> t
val lerp_ : t -> end_:t -> weight:'a Torch_core.Wrapper.Scalar.t -> t
val lerp_1 : t -> end_:t -> weight:t -> t
val lerp_out : out:t -> t -> end_:t -> weight:'a Torch_core.Wrapper.Scalar.t -> t
val lerp_out1 : out:t -> t -> end_:t -> weight:t -> t
val less : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val less1 : t -> t -> t
val less_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val less_1 : t -> t -> t
val less_equal : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val less_equal1 : t -> t -> t
val less_equal_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val less_equal_1 : t -> t -> t
val less_equal_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val less_equal_out1 : out:t -> t -> t -> t
val less_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val less_out1 : out:t -> t -> t -> t
val lgamma : t -> t
val lgamma_ : t -> t
val lgamma_out : out:t -> t -> t
val linalg_cholesky : t -> t
val linalg_cholesky_out : out:t -> t -> t
val linalg_cond : t -> p:'a Torch_core.Wrapper.Scalar.t -> t
val linalg_cond1 : t -> p:string -> t
val linalg_cond_out : out:t -> t -> p:'a Torch_core.Wrapper.Scalar.t -> t
val linalg_cond_out1 : out:t -> t -> p:string -> t
val linalg_det : t -> t
val linalg_eigh : t -> uplo:string -> t * t
val linalg_eigh_out : eigvals:t -> eigvecs:t -> t -> uplo:string -> t * t
val linalg_eigvalsh : t -> uplo:string -> t
val linalg_eigvalsh_out : out:t -> t -> uplo:string -> t
val linalg_inv : t -> t
val linalg_inv_out : out:t -> t -> 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_norm : t -> ord:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val linalg_norm1 : t -> ord:string -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val linalg_norm_out : out:t -> t -> ord:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val linalg_norm_out1 : out:t -> t -> ord:string -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val linalg_pinv : t -> rcond:float -> hermitian:bool -> t
val linalg_pinv1 : t -> rcond:t -> hermitian:bool -> t
val linalg_pinv_out : out:t -> t -> rcond:float -> hermitian:bool -> t
val linalg_pinv_out1 : out:t -> 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 -> compute_uv:bool -> t * t * t
val linalg_svd_out : u:t -> s:t -> v:t -> t -> full_matrices:bool -> compute_uv:bool -> 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 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_out : 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_out : 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 lstm1 : data:t -> batch_sizes:t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t * t
val lstm_cell : t -> hx:t list -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t * t
val lstsq : t -> a:t -> t * t
val lstsq_out : x:t -> qr:t -> t -> a:t -> t * t
val lt : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val lt1 : t -> t -> t
val lt_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val lt_1 : t -> t -> t
val lt_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val lt_out1 : 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 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_fill1 : t -> mask:t -> value:t -> t
val masked_fill_ : t -> mask:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
val masked_fill_1 : 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_rank : t -> symmetric:bool -> t
val matrix_rank1 : t -> tol:float -> symmetric:bool -> t
val max1 : t -> t -> t
val max2 : t -> dim:int -> keepdim:bool -> t * t
val max_out : out:t -> t -> t -> t
val max_out1 : max:t -> max_values:t -> t -> dim:int -> keepdim:bool -> 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_out : 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_out : 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_out : 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_out : 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 mean1 : 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 median1 : t -> dim:int -> keepdim:bool -> t * t
val median_out : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val meshgrid : t list -> t list
val min1 : t -> t -> t
val min2 : t -> dim:int -> keepdim:bool -> t * t
val min_out : out:t -> t -> t -> t
val min_out1 : min:t -> min_indices:t -> t -> dim:int -> keepdim:bool -> 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 mkldnn_adaptive_avg_pool2d : t -> output_size:int list -> t
val mkldnn_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
val mkldnn_convolution_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_pool3d : 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_out : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val moveaxis : t -> source:int list -> destination:int list -> t
val moveaxis1 : t -> source:int -> destination:int -> t
val movedim : t -> source:int list -> destination:int list -> t
val movedim1 : 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_out : 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 mul1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val mul_ : t -> t -> t
val mul_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val mul_out : out:t -> t -> 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_out : 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_out : 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 multiply1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val multiply_ : t -> t -> t
val multiply_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val multiply_out : out:t -> t -> 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 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 nanmedian : t -> t
val nanmedian1 : t -> dim:int -> keepdim:bool -> t * t
val nanmedian_out : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val nanquantile : t -> q:float -> dim:int -> keepdim:bool -> t
val nanquantile1 : t -> q:t -> dim:int -> keepdim:bool -> t
val nanquantile_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> t
val nanquantile_out1 : out:t -> t -> q:t -> dim:int -> keepdim:bool -> t
val nansum : t -> dtype:Torch_core.Kind.packed -> t
val nansum1 : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val nansum_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 narrow1 : t -> dim:int -> start:t -> 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 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_norm1 : 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 ne1 : t -> t -> t
val ne_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val ne_1 : t -> t -> t
val ne_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val ne_out1 : 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_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_out : 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_out : 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_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 norm1 : t -> p:'a Torch_core.Wrapper.Scalar.t -> dtype:Torch_core.Kind.packed -> t
val norm2 : t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> t
val norm3 : 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_out1 : out:t -> t -> p:'a Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val normal_ : t -> mean:float -> std:float -> t
val normal_out : out:t -> mean:t -> std:float -> t
val normal_out1 : out:t -> mean:float -> std:t -> t
val normal_out2 : out:t -> mean:t -> std:t -> t
val normal_out3 : out:t -> mean:float -> std:float -> size:int list -> t
val not_equal : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val not_equal1 : t -> t -> t
val not_equal_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val not_equal_1 : t -> t -> t
val not_equal_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val not_equal_out1 : out:t -> t -> t -> t
val nuclear_norm : t -> keepdim:bool -> t
val nuclear_norm1 : t -> dim:int list -> keepdim:bool -> t
val nuclear_norm_out : out:t -> t -> keepdim:bool -> t
val nuclear_norm_out1 : out:t -> t -> dim:int list -> 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 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 -> 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 pow : t -> exponent:'a Torch_core.Wrapper.Scalar.t -> t
val pow1 : t -> exponent:t -> t
val pow2 : 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
val pow_ : t -> exponent:'a Torch_core.Wrapper.Scalar.t -> t
val pow_1 : t -> exponent:t -> t
val pow_out : out:t -> t -> exponent:t -> t
val pow_out1 : out:t -> 'a Torch_core.Wrapper.Scalar.t -> exponent:t -> t
val pow_out2 : out:t -> t -> exponent:'a Torch_core.Wrapper.Scalar.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 prod1 : t -> dim:int -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val prod_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 q_per_channel_scales : t -> t
val q_per_channel_zero_points : t -> t
val qr : t -> some:bool -> t * t
val qr_out : q:t -> r:t -> t -> some:bool -> t * t
val quantile : t -> q:float -> dim:int -> keepdim:bool -> t
val quantile1 : t -> q:t -> dim:int -> keepdim:bool -> t
val quantile_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> t
val quantile_out1 : out:t -> t -> q:t -> 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_tensor1 : 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 randint1 : low:int -> high:int -> size:int list -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val randint_like : t -> high:int -> t
val randint_like1 : t -> low:int -> high:int -> t
val randint_out : out:t -> high:int -> size:int list -> t
val randint_out1 : out:t -> low:int -> 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_1 : t -> to_:int -> t
val random_2 : t -> from:int -> 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_out : 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_out : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val reflection_pad2d_out : out:t -> t -> padding:int list -> t
val relu : t -> t
val relu_ : t -> t
val remainder : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val remainder1 : t -> t -> t
val remainder_ : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val remainder_1 : t -> t -> t
val remainder_out : out:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val remainder_out1 : 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 -> t
val repeat_interleave1 : t -> repeats:t -> dim:int -> t
val repeat_interleave2 : t -> repeats:int -> dim: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_out : 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_out : 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_out : 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 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_relu1 : 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_relu_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> 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_tanh1 : 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_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> 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 rsub1 : 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 scatter1 : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
val scatter_ : t -> dim:int -> index:t -> src:t -> t
val scatter_1 : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> t
val scatter_2 : t -> dim:int -> index:t -> src:t -> reduce:string -> t
val scatter_3 : t -> dim:int -> index:t -> value:'a Torch_core.Wrapper.Scalar.t -> reduce:string -> t
val scatter_add : t -> dim:int -> index:t -> src:t -> t
val scatter_add_ : t -> dim:int -> index:t -> src:t -> t
val searchsorted : sorted_sequence:t -> t -> out_int32:bool -> right:bool -> t
val searchsorted1 : sorted_sequence:t -> 'a Torch_core.Wrapper.Scalar.t -> out_int32:bool -> right:bool -> t
val searchsorted_out : out:t -> sorted_sequence:t -> t -> out_int32:bool -> right:bool -> t
val select_backward : grad:t -> input_sizes:int list -> dim:int -> index:int -> t
val selu : t -> t
val selu_ : t -> t
val set_ : t -> t
val set_1 : t -> source:t -> t
val set_requires_grad : t -> r:bool -> 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_out : 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_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: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_out : 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_out : 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_out : 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_out : 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_out : solution:t -> lu:t -> t -> a:t -> t * t
val sort : t -> dim:int -> descending:bool -> t * t
val sort_out : values:t -> indices:t -> t -> 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_tensor1 : indices:t -> values:t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> t
val sparse_coo_tensor2 : 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 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 squeeze : t -> t
val squeeze1 : t -> dim:int -> t
val squeeze_ : t -> t
val squeeze_1 : 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 std1 : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val std_mean : t -> unbiased:bool -> t * t
val std_mean1 : 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 sub1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val sub_ : t -> t -> t
val sub_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val sub_out : out:t -> t -> t -> t
val subtract : t -> t -> t
val subtract1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val subtract_ : t -> t -> t
val subtract_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val subtract_out : out:t -> t -> t -> t
val sum1 : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.packed -> t
val sum_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_out : 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_out : 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_backward : grad:t -> t -> index:t -> 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_out : 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_split1 : t -> indices:int list -> dim:int -> t list
val tensor_split2 : 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_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 to1 : t -> options:(Torch_core.Kind.packed * Torch_core.Device.t) -> non_blocking:bool -> copy:bool -> t
val to2 : t -> dtype:Torch_core.Kind.packed -> non_blocking:bool -> copy:bool -> t
val to3 : t -> t -> non_blocking:bool -> copy:bool -> t
val to4 : t -> device:Torch_core.Device.t -> dtype:Torch_core.Kind.packed -> non_blocking:bool -> copy:bool -> t
val to_dense : t -> dtype:Torch_core.Kind.packed -> t
val to_dense_backward : grad:t -> t -> t
val to_mkldnn : t -> dtype:Torch_core.Kind.packed -> t
val to_mkldnn_backward : grad:t -> t -> t
val to_sparse : t -> t
val to_sparse1 : t -> sparse_dim:int -> t
val topk : t -> k:int -> dim:int -> largest:bool -> sorted:bool -> t * t
val topk_out : 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 trapz : y:t -> x:t -> dim:int -> t
val trapz1 : y:t -> dx:float -> dim:int -> t
val triangular_solve : t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
val triangular_solve_out : 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_divide1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val true_divide_ : t -> t -> t
val true_divide_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val true_divide_out : out:t -> t -> 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 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_out : 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_out : 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_out : 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_out : 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_out : 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_out : 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_out : 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 var1 : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val var_mean : t -> unbiased:bool -> t * t
val var_mean1 : 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 view1 : t -> dtype:Torch_core.Kind.packed -> t
val view_as : t -> t -> t
val view_as_complex : t -> t
val view_as_real : t -> t
val vstack : t list -> t
val vstack_out : out:t -> t list -> t
val where : condition:t -> t list
val where1 : condition:t -> t -> t -> t
val where2 : condition:t -> 'a Torch_core.Wrapper.Scalar.t -> t -> t
val where3 : condition:t -> t -> 'a Torch_core.Wrapper.Scalar.t -> t
val where4 : condition:t -> 'a Torch_core.Wrapper.Scalar.t -> 'a Torch_core.Wrapper.Scalar.t -> t
val xlogy : t -> t -> t
val xlogy1 : 'a Torch_core.Wrapper.Scalar.t -> t -> t
val xlogy2 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val xlogy_ : t -> t -> t
val xlogy_1 : t -> 'a Torch_core.Wrapper.Scalar.t -> t
val xlogy_out : out:t -> t -> t -> t
val xlogy_out1 : out:t -> 'a Torch_core.Wrapper.Scalar.t -> t -> t
val xlogy_out2 : out:t -> t -> 'a Torch_core.Wrapper.Scalar.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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