package torch
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Module type
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Class
Class type
type t
include Wrapper_generated_intf.S with type t := t and type 'a scalar := 'a Scalar.t
val __and__ : t -> 'a Scalar.t -> t
val __and__tensor_ : t -> t -> t
val __iand__ : t -> 'a Scalar.t -> t
val __iand__tensor_ : t -> t -> t
val __ilshift__ : t -> 'a Scalar.t -> t
val __ilshift__tensor_ : t -> t -> t
val __ior__ : t -> 'a Scalar.t -> t
val __ior__tensor_ : t -> t -> t
val __irshift__ : t -> 'a Scalar.t -> t
val __irshift__tensor_ : t -> t -> t
val __ixor__ : t -> 'a Scalar.t -> t
val __ixor__tensor_ : t -> t -> t
val __lshift__ : t -> 'a Scalar.t -> t
val __lshift__tensor_ : t -> t -> t
val __or__ : t -> 'a Scalar.t -> t
val __or__tensor_ : t -> t -> t
val __rshift__ : t -> 'a Scalar.t -> t
val __rshift__tensor_ : t -> t -> t
val __xor__ : t -> 'a Scalar.t -> t
val __xor__tensor_ : t -> t -> t
val _adaptive_avg_pool2d : t -> output_size:int list -> t
val _adaptive_avg_pool2d_backward : grad_output:t -> t -> t
val _adaptive_avg_pool3d : t -> output_size:int list -> t
val _adaptive_avg_pool3d_backward : grad_output:t -> t -> t
val _add_batch_dim : t -> batch_dim:int -> level:int -> t
val _add_relu : t -> t -> t
val _add_relu_ : t -> t -> t
val _add_relu_out : out:t -> t -> t -> t
val _add_relu_scalar : t -> 'a Scalar.t -> t
val _add_relu_scalar_ : t -> 'a Scalar.t -> t
val _aminmax : t -> t * t
val _aminmax_dim : t -> dim:int -> keepdim:bool -> t * t
val _amp_update_scale_ : t -> growth_tracker:t -> found_inf:t -> scale_growth_factor:float -> scale_backoff_factor:float -> growth_interval:int -> t
val _baddbmm_mkl_ : t -> batch1:t -> batch2:t -> t
val _cast_byte : t -> non_blocking:bool -> t
val _cast_char : t -> non_blocking:bool -> t
val _cast_double : t -> non_blocking:bool -> t
val _cast_float : t -> non_blocking:bool -> t
val _cast_half : t -> non_blocking:bool -> t
val _cast_int : t -> non_blocking:bool -> t
val _cast_long : t -> non_blocking:bool -> t
val _cast_short : t -> non_blocking:bool -> t
val _cat : t list -> dim:int -> t
val _cat_out : out:t -> t list -> dim:int -> t
val _cdist_backward : grad:t -> x1:t -> x2:t -> p:float -> cdist:t -> t
val _cholesky_solve_helper : t -> a:t -> upper:bool -> t
val _coalesce : t -> t
val _coalesced_ : t -> coalesced:bool -> t
val _compute_linear_combination : t -> coefficients:t -> t
val _compute_linear_combination_out : out:t -> t -> coefficients:t -> t
val _conj : t -> t
val _conj_physical : t -> t
val _conv_depthwise2d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
val _conv_depthwise2d_backward : grad_input:t -> grad_weight:t -> grad_output:t -> t -> weight:t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> t * t
val _conv_depthwise2d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
val _convert_indices_from_coo_to_csr : t -> size:int -> out_int32:bool -> t
val _convert_indices_from_coo_to_csr_out : out:t -> t -> size:int -> out_int32:bool -> t
val _convolution : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> benchmark:bool -> deterministic:bool -> cudnn_enabled:bool -> allow_tf32:bool -> t
val _convolution_deprecated : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> benchmark:bool -> deterministic:bool -> cudnn_enabled:bool -> t
val _convolution_mode : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
val _convolution_nogroup : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> t
val _copy_from : t -> dst:t -> non_blocking:bool -> t
val _copy_from_and_resize : t -> dst:t -> t
val _ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> zero_infinity:bool -> t * t
val _ctc_loss_backward : grad:t -> log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> neg_log_likelihood:t -> log_alpha:t -> blank:int -> zero_infinity:bool -> t
val _cudnn_ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> deterministic:bool -> zero_infinity:bool -> t * t
val _cudnn_init_dropout_state : dropout:float -> train:bool -> dropout_seed:int -> options:(Kind.packed * Device.t) -> t
val _cudnn_rnn : t -> weight:t list -> weight_stride0:int -> weight_buf:t option -> hx:t -> cx:t option -> mode:int -> hidden_size:int -> proj_size:int -> num_layers:int -> batch_first:bool -> dropout:float -> train:bool -> bidirectional:bool -> batch_sizes:int list -> dropout_state:t option -> t * t * t * t * t
val _cudnn_rnn_flatten_weight : weight_arr:t list -> weight_stride0:int -> input_size:int -> mode:int -> hidden_size:int -> proj_size:int -> num_layers:int -> batch_first:bool -> bidirectional:bool -> t
val _det_lu_based_helper : t -> t * t * t
val _det_lu_based_helper_backward_helper : det_grad:t -> det:t -> t -> lu:t -> pivs:t -> t
val _dim_arange : like:t -> dim:int -> t
val _dirichlet_grad : x:t -> alpha:t -> total:t -> t
val _embedding_bag : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int -> t * t * t * t
val _embedding_bag_backward : grad:t -> indices:t -> offsets:t -> offset2bag:t -> bag_size:t -> maximum_indices:t -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> padding_idx:int -> t
val _embedding_bag_dense_backward : grad:t -> indices:t -> offset2bag:t -> bag_size:t -> maximum_indices:t -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> per_sample_weights:t option -> padding_idx:int -> t
val _embedding_bag_forward_only : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int -> t * t * t * t
val _embedding_bag_per_sample_weights_backward : grad:t -> weight:t -> indices:t -> offsets:t -> offset2bag:t -> mode:int -> padding_idx:int -> t
val _embedding_bag_sparse_backward : grad:t -> indices:t -> offsets:t -> offset2bag:t -> bag_size:t -> num_weights:int -> scale_grad_by_freq:bool -> mode:int -> per_sample_weights:t option -> padding_idx:int -> t
val _empty_affine_quantized : size:int list -> options:(Kind.packed * 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:(Kind.packed * Device.t) -> t
val _euclidean_dist : x1:t -> x2:t -> t
val _fake_quantize_learnable_per_channel_affine : t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> grad_factor:float -> t
val _fake_quantize_learnable_per_channel_affine_backward : grad:t -> t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> grad_factor:float -> t * t * t
val _fake_quantize_learnable_per_tensor_affine : t -> scale:t -> zero_point:t -> quant_min:int -> quant_max:int -> grad_factor:float -> t
val _fake_quantize_learnable_per_tensor_affine_backward : grad:t -> t -> scale:t -> zero_point:t -> quant_min:int -> quant_max:int -> grad_factor:float -> t * t * t
val _fake_quantize_per_tensor_affine_cachemask_tensor_qparams : t -> scale:t -> zero_point:t -> fake_quant_enabled:t -> quant_min:int -> quant_max:int -> t * t
val _fft_c2c : t -> dim:int list -> normalization:int -> forward:bool -> t
val _fft_c2c_out : out:t -> t -> dim:int list -> normalization:int -> forward:bool -> t
val _fft_c2r : t -> dim:int list -> normalization:int -> last_dim_size:int -> t
val _fft_c2r_out : out:t -> t -> dim:int list -> normalization:int -> last_dim_size:int -> t
val _fft_r2c : t -> dim:int list -> normalization:int -> onesided:bool -> t
val _fft_r2c_out : out:t -> t -> dim:int list -> normalization:int -> onesided:bool -> t
val _fused_dropout : t -> p:float -> t * t
val _fused_moving_avg_obs_fq_helper : t -> observer_on:t -> fake_quant_on:t -> running_min:t -> running_max:t -> scale:t -> zero_point:t -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> t * t
val _fw_primal : t -> level:int -> t
val _gather_sparse_backward : t -> dim:int -> index:t -> grad:t -> t
val _grid_sampler_2d_cpu_fallback : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
val _grid_sampler_2d_cpu_fallback_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t * t
val _index_copy_ : t -> dim:int -> index:t -> source:t -> t
val _index_put_impl_ : t -> indices:t option list -> values:t -> accumulate:bool -> unsafe:bool -> t
val _indices : t -> t
val _inverse_helper : t -> t
val _linalg_inv_out_helper_ : t -> infos_lu:t -> infos_getri:t -> t
val _linalg_qr_helper : t -> mode:string -> t * t
val _log_softmax : t -> dim:int -> half_to_float:bool -> t
val _log_softmax_backward_data : grad_output:t -> output:t -> dim:int -> t -> t
val _log_softmax_backward_data_out : out:t -> grad_output:t -> output:t -> dim:int -> t -> t
val _log_softmax_out : out:t -> t -> dim:int -> half_to_float:bool -> t
val _logcumsumexp : t -> dim:int -> t
val _logcumsumexp_out : out:t -> t -> dim:int -> t
val _lu_with_info : t -> pivot:bool -> check_errors:bool -> t * t * t
val _make_dual : primal:t -> tangent:t -> level:int -> t
val _make_per_channel_quantized_tensor : t -> scale:t -> zero_point:t -> axis:int -> t
val _make_per_tensor_quantized_tensor : t -> scale:float -> zero_point:int -> t
val _masked_scale : t -> mask:t -> scale:float -> t
val _mkldnn_reshape : t -> shape:int list -> t
val _mkldnn_transpose : t -> dim0:int -> dim1:int -> t
val _mkldnn_transpose_ : t -> dim0:int -> dim1:int -> t
val _neg_view : t -> t
val _nnpack_spatial_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> t
val _nnpack_spatial_convolution_backward_input : t -> grad_output:t -> weight:t -> padding:int list -> t
val _nnpack_spatial_convolution_backward_weight : t -> weightsize:int list -> grad_output:t -> padding:int list -> t
val _pack_padded_sequence : t -> lengths:t -> batch_first:bool -> t * t
val _pack_padded_sequence_backward : grad:t -> input_size:int list -> batch_sizes:t -> batch_first:bool -> t
val _pad_packed_sequence : data:t -> batch_sizes:t -> batch_first:bool -> padding_value:'a Scalar.t -> total_length:int -> t * t
val _pdist_backward : grad:t -> t -> p:float -> pdist:t -> t
val _pin_memory : t -> device:Device.t -> t
val _remove_batch_dim : t -> level:int -> batch_size:int -> out_dim:int -> t
val _reshape_alias : t -> size:int list -> stride:int list -> t
val _reshape_from_tensor : t -> shape:t -> t
val _rowwise_prune : weight:t -> mask:t -> compressed_indices_dtype:Kind.packed -> t * t
val _s_where : condition:t -> t -> t -> t
val _sample_dirichlet : t -> t
val _saturate_weight_to_fp16 : weight:t -> t
val _segment_reduce_backward : grad:t -> output:t -> data:t -> reduce:string -> lengths:t option -> axis:int -> t
val _shape_as_tensor : t -> t
val _sobol_engine_draw : quasi:t -> n:int -> sobolstate:t -> dimension:int -> num_generated:int -> dtype:Kind.packed -> t * t
val _sobol_engine_ff_ : t -> n:int -> sobolstate:t -> dimension:int -> num_generated:int -> t
val _sobol_engine_initialize_state_ : t -> dimension:int -> t
val _sobol_engine_scramble_ : t -> ltm:t -> dimension:int -> t
val _softmax : t -> dim:int -> half_to_float:bool -> t
val _softmax_backward_data : grad_output:t -> output:t -> dim:int -> t -> t
val _softmax_backward_data_out : grad_input:t -> grad_output:t -> output:t -> dim:int -> t -> t
val _softmax_out : out:t -> t -> dim:int -> half_to_float:bool -> t
val _solve_helper : t -> a:t -> t * t
val _sparse_addmm : t -> sparse:t -> dense:t -> t
val _sparse_coo_tensor_unsafe : indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
val _sparse_coo_tensor_with_dims : sparse_dim:int -> dense_dim:int -> size:int list -> options:(Kind.packed * 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:(Kind.packed * Device.t) -> t
val _sparse_csr_tensor_unsafe : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
val _sparse_log_softmax : t -> dim:int -> half_to_float:bool -> t
val _sparse_log_softmax_backward_data : grad_output:t -> output:t -> dim:int -> t -> t
val _sparse_log_softmax_int : t -> dim:int -> dtype:Kind.packed -> t
val _sparse_mask_helper : tr:t -> mask_indices:t -> t
val _sparse_mm : sparse:t -> dense:t -> t
val _sparse_softmax : t -> dim:int -> half_to_float:bool -> t
val _sparse_softmax_backward_data : grad_output:t -> output:t -> dim:int -> t -> t
val _sparse_softmax_int : t -> dim:int -> dtype:Kind.packed -> t
val _sparse_sparse_matmul : t -> t -> t
val _sparse_sum : t -> t
val _sparse_sum_backward : grad:t -> t -> dim:int list -> t
val _sparse_sum_dim : t -> dim:int list -> t
val _sparse_sum_dim_dtype : t -> dim:int list -> dtype:Kind.packed -> t
val _sparse_sum_dtype : t -> dtype:Kind.packed -> t
val _stack : t list -> dim:int -> t
val _stack_out : out:t -> t list -> dim:int -> t
val _standard_gamma : t -> t
val _standard_gamma_grad : t -> output:t -> t
val _svd_helper : t -> some:bool -> compute_uv:bool -> t * t * t
val _symeig_helper : t -> eigenvectors:bool -> upper:bool -> t * t
val _test_ambiguous_defaults : dummy:t -> a:int -> b:int -> t
val _test_ambiguous_defaults_b : dummy:t -> a:int -> b:string -> t
val _test_optional_filled_intlist : values:t -> addends:int list -> t
val _test_optional_intlist : values:t -> addends:int list -> t
val _test_serialization_subcmul : t -> t -> t
val _test_string_default : dummy:t -> a:string -> b:string -> t
val _thnn_differentiable_gru_cell_backward : grad_hy:t -> input_gates:t -> hidden_gates:t -> hx:t -> input_bias:t option -> hidden_bias:t option -> t * t * t * t * t
val _thnn_differentiable_lstm_cell_backward : grad_hy:t option -> grad_cy:t option -> input_gates:t -> hidden_gates:t -> input_bias:t option -> hidden_bias:t option -> cx:t -> cy:t -> t * t * t * t * t
val _thnn_fused_gru_cell : input_gates:t -> hidden_gates:t -> hx:t -> input_bias:t option -> hidden_bias:t option -> t * t
val _thnn_fused_gru_cell_backward : grad_hy:t -> workspace:t -> has_bias:bool -> t * t * t * t * t
val _thnn_fused_lstm_cell : input_gates:t -> hidden_gates:t -> cx:t -> input_bias:t option -> hidden_bias:t option -> t * t * t
val _thnn_fused_lstm_cell_backward : grad_hy:t option -> grad_cy:t option -> cx:t -> cy:t -> workspace:t -> has_bias:bool -> t * t * t * t * t
val _to_copy : t -> options:(Kind.packed * Device.t) -> non_blocking:bool -> t
val _to_cpu : t list -> t list
val _trilinear : i1:t -> i2:t -> i3:t -> expand1:int list -> expand2:int list -> expand3:int list -> sumdim:int list -> unroll_dim:int -> t
val _unique : t -> sorted:bool -> return_inverse:bool -> t * t
val _unique2 : t -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
val _unpack_dual : dual:t -> level:int -> t * t
val _unsafe_view : t -> size:int list -> t
val _values : t -> t
val _weight_norm : v:t -> g:t -> dim:int -> t
val _weight_norm_cuda_interface : v:t -> g:t -> dim:int -> t * t
val _weight_norm_cuda_interface_backward : grad_w:t -> saved_v:t -> saved_g:t -> saved_norms:t -> dim:int -> t * t
val _weight_norm_differentiable_backward : grad_w:t -> saved_v:t -> saved_g:t -> saved_norms:t -> dim:int -> t * t
val abs : t -> t
val abs_ : t -> t
val abs_out : out:t -> t -> t
val absolute : t -> t
val absolute_ : t -> t
val absolute_out : out:t -> t -> t
val acos : t -> t
val acos_ : t -> t
val acos_out : out:t -> t -> t
val acosh : t -> t
val acosh_ : t -> t
val acosh_out : out:t -> t -> t
val adaptive_avg_pool1d : t -> output_size:int list -> t
val adaptive_avg_pool2d : t -> output_size:int list -> t
val adaptive_avg_pool2d_out : out:t -> t -> output_size:int list -> t
val adaptive_avg_pool3d : t -> output_size:int list -> t
val adaptive_avg_pool3d_backward : grad_input:t -> grad_output:t -> t -> t
val adaptive_avg_pool3d_out : out:t -> t -> output_size:int list -> t
val adaptive_max_pool1d : t -> output_size:int list -> t * t
val adaptive_max_pool2d : t -> output_size:int list -> t * t
val adaptive_max_pool2d_backward : grad_output:t -> t -> indices:t -> t
val adaptive_max_pool2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> indices:t -> t
val adaptive_max_pool2d_out : out:t -> indices:t -> t -> output_size:int list -> t * t
val adaptive_max_pool3d : t -> output_size:int list -> t * t
val adaptive_max_pool3d_backward : grad_output:t -> t -> indices:t -> t
val adaptive_max_pool3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> indices:t -> t
val adaptive_max_pool3d_out : out:t -> indices:t -> t -> output_size:int list -> t * t
val add : t -> t -> t
val add_ : t -> t -> t
val add_out : out:t -> t -> t -> t
val add_scalar : t -> 'a Scalar.t -> t
val add_scalar_ : t -> 'a Scalar.t -> t
val addbmm : t -> batch1:t -> batch2:t -> t
val addbmm_ : t -> batch1:t -> batch2:t -> t
val addbmm_out : out:t -> t -> batch1:t -> batch2:t -> t
val addcdiv : t -> tensor1:t -> tensor2:t -> t
val addcdiv_ : t -> tensor1:t -> tensor2:t -> t
val addcdiv_out : out:t -> t -> tensor1:t -> tensor2:t -> t
val addcmul : t -> tensor1:t -> tensor2:t -> t
val addcmul_ : t -> tensor1:t -> tensor2:t -> t
val addcmul_out : out:t -> t -> tensor1:t -> tensor2:t -> t
val addmm : t -> mat1:t -> mat2:t -> t
val addmm_ : t -> mat1:t -> mat2:t -> t
val addmm_out : out:t -> t -> mat1:t -> mat2:t -> t
val addmv : t -> mat:t -> vec:t -> t
val addmv_ : t -> mat:t -> vec:t -> t
val addmv_out : out:t -> t -> mat:t -> vec:t -> t
val addr : t -> vec1:t -> vec2:t -> t
val addr_ : t -> vec1:t -> vec2:t -> t
val addr_out : out:t -> t -> vec1:t -> vec2:t -> t
val affine_grid_generator : theta:t -> size:int list -> align_corners:bool -> t
val affine_grid_generator_backward : grad:t -> size:int list -> align_corners:bool -> t
val alias : t -> t
val align_as : t -> t -> t
val align_tensors : t list -> t list
val all : t -> t
val all_all_out : out:t -> t -> t
val all_dim : t -> dim:int -> keepdim:bool -> t
val all_out : out:t -> t -> dim:int -> keepdim:bool -> t
val alpha_dropout : t -> p:float -> train:bool -> t
val alpha_dropout_ : t -> p:float -> train:bool -> t
val amax : t -> dim:int list -> keepdim:bool -> t
val amax_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val amin : t -> dim:int list -> keepdim:bool -> t
val amin_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val aminmax : t -> dim:int -> keepdim:bool -> t * t
val aminmax_out : min:t -> max:t -> t -> dim:int -> keepdim:bool -> t * t
val angle : t -> t
val angle_out : out:t -> t -> t
val any : t -> t
val any_all_out : out:t -> t -> t
val any_dim : t -> dim:int -> keepdim:bool -> t
val any_out : out:t -> t -> dim:int -> keepdim:bool -> t
val arange : end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val arange_out : out:t -> end_:'a Scalar.t -> t
val arange_start : start:'a Scalar.t -> end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val arange_start_out : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> t
val arange_start_step : start:'a Scalar.t -> end_:'a Scalar.t -> step:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val arccos : t -> t
val arccos_ : t -> t
val arccos_out : out:t -> t -> t
val arccosh : t -> t
val arccosh_ : t -> t
val arccosh_out : out:t -> t -> t
val arcsin : t -> t
val arcsin_ : t -> t
val arcsin_out : out:t -> t -> t
val arcsinh : t -> t
val arcsinh_ : t -> t
val arcsinh_out : out:t -> t -> t
val arctan : t -> t
val arctan_ : t -> t
val arctan_out : out:t -> t -> t
val arctanh : t -> t
val arctanh_ : t -> t
val arctanh_out : out:t -> t -> t
val argmax_out : out:t -> t -> dim:int -> keepdim:bool -> t
val argmin : t -> dim:int -> keepdim:bool -> t
val argmin_out : out:t -> t -> dim:int -> keepdim:bool -> t
val argsort : t -> dim:int -> descending:bool -> t
val as_strided : t -> size:int list -> stride:int list -> storage_offset:int -> t
val as_strided_ : t -> size:int list -> stride:int list -> storage_offset:int -> t
val asin : t -> t
val asin_ : t -> t
val asin_out : out:t -> t -> t
val asinh : t -> t
val asinh_ : t -> t
val asinh_out : out:t -> t -> t
val atan : t -> t
val atan2 : t -> t -> t
val atan2_ : t -> t -> t
val atan2_out : out:t -> t -> t -> t
val atan_ : t -> t
val atan_out : out:t -> t -> t
val atanh : t -> t
val atanh_ : t -> t
val atanh_out : out:t -> t -> t
val atleast_1d : t -> t
val atleast_1d_sequence : t list -> t list
val atleast_2d : t -> t
val atleast_2d_sequence : t list -> t list
val atleast_3d : t -> t
val atleast_3d_sequence : t list -> t list
val avg_pool1d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val avg_pool2d_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val avg_pool2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val avg_pool2d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val avg_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val avg_pool3d_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val avg_pool3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val avg_pool3d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> divisor_override:int -> t
val baddbmm : t -> batch1:t -> batch2:t -> t
val baddbmm_ : t -> batch1:t -> batch2:t -> t
val baddbmm_out : out:t -> t -> batch1:t -> batch2:t -> t
val bartlett_window : window_length:int -> options:(Kind.packed * Device.t) -> t
val bartlett_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
val batch_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> t
val batch_norm_backward_elemt : grad_out:t -> t -> mean:t -> invstd:t -> weight:t option -> mean_dy:t -> mean_dy_xmu:t -> count:t -> t
val batch_norm_backward_reduce : grad_out:t -> t -> mean:t -> invstd:t -> weight:t option -> input_g:bool -> weight_g:bool -> bias_g:bool -> t * t * t * t
val batch_norm_elemt : t -> weight:t option -> bias:t option -> mean:t -> invstd:t -> eps:float -> t
val batch_norm_elemt_out : out:t -> t -> weight:t option -> bias:t option -> mean:t -> invstd:t -> eps:float -> t
val batch_norm_gather_stats : t -> mean:t -> invstd:t -> running_mean:t option -> running_var:t option -> momentum:float -> eps:float -> count:int -> t * t
val batch_norm_gather_stats_with_counts : t -> mean:t -> invstd:t -> running_mean:t option -> running_var:t option -> momentum:float -> eps:float -> counts:t -> t * t
val batch_norm_stats : t -> eps:float -> t * t
val batch_norm_update_stats : t -> running_mean:t option -> running_var:t option -> momentum:float -> t * t
val bernoulli : t -> t
val bernoulli_ : t -> p:t -> t
val bernoulli_float_ : t -> p:float -> t
val bernoulli_out : out:t -> t -> t
val bernoulli_p : t -> p:float -> t
val bilinear : input1:t -> input2:t -> weight:t -> bias:t option -> t
val binary_cross_entropy : t -> target:t -> weight:t option -> reduction:Reduction.t -> t
val binary_cross_entropy_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> t
val binary_cross_entropy_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> t
val binary_cross_entropy_out : out:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> t
val binary_cross_entropy_with_logits : t -> target:t -> weight:t option -> pos_weight:t option -> reduction:Reduction.t -> t
val binary_cross_entropy_with_logits_backward : grad_output:t -> t -> target:t -> weight:t option -> pos_weight:t option -> reduction:Reduction.t -> t
val bincount : t -> weights:t option -> minlength:int -> t
val binomial : count:t -> prob:t -> t
val bitwise_and : t -> 'a Scalar.t -> t
val bitwise_and_ : t -> 'a Scalar.t -> t
val bitwise_and_scalar_out : out:t -> t -> 'a Scalar.t -> t
val bitwise_and_tensor : t -> t -> t
val bitwise_and_tensor_ : t -> t -> t
val bitwise_and_tensor_out : out:t -> t -> t -> t
val bitwise_left_shift : t -> t -> t
val bitwise_left_shift_ : t -> t -> t
val bitwise_left_shift_scalar_tensor : 'a Scalar.t -> t -> t
val bitwise_left_shift_tensor_out : out:t -> t -> t -> t
val bitwise_left_shift_tensor_scalar : t -> 'a Scalar.t -> t
val bitwise_left_shift_tensor_scalar_ : t -> 'a Scalar.t -> t
val bitwise_left_shift_tensor_scalar_out : out:t -> t -> 'a 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 Scalar.t -> t
val bitwise_or_ : t -> 'a Scalar.t -> t
val bitwise_or_scalar_out : out:t -> t -> 'a Scalar.t -> t
val bitwise_or_tensor : t -> t -> t
val bitwise_or_tensor_ : t -> t -> t
val bitwise_or_tensor_out : out:t -> t -> t -> t
val bitwise_right_shift : t -> t -> t
val bitwise_right_shift_ : t -> t -> t
val bitwise_right_shift_scalar_tensor : 'a Scalar.t -> t -> t
val bitwise_right_shift_tensor_out : out:t -> t -> t -> t
val bitwise_right_shift_tensor_scalar : t -> 'a Scalar.t -> t
val bitwise_right_shift_tensor_scalar_ : t -> 'a Scalar.t -> t
val bitwise_right_shift_tensor_scalar_out : out:t -> t -> 'a Scalar.t -> t
val bitwise_xor : t -> 'a Scalar.t -> t
val bitwise_xor_ : t -> 'a Scalar.t -> t
val bitwise_xor_scalar_out : out:t -> t -> 'a Scalar.t -> t
val bitwise_xor_tensor : t -> t -> t
val bitwise_xor_tensor_ : t -> t -> t
val bitwise_xor_tensor_out : out:t -> t -> t -> t
val blackman_window : window_length:int -> options:(Kind.packed * Device.t) -> t
val blackman_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
val block_diag : t list -> t
val bmm : t -> mat2:t -> t
val bmm_out : out:t -> t -> mat2:t -> t
val broadcast_tensors : t list -> t list
val broadcast_to : t -> size:int list -> t
val bucketize : t -> boundaries:t -> out_int32:bool -> right:bool -> t
val bucketize_scalar : 'a Scalar.t -> boundaries:t -> out_int32:bool -> right:bool -> t
val bucketize_tensor_out : out:t -> t -> boundaries:t -> out_int32:bool -> right:bool -> t
val cartesian_prod : t list -> t
val cat : t list -> dim:int -> t
val cat_out : out:t -> t list -> dim:int -> t
val cauchy_ : t -> median:float -> sigma:float -> t
val cdist : x1:t -> x2:t -> p:float -> compute_mode:int -> t
val ceil : t -> t
val ceil_ : t -> t
val ceil_out : out:t -> t -> t
val celu : t -> t
val celu_ : t -> t
val chain_matmul : matrices:t list -> t
val chain_matmul_out : out:t -> matrices:t list -> t
val channel_shuffle : t -> groups:int -> t
val cholesky : t -> upper:bool -> t
val cholesky_inverse : t -> upper:bool -> t
val cholesky_inverse_out : out:t -> t -> upper:bool -> t
val cholesky_out : out:t -> t -> upper:bool -> t
val cholesky_solve : t -> input2:t -> upper:bool -> t
val cholesky_solve_out : out:t -> t -> input2:t -> upper:bool -> t
val choose_qparams_optimized : t -> numel:int -> n_bins:int -> ratio:float -> bit_width:int -> t * t
val chunk : t -> chunks:int -> dim:int -> t list
val clamp : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
val clamp_ : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
val clamp_max : t -> max:'a Scalar.t -> t
val clamp_max_ : t -> max:'a Scalar.t -> t
val clamp_max_out : out:t -> t -> max:'a Scalar.t -> t
val clamp_max_tensor : t -> max:t -> t
val clamp_max_tensor_ : t -> max:t -> t
val clamp_max_tensor_out : out:t -> t -> max:t -> t
val clamp_min : t -> min:'a Scalar.t -> t
val clamp_min_ : t -> min:'a Scalar.t -> t
val clamp_min_out : out:t -> t -> min:'a Scalar.t -> t
val clamp_min_tensor : t -> min:t -> t
val clamp_min_tensor_ : t -> min:t -> t
val clamp_min_tensor_out : out:t -> t -> min:t -> t
val clamp_out : out:t -> t -> min:'a Scalar.t -> max:'a Scalar.t -> t
val clamp_tensor : t -> min:t option -> max:t option -> t
val clamp_tensor_ : t -> min:t option -> max:t option -> t
val clamp_tensor_out : out:t -> t -> min:t option -> max:t option -> t
val clip : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
val clip_ : t -> min:'a Scalar.t -> max:'a Scalar.t -> t
val clip_out : out:t -> t -> min:'a Scalar.t -> max:'a Scalar.t -> t
val clip_tensor : t -> min:t option -> max:t option -> t
val clip_tensor_ : t -> min:t option -> max:t option -> t
val clip_tensor_out : out:t -> t -> min:t option -> max:t option -> t
val clone : t -> t
val coalesce : t -> t
val col2im : t -> output_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val col2im_backward : grad_output:t -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val col2im_backward_grad_input : grad_input:t -> grad_output:t -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val col2im_out : out:t -> t -> output_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val col_indices : t -> t
val column_stack : t list -> t
val column_stack_out : out:t -> t list -> t
val combinations : t -> r:int -> with_replacement:bool -> t
val complex : real:t -> imag:t -> t
val complex_out : out:t -> real:t -> imag:t -> t
val concat : t list -> dim:int -> t
val concat_out : out:t -> t list -> dim:int -> t
val conj : t -> t
val conj_physical : t -> t
val conj_physical_ : t -> t
val conj_physical_out : out:t -> t -> t
val constant_pad_nd : t -> pad:int list -> t
val contiguous : t -> t
val conv1d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
val conv1d_padding : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
val conv2d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
val conv2d_padding : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
val conv3d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
val conv3d_padding : t -> weight:t -> bias:t option -> stride:int list -> padding:string -> dilation:int list -> groups:int -> t
val conv_depthwise3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
val conv_depthwise3d_backward : grad_input:t -> grad_weight:t -> grad_bias:t -> grad_output:t -> t -> weight:t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> t * t * t
val conv_tbc : t -> weight:t -> bias:t -> pad:int -> t
val conv_tbc_backward : t -> t -> weight:t -> bias:t -> pad:int -> t * t * t
val conv_transpose1d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
val conv_transpose2d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
val conv_transpose3d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
val convolution : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> t
val convolution_overrideable : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> t
val copy_sparse_to_sparse_ : t -> src:t -> non_blocking:bool -> t
val copysign : t -> t -> t
val copysign_ : t -> t -> t
val copysign_out : out:t -> t -> t -> t
val copysign_scalar : t -> 'a Scalar.t -> t
val copysign_scalar_ : t -> 'a Scalar.t -> t
val copysign_scalar_out : out:t -> t -> 'a Scalar.t -> t
val corrcoef : t -> t
val cos : t -> t
val cos_ : t -> t
val cos_out : out:t -> t -> t
val cosh : t -> t
val cosh_ : t -> t
val cosh_out : out:t -> t -> t
val cosine_embedding_loss : input1:t -> input2:t -> target:t -> margin:float -> reduction:Reduction.t -> t
val cosine_similarity : x1:t -> x2:t -> dim:int -> eps:float -> t
val cov : t -> correction:int -> fweights:t option -> aweights:t option -> t
val cross : t -> t -> dim:int -> t
val cross_entropy_loss : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> label_smoothing:float -> t
val cross_out : out:t -> t -> t -> dim:int -> t
val crow_indices : t -> t
val ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> reduction:Reduction.t -> zero_infinity:bool -> t
val ctc_loss_tensor : log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> blank:int -> reduction:Reduction.t -> zero_infinity:bool -> t
val cudnn_affine_grid_generator : theta:t -> n:int -> c:int -> h:int -> w:int -> t
val cudnn_affine_grid_generator_backward : grad:t -> n:int -> c:int -> h:int -> w:int -> t
val cudnn_batch_norm : t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t * t
val cudnn_batch_norm_backward : t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> reservespace:t -> t * t * t
val cudnn_convolution : t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_add_relu : t -> weight:t -> z:t -> alpha:'a Scalar.t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
val cudnn_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_deprecated : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_deprecated2 : t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_relu : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
val cudnn_convolution_transpose : t -> weight:t -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_transpose_backward_input : grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_transpose_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> allow_tf32:bool -> t
val cudnn_convolution_transpose_deprecated : t -> weight:t -> bias:t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_transpose_deprecated2 : t -> weight:t -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_grid_sampler : t -> grid:t -> t
val cudnn_grid_sampler_backward : t -> grid:t -> grad_output:t -> t * t
val cummax : t -> dim:int -> t * t
val cummax_out : values:t -> indices:t -> t -> dim:int -> t * t
val cummaxmin_backward : grad:t -> t -> indices:t -> dim:int -> t
val cummin : t -> dim:int -> t * t
val cummin_out : values:t -> indices:t -> t -> dim:int -> t * t
val cumprod : t -> dim:int -> dtype:Kind.packed -> t
val cumprod_ : t -> dim:int -> dtype:Kind.packed -> t
val cumprod_backward : grad:t -> t -> dim:int -> output:t -> t
val cumprod_out : out:t -> t -> dim:int -> dtype:Kind.packed -> t
val cumsum : t -> dim:int -> dtype:Kind.packed -> t
val cumsum_ : t -> dim:int -> dtype:Kind.packed -> t
val cumsum_out : out:t -> t -> dim:int -> dtype:Kind.packed -> t
val cumulative_trapezoid : y:t -> dim:int -> t
val cumulative_trapezoid_x : y:t -> x:t -> dim:int -> t
val data : t -> t
val deg2rad : t -> t
val deg2rad_ : t -> t
val deg2rad_out : out:t -> t -> t
val dequantize : t -> t
val dequantize_tensors : t list -> t list
val det : t -> t
val detach : t -> t
val detach_ : t -> t
val diag : t -> diagonal:int -> t
val diag_backward : grad:t -> input_sizes:int list -> diagonal:int -> t
val diag_embed : t -> offset:int -> dim1:int -> dim2:int -> t
val diag_out : out:t -> t -> diagonal:int -> t
val diagflat : t -> offset:int -> t
val diagonal : t -> offset:int -> dim1:int -> dim2:int -> t
val diagonal_backward : grad_output:t -> input_sizes:int list -> offset:int -> dim1:int -> dim2:int -> t
val diff : t -> n:int -> dim:int -> prepend:t option -> append:t option -> t
val diff_out : out:t -> t -> n:int -> dim:int -> prepend:t option -> append:t option -> t
val digamma : t -> t
val digamma_ : t -> t
val digamma_out : out:t -> t -> t
val dist : t -> t -> t
val div : t -> t -> t
val div_ : t -> t -> t
val div_out : out:t -> t -> t -> t
val div_out_mode : out:t -> t -> t -> rounding_mode:string -> t
val div_scalar : t -> 'a Scalar.t -> t
val div_scalar_ : t -> 'a Scalar.t -> t
val div_scalar_mode : t -> 'a Scalar.t -> rounding_mode:string -> t
val div_scalar_mode_ : t -> 'a Scalar.t -> rounding_mode:string -> t
val div_tensor_mode : t -> t -> rounding_mode:string -> t
val div_tensor_mode_ : t -> t -> rounding_mode:string -> t
val divide : t -> t -> t
val divide_ : t -> t -> t
val divide_out : out:t -> t -> t -> t
val divide_out_mode : out:t -> t -> t -> rounding_mode:string -> t
val divide_scalar : t -> 'a Scalar.t -> t
val divide_scalar_ : t -> 'a Scalar.t -> t
val divide_scalar_mode : t -> 'a Scalar.t -> rounding_mode:string -> t
val divide_scalar_mode_ : t -> 'a Scalar.t -> rounding_mode:string -> t
val divide_tensor_mode : t -> t -> rounding_mode:string -> t
val divide_tensor_mode_ : t -> t -> rounding_mode:string -> t
val dot : t -> t -> t
val dot_out : out:t -> t -> t -> t
val dropout : t -> p:float -> train:bool -> t
val dropout_ : t -> p:float -> train:bool -> t
val dsplit : t -> sections:int -> t list
val dsplit_array : t -> indices:int list -> t list
val dstack : t list -> t
val dstack_out : out:t -> t list -> t
val eig : t -> eigenvectors:bool -> t * t
val eig_e : e:t -> v:t -> t -> eigenvectors:bool -> t * t
val einsum : equation:string -> t list -> t
val elu : t -> t
val elu_ : t -> t
val elu_backward : grad_output:t -> alpha:'a Scalar.t -> scale:'a Scalar.t -> input_scale:'a Scalar.t -> is_result:bool -> self_or_result:t -> t
val elu_backward_grad_input : grad_input:t -> grad_output:t -> alpha:'a Scalar.t -> scale:'a Scalar.t -> input_scale:'a Scalar.t -> is_result:bool -> self_or_result:t -> t
val elu_out : out:t -> t -> t
val embedding : weight:t -> indices:t -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> t
val embedding_backward : grad:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> t
val embedding_bag : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> t * t * t * t
val embedding_bag_padding_idx : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> include_last_offset:bool -> padding_idx:int -> t * t * t * t
val embedding_dense_backward : grad_output:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> t
val embedding_renorm_ : t -> indices:t -> max_norm:float -> norm_type:float -> t
val embedding_sparse_backward : grad:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> t
val empty : size:int list -> options:(Kind.packed * Device.t) -> t
val empty_like : t -> t
val empty_out : out:t -> size:int list -> t
val empty_quantized : size:int list -> qtensor:t -> options:(Kind.packed * Device.t) -> t
val empty_strided : size:int list -> stride:int list -> options:(Kind.packed * Device.t) -> t
val eq : t -> 'a Scalar.t -> t
val eq_ : t -> 'a Scalar.t -> t
val eq_scalar_out : out:t -> t -> 'a Scalar.t -> t
val eq_tensor : t -> t -> t
val eq_tensor_ : t -> t -> t
val eq_tensor_out : out:t -> t -> t -> t
val erf : t -> t
val erf_ : t -> t
val erf_out : out:t -> t -> t
val erfc : t -> t
val erfc_ : t -> t
val erfc_out : out:t -> t -> t
val erfinv : t -> t
val erfinv_ : t -> t
val erfinv_out : out:t -> t -> t
val exp : t -> t
val exp2 : t -> t
val exp2_ : t -> t
val exp2_out : out:t -> t -> t
val exp_ : t -> t
val exp_out : out:t -> t -> t
val expand : t -> size:int list -> implicit:bool -> t
val expand_as : t -> t -> t
val expm1 : t -> t
val expm1_ : t -> t
val expm1_out : out:t -> t -> t
val exponential_ : t -> lambd:float -> t
val eye : n:int -> options:(Kind.packed * Device.t) -> t
val eye_m : n:int -> m:int -> options:(Kind.packed * Device.t) -> t
val eye_m_out : out:t -> n:int -> m:int -> t
val eye_out : out:t -> n:int -> t
val fake_quantize_per_channel_affine : t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> t
val fake_quantize_per_channel_affine_cachemask : t -> scale:t -> zero_point:t -> axis:int -> quant_min:int -> quant_max:int -> t * t
val fake_quantize_per_channel_affine_cachemask_backward : grad:t -> mask:t -> t
val fake_quantize_per_tensor_affine : t -> scale:float -> zero_point:int -> quant_min:int -> quant_max:int -> t
val fake_quantize_per_tensor_affine_cachemask : t -> scale:float -> zero_point:int -> quant_min:int -> quant_max:int -> t * t
val fake_quantize_per_tensor_affine_cachemask_backward : grad:t -> mask:t -> t
val fake_quantize_per_tensor_affine_tensor_qparams : t -> scale:t -> zero_point:t -> quant_min:int -> quant_max:int -> t
val fbgemm_linear_fp16_weight : t -> packed_weight:t -> bias:t -> t
val fbgemm_linear_fp16_weight_fp32_activation : t -> packed_weight:t -> bias:t -> t
val fbgemm_linear_int8_weight : t -> weight:t -> packed:t -> col_offsets:t -> weight_scale:'a Scalar.t -> weight_zero_point:'a Scalar.t -> bias:t -> t
val fbgemm_linear_int8_weight_fp32_activation : t -> weight:t -> packed:t -> col_offsets:t -> weight_scale:'a Scalar.t -> weight_zero_point:'a Scalar.t -> bias:t -> t
val fbgemm_pack_gemm_matrix_fp16 : t -> t
val fbgemm_pack_quantized_matrix : t -> t
val fbgemm_pack_quantized_matrix_kn : t -> k:int -> n:int -> t
val feature_alpha_dropout : t -> p:float -> train:bool -> t
val feature_alpha_dropout_ : t -> p:float -> train:bool -> t
val feature_dropout : t -> p:float -> train:bool -> t
val feature_dropout_ : t -> p:float -> train:bool -> t
val fft_fft : t -> n:int -> dim:int -> norm:string -> t
val fft_fft2 : t -> s:int list -> dim:int list -> norm:string -> t
val fft_fft2_out : out:t -> t -> s:int list -> dim:int list -> norm:string -> t
val fft_fft_out : out:t -> t -> n:int -> dim:int -> norm:string -> t
val fft_fftfreq : n:int -> d:float -> options:(Kind.packed * 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:(Kind.packed * 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 Scalar.t -> t
val fill_diagonal_ : t -> fill_value:'a Scalar.t -> wrap:bool -> t
val fill_tensor_ : t -> value:t -> t
val fix : t -> t
val fix_ : t -> t
val fix_out : out:t -> t -> t
val flatten : t -> start_dim:int -> end_dim:int -> t
val flatten_dense_tensors : t list -> t
val flip : t -> dims:int list -> t
val fliplr : t -> t
val flipud : t -> t
val float_power : t -> exponent:t -> t
val float_power_ : t -> exponent:'a Scalar.t -> t
val float_power_scalar : 'a Scalar.t -> exponent:t -> t
val float_power_scalar_out : out:t -> 'a Scalar.t -> exponent:t -> t
val float_power_tensor_ : t -> exponent:t -> t
val float_power_tensor_scalar : t -> exponent:'a Scalar.t -> t
val float_power_tensor_scalar_out : out:t -> t -> exponent:'a Scalar.t -> t
val float_power_tensor_tensor_out : out:t -> t -> exponent:t -> t
val floor : t -> t
val floor_ : t -> t
val floor_divide : t -> t -> t
val floor_divide_ : t -> t -> t
val floor_divide_out : out:t -> t -> t -> t
val floor_divide_scalar : t -> 'a Scalar.t -> t
val floor_divide_scalar_ : t -> 'a Scalar.t -> t
val floor_out : out:t -> t -> t
val fmax : t -> t -> t
val fmax_out : out:t -> t -> t -> t
val fmin : t -> t -> t
val fmin_out : out:t -> t -> t -> t
val fmod : t -> 'a Scalar.t -> t
val fmod_ : t -> 'a Scalar.t -> t
val fmod_scalar_out : out:t -> t -> 'a Scalar.t -> t
val fmod_tensor : t -> t -> t
val fmod_tensor_ : t -> t -> t
val fmod_tensor_out : out:t -> t -> t -> t
val frac : t -> t
val frac_ : t -> t
val frac_out : out:t -> t -> t
val fractional_max_pool2d : t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val fractional_max_pool2d_backward : grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool2d_output : output:t -> indices:t -> t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val fractional_max_pool3d : t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val fractional_max_pool3d_backward : grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool3d_output : output:t -> indices:t -> t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val frexp : t -> t * t
val frexp_tensor_out : mantissa:t -> exponent:t -> t -> t * t
val frobenius_norm : t -> t
val frobenius_norm_dim : t -> dim:int list -> keepdim:bool -> t
val frobenius_norm_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val from_file : filename:string -> shared:bool -> size:int -> options:(Kind.packed * Device.t) -> t
val full : size:int list -> fill_value:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val full_like : t -> fill_value:'a Scalar.t -> t
val full_out : out:t -> size:int list -> fill_value:'a Scalar.t -> t
val fused_moving_avg_obs_fake_quant : t -> observer_on:t -> fake_quant_on:t -> running_min:t -> running_max:t -> scale:t -> zero_point:t -> averaging_const:float -> quant_min:int -> quant_max:int -> ch_axis:int -> per_row_fake_quant:bool -> symmetric_quant:bool -> t
val gather : t -> dim:int -> index:t -> sparse_grad:bool -> t
val gather_backward : grad:t -> t -> dim:int -> index:t -> sparse_grad:bool -> t
val gather_out : out:t -> t -> dim:int -> index:t -> sparse_grad:bool -> t
val gcd : t -> t -> t
val gcd_ : t -> t -> t
val gcd_out : out:t -> t -> t -> t
val ge : t -> 'a Scalar.t -> t
val ge_ : t -> 'a Scalar.t -> t
val ge_scalar_out : out:t -> t -> 'a Scalar.t -> t
val ge_tensor : t -> t -> t
val ge_tensor_ : t -> t -> t
val ge_tensor_out : out:t -> t -> t -> t
val gelu : t -> t
val gelu_backward : grad:t -> t -> t
val gelu_backward_grad_input : grad_input:t -> grad:t -> t -> t
val gelu_out : out:t -> t -> t
val geometric_ : t -> p:float -> t
val geqrf : t -> t * t
val geqrf_a : a:t -> tau:t -> t -> t * t
val ger : t -> vec2:t -> t
val ger_out : out:t -> t -> vec2:t -> t
val glu : t -> dim:int -> t
val glu_backward : grad_output:t -> t -> dim:int -> t
val glu_backward_grad_input : grad_input:t -> grad_output:t -> t -> dim:int -> t
val glu_out : out:t -> t -> dim:int -> t
val grad : t -> t
val greater : t -> 'a Scalar.t -> t
val greater_ : t -> 'a Scalar.t -> t
val greater_equal : t -> 'a Scalar.t -> t
val greater_equal_ : t -> 'a Scalar.t -> t
val greater_equal_scalar_out : out:t -> t -> 'a Scalar.t -> t
val greater_equal_tensor : t -> t -> t
val greater_equal_tensor_ : t -> t -> t
val greater_equal_tensor_out : out:t -> t -> t -> t
val greater_scalar_out : out:t -> t -> 'a Scalar.t -> t
val greater_tensor : t -> t -> t
val greater_tensor_ : t -> t -> t
val greater_tensor_out : out:t -> t -> t -> t
val grid_sampler : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
val grid_sampler_2d : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
val grid_sampler_2d_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t * t
val grid_sampler_3d : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t
val grid_sampler_3d_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> align_corners:bool -> t * t
val group_norm : t -> num_groups:int -> weight:t option -> bias:t option -> eps:float -> cudnn_enabled:bool -> t
val gru : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
val gru_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
val gru_data : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
val gt : t -> 'a Scalar.t -> t
val gt_ : t -> 'a Scalar.t -> t
val gt_scalar_out : out:t -> t -> 'a Scalar.t -> t
val gt_tensor : t -> t -> t
val gt_tensor_ : t -> t -> t
val gt_tensor_out : out:t -> t -> t -> t
val hamming_window : window_length:int -> options:(Kind.packed * Device.t) -> t
val hamming_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
val hamming_window_periodic_alpha : window_length:int -> periodic:bool -> alpha:float -> options:(Kind.packed * Device.t) -> t
val hamming_window_periodic_alpha_beta : window_length:int -> periodic:bool -> alpha:float -> beta:float -> options:(Kind.packed * Device.t) -> t
val hann_window : window_length:int -> options:(Kind.packed * Device.t) -> t
val hann_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
val hardshrink : t -> t
val hardshrink_backward : grad_out:t -> t -> lambd:'a Scalar.t -> t
val hardshrink_backward_grad_input : grad_input:t -> grad_out:t -> t -> lambd:'a Scalar.t -> t
val hardshrink_out : out:t -> t -> t
val hardsigmoid : t -> t
val hardsigmoid_ : t -> t
val hardsigmoid_backward : grad_output:t -> t -> t
val hardsigmoid_backward_grad_input : grad_input:t -> grad_output:t -> t -> t
val hardsigmoid_out : out:t -> t -> t
val hardswish : t -> t
val hardswish_ : t -> t
val hardswish_backward : grad_output:t -> t -> t
val hardswish_out : out:t -> t -> t
val hardtanh : t -> t
val hardtanh_ : t -> t
val hardtanh_backward : grad_output:t -> t -> min_val:'a Scalar.t -> max_val:'a Scalar.t -> t
val hardtanh_backward_grad_input : grad_input:t -> grad_output:t -> t -> min_val:'a Scalar.t -> max_val:'a 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:Reduction.t -> t
val histc : t -> bins:int -> t
val histc_out : out:t -> t -> bins:int -> t
val hsplit : t -> sections:int -> t list
val hsplit_array : t -> indices:int list -> t list
val hspmm : mat1:t -> mat2:t -> t
val hspmm_out : out:t -> mat1:t -> mat2:t -> t
val hstack : t list -> t
val hstack_out : out:t -> t list -> t
val huber_loss : t -> target:t -> reduction:Reduction.t -> delta:float -> t
val huber_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> delta:float -> t
val huber_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> delta:float -> t
val huber_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> delta:float -> t
val hypot : t -> t -> t
val hypot_ : t -> t -> t
val hypot_out : out:t -> t -> t -> t
val i0 : t -> t
val i0_ : t -> t
val i0_out : out:t -> t -> t
val igamma : t -> t -> t
val igamma_ : t -> t -> t
val igamma_out : out:t -> t -> t -> t
val igammac : t -> t -> t
val igammac_ : t -> t -> t
val igammac_out : out:t -> t -> t -> t
val im2col : t -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val im2col_backward : grad_output:t -> input_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val im2col_backward_grad_input : grad_input:t -> grad_output:t -> input_size:int list -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val im2col_out : out:t -> t -> kernel_size:int list -> dilation:int list -> padding:int list -> stride:int list -> t
val imag : t -> t
val index : t -> indices:t option list -> t
val index_add : t -> dim:int -> index:t -> source:t -> t
val index_add_ : t -> dim:int -> index:t -> source:t -> t
val index_add_alpha : t -> dim:int -> index:t -> source:t -> alpha:'a Scalar.t -> t
val index_add_alpha_ : t -> dim:int -> index:t -> source:t -> alpha:'a Scalar.t -> t
val index_copy : t -> dim:int -> index:t -> source:t -> t
val index_copy_ : t -> dim:int -> index:t -> source:t -> t
val index_fill : t -> dim:int -> index:t -> value:'a Scalar.t -> t
val index_fill_ : t -> dim:int -> index:t -> value:'a Scalar.t -> t
val index_fill_int_tensor : t -> dim:int -> index:t -> value:t -> t
val index_fill_int_tensor_ : t -> dim:int -> index:t -> value:t -> t
val index_put : t -> indices:t option list -> values:t -> accumulate:bool -> t
val index_put_ : t -> indices:t option list -> values:t -> accumulate:bool -> t
val index_select : t -> dim:int -> index:t -> t
val index_select_backward : grad:t -> self_sizes:int list -> dim:int -> index:t -> t
val index_select_out : out:t -> t -> dim:int -> index:t -> t
val indices : t -> t
val infinitely_differentiable_gelu_backward : grad:t -> t -> t
val inner : t -> t -> t
val inner_out : out:t -> t -> t -> t
val instance_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> use_input_stats:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> t
val int_repr : t -> t
val inverse : t -> t
val inverse_out : out:t -> t -> t
val isclose : t -> t -> rtol:float -> atol:float -> equal_nan:bool -> t
val isfinite : t -> t
val isin : elements:t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
val isin_scalar_tensor : element:'a Scalar.t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
val isin_scalar_tensor_out : out:t -> element:'a Scalar.t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
val isin_tensor_scalar : elements:t -> test_element:'a Scalar.t -> assume_unique:bool -> invert:bool -> t
val isin_tensor_scalar_out : out:t -> elements:t -> test_element:'a Scalar.t -> assume_unique:bool -> invert:bool -> t
val isin_tensor_tensor_out : out:t -> elements:t -> test_elements:t -> assume_unique:bool -> invert:bool -> t
val isinf : t -> t
val isnan : t -> t
val isneginf : t -> t
val isneginf_out : out:t -> t -> t
val isposinf : t -> t
val isposinf_out : out:t -> t -> t
val isreal : t -> t
val istft : t -> n_fft:int -> hop_length:int -> win_length:int -> window:t option -> center:bool -> normalized:bool -> onesided:bool -> length:int -> return_complex:bool -> t
val kaiser_window : window_length:int -> options:(Kind.packed * Device.t) -> t
val kaiser_window_beta : window_length:int -> periodic:bool -> beta:float -> options:(Kind.packed * Device.t) -> t
val kaiser_window_periodic : window_length:int -> periodic:bool -> options:(Kind.packed * Device.t) -> t
val kl_div : t -> target:t -> reduction:Reduction.t -> log_target:bool -> t
val kl_div_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> log_target:bool -> t
val kron : t -> t -> t
val kron_out : out:t -> t -> t -> t
val kthvalue : t -> k:int -> dim:int -> keepdim:bool -> t * t
val kthvalue_values : values:t -> indices:t -> t -> k:int -> dim:int -> keepdim:bool -> t * t
val l1_loss : t -> target:t -> reduction:Reduction.t -> t
val l1_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
val l1_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
val l1_loss_out : out:t -> t -> target:t -> reduction: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 Scalar.t -> t
val le_ : t -> 'a Scalar.t -> t
val le_scalar_out : out:t -> t -> 'a Scalar.t -> t
val le_tensor : t -> t -> t
val le_tensor_ : t -> t -> t
val le_tensor_out : out:t -> t -> t -> t
val leaky_relu : t -> t
val leaky_relu_ : t -> t
val leaky_relu_backward : grad_output:t -> t -> negative_slope:'a Scalar.t -> self_is_result:bool -> t
val leaky_relu_backward_grad_input : grad_input:t -> grad_output:t -> t -> negative_slope:'a Scalar.t -> self_is_result:bool -> t
val leaky_relu_out : out:t -> t -> t
val lerp : t -> end_:t -> weight:'a Scalar.t -> t
val lerp_ : t -> end_:t -> weight:'a Scalar.t -> t
val lerp_scalar_out : out:t -> t -> end_:t -> weight:'a Scalar.t -> t
val lerp_tensor : t -> end_:t -> weight:t -> t
val lerp_tensor_ : t -> end_:t -> weight:t -> t
val lerp_tensor_out : out:t -> t -> end_:t -> weight:t -> t
val less : t -> 'a Scalar.t -> t
val less_ : t -> 'a Scalar.t -> t
val less_equal : t -> 'a Scalar.t -> t
val less_equal_ : t -> 'a Scalar.t -> t
val less_equal_scalar_out : out:t -> t -> 'a Scalar.t -> t
val less_equal_tensor : t -> t -> t
val less_equal_tensor_ : t -> t -> t
val less_equal_tensor_out : out:t -> t -> t -> t
val less_scalar_out : out:t -> t -> 'a Scalar.t -> t
val less_tensor : t -> t -> t
val less_tensor_ : t -> t -> t
val less_tensor_out : out:t -> t -> t -> t
val lgamma : t -> t
val lgamma_ : t -> t
val lgamma_out : out:t -> t -> t
val linalg_cholesky : t -> upper:bool -> t
val linalg_cholesky_ex : t -> upper:bool -> check_errors:bool -> t * t
val linalg_cholesky_ex_l : l:t -> info:t -> t -> upper:bool -> check_errors:bool -> t * t
val linalg_cholesky_out : out:t -> t -> upper:bool -> t
val linalg_cond : t -> p:'a Scalar.t -> t
val linalg_cond_out : out:t -> t -> p:'a Scalar.t -> t
val linalg_cond_p_str : t -> p:string -> t
val linalg_cond_p_str_out : out:t -> t -> p:string -> t
val linalg_det : t -> t
val linalg_det_out : out:t -> t -> t
val linalg_eig : t -> t * t
val linalg_eig_out : eigenvalues:t -> eigenvectors:t -> t -> t * t
val linalg_eigh : t -> uplo:string -> t * t
val linalg_eigh_eigvals : eigvals:t -> eigvecs:t -> t -> uplo:string -> t * t
val linalg_eigvals : t -> t
val linalg_eigvals_out : out:t -> t -> t
val linalg_eigvalsh : t -> uplo:string -> t
val linalg_eigvalsh_out : out:t -> t -> uplo:string -> t
val linalg_householder_product : t -> tau:t -> t
val linalg_householder_product_out : out:t -> t -> tau:t -> t
val linalg_inv : t -> t
val linalg_inv_ex : t -> check_errors:bool -> t * t
val linalg_inv_ex_inverse : inverse:t -> info:t -> t -> check_errors:bool -> t * t
val linalg_inv_out : out:t -> t -> t
val linalg_lstsq : t -> b:t -> rcond:float -> driver:string -> t * t * t * t
val linalg_lstsq_out : solution:t -> residuals:t -> rank:t -> singular_values:t -> t -> b:t -> rcond:float -> driver:string -> t * t * t * t
val linalg_matmul : t -> t -> t
val linalg_matmul_out : out:t -> t -> t -> t
val linalg_matrix_power : t -> n:int -> t
val linalg_matrix_power_out : out:t -> t -> n:int -> t
val linalg_matrix_rank : t -> tol:float -> hermitian:bool -> t
val linalg_matrix_rank_out : out:t -> t -> tol:float -> hermitian:bool -> t
val linalg_matrix_rank_out_tol_tensor : out:t -> t -> tol:t -> hermitian:bool -> t
val linalg_matrix_rank_tol_tensor : t -> tol:t -> hermitian:bool -> t
val linalg_multi_dot : t list -> t
val linalg_multi_dot_out : out:t -> t list -> t
val linalg_pinv : t -> rcond:float -> hermitian:bool -> t
val linalg_pinv_out : out:t -> t -> rcond:float -> hermitian:bool -> t
val linalg_pinv_out_rcond_tensor : out:t -> t -> rcond:t -> hermitian:bool -> t
val linalg_pinv_rcond_tensor : t -> rcond:t -> hermitian:bool -> t
val linalg_qr : t -> mode:string -> t * t
val linalg_qr_out : q:t -> r:t -> t -> mode:string -> t * t
val linalg_slogdet : t -> t * t
val linalg_slogdet_out : sign:t -> logabsdet:t -> t -> t * t
val linalg_solve : t -> t -> t
val linalg_solve_out : out:t -> t -> t -> t
val linalg_svd : t -> full_matrices:bool -> t * t * t
val linalg_svd_u : u:t -> s:t -> vh:t -> t -> full_matrices:bool -> t * t * t
val linalg_svdvals : t -> t
val linalg_svdvals_out : out:t -> t -> t
val linalg_tensorinv : t -> ind:int -> t
val linalg_tensorinv_out : out:t -> t -> ind:int -> t
val linalg_tensorsolve : t -> t -> dims:int list -> t
val linalg_tensorsolve_out : out:t -> t -> t -> dims:int list -> t
val linear : t -> weight:t -> bias:t option -> t
val linear_out : out:t -> t -> weight:t -> bias:t option -> t
val linspace : start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> options:(Kind.packed * Device.t) -> t
val linspace_out : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> t
val log : t -> t
val log10 : t -> t
val log10_ : t -> t
val log10_out : out:t -> t -> t
val log1p : t -> t
val log1p_ : t -> t
val log1p_out : out:t -> t -> t
val log2 : t -> t
val log2_ : t -> t
val log2_out : out:t -> t -> t
val log_ : t -> t
val log_normal_ : t -> mean:float -> std:float -> t
val log_out : out:t -> t -> t
val log_sigmoid : t -> t
val log_sigmoid_backward : grad_output:t -> t -> buffer:t -> t
val log_sigmoid_backward_grad_input : grad_input:t -> grad_output:t -> t -> buffer:t -> t
val log_sigmoid_out : out:t -> t -> t
val log_softmax : t -> dim:int -> dtype:Kind.packed -> t
val logaddexp : t -> t -> t
val logaddexp2 : t -> t -> t
val logaddexp2_out : out:t -> t -> t -> t
val logaddexp_out : out:t -> t -> t -> t
val logcumsumexp : t -> dim:int -> t
val logcumsumexp_out : out:t -> t -> dim:int -> t
val logdet : t -> t
val logical_and : t -> t -> t
val logical_and_ : t -> t -> t
val logical_and_out : out:t -> t -> t -> t
val logical_not : t -> t
val logical_not_ : t -> t
val logical_not_out : out:t -> t -> t
val logical_or : t -> t -> t
val logical_or_ : t -> t -> t
val logical_or_out : out:t -> t -> t -> t
val logical_xor : t -> t -> t
val logical_xor_ : t -> t -> t
val logical_xor_out : out:t -> t -> t -> t
val logit : t -> eps:float -> t
val logit_ : t -> eps:float -> t
val logit_backward : grad_output:t -> t -> eps:float -> t
val logit_backward_grad_input : grad_input:t -> grad_output:t -> t -> eps:float -> t
val logit_out : out:t -> t -> eps:float -> t
val logspace : start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> base:float -> options:(Kind.packed * Device.t) -> t
val logspace_out : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> steps:int -> base:float -> t
val logsumexp : t -> dim:int list -> keepdim:bool -> t
val logsumexp_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val lstm : t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t * t
val lstm_cell : t -> hx:t list -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t * t
val lstm_data : data:t -> batch_sizes:t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t * t
val lstsq : t -> a:t -> t * t
val lstsq_x : x:t -> qr:t -> t -> a:t -> t * t
val lt : t -> 'a Scalar.t -> t
val lt_ : t -> 'a Scalar.t -> t
val lt_scalar_out : out:t -> t -> 'a Scalar.t -> t
val lt_tensor : t -> t -> t
val lt_tensor_ : t -> t -> t
val lt_tensor_out : out:t -> t -> t -> t
val lu_solve : t -> lu_data:t -> lu_pivots:t -> t
val lu_solve_out : out:t -> t -> lu_data:t -> lu_pivots:t -> t
val lu_unpack : lu_data:t -> lu_pivots:t -> unpack_data:bool -> unpack_pivots:bool -> t * t * t
val lu_unpack_out : p:t -> l:t -> u:t -> lu_data:t -> lu_pivots:t -> unpack_data:bool -> unpack_pivots:bool -> t * t * t
val margin_ranking_loss : input1:t -> input2:t -> target:t -> margin:float -> reduction:Reduction.t -> t
val masked_fill : t -> mask:t -> value:'a Scalar.t -> t
val masked_fill_ : t -> mask:t -> value:'a Scalar.t -> t
val masked_fill_tensor : t -> mask:t -> value:t -> t
val masked_fill_tensor_ : t -> mask:t -> value:t -> t
val masked_scatter : t -> mask:t -> source:t -> t
val masked_scatter_ : t -> mask:t -> source:t -> t
val masked_select : t -> mask:t -> t
val masked_select_backward : grad:t -> t -> mask:t -> t
val masked_select_out : out:t -> t -> mask:t -> t
val matmul : t -> t -> t
val matmul_out : out:t -> t -> t -> t
val matrix_exp : t -> t
val matrix_exp_backward : t -> grad:t -> t
val matrix_power : t -> n:int -> t
val matrix_power_out : out:t -> t -> n:int -> t
val matrix_rank : t -> symmetric:bool -> t
val matrix_rank_tol : t -> tol:float -> symmetric:bool -> t
val max_dim : t -> dim:int -> keepdim:bool -> t * t
val max_dim_max : max:t -> max_values:t -> t -> dim:int -> keepdim:bool -> t * t
val max_other : t -> t -> t
val max_out : out:t -> t -> t -> t
val max_pool1d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val max_pool1d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val max_pool2d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool2d_with_indices_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool2d_with_indices_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool2d_with_indices_out : out:t -> indices:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val max_pool3d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool3d_with_indices_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool3d_with_indices_backward_grad_input : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool3d_with_indices_out : out:t -> indices:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_unpool2d : t -> indices:t -> output_size:int list -> t
val max_unpool2d_backward : grad_output:t -> t -> indices:t -> output_size:int list -> t
val max_unpool2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> indices:t -> output_size:int list -> t
val max_unpool2d_out : out:t -> t -> indices:t -> output_size:int list -> t
val max_unpool3d : t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_unpool3d_backward : grad_output:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_unpool3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_unpool3d_out : out:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val maximum : t -> t -> t
val maximum_out : out:t -> t -> t -> t
val mean_dim : t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val mean_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val median : t -> t
val median_dim : t -> dim:int -> keepdim:bool -> t * t
val median_dim_values : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val meshgrid : t list -> t list
val meshgrid_indexing : t list -> indexing:string -> t list
val min_dim : t -> dim:int -> keepdim:bool -> t * t
val min_dim_min : min:t -> min_indices:t -> t -> dim:int -> keepdim:bool -> t * t
val min_other : t -> t -> t
val min_out : out:t -> t -> t -> t
val minimum : t -> t -> t
val minimum_out : out:t -> t -> t -> t
val miopen_batch_norm : t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t
val miopen_batch_norm_backward : t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> t * t * t
val miopen_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_backward_bias : grad_output:t -> t
val miopen_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_transpose : t -> weight:t -> bias:t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_transpose_backward_input : grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_transpose_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_depthwise_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_depthwise_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_depthwise_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_rnn : t -> weight:t list -> weight_stride0:int -> hx:t -> cx:t option -> mode:int -> hidden_size:int -> num_layers:int -> batch_first:bool -> dropout:float -> train:bool -> bidirectional:bool -> batch_sizes:int list -> dropout_state:t option -> t * t * t * t * t
val mish : t -> t
val mish_ : t -> t
val mish_backward : grad_output:t -> t -> t
val mish_out : out:t -> t -> t
val mkldnn_adaptive_avg_pool2d : t -> output_size:int list -> t
val mkldnn_adaptive_avg_pool2d_backward : grad_output:t -> t -> t
val mkldnn_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
val mkldnn_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> t
val mkldnn_convolution_backward_weights : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> t * t
val mkldnn_linear : t -> weight:t -> bias:t option -> t
val mkldnn_linear_backward_input : input_size:int list -> grad_output:t -> weight:t -> t
val mkldnn_linear_backward_weights : grad_output:t -> t -> weight:t -> bias_defined:bool -> t * t
val mkldnn_max_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val mkldnn_max_pool2d_backward : grad_output:t -> output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val mkldnn_max_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val mkldnn_max_pool3d_backward : grad_output:t -> output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val mkldnn_reorder_conv2d_weight : t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
val mkldnn_reorder_conv3d_weight : t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
val mm : t -> mat2:t -> t
val mm_out : out:t -> t -> mat2:t -> t
val mode : t -> dim:int -> keepdim:bool -> t * t
val mode_values : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val moveaxis : t -> source:int list -> destination:int list -> t
val moveaxis_int : t -> source:int -> destination:int -> t
val movedim : t -> source:int list -> destination:int list -> t
val movedim_int : t -> source:int -> destination:int -> t
val mse_loss : t -> target:t -> reduction:Reduction.t -> t
val mse_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
val mse_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
val mse_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> t
val msort : t -> t
val msort_out : out:t -> t -> t
val mul : t -> t -> t
val mul_ : t -> t -> t
val mul_out : out:t -> t -> t -> t
val mul_scalar : t -> 'a Scalar.t -> t
val mul_scalar_ : t -> 'a Scalar.t -> t
val multi_margin_loss_backward : grad_output:t -> t -> target:t -> p:'a Scalar.t -> margin:'a Scalar.t -> weight:t option -> reduction:Reduction.t -> t
val multi_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> p:'a Scalar.t -> margin:'a Scalar.t -> weight:t option -> reduction:Reduction.t -> t
val multilabel_margin_loss : t -> target:t -> reduction:Reduction.t -> t
val multilabel_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> is_target:t -> t
val multilabel_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> is_target:t -> t
val multilabel_margin_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> t
val multinomial : t -> num_samples:int -> replacement:bool -> t
val multinomial_out : out:t -> t -> num_samples:int -> replacement:bool -> t
val multiply : t -> t -> t
val multiply_ : t -> t -> t
val multiply_out : out:t -> t -> t -> t
val multiply_scalar : t -> 'a Scalar.t -> t
val multiply_scalar_ : t -> 'a Scalar.t -> t
val mv : t -> vec:t -> t
val mv_out : out:t -> t -> vec:t -> t
val mvlgamma : t -> p:int -> t
val mvlgamma_ : t -> p:int -> t
val mvlgamma_out : out:t -> t -> p:int -> t
val nan_to_num : t -> nan:float -> posinf:float -> neginf:float -> t
val nan_to_num_ : t -> nan:float -> posinf:float -> neginf:float -> t
val nan_to_num_out : out:t -> t -> nan:float -> posinf:float -> neginf:float -> t
val nanmean : t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val nanmean_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val nanmedian : t -> t
val nanmedian_dim : t -> dim:int -> keepdim:bool -> t * t
val nanmedian_dim_values : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val nanquantile : t -> q:t -> dim:int -> keepdim:bool -> t
val nanquantile_new : t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_new_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_new_scalar : t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_new_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val nanquantile_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> t
val nanquantile_scalar : t -> q:float -> dim:int -> keepdim:bool -> t
val nanquantile_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> t
val nansum : t -> dtype:Kind.packed -> t
val nansum_dim_intlist : t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val nansum_intlist_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val narrow : t -> dim:int -> start:int -> length:int -> t
val narrow_copy : t -> dim:int -> start:int -> length:int -> t
val narrow_copy_out : out:t -> t -> dim:int -> start:int -> length:int -> t
val narrow_tensor : t -> dim:int -> start:t -> length:int -> t
val native_batch_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> t * t * t
val native_batch_norm_out : out:t -> save_mean:t -> save_invstd:t -> t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> t * t * t
val native_group_norm : t -> weight:t option -> bias:t option -> n:int -> c:int -> hxw:int -> group:int -> eps:float -> t * t * t
val native_layer_norm : t -> normalized_shape:int list -> weight:t option -> bias:t option -> eps:float -> t * t * t
val native_norm : t -> t
val native_norm_scalaropt_dim_dtype : t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val ne : t -> 'a Scalar.t -> t
val ne_ : t -> 'a Scalar.t -> t
val ne_scalar_out : out:t -> t -> 'a Scalar.t -> t
val ne_tensor : t -> t -> t
val ne_tensor_ : t -> t -> t
val ne_tensor_out : out:t -> t -> t -> t
val neg : t -> t
val neg_ : t -> t
val neg_out : out:t -> t -> t
val negative : t -> t
val negative_ : t -> t
val negative_out : out:t -> t -> t
val new_empty : t -> size:int list -> options:(Kind.packed * Device.t) -> t
val new_empty_strided : t -> size:int list -> stride:int list -> options:(Kind.packed * Device.t) -> t
val new_full : t -> size:int list -> fill_value:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val new_ones : t -> size:int list -> options:(Kind.packed * Device.t) -> t
val new_zeros : t -> size:int list -> options:(Kind.packed * Device.t) -> t
val nextafter : t -> t -> t
val nextafter_ : t -> t -> t
val nextafter_out : out:t -> t -> t -> t
val nll_loss : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
val nll_loss2d : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
val nll_loss2d_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
val nll_loss2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
val nll_loss2d_out : out:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
val nll_loss_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
val nll_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> total_weight:t -> t
val nll_loss_nd : t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
val nll_loss_out : out:t -> t -> target:t -> weight:t option -> reduction:Reduction.t -> ignore_index:int -> t
val nonzero : t -> t
val nonzero_numpy : t -> t list
val nonzero_out : out:t -> t -> t
val norm : t -> t
val norm_dtype_out : out:t -> t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val norm_except_dim : v:t -> pow:int -> dim:int -> t
val norm_out : out:t -> t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> t
val norm_scalaropt_dim : t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> t
val norm_scalaropt_dim_dtype : t -> p:'a Scalar.t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val norm_scalaropt_dtype : t -> p:'a Scalar.t -> dtype:Kind.packed -> t
val normal : out:t -> mean:t -> std:float -> t
val normal_ : t -> mean:float -> std:float -> t
val normal_float_float_out : out:t -> mean:float -> std:float -> size:int list -> t
val normal_float_tensor_out : out:t -> mean:float -> std:t -> t
val normal_tensor_tensor_out : out:t -> mean:t -> std:t -> t
val not_equal : t -> 'a Scalar.t -> t
val not_equal_ : t -> 'a Scalar.t -> t
val not_equal_scalar_out : out:t -> t -> 'a Scalar.t -> t
val not_equal_tensor : t -> t -> t
val not_equal_tensor_ : t -> t -> t
val not_equal_tensor_out : out:t -> t -> t -> t
val nuclear_norm : t -> keepdim:bool -> t
val nuclear_norm_dim : t -> dim:int list -> keepdim:bool -> t
val nuclear_norm_dim_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val nuclear_norm_out : out:t -> t -> keepdim:bool -> t
val numpy_t : t -> t
val one_hot : t -> num_classes:int -> t
val ones : size:int list -> options:(Kind.packed * Device.t) -> t
val ones_like : t -> t
val ones_out : out:t -> size:int list -> t
val orgqr : t -> input2:t -> t
val orgqr_out : out:t -> t -> input2:t -> t
val ormqr : t -> input2:t -> input3:t -> left:bool -> transpose:bool -> t
val ormqr_out : out:t -> t -> input2:t -> input3:t -> left:bool -> transpose:bool -> t
val outer : t -> vec2:t -> t
val outer_out : out:t -> t -> vec2:t -> t
val pad_sequence : sequences:t list -> batch_first:bool -> padding_value:float -> t
val pairwise_distance : x1:t -> x2:t -> p:float -> eps:float -> keepdim:bool -> t
val pdist : t -> p:float -> t
val permute : t -> dims:int list -> t
val pin_memory : t -> device:Device.t -> t
val pinverse : t -> rcond:float -> t
val pixel_shuffle : t -> upscale_factor:int -> t
val pixel_unshuffle : t -> downscale_factor:int -> t
val poisson : t -> t
val poisson_nll_loss : t -> target:t -> log_input:bool -> full:bool -> eps:float -> reduction:Reduction.t -> t
val polar : abs:t -> angle:t -> t
val polar_out : out:t -> abs:t -> angle:t -> t
val polygamma : n:int -> t -> t
val polygamma_ : t -> n:int -> t
val polygamma_out : out:t -> n:int -> t -> t
val positive : t -> t
val pow : t -> exponent:t -> t
val pow_ : t -> exponent:'a Scalar.t -> t
val pow_scalar : 'a Scalar.t -> exponent:t -> t
val pow_scalar_out : out:t -> 'a Scalar.t -> exponent:t -> t
val pow_tensor_ : t -> exponent:t -> t
val pow_tensor_scalar : t -> exponent:'a Scalar.t -> t
val pow_tensor_scalar_out : out:t -> t -> exponent:'a Scalar.t -> t
val pow_tensor_tensor_out : out:t -> t -> exponent:t -> t
val prelu : t -> weight:t -> t
val prelu_backward : grad_output:t -> t -> weight:t -> t * t
val prod : t -> dtype:Kind.packed -> t
val prod_dim_int : t -> dim:int -> keepdim:bool -> dtype:Kind.packed -> t
val prod_int_out : out:t -> t -> dim:int -> keepdim:bool -> dtype:Kind.packed -> t
val put : t -> index:t -> source:t -> accumulate:bool -> t
val put_ : t -> index:t -> source:t -> accumulate:bool -> t
val q_per_channel_scales : t -> t
val q_per_channel_zero_points : t -> t
val qr : t -> some:bool -> t * t
val qr_q : q:t -> r:t -> t -> some:bool -> t * t
val quantile : t -> q:t -> dim:int -> keepdim:bool -> t
val quantile_new : t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_new_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_new_scalar : t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_new_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> interpolation:string -> t
val quantile_out : out:t -> t -> q:t -> dim:int -> keepdim:bool -> t
val quantile_scalar : t -> q:float -> dim:int -> keepdim:bool -> t
val quantile_scalar_out : out:t -> t -> q:float -> dim:int -> keepdim:bool -> t
val quantize_per_channel : t -> scales:t -> zero_points:t -> axis:int -> dtype:Kind.packed -> t
val quantize_per_tensor : t -> scale:float -> zero_point:int -> dtype:Kind.packed -> t
val quantize_per_tensor_tensor_qparams : t -> scale:t -> zero_point:t -> dtype:Kind.packed -> t
val quantize_per_tensor_tensors : t list -> scales:t -> zero_points:t -> dtype: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 Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a 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 Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a 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 Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a 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 Scalar.t -> scale_hh:'a Scalar.t -> zero_point_ih:'a Scalar.t -> zero_point_hh:'a Scalar.t -> t
val rad2deg : t -> t
val rad2deg_ : t -> t
val rad2deg_out : out:t -> t -> t
val rand : size:int list -> options:(Kind.packed * Device.t) -> t
val rand_like : t -> t
val rand_out : out:t -> size:int list -> t
val randint : high:int -> size:int list -> options:(Kind.packed * Device.t) -> t
val randint_like : t -> high:int -> t
val randint_like_low_dtype : t -> low:int -> high:int -> t
val randint_low : low:int -> high:int -> size:int list -> options:(Kind.packed * Device.t) -> t
val randint_low_out : out:t -> low:int -> high:int -> size:int list -> t
val randint_out : out:t -> high:int -> size:int list -> t
val randn : size:int list -> options:(Kind.packed * Device.t) -> t
val randn_like : t -> t
val randn_out : out:t -> size:int list -> t
val random_ : t -> t
val random_from_ : t -> from:int -> to_:int -> t
val random_to_ : t -> to_:int -> t
val randperm : n:int -> options:(Kind.packed * Device.t) -> t
val randperm_out : out:t -> n:int -> t
val range : start:'a Scalar.t -> end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val range_out : out:t -> start:'a Scalar.t -> end_:'a Scalar.t -> t
val range_step : start:'a Scalar.t -> end_:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val ravel : t -> t
val real : t -> t
val reciprocal : t -> t
val reciprocal_ : t -> t
val reciprocal_out : out:t -> t -> t
val reflection_pad1d : t -> padding:int list -> t
val reflection_pad1d_backward : grad_output:t -> t -> padding:int list -> t
val reflection_pad1d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val reflection_pad1d_out : out:t -> t -> padding:int list -> t
val reflection_pad2d : t -> padding:int list -> t
val reflection_pad2d_backward : grad_output:t -> t -> padding:int list -> t
val reflection_pad2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val reflection_pad2d_out : out:t -> t -> padding:int list -> t
val reflection_pad3d : t -> padding:int list -> t
val reflection_pad3d_backward : grad_output:t -> t -> padding:int list -> t
val reflection_pad3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val reflection_pad3d_out : out:t -> t -> padding:int list -> t
val relu : t -> t
val relu6 : t -> t
val relu6_ : t -> t
val relu_ : t -> t
val remainder : t -> 'a Scalar.t -> t
val remainder_ : t -> 'a Scalar.t -> t
val remainder_scalar_out : out:t -> t -> 'a Scalar.t -> t
val remainder_scalar_tensor : 'a Scalar.t -> t -> t
val remainder_tensor : t -> t -> t
val remainder_tensor_ : t -> t -> t
val remainder_tensor_out : out:t -> t -> t -> t
val renorm : t -> p:'a Scalar.t -> dim:int -> maxnorm:'a Scalar.t -> t
val renorm_ : t -> p:'a Scalar.t -> dim:int -> maxnorm:'a Scalar.t -> t
val renorm_out : out:t -> t -> p:'a Scalar.t -> dim:int -> maxnorm:'a Scalar.t -> t
val repeat : t -> repeats:int list -> t
val repeat_interleave : repeats:t -> output_size:int -> t
val repeat_interleave_self_int : t -> repeats:int -> dim:int -> output_size:int -> t
val repeat_interleave_self_tensor : t -> repeats:t -> dim:int -> output_size:int -> t
val replication_pad1d : t -> padding:int list -> t
val replication_pad1d_backward : grad_output:t -> t -> padding:int list -> t
val replication_pad1d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val replication_pad1d_out : out:t -> t -> padding:int list -> t
val replication_pad2d : t -> padding:int list -> t
val replication_pad2d_backward : grad_output:t -> t -> padding:int list -> t
val replication_pad2d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val replication_pad2d_out : out:t -> t -> padding:int list -> t
val replication_pad3d : t -> padding:int list -> t
val replication_pad3d_backward : grad_output:t -> t -> padding:int list -> t
val replication_pad3d_backward_grad_input : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val replication_pad3d_out : out:t -> t -> padding:int list -> t
val requires_grad_ : t -> requires_grad:bool -> t
val reshape : t -> shape:int list -> t
val reshape_as : t -> t -> t
val resize_ : t -> size:int list -> t
val resize_as_ : t -> the_template:t -> t
val resize_as_sparse_ : t -> the_template:t -> t
val resolve_conj : t -> t
val resolve_neg : t -> t
val rnn_relu : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
val rnn_relu_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
val rnn_relu_data : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
val rnn_tanh : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
val rnn_tanh_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
val rnn_tanh_data : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
val roll : t -> shifts:int list -> dims:int list -> t
val rot90 : t -> k:int -> dims:int list -> t
val round : t -> t
val round_ : t -> t
val round_out : out:t -> t -> t
val row_stack : t list -> t
val row_stack_out : out:t -> t list -> t
val rrelu : t -> training:bool -> t
val rrelu_ : t -> training:bool -> t
val rrelu_with_noise : t -> noise:t -> training:bool -> t
val rrelu_with_noise_ : t -> noise:t -> training:bool -> t
val rrelu_with_noise_backward : grad_output:t -> t -> noise:t -> lower:'a Scalar.t -> upper:'a Scalar.t -> training:bool -> self_is_result:bool -> t
val rrelu_with_noise_out : out:t -> t -> noise:t -> training:bool -> t
val rsqrt : t -> t
val rsqrt_ : t -> t
val rsqrt_out : out:t -> t -> t
val rsub : t -> t -> t
val rsub_scalar : t -> 'a Scalar.t -> t
val scalar_tensor : s:'a Scalar.t -> options:(Kind.packed * Device.t) -> t
val scatter : t -> dim:int -> index:t -> src:t -> t
val scatter_ : t -> dim:int -> index:t -> src:t -> t
val scatter_add : t -> dim:int -> index:t -> src:t -> t
val scatter_add_ : t -> dim:int -> index:t -> src:t -> t
val scatter_add_out : out:t -> t -> dim:int -> index:t -> src:t -> t
val scatter_reduce : t -> dim:int -> index:t -> src:t -> reduce:string -> t
val scatter_reduce_ : t -> dim:int -> index:t -> src:t -> reduce:string -> t
val scatter_reduce_out : out:t -> t -> dim:int -> index:t -> src:t -> reduce:string -> t
val scatter_src_out : out:t -> t -> dim:int -> index:t -> src:t -> t
val scatter_value : t -> dim:int -> index:t -> value:'a Scalar.t -> t
val scatter_value_ : t -> dim:int -> index:t -> value:'a Scalar.t -> t
val scatter_value_out : out:t -> t -> dim:int -> index:t -> value:'a Scalar.t -> t
val scatter_value_reduce : t -> dim:int -> index:t -> value:'a Scalar.t -> reduce:string -> t
val scatter_value_reduce_ : t -> dim:int -> index:t -> value:'a Scalar.t -> reduce:string -> t
val scatter_value_reduce_out : out:t -> t -> dim:int -> index:t -> value:'a Scalar.t -> reduce:string -> t
val searchsorted : sorted_sequence:t -> t -> out_int32:bool -> right:bool -> t
val searchsorted_scalar : sorted_sequence:t -> 'a Scalar.t -> out_int32:bool -> right:bool -> t
val searchsorted_tensor_out : out:t -> sorted_sequence:t -> t -> out_int32:bool -> right:bool -> t
val segment_reduce : data:t -> reduce:string -> lengths:t option -> indices:t option -> axis:int -> unsafe:bool -> initial:'a Scalar.t -> t
val select_backward : grad_output:t -> input_sizes:int list -> dim:int -> index:int -> t
val selu : t -> t
val selu_ : t -> t
val set_ : t -> t
val set_requires_grad : t -> r:bool -> t
val set_source_tensor_ : t -> source:t -> t
val sgn : t -> t
val sgn_ : t -> t
val sgn_out : out:t -> t -> t
val sigmoid : t -> t
val sigmoid_ : t -> t
val sigmoid_backward : grad_output:t -> output:t -> t
val sigmoid_backward_grad_input : grad_input:t -> grad_output:t -> output:t -> t
val sigmoid_out : out:t -> t -> t
val sign : t -> t
val sign_ : t -> t
val sign_out : out:t -> t -> t
val signbit : t -> t
val signbit_out : out:t -> t -> t
val silu : t -> t
val silu_ : t -> t
val silu_backward : grad_output:t -> t -> t
val silu_backward_grad_input : grad_input:t -> grad_output:t -> t -> t
val silu_out : out:t -> t -> t
val sin : t -> t
val sin_ : t -> t
val sin_out : out:t -> t -> t
val sinc : t -> t
val sinc_ : t -> t
val sinc_out : out:t -> t -> t
val sinh : t -> t
val sinh_ : t -> t
val sinh_out : out:t -> t -> t
val slice : t -> dim:int -> start:int -> end_:int -> step:int -> t
val slice_backward : grad_output:t -> input_sizes:int list -> dim:int -> start:int -> end_:int -> step:int -> t
val slogdet : t -> t * t
val slow_conv3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> t
val slow_conv3d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> t
val slow_conv_dilated2d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
val slow_conv_dilated3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> t
val slow_conv_transpose2d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
val slow_conv_transpose2d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
val slow_conv_transpose3d : t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
val slow_conv_transpose3d_out : out:t -> t -> weight:t -> kernel_size:int list -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> dilation:int list -> t
val smm : t -> mat2:t -> t
val smooth_l1_loss : t -> target:t -> reduction:Reduction.t -> beta:float -> t
val smooth_l1_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> beta:float -> t
val smooth_l1_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> beta:float -> t
val smooth_l1_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> beta:float -> t
val soft_margin_loss : t -> target:t -> reduction:Reduction.t -> t
val soft_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
val soft_margin_loss_backward_grad_input : grad_input:t -> grad_output:t -> t -> target:t -> reduction:Reduction.t -> t
val soft_margin_loss_out : out:t -> t -> target:t -> reduction:Reduction.t -> t
val softmax : t -> dim:int -> dtype:Kind.packed -> t
val softplus : t -> t
val softplus_backward : grad_output:t -> t -> beta:'a Scalar.t -> threshold:'a Scalar.t -> output:t -> t
val softplus_backward_grad_input : grad_input:t -> grad_output:t -> t -> beta:'a Scalar.t -> threshold:'a 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 Scalar.t -> t
val softshrink_backward_grad_input : grad_input:t -> grad_output:t -> t -> lambd:'a Scalar.t -> t
val softshrink_out : out:t -> t -> t
val solve : t -> a:t -> t * t
val solve_solution : solution:t -> lu:t -> t -> a:t -> t * t
val sort : t -> dim:int -> descending:bool -> t * t
val sort_stable : t -> stable:bool -> dim:int -> descending:bool -> t * t
val sort_values : values:t -> indices:t -> t -> dim:int -> descending:bool -> t * t
val sort_values_stable : values:t -> indices:t -> t -> stable:bool -> dim:int -> descending:bool -> t * t
val sparse_coo_tensor : size:int list -> options:(Kind.packed * Device.t) -> t
val sparse_coo_tensor_indices : indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
val sparse_coo_tensor_indices_size : indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
val sparse_csr_tensor : crow_indices:t -> col_indices:t -> values:t -> options:(Kind.packed * Device.t) -> t
val sparse_csr_tensor_crow_col_value_size : crow_indices:t -> col_indices:t -> values:t -> size:int list -> options:(Kind.packed * Device.t) -> t
val sparse_mask : t -> mask:t -> t
val sparse_resize_ : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
val sparse_resize_and_clear_ : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
val special_digamma : t -> t
val special_digamma_out : out:t -> t -> t
val special_entr : t -> t
val special_entr_out : out:t -> t -> t
val special_erf : t -> t
val special_erf_out : out:t -> t -> t
val special_erfc : t -> t
val special_erfc_out : out:t -> t -> t
val special_erfcx : t -> t
val special_erfcx_out : out:t -> t -> t
val special_erfinv : t -> t
val special_erfinv_out : out:t -> t -> t
val special_exp2 : t -> t
val special_exp2_out : out:t -> t -> t
val special_expit : t -> t
val special_expit_out : out:t -> t -> t
val special_expm1 : t -> t
val special_expm1_out : out:t -> t -> t
val special_gammainc : t -> t -> t
val special_gammainc_out : out:t -> t -> t -> t
val special_gammaincc : t -> t -> t
val special_gammaincc_out : out:t -> t -> t -> t
val special_gammaln : t -> t
val special_gammaln_out : out:t -> t -> t
val special_i0 : t -> t
val special_i0_out : out:t -> t -> t
val special_i0e : t -> t
val special_i0e_out : out:t -> t -> t
val special_i1 : t -> t
val special_i1_out : out:t -> t -> t
val special_i1e : t -> t
val special_i1e_out : out:t -> t -> t
val special_log1p : t -> t
val special_log1p_out : out:t -> t -> t
val special_log_softmax : t -> dim:int -> dtype:Kind.packed -> t
val special_logit : t -> eps:float -> t
val special_logit_out : out:t -> t -> eps:float -> t
val special_logsumexp : t -> dim:int list -> keepdim:bool -> t
val special_logsumexp_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val special_multigammaln : t -> p:int -> t
val special_multigammaln_out : out:t -> t -> p:int -> t
val special_ndtr : t -> t
val special_ndtr_out : out:t -> t -> t
val special_ndtri : t -> t
val special_ndtri_out : out:t -> t -> t
val special_polygamma : n:int -> t -> t
val special_polygamma_out : out:t -> n:int -> t -> t
val special_psi : t -> t
val special_psi_out : out:t -> t -> t
val special_round : t -> t
val special_round_out : out:t -> t -> t
val special_sinc : t -> t
val special_sinc_out : out:t -> t -> t
val special_xlog1py : t -> t -> t
val special_xlog1py_other_scalar : t -> 'a Scalar.t -> t
val special_xlog1py_other_scalar_out : out:t -> t -> 'a Scalar.t -> t
val special_xlog1py_out : out:t -> t -> t -> t
val special_xlog1py_self_scalar : 'a Scalar.t -> t -> t
val special_xlog1py_self_scalar_out : out:t -> 'a Scalar.t -> t -> t
val special_xlogy : t -> t -> t
val special_xlogy_other_scalar : t -> 'a Scalar.t -> t
val special_xlogy_other_scalar_out : out:t -> t -> 'a Scalar.t -> t
val special_xlogy_out : out:t -> t -> t -> t
val special_xlogy_self_scalar : 'a Scalar.t -> t -> t
val special_xlogy_self_scalar_out : out:t -> 'a Scalar.t -> t -> t
val special_zeta : t -> t -> t
val special_zeta_other_scalar : t -> 'a Scalar.t -> t
val special_zeta_other_scalar_out : out:t -> t -> 'a Scalar.t -> t
val special_zeta_out : out:t -> t -> t -> t
val special_zeta_self_scalar : 'a Scalar.t -> t -> t
val special_zeta_self_scalar_out : out:t -> 'a Scalar.t -> t -> t
val split : t -> split_size:int -> dim:int -> t list
val split_with_sizes : t -> split_sizes:int list -> dim:int -> t list
val sqrt : t -> t
val sqrt_ : t -> t
val sqrt_out : out:t -> t -> t
val square : t -> t
val square_ : t -> t
val square_out : out:t -> t -> t
val squeeze : t -> t
val squeeze_ : t -> t
val squeeze_dim : t -> dim:int -> t
val squeeze_dim_ : t -> dim:int -> t
val sspaddmm : t -> mat1:t -> mat2:t -> t
val sspaddmm_out : out:t -> t -> mat1:t -> mat2:t -> t
val stack : t list -> dim:int -> t
val stack_out : out:t -> t list -> dim:int -> t
val std : t -> unbiased:bool -> t
val std_correction : t -> dim:int list -> correction:int -> keepdim:bool -> t
val std_correction_out : out:t -> t -> dim:int list -> correction:int -> keepdim:bool -> t
val std_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val std_mean : t -> unbiased:bool -> t * t
val std_mean_correction : t -> dim:int list -> correction:int -> keepdim:bool -> t * t
val std_mean_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t * t
val std_out : out:t -> t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val stft : t -> n_fft:int -> hop_length:int -> win_length:int -> window:t option -> normalized:bool -> onesided:bool -> return_complex:bool -> t
val sub : t -> t -> t
val sub_ : t -> t -> t
val sub_out : out:t -> t -> t -> t
val sub_scalar : t -> 'a Scalar.t -> t
val sub_scalar_ : t -> 'a Scalar.t -> t
val subtract : t -> t -> t
val subtract_ : t -> t -> t
val subtract_out : out:t -> t -> t -> t
val subtract_scalar : t -> 'a Scalar.t -> t
val subtract_scalar_ : t -> 'a Scalar.t -> t
val sum_dim_intlist : t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val sum_intlist_out : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Kind.packed -> t
val sum_to_size : t -> size:int list -> t
val svd : t -> some:bool -> compute_uv:bool -> t * t * t
val svd_u : u:t -> s:t -> v:t -> t -> some:bool -> compute_uv:bool -> t * t * t
val swapaxes : t -> axis0:int -> axis1:int -> t
val swapaxes_ : t -> axis0:int -> axis1:int -> t
val swapdims : t -> dim0:int -> dim1:int -> t
val swapdims_ : t -> dim0:int -> dim1:int -> t
val symeig : t -> eigenvectors:bool -> upper:bool -> t * t
val symeig_e : e:t -> v:t -> t -> eigenvectors:bool -> upper:bool -> t * t
val tr : t -> t
val t_ : t -> t
val take : t -> index:t -> t
val take_along_dim : t -> indices:t -> dim:int -> t
val take_along_dim_out : out:t -> t -> indices:t -> dim:int -> t
val take_out : out:t -> t -> index:t -> t
val tan : t -> t
val tan_ : t -> t
val tan_out : out:t -> t -> t
val tanh : t -> t
val tanh_ : t -> t
val tanh_backward : grad_output:t -> output:t -> t
val tanh_backward_grad_input : grad_input:t -> grad_output:t -> output:t -> t
val tanh_out : out:t -> t -> t
val tensor_split : t -> sections:int -> dim:int -> t list
val tensor_split_indices : t -> indices:int list -> dim:int -> t list
val tensor_split_tensor_indices_or_sections : t -> tensor_indices_or_sections:t -> dim:int -> t list
val tensordot : t -> t -> dims_self:int list -> dims_other:int list -> t
val tensordot_out : out:t -> t -> t -> dims_self:int list -> dims_other:int list -> t
val threshold : t -> threshold:'a Scalar.t -> value:'a Scalar.t -> t
val threshold_ : t -> threshold:'a Scalar.t -> value:'a Scalar.t -> t
val threshold_backward : grad_output:t -> t -> threshold:'a Scalar.t -> t
val threshold_backward_grad_input : grad_input:t -> grad_output:t -> t -> threshold:'a Scalar.t -> t
val threshold_out : out:t -> t -> threshold:'a Scalar.t -> value:'a Scalar.t -> t
val tile : t -> dims:int list -> t
val to_ : t -> device:Device.t -> t
val to_dense : t -> dtype:Kind.packed -> t
val to_dense_backward : grad:t -> t -> t
val to_device : t -> device:Device.t -> dtype:Kind.packed -> non_blocking:bool -> copy:bool -> t
val to_dtype : t -> dtype:Kind.packed -> non_blocking:bool -> copy:bool -> t
val to_dtype_layout : t -> options:(Kind.packed * Device.t) -> non_blocking:bool -> copy:bool -> t
val to_mkldnn : t -> dtype:Kind.packed -> t
val to_mkldnn_backward : grad:t -> t -> t
val to_other : t -> t -> non_blocking:bool -> copy:bool -> t
val to_sparse : t -> t
val to_sparse_sparse_dim : t -> sparse_dim:int -> t
val topk : t -> k:int -> dim:int -> largest:bool -> sorted:bool -> t * t
val topk_values : values:t -> indices:t -> t -> k:int -> dim:int -> largest:bool -> sorted:bool -> t * t
val totype : t -> scalar_type:Kind.packed -> t
val trace : t -> t
val trace_backward : grad:t -> sizes:int list -> t
val transpose : t -> dim0:int -> dim1:int -> t
val transpose_ : t -> dim0:int -> dim1:int -> t
val trapezoid : y:t -> dim:int -> t
val trapezoid_x : y:t -> x:t -> dim:int -> t
val trapz : y:t -> x:t -> dim:int -> t
val trapz_dx : y:t -> dx:float -> dim:int -> t
val triangular_solve : t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
val triangular_solve_x : x:t -> m:t -> t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
val tril : t -> diagonal:int -> t
val tril_ : t -> diagonal:int -> t
val tril_indices : row:int -> col:int -> offset:int -> options:(Kind.packed * 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: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:(Kind.packed * Device.t) -> t
val triu_out : out:t -> t -> diagonal:int -> t
val true_divide : t -> t -> t
val true_divide_ : t -> t -> t
val true_divide_out : out:t -> t -> t -> t
val true_divide_scalar : t -> 'a Scalar.t -> t
val true_divide_scalar_ : t -> 'a Scalar.t -> t
val trunc : t -> t
val trunc_ : t -> t
val trunc_out : out:t -> t -> t
val type_as : t -> t -> t
val unbind : t -> dim:int -> t list
val unflatten : t -> dim:int -> sizes:int list -> t
val unflatten_dense_tensors : flat:t -> t list -> t list
val unfold : t -> dimension:int -> size:int -> step:int -> t
val unfold_backward : grad_in:t -> input_sizes:int list -> dim:int -> size:int -> step:int -> t
val uniform_ : t -> from:float -> to_:float -> t
val unique_consecutive : t -> return_inverse:bool -> return_counts:bool -> dim:int -> t * t * t
val unique_dim : t -> dim:int -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
val unique_dim_consecutive : t -> dim:int -> return_inverse:bool -> return_counts:bool -> t * t * t
val unsafe_chunk : t -> chunks:int -> dim:int -> t list
val unsafe_split : t -> split_size:int -> dim:int -> t list
val unsafe_split_with_sizes : t -> split_sizes:int list -> dim:int -> t list
val unsqueeze : t -> dim:int -> t
val unsqueeze_ : t -> dim:int -> t
val upsample_bicubic2d : t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bicubic2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bicubic2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bicubic2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bilinear2d : t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bilinear2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bilinear2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_bilinear2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_h:float -> scales_w:float -> t
val upsample_linear1d : t -> output_size:int list -> align_corners:bool -> scales:float -> t
val upsample_linear1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales:float -> t
val upsample_linear1d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales:float -> t
val upsample_linear1d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales:float -> t
val upsample_nearest1d : t -> output_size:int list -> scales:float -> t
val upsample_nearest1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales:float -> t
val upsample_nearest1d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales:float -> t
val upsample_nearest1d_out : out:t -> t -> output_size:int list -> scales:float -> t
val upsample_nearest2d : t -> output_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest2d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest2d_out : out:t -> t -> output_size:int list -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d : t -> output_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_nearest3d_out : out:t -> t -> output_size:int list -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_trilinear3d : t -> output_size:int list -> align_corners:bool -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_trilinear3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_trilinear3d_backward_grad_input : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> scales_d:float -> scales_h:float -> scales_w:float -> t
val upsample_trilinear3d_out : out:t -> t -> output_size:int list -> align_corners:bool -> scales_d:float -> scales_h:float -> scales_w:float -> t
val value_selecting_reduction_backward : grad:t -> dim:int -> indices:t -> sizes:int list -> keepdim:bool -> t
val values : t -> t
val vander : x:t -> n:int -> increasing:bool -> t
val var : t -> unbiased:bool -> t
val var_correction : t -> dim:int list -> correction:int -> keepdim:bool -> t
val var_correction_out : out:t -> t -> dim:int list -> correction:int -> keepdim:bool -> t
val var_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val var_mean : t -> unbiased:bool -> t * t
val var_mean_correction : t -> dim:int list -> correction:int -> keepdim:bool -> t * t
val var_mean_dim : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t * t
val var_out : out:t -> t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val vdot : t -> t -> t
val vdot_out : out:t -> t -> t -> t
val view : t -> size:int list -> t
val view_as : t -> t -> t
val view_as_complex : t -> t
val view_as_real : t -> t
val view_dtype : t -> dtype:Kind.packed -> t
val vsplit : t -> sections:int -> t list
val vsplit_array : t -> indices:int list -> t list
val vstack : t list -> t
val vstack_out : out:t -> t list -> t
val where : condition:t -> t list
val where_scalar : condition:t -> 'a Scalar.t -> 'a Scalar.t -> t
val where_scalarother : condition:t -> t -> 'a Scalar.t -> t
val where_scalarself : condition:t -> 'a Scalar.t -> t -> t
val where_self : condition:t -> t -> t -> t
val xlogy : t -> t -> t
val xlogy_ : t -> t -> t
val xlogy_outscalar_other : out:t -> t -> 'a Scalar.t -> t
val xlogy_outscalar_self : out:t -> 'a Scalar.t -> t -> t
val xlogy_outtensor : out:t -> t -> t -> t
val xlogy_scalar_other : t -> 'a Scalar.t -> t
val xlogy_scalar_other_ : t -> 'a Scalar.t -> t
val xlogy_scalar_self : 'a Scalar.t -> t -> t
val zero_ : t -> t
val zeros : size:int list -> options:(Kind.packed * Device.t) -> t
val zeros_like : t -> t
val zeros_out : out:t -> size:int list -> t
val new_tensor : unit -> t
val float_vec : ?kind:[ `double | `float | `half ] -> float list -> t
val int_vec : ?kind:[ `int | `int16 | `int64 | `int8 | `uint8 ] -> int list -> t
val of_bigarray : (_, _, Bigarray.c_layout) Bigarray.Genarray.t -> t
val copy_to_bigarray : t -> (_, _, Bigarray.c_layout) Bigarray.Genarray.t -> unit
val shape : t -> int list
val size : t -> int list
val shape1_exn : t -> int