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let upsample_trilinear3d_backward ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_trilinear3d_backward ( CArray . start out__ ) grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_trilinear3d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_trilinear3d_backward_grad_input ( CArray . start out__ ) grad_input grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_trilinear3d_out ~ out self ~ output_size ~ align_corners ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_trilinear3d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let value_selecting_reduction_backward ~ grad ~ dim ~ indices ~ sizes ~ keepdim = let out__ = CArray . make t 1 in stubs_value_selecting_reduction_backward ( CArray . start out__ ) grad ( Int64 . of_int dim ) indices ( List . map Int64 . of_int sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length sizes ) ( if keepdim then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let values self = let out__ = CArray . make t 1 in stubs_values ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let vander ~ x ~ n ~ increasing = let out__ = CArray . make t 1 in stubs_vander ( CArray . start out__ ) x ( Int64 . of_int n ) ( if increasing then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let var self ~ unbiased = let out__ = CArray . make t 1 in stubs_var ( CArray . start out__ ) self ( if unbiased then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let var_correction self ~ dim ~ correction ~ keepdim = let out__ = CArray . make t 1 in stubs_var_correction ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int correction ) ( if keepdim then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let var_correction_out ~ out self ~ dim ~ correction ~ keepdim = let out__ = CArray . make t 1 in stubs_var_correction_out ( CArray . start out__ ) out self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int correction ) ( if keepdim then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let var_dim self ~ dim ~ unbiased ~ keepdim = let out__ = CArray . make t 1 in stubs_var_dim ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( if unbiased then 1 else 0 ) ( if keepdim then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let var_mean self ~ unbiased = let out__ = CArray . make t 2 in stubs_var_mean ( CArray . start out__ ) self ( if unbiased then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let var_mean_correction self ~ dim ~ correction ~ keepdim = let out__ = CArray . make t 2 in stubs_var_mean_correction ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int correction ) ( if keepdim then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let var_mean_dim self ~ dim ~ unbiased ~ keepdim = let out__ = CArray . make t 2 in stubs_var_mean_dim ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( if unbiased then 1 else 0 ) ( if keepdim then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let var_out ~ out self ~ dim ~ unbiased ~ keepdim = let out__ = CArray . make t 1 in stubs_var_out ( CArray . start out__ ) out self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( if unbiased then 1 else 0 ) ( if keepdim then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let vdot self other = let out__ = CArray . make t 1 in stubs_vdot ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let vdot_out ~ out self other = let out__ = CArray . make t 1 in stubs_vdot_out ( CArray . start out__ ) out self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let view self ~ size = let out__ = CArray . make t 1 in stubs_view ( CArray . start out__ ) self ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let view_as self other = let out__ = CArray . make t 1 in stubs_view_as ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let view_as_complex self = let out__ = CArray . make t 1 in stubs_view_as_complex ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let view_as_real self = let out__ = CArray . make t 1 in stubs_view_as_real ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let view_dtype self ~ dtype = let out__ = CArray . make t 1 in stubs_view_dtype ( CArray . start out__ ) self ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let vsplit self ~ sections = stubs_vsplit self ( Int64 . of_int sections ) |> to_tensor_list |
let vsplit_array self ~ indices = stubs_vsplit_array self ( List . map Int64 . of_int indices |> CArray . of_list int64_t |> CArray . start ) ( List . length indices ) |> to_tensor_list |
let vstack tensors = let out__ = CArray . make t 1 in stubs_vstack ( CArray . start out__ ) ( CArray . of_list t tensors |> CArray . start ) ( List . length tensors ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let vstack_out ~ out tensors = let out__ = CArray . make t 1 in stubs_vstack_out ( CArray . start out__ ) out ( CArray . of_list t tensors |> CArray . start ) ( List . length tensors ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let where ~ condition = stubs_where condition |> to_tensor_list |
let where_scalar ~ condition self other = let out__ = CArray . make t 1 in stubs_where_scalar ( CArray . start out__ ) condition self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let where_scalarother ~ condition self other = let out__ = CArray . make t 1 in stubs_where_scalarother ( CArray . start out__ ) condition self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let where_scalarself ~ condition self other = let out__ = CArray . make t 1 in stubs_where_scalarself ( CArray . start out__ ) condition self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let where_self ~ condition self other = let out__ = CArray . make t 1 in stubs_where_self ( CArray . start out__ ) condition self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy self other = let out__ = CArray . make t 1 in stubs_xlogy ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy_ self other = let out__ = CArray . make t 1 in stubs_xlogy_ ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy_outscalar_other ~ out self other = let out__ = CArray . make t 1 in stubs_xlogy_outscalar_other ( CArray . start out__ ) out self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy_outscalar_self ~ out self other = let out__ = CArray . make t 1 in stubs_xlogy_outscalar_self ( CArray . start out__ ) out self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy_outtensor ~ out self other = let out__ = CArray . make t 1 in stubs_xlogy_outtensor ( CArray . start out__ ) out self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy_scalar_other self other = let out__ = CArray . make t 1 in stubs_xlogy_scalar_other ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy_scalar_other_ self other = let out__ = CArray . make t 1 in stubs_xlogy_scalar_other_ ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let xlogy_scalar_self self other = let out__ = CArray . make t 1 in stubs_xlogy_scalar_self ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let zero_ self = let out__ = CArray . make t 1 in stubs_zero_ ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let zeros ~ size ~ options = let out__ = CArray . make t 1 in stubs_zeros ( CArray . start out__ ) ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let zeros_like self = let out__ = CArray . make t 1 in stubs_zeros_like ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let zeros_out ~ out ~ size = let out__ = CArray . make t 1 in stubs_zeros_out ( CArray . start out__ ) out ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
module type S = sig type t type _ scalar val __and__ : t -> ' a scalar -> t val __and__tensor_ : t -> t -> t val __iand__ : t -> ' a scalar -> t val __iand__tensor_ : t -> t -> t val __ilshift__ : t -> ' a scalar -> t val __ilshift__tensor_ : t -> t -> t val __ior__ : t -> ' a scalar -> t val __ior__tensor_ : t -> t -> t val __irshift__ : t -> ' a scalar -> t val __irshift__tensor_ : t -> t -> t val __ixor__ : t -> ' a scalar -> t val __ixor__tensor_ : t -> t -> t val __lshift__ : t -> ' a scalar -> t val __lshift__tensor_ : t -> t -> t val __or__ : t -> ' a scalar -> t val __or__tensor_ : t -> t -> t val __rshift__ : t -> ' a scalar -> t val __rshift__tensor_ : t -> t -> t val __xor__ : t -> ' a scalar -> 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 val _add_relu_scalar_ : t -> ' a scalar -> 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 -> 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 val add_scalar_ : t -> ' a scalar -> 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 -> options : Kind . packed * Device . t -> t val arange_out : out : t -> end_ ' : a scalar -> t val arange_start : start ' : a scalar -> end_ ' : a scalar -> options : Kind . packed * Device . t -> t val arange_start_out : out : t -> start ' : a scalar -> end_ ' : a scalar -> t val arange_start_step : start ' : a scalar -> end_ ' : a scalar -> step ' : a scalar -> 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 : t -> dim : int -> keepdim : bool -> 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 val bitwise_and_ : t -> ' a scalar -> t val bitwise_and_scalar_out : out : t -> t -> ' a scalar -> 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 val bitwise_left_shift_tensor_out : out : t -> t -> t -> t val bitwise_left_shift_tensor_scalar : t -> ' a scalar -> t val bitwise_left_shift_tensor_scalar_ : t -> ' a scalar -> t val bitwise_left_shift_tensor_scalar_out : out : t -> t -> ' a scalar -> 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 val bitwise_or_ : t -> ' a scalar -> t val bitwise_or_scalar_out : out : t -> t -> ' a scalar -> 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 val bitwise_right_shift_tensor_out : out : t -> t -> t -> t val bitwise_right_shift_tensor_scalar : t -> ' a scalar -> t val bitwise_right_shift_tensor_scalar_ : t -> ' a scalar -> t val bitwise_right_shift_tensor_scalar_out : out : t -> t -> ' a scalar -> t val bitwise_xor : t -> ' a scalar -> t val bitwise_xor_ : t -> ' a scalar -> t val bitwise_xor_scalar_out : out : t -> t -> ' a scalar -> 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 -> 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 -> max ' : a scalar -> t val clamp_ : t -> min ' : a scalar -> max ' : a scalar -> t val clamp_max : t -> max ' : a scalar -> t val clamp_max_ : t -> max ' : a scalar -> t val clamp_max_out : out : t -> t -> max ' : a scalar -> 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 val clamp_min_ : t -> min ' : a scalar -> t val clamp_min_out : out : t -> t -> min ' : a scalar -> 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 -> max ' : a scalar -> 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 -> max ' : a scalar -> t val clip_ : t -> min ' : a scalar -> max ' : a scalar -> t val clip_out : out : t -> t -> min ' : a scalar -> max ' : a scalar -> 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 val copysign_scalar_ : t -> ' a scalar -> t val copysign_scalar_out : out : t -> t -> ' a scalar -> 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 -> 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 val div_scalar_ : t -> ' a scalar -> t val div_scalar_mode : t -> ' a scalar -> rounding_mode : string -> t val div_scalar_mode_ : t -> ' a scalar -> 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 val divide_scalar_ : t -> ' a scalar -> t val divide_scalar_mode : t -> ' a scalar -> rounding_mode : string -> t val divide_scalar_mode_ : t -> ' a scalar -> 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 -> scale ' : a scalar -> input_scale ' : a scalar -> is_result : bool -> self_or_result : t -> t val elu_backward_grad_input : grad_input : t -> grad_output : t -> alpha ' : a scalar -> scale ' : a scalar -> input_scale ' : a scalar -> 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 val eq_ : t -> ' a scalar -> t val eq_scalar_out : out : t -> t -> ' a scalar -> 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 -> weight_zero_point ' : a scalar -> bias : t -> t val fbgemm_linear_int8_weight_fp32_activation : t -> weight : t -> packed : t -> col_offsets : t -> weight_scale ' : a scalar -> weight_zero_point ' : a scalar -> 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 val fill_diagonal_ : t -> fill_value ' : a scalar -> 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 val float_power_scalar : ' a scalar -> exponent : t -> t val float_power_scalar_out : out : t -> ' a scalar -> exponent : t -> t val float_power_tensor_ : t -> exponent : t -> t val float_power_tensor_scalar : t -> exponent ' : a scalar -> t val float_power_tensor_scalar_out : out : t -> t -> exponent ' : a scalar -> 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 val floor_divide_scalar_ : t -> ' a scalar -> 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 val fmod_ : t -> ' a scalar -> t val fmod_scalar_out : out : t -> t -> ' a scalar -> 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 -> options : Kind . packed * Device . t -> t val full_like : t -> fill_value ' : a scalar -> t val full_out : out : t -> size : int list -> fill_value ' : a scalar -> 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 val ge_ : t -> ' a scalar -> t val ge_scalar_out : out : t -> t -> ' a scalar -> 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 val greater_ : t -> ' a scalar -> t val greater_equal : t -> ' a scalar -> t val greater_equal_ : t -> ' a scalar -> t val greater_equal_scalar_out : out : t -> t -> ' a scalar -> 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 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 val gt_ : t -> ' a scalar -> t val gt_scalar_out : out : t -> t -> ' a scalar -> 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 val hardshrink_backward_grad_input : grad_input : t -> grad_out : t -> t -> lambd ' : a scalar -> 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 -> max_val ' : a scalar -> t val hardtanh_backward_grad_input : grad_input : t -> grad_output : t -> t -> min_val ' : a scalar -> max_val ' : a scalar -> 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 val index_add_alpha_ : t -> dim : int -> index : t -> source : t -> alpha ' : a scalar -> 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 val index_fill_ : t -> dim : int -> index : t -> value ' : a scalar -> 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 -> test_elements : t -> assume_unique : bool -> invert : bool -> t val isin_scalar_tensor_out : out : t -> element ' : a scalar -> test_elements : t -> assume_unique : bool -> invert : bool -> t val isin_tensor_scalar : elements : t -> test_element ' : a scalar -> assume_unique : bool -> invert : bool -> t val isin_tensor_scalar_out : out : t -> elements : t -> test_element ' : a scalar -> 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 val le_ : t -> ' a scalar -> t val le_scalar_out : out : t -> t -> ' a scalar -> 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 -> self_is_result : bool -> t val leaky_relu_backward_grad_input : grad_input : t -> grad_output : t -> t -> negative_slope ' : a scalar -> self_is_result : bool -> t val leaky_relu_out : out : t -> t -> t val lerp : t -> end_ : t -> weight ' : a scalar -> t val lerp_ : t -> end_ : t -> weight ' : a scalar -> t val lerp_scalar_out : out : t -> t -> end_ : t -> weight ' : a scalar -> 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 val less_ : t -> ' a scalar -> t val less_equal : t -> ' a scalar -> t val less_equal_ : t -> ' a scalar -> t val less_equal_scalar_out : out : t -> t -> ' a scalar -> 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 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 val linalg_cond_out : out : t -> t -> p ' : a scalar -> 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 -> end_ ' : a scalar -> steps : int -> options : Kind . packed * Device . t -> t val linspace_out : out : t -> start ' : a scalar -> end_ ' : a scalar -> 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 -> end_ ' : a scalar -> steps : int -> base : float -> options : Kind . packed * Device . t -> t val logspace_out : out : t -> start ' : a scalar -> end_ ' : a scalar -> 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 val lt_ : t -> ' a scalar -> t val lt_scalar_out : out : t -> t -> ' a scalar -> 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 val masked_fill_ : t -> mask : t -> value ' : a scalar -> 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 : t -> 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 : t -> dtype : Kind . packed -> 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 : t -> t val min_dim : t -> dim : int -> keepdim : bool -> t * t val min_dim_min : min : t -> min_indices : t -> t -> dim : int -> keepdim : bool -> t * t val min_other : t -> t -> t val min_out : out : t -> t -> t -> t val minimum : 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 val mul_scalar_ : t -> ' a scalar -> t val multi_margin_loss_backward : grad_output : t -> t -> target : t -> p ' : a scalar -> margin ' : a scalar -> 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 -> margin ' : a scalar -> 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 val multiply_scalar_ : t -> ' a scalar -> 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 -> dim : int list -> keepdim : bool -> dtype : Kind . packed -> t val ne : t -> ' a scalar -> t val ne_ : t -> ' a scalar -> t val ne_scalar_out : out : t -> t -> ' a scalar -> 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 -> 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 -> 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 -> dim : int list -> keepdim : bool -> t val norm_scalaropt_dim : t -> p ' : a scalar -> dim : int list -> keepdim : bool -> t val norm_scalaropt_dim_dtype : t -> p ' : a scalar -> dim : int list -> keepdim : bool -> dtype : Kind . packed -> t val norm_scalaropt_dtype : t -> p ' : a scalar -> 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 val not_equal_ : t -> ' a scalar -> t val not_equal_scalar_out : out : t -> t -> ' a scalar -> 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 val pow_scalar : ' a scalar -> exponent : t -> t val pow_scalar_out : out : t -> ' a scalar -> exponent : t -> t val pow_tensor_ : t -> exponent : t -> t val pow_tensor_scalar : t -> exponent ' : a scalar -> t val pow_tensor_scalar_out : out : t -> t -> exponent ' : a scalar -> 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 -> scale_hh ' : a scalar -> zero_point_ih ' : a scalar -> zero_point_hh ' : a scalar -> 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 -> scale_hh ' : a scalar -> zero_point_ih ' : a scalar -> zero_point_hh ' : a scalar -> 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 -> scale_hh ' : a scalar -> zero_point_ih ' : a scalar -> zero_point_hh ' : a scalar -> 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 -> scale_hh ' : a scalar -> zero_point_ih ' : a scalar -> zero_point_hh ' : a scalar -> 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 -> end_ ' : a scalar -> options : Kind . packed * Device . t -> t val range_out : out : t -> start ' : a scalar -> end_ ' : a scalar -> t val range_step : start ' : a scalar -> end_ ' : a scalar -> 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 val remainder_ : t -> ' a scalar -> t val remainder_scalar_out : out : t -> t -> ' a scalar -> t val remainder_scalar_tensor : ' a scalar -> 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 -> dim : int -> maxnorm ' : a scalar -> t val renorm_ : t -> p ' : a scalar -> dim : int -> maxnorm ' : a scalar -> t val renorm_out : out : t -> t -> p ' : a scalar -> dim : int -> maxnorm ' : a scalar -> 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 -> upper ' : a scalar -> 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 val scalar_tensor : s ' : a scalar -> 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 val scatter_value_ : t -> dim : int -> index : t -> value ' : a scalar -> t val scatter_value_out : out : t -> t -> dim : int -> index : t -> value ' : a scalar -> t val scatter_value_reduce : t -> dim : int -> index : t -> value ' : a scalar -> reduce : string -> t val scatter_value_reduce_ : t -> dim : int -> index : t -> value ' : a scalar -> reduce : string -> t val scatter_value_reduce_out : out : t -> t -> dim : int -> index : t -> value ' : a scalar -> reduce : string -> t val searchsorted : sorted_sequence : t -> t -> out_int32 : bool -> right : bool -> t val searchsorted_scalar : sorted_sequence : t -> ' a scalar -> 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 val select : t -> dim : int -> index : int -> 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 -> threshold ' : a scalar -> output : t -> t val softplus_backward_grad_input : grad_input : t -> grad_output : t -> t -> beta ' : a scalar -> threshold ' : a scalar -> 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 val softshrink_backward_grad_input : grad_input : t -> grad_output : t -> t -> lambd ' : a scalar -> 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 val special_xlog1py_other_scalar_out : out : t -> t -> ' a scalar -> t val special_xlog1py_out : out : t -> t -> t -> t val special_xlog1py_self_scalar : ' a scalar -> t -> t val special_xlog1py_self_scalar_out : out : t -> ' a scalar -> t -> t val special_xlogy : t -> t -> t val special_xlogy_other_scalar : t -> ' a scalar -> t val special_xlogy_other_scalar_out : out : t -> t -> ' a scalar -> t val special_xlogy_out : out : t -> t -> t -> t val special_xlogy_self_scalar : ' a scalar -> t -> t val special_xlogy_self_scalar_out : out : t -> ' a scalar -> t -> t val special_zeta : t -> t -> t val special_zeta_other_scalar : t -> ' a scalar -> t val special_zeta_other_scalar_out : out : t -> t -> ' a scalar -> t val special_zeta_out : out : t -> t -> t -> t val special_zeta_self_scalar : ' a scalar -> t -> t val special_zeta_self_scalar_out : out : t -> ' a scalar -> 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 val sub_scalar_ : t -> ' a scalar -> 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 val subtract_scalar_ : t -> ' a scalar -> t val sum : t -> dtype : Kind . packed -> 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 -> value ' : a scalar -> t val threshold_ : t -> threshold ' : a scalar -> value ' : a scalar -> t val threshold_backward : grad_output : t -> t -> threshold ' : a scalar -> t val threshold_backward_grad_input : grad_input : t -> grad_output : t -> t -> threshold ' : a scalar -> t val threshold_out : out : t -> t -> threshold ' : a scalar -> value ' : a scalar -> 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 val true_divide_scalar_ : t -> ' a scalar -> 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 -> ' a scalar -> t val where_scalarother : condition : t -> t -> ' a scalar -> t val where_scalarself : condition : t -> ' a scalar -> 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 val xlogy_outscalar_self : out : t -> ' a scalar -> t -> t val xlogy_outtensor : out : t -> t -> t -> t val xlogy_scalar_other : t -> ' a scalar -> t val xlogy_scalar_other_ : t -> ' a scalar -> t val xlogy_scalar_self : ' a scalar -> 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 end |
let wrap ident type_parameters = let open JavaAST in let name = Ident . name ident in TypeMap . add_local ident false ; let wrapper_fields , get_wrapper_meth , get_wrapper_idx_meth , wrapper_meth , wrap_meth = Wrap_common . make_wrapper_elements name type_parameters in let ident_v = Identifier " v " in let cstr = let wrapper_parameters , inits = Wrap_common . make_wrapper_cstr_elements wrapper_fields type_parameters in constructor [ Private ] Private name ~ parameters ( : wrapper_parameters @ [ type_Value , " v ] ) " ( [ Super_constructor [ ident_v ] ident_v ] ident_v @ inits ) inits in let hash_code_meth , equals_meth , to_string_meth = Wrap_common . make_basic_object_methods name " value " true in let full_name = if type_parameters = [ ] then name else let tmp = List . map ( fun ( id , _ ) _ -> Printf . sprintf " % s extends OCamlValue " id ) id type_parameters in name ^ " " < ^ ( String . concat " , " tmp ) tmp ^ " " > in class_ [ Public ; Static ; Final ] Final full_name ~ extends ( : Some " OCamlValue ) " ~ fields : wrapper_fields ~ methods [ : cstr ; get_wrapper_meth ; get_wrapper_idx_meth ; hash_code_meth ; equals_meth ; to_string_meth ; wrap_meth ; wrapper_meth ] wrapper_meth ( ) |
let throws_list = [ " OCamlException " ] |
let return_type_of_type_expr ( ? reverse = false ) false generics t = if is_unit t then None else let gen = ( match t . Types . desc with Types . Tvar ( Some _ ) _ -> true | _ -> false ) false in let t = TypeMap . find ~ generics false t in let conv = if reverse then t . TypeInfo . ocaml_of_java else t . TypeInfo . java_of_ocaml in Some ( t . TypeInfo . java_type , conv , gen ) gen |
let rec expand_if_needed te = let open Types in match te . desc with | Tlink te -> expand_if_needed te | Tconstr ( path , _ , _ ) _ when TypeMap . is_defined_and_doesnt_expand path -> te | Tobject ( _ , { contents = Some ( path , _ ) _ } ) when TypeMap . is_defined_and_doesnt_expand path -> te | _ -> try Ctype . full_expand ! State . environment ( Ctype . repr te ) te with _ -> te |
let rec visit t path te = let open Types in match te . desc with | Tvar None -> ( ) | Tvar ( Some id ) id -> add_parameter t id path | Tarrow ( _ , te1 , te2 , _ ) _ -> visit_arrow t path 0 te1 te2 | Ttuple l -> visit_list t path l | Tconstr ( _path , l , _ ) _ -> visit_list t path l | Tobject _ | Tfield _ | Tnil | Tsubst _ | Tvariant _ | Tunivar _ | Tpoly _ | Tpackage _ -> ( ) | Tlink te -> visit t path te List . iteri ( fun i e -> visit t ( i :: path ) path e ) e l let open Types in visit t ( i :: path ) path te1 ; match te2 . desc with | Tarrow ( _ , te1 ' , te2 ' , _ ) _ -> visit_arrow t path ( succ i ) i te1 ' te2 ' | _ -> visit t ( ( succ i ) i :: path ) path te2 |
let get_type_parameters_for_params type_expr_list = let parameters = ref TypeParametersTable . empty in let type_expr_list = List . map expand_if_needed type_expr_list in List . iteri ( fun i e -> visit parameters [ i ] i e ) e type_expr_list ; ! parameters |
let get_type_parameters_for_return type_expr = let parameters = ref TypeParametersTable . empty in let type_expr = expand_if_needed type_expr in visit parameters [ ] type_expr ; ! parameters |
let merge parameters return = TypeParametersTable . fold ( fun k _ acc -> TypeParametersTable . add k TypeParametersTable . Nowhere acc ) acc return parameters |
let get_type_parameters meth_parameters meth_return_type = let meth_parameters = get_type_parameters_for_params meth_parameters in let meth_return_type = get_type_parameters_for_return meth_return_type in let res = merge meth_parameters meth_return_type in res |
let generics_of_type_parameters type_parameters = TypeParametersTable . fold ( fun k _ acc -> k :: acc ) acc type_parameters [ ] |
let make_body meth_return_type type_parameters call = let open JavaAST in match meth_return_type with | Some ( t , conv , gen ) gen -> let ret = if gen then let base = ( Identifier " res ) " in let w = TypeMap . make_wrapper type_parameters t in let w = Cast ( Reference ( " Wrapper " , [ t ] t ) t , w ) w in return ( cast_if_needed t ( Call ( w , " wrap " , [ base ] base ) base ) base ) base else return ( cast_if_needed t ( conv ( Identifier " res ) ) ) " in [ Variable_declaration ( Reference ( " Value " , [ ] ) , " res " , call ) call ; ret ] | None -> [ Expression call ] |
let make_try_catch ( ? catch_all = false ) false body = let open JavaAST in let handlers = [ " FailException " , " fe " , [ Throw ( Static_call ( " OCamlException " , " wrap " , [ Identifier " fe ] ) ) " ] ; ] in let handlers = if catch_all then handlers @ [ " Throwable " , " t " , [ Throw ( New ( " RuntimeException " , [ Identifier " t ] ) ) " ] ] else handlers in let body = [ Try_catch ( body , handlers ) handlers ] handlers in body |
let wrap modname name type_expr approx global_idx = Output . verbose ( Printf . sprintf " wrapping function % S . . . " ( Ident . name name ) name ) name ; let open JavaAST in let meth_return_type , meth_parameters = flatten_arrow type_expr in let type_parameters = get_type_parameters meth_parameters meth_return_type in let generics = generics_of_type_parameters type_parameters in let meth_return_type = return_type_of_type_expr type_parameters meth_return_type in let meth_parameters = List . mapi ( fun i te -> if is_unit te then None , JavaAST . Identifier " Value . UNIT " else let id = Printf . sprintf " p % d " i in let tt = TypeMap . find ~ generics : type_parameters false te in let t = tt . TypeInfo . java_type in let conv = tt . TypeInfo . ocaml_of_java in let formal = t , id in let effective = conv ( Identifier id ) id in Some formal , effective ) effective meth_parameters in let formal_meth_parameters , effective_meth_parameters = List . split meth_parameters in let formal_meth_parameters = map_option formal_meth_parameters in let class_name , func_name , closed = match approx with | Some ( Jlambda . Value_closure ( fundesc , _ ) _ ) _ -> fundesc . Jlambda . fun_label . Jlambda . fl_class , fundesc . Jlambda . fun_label . Jlambda . fl_method , fundesc . Jlambda . fun_closed | _ -> fail ( Cannot_determine_function name ) name in let effective_meth_parameters = if closed then effective_meth_parameters else effective_meth_parameters @ [ get_global global_idx ] global_idx in let call = Static_call ( class_name , func_name , effective_meth_parameters ) effective_meth_parameters in let body = make_body meth_return_type type_parameters call |> make_try_catch in let javadoc = [ Printf . sprintf " Calls function { @ code % s . % s } s . " modname ( Ident . name name ) name ] in let additional_parameters = TypeParametersTable . fold ( fun k v acc -> if v = TypeParametersTable . Nowhere then begin let ty = Reference ( " Wrapper " , [ Reference ( k , [ ] ) ] ) in let id = Printf . sprintf " w % s " k in ( ty , id ) id :: acc end else acc ) acc type_parameters [ ] in let generics = List . map ( Printf . sprintf " % s extends OCamlValue ) " generics in method_ ~ javadoc ~ generics [ Public ; Static ] ~ return ( : match meth_return_type with None -> None | Some ( x , _ , _ ) _ -> Some x ) x ( Ident . name name ) name ~ parameters ( : formal_meth_parameters @ additional_parameters ) additional_parameters ~ throws : throws_list body |
let wrap_closure modname name type_expr approx global_idx = let open JavaAST in match approx with | Some ( Jlambda . Value_closure ( fundesc , _ ) _ ) _ when fundesc . Jlambda . fun_closed -> let type_parameters = let meth_return_type , meth_parameters = flatten_arrow type_expr in get_type_parameters meth_parameters meth_return_type in let generics = generics_of_type_parameters type_parameters in let javadoc = [ Printf . sprintf " Returns closure for function { @ code % s . % s } s . " modname ( Ident . name name ) name ] in let return_info = TypeMap . find ~ generics : type_parameters true type_expr in let return_type = Some return_info . TypeInfo . java_type in let value = get_global global_idx in let closure = return_info . TypeInfo . java_of_ocaml ( Identifier " res ) " in let generics = List . map ( Printf . sprintf " % s extends OCamlValue ) " generics in let res = method_ ~ javadoc ~ generics [ Public ; Static ] ~ return : return_type ( ( Ident . name name ) name ^ " $ closure ) " [ Variable_declaration ( type_Value , " res " , value ) value ; return closure ] closure in Some res | _ -> None |
let extract_methods self = let open Types in let rec extract acc te = match te . desc with | Tfield ( " * dummy method " , * _ , _ , te ' ) te ' -> extract acc te ' | Tfield ( name , Fpresent , te1 , te2 ) te2 -> let acc = match te1 . desc with | Tpoly ( te , _ ) _ -> ( name , te , None ) None :: acc | _ -> acc in extract acc te2 | Tfield ( _ , _ , _ , te ' ) te ' -> extract acc te ' | _ -> List . rev acc in match self . desc with | Tobject ( te , _ ) _ -> extract [ ] te | _ -> [ ] |
let wrap name class_type_declaration = let class_name = Ident . name name in Output . verbose ( Printf . sprintf " wrapping class type % S . . . " class_name ) class_name ; TypeMap . add_local name true ; let open Types in let open JavaAST in let type_parameters = List . mapi ( fun i te -> match te . desc with | Tvar ( Some id ) id -> id , i | _ -> assert false ) false class_type_declaration . clty_params in let wrapper_fields , get_wrapper_meth , get_wrapper_idx_meth , wrapper_meth , wrap_meth = Wrap_common . make_wrapper_elements ( Ident . name name ) name ~ suffix " :$ impl " type_parameters in let cstr , cstrc , cstr2 = let wrapper_parameters , inits = Wrap_common . make_wrapper_cstr_elements wrapper_fields type_parameters in let c1 = constructor [ Public ] Public ( Ident . name name ) name ~ parameters : wrapper_parameters ( [ Super_constructor [ Identifier " Value . UNIT ] " ; Assign ( " this . oid " , Static_call ( " OCamlWrappers " , " getAndIncrementOid " , [ ] ) ) ] @ inits ) inits in let cc = constructor [ Protected ] Protected ( Ident . name name ) name ~ parameters ( : wrapper_parameters @ [ type_Value , " v ] ) " ( [ Super_constructor [ Identifier " v ] " ; Assign ( " this . oid " , Identifier " Value . MINUS_ONE ) ] " @ inits ) inits in let effective_parameters = List . map ( fun ( _ , id ) id -> Identifier id ) id wrapper_parameters in let c2 = constructor ~ annotations ( : if effective_parameters = [ ] then [ ] else [ " @ SuppressWarnings ( SuppressWarnings " \ unchecked ) ] ) " " \ [ Public ] Public ( ( Ident . name name ) name ^ " $ impl ) " ~ parameters ( : wrapper_parameters @ [ type_Value , " v ] ) " [ Super_constructor ( effective_parameters @ [ Identifier " v ] ) ] " in c1 , cc , c2 in let methods , values = match class_type_declaration . clty_type with | Cty_signature x -> if x . cty_inher <> [ ] then fail Cannot_inherit ; extract_methods x . cty_self , x . cty_vars | _ -> fail ( Cannot_translate_class_type name ) name in if not ( Types . Vars . is_empty values ) values then fail Cannot_contain_value ; let implementations , callers , closures = Wrap_structure . make_methods true methods in let create_long x = Static_call ( " Value " , " createLong " , [ Int_literal ( Int32 . of_int x ) x ] x ) x in let create_method_table_meth = let sz = 2 + ( 2 * ( List . length methods ) methods ) methods in let sz = Int32 . of_int sz in let res = Identifier " res " in let l = List . map ( fun m -> let hash = Btype . hash_variant m . meth_name in let arity = List . length m . meth_parameters in hash , m . meth_name , succ arity ) arity implementations in let l = List . sort Pervasives . compare l in let l = List . mapi ( fun i ( hash , name , arity ) arity -> let clos = Static_call ( " OCamlWrappers " , " createClosure " , [ Identifier ( class_name ^ " . class ) " ; String_literal ( name ^ " $ impl ) " ; Identifier " this " ; Int_literal ( Int32 . of_int arity ) arity ] ) in let idx = 2 + ( i * 2 ) 2 in [ Expression ( set res idx clos ) clos ; Expression ( set res ( succ idx ) idx ( create_long hash ) hash ) hash ] hash ) hash l in let l = List . flatten l in method_ [ Private ] Private ~ return ( : Some type_Value ) type_Value " createMethodTable " ~ parameters [ ] : ( [ Variable_declaration ( type_Value , " res " , Static_call ( " Value " , " createBlock " , [ Int_literal 0l ; Int_literal sz ] sz ) sz ) sz ; Expression ( set res 0 ( create_long ( List . length methods ) methods ) methods ) methods ; Expression ( set res 1 ( create_long 0 ) 0 ) 0 ] 0 @ l @ [ return res ] res ) res in let value_meth = let cached_id = Identifier " this . cached " in let sz = Int32 . of_int ( 2 + 0 ) in let build_cached = [ Assign ( " this . cached " , Static_call ( " Value " , " createBlock " , [ Identifier " BlockValue . OBJECT_TAG " ; Int_literal sz ] sz ) sz ) sz ; Expression ( set cached_id 0 ( Call ( Identifier " this " , " createMethodTable " , [ ] ) ) ) ; Expression ( set cached_id 1 ( Identifier " this . oid ) ) ] " in method_ ~ annotations [ " :@ Override ] " [ Public ] Public ~ return ( : Some type_Value ) type_Value " value " [ If ( Infix ( " " , == cached_id , Null_literal ) Null_literal , Block build_cached ) build_cached ; JavaAST . return cached_id ] cached_id in let name = Ident . name name in let full_name name = if type_parameters = [ ] then name else let tmp = List . map ( fun ( id , _ ) _ -> Printf . sprintf " % s extends OCamlValue " id ) id type_parameters in name ^ " " < ^ ( String . concat " , " tmp ) tmp ^ " " > in let full_name ' name = if type_parameters = [ ] then name else let tmp = List . map ( fun ( id , _ ) _ -> Printf . sprintf " % s " id ) id type_parameters in name ^ " " < ^ ( String . concat " , " tmp ) tmp ^ " " > in let oid_field = [ Private ; Final ] Final , type_Value , " oid " , None in let cached_field = [ Private ] Private , type_Value , " cached " , None in let hash_code , equals , to_string = make_basic_object_methods name " oid " false in let abstract = class_ [ Public ; Static ; Abstract ] Abstract ( full_name name ) name ~ extends ( : Some " OCamlValue ) " ~ fields ( [ : oid_field ; cached_field ] cached_field @ wrapper_fields ) wrapper_fields ~ methods ( [ : cstr ; cstrc ; value_meth ; create_method_table_meth ] create_method_table_meth @ implementations @ closures @ [ hash_code ; equals ; to_string ] to_string @ [ get_wrapper_meth ; get_wrapper_idx_meth ; wrap_meth ; wrapper_meth ] wrapper_meth ) wrapper_meth ( ) in let orig_value_meth = method_ ~ annotations [ " :@ Override ] " [ Public ; Final ] Final ~ return ( : Some type_Value ) type_Value " value " [ return ( Identifier " this . value ) ] " in let final = class_ [ Private ; Static ; Final ] Final ( full_name ( name ^ " $ impl ) ) " ~ extends ( : Some ( full_name ' name ) name ) name ~ methods ( [ : cstr2 ] cstr2 @ callers @ [ orig_value_meth ] orig_value_meth ) orig_value_meth ( ) in abstract , final |
type t = | Aaaaaaaaaa | Bbbbbbbbbb |
let _ = [ " a " ; " b " ; " c " ; " d " ; " e " ; " f " ; " g " ] |
let _ = let _ = 0 in 0 |
let _ = ( ) ; ; |
type t = | Aaaaaaaaaa | Bbbbbbbbbb |
let rex = Pcre . regexp ( " [ ^ 0 - 9 ] { 2 } " ^ " ( . { 12 } ) " ^ " ( . { 4 } ) " ^ " ( [ 0 - 9 ] { 3 } ) " ^ " ( . { 60 } ) " ^ " ( . { 12 } ) " ^ " ( . { 12 } ) " ^ " ( [ 0 - 9 ] { 3 } ) " ^ " ( [ 0 - 9 ] { 3 } ) " ^ " ( . { 15 } ) " ^ " ( [ 0 - 9 ] { 7 } ) " ^ " ( . { 10 } ) " ^ date_fmt ^ " ( [ 0 - 9 ] { 18 } ) " ^ " ( . ) " ^ " ( [ 0 - 9 ] { 3 } ) " ^ " ( . { 15 } ) " ^ " ( . { 3 } ) " ^ " ( . { 27 } ) " ) $ ; ; |
type foo = { some_field : int ; another_field : string } |
let _ = [ " a " ; " b " ; " c " ; " d " ; " e " ; " f " ; " g " ] |
let _ = let _ = 0 in 0 |
let _ = ( ) ; ; |
let _ = ( ) ; ; |
type error = | Command_line_inconsistency of string | Cannot_find_cmi_file of string | Invalid_cmi_file of string | Invalid_cmj_file of string | Cannot_determine_function of Ident . t | Cannot_translate_open_polymorphic_variant of Ident . t | Cannot_translate_polymorphic_variant of Ident . t | Cannot_translate_class_type of Ident . t | Cannot_map_type of string | Cannot_find_type of string | Tuple_is_too_large of int | Function_arity_is_too_large of int | Cannot_inherit | Cannot_contain_value | Only_asbtract_types_and_functions | Cannot_determine_functor_signature |
let fail e = raise ( Exception e ) e |
let map_option l = List . fold_right ( fun elem acc -> match elem with | Some e -> e :: acc | None -> acc ) acc l [ ] |
let string_of_error = function | Command_line_inconsistency s -> Printf . sprintf " command - line inconsistency : % s " s | Cannot_find_cmi_file s -> Printf . sprintf " cannot find cmi file % S " s | Invalid_cmi_file s -> Printf . sprintf " invalid cmi file % S " s | Invalid_cmj_file s -> Printf . sprintf " invalid cmj file % S " s | Cannot_determine_function i -> Printf . sprintf " cannot determine function for % S " ( Ident . name i ) i | Cannot_translate_open_polymorphic_variant i -> Printf . sprintf " cannot translate open polymorphic variant % S " ( Ident . name i ) i | Cannot_translate_polymorphic_variant i -> Printf . sprintf " cannot translate polymorphic variant % S " ( Ident . name i ) i | Cannot_translate_class_type i -> Printf . sprintf " cannot translate class type % S " ( Ident . name i ) i | Cannot_map_type s -> Printf . sprintf " cannot map type % S " s | Cannot_find_type s -> Printf . sprintf " cannot find type % S " s | Tuple_is_too_large n -> Printf . sprintf " tuple is too large ( size is % d ) d " n | Function_arity_is_too_large n -> Printf . sprintf " function arity is too large ( arity is % d ) d " n | Cannot_inherit -> " class type cannot inherit from a parent class type " | Cannot_contain_value -> " class type cannot contain value " | Only_asbtract_types_and_functions -> " module type can contain only abstract types and functions " | Cannot_determine_functor_signature -> " cannot determine functor signature " |
let ( ) = Printexc . register_printer ( function | Exception e -> Some ( string_of_error e ) e | _ -> None ) None |
let main_static_block = ref [ ] |
let add_static_block l = main_static_block := ! main_static_block @ l |
let clear_static_block ( ) = main_static_block := [ ] |
let get e idx = let open JavaAST in if ( idx >= 0 ) 0 && ( idx <= 7 ) 7 then Call ( e , Printf . sprintf " get % d " idx , [ ] ) else Call ( e , " get " , [ Int_literal ( Int32 . of_int idx ) idx ] idx ) idx |
let set e idx e ' = let open JavaAST in if ( idx >= 0 ) 0 && ( idx <= 7 ) 7 then Call ( e , Printf . sprintf " set % d " idx , [ e ' ] e ' ) e ' else Call ( e , " set " , [ Int_literal ( Int32 . of_int idx ) idx ; e ' ] e ' ) e ' |
let get_double e idx = let open JavaAST in if ( idx >= 0 ) 0 && ( idx <= 7 ) 7 then Call ( e , Printf . sprintf " getDouble % d " idx , [ ] ) else Call ( e , " getDouble " , [ Int_literal ( Int32 . of_int idx ) idx ] idx ) idx |
let set_double e idx e ' = let open JavaAST in if ( idx >= 0 ) 0 && ( idx <= 7 ) 7 then Call ( e , Printf . sprintf " setDouble % d " idx , [ e ' ] e ' ) e ' else Call ( e , " setDouble " , [ Int_literal ( Int32 . of_int idx ) idx ; e ' ] e ' ) e ' |
let get_global idx = let open JavaAST in match ! State . java_class with | Some x -> let e = Static_call ( x , " getGlobal " , [ ] ) in get e idx | None -> assert false |
let create_block tag expr_list = let open JavaAST in let tag = Int_literal ( Int32 . of_int tag ) tag in if ( List . length expr_list ) expr_list <= 8 then Static_call ( " Value " , " createBlock " , tag :: expr_list ) expr_list else let array = New_array ( type_Value , expr_list ) expr_list in Static_call ( " Value " , " createBlock " , [ tag ; array ] array ) array |
let create_double_array expr_list = let open JavaAST in if ( List . length expr_list ) expr_list <= 8 then Static_call ( " Value " , " createDoubleArray " , expr_list ) expr_list else let array = New_array ( Double , expr_list ) expr_list in Static_call ( " Value " , " createDoubleArray " , [ array ] array ) array |
let create_long x = let open JavaAST in Static_call ( " Value " , " createLong " , [ Int_literal ( Int32 . of_int x ) x ] x ) x |
let make_wrapper_elements_no_type_parameter class_name suffix = let open JavaAST in let wrapper_type = Reference ( " Wrapper " , [ Reference ( " ? extends " ^ ( checked_name class_name ) class_name , [ ] ) ] ) in let wrapper_field = let ret_type = Reference ( " Wrapper " , [ Reference ( class_name , [ ] ) ] ) in [ Public ; Static ; Final ] Final , ret_type , " WRAPPER " , Some ( Anonymous_class ( Reference ( " SimpleWrapper " , [ Reference ( class_name , [ ] ) ] ) , [ ] , Reference ( class_name , [ ] ) , " wrap " , [ Reference ( " Value " , [ ] ) , " v ] " , New ( class_name ^ suffix , [ Identifier " v ] ) ) ) " in let get_wrapper_meth = method_ [ Public ] Public ~ return ( : Some wrapper_type ) wrapper_type " getWrapper " [ Return ( Some ( Identifier ( ( checked_name class_name ) class_name ^ " . WRAPPER ) ) ) ] " in let get_wrapper_idx_meth = method_ [ Public ] Public ~ return ( : Some wrapper_type ) wrapper_type " getWrapper " ~ parameters [ : Int , " idx ] " [ Return ( Some ( Identifier ( ( checked_name class_name ) class_name ^ " . WRAPPER ) ) ) ] " in let wrapper_meth = method_ [ Public ; Static ] Static ~ return ( : Some wrapper_type ) wrapper_type " wrapper " [ Return ( Some ( Identifier ( ( checked_name class_name ) class_name ^ " . WRAPPER ) ) ) ] " in let class_type = Reference ( class_name , [ ] ) in let wrap_meth = method_ [ Public ; Static ] Static ~ return ( : Some class_type ) class_type " wrap " ~ parameters [ : type_Value , " v ] " [ return ( New ( class_name ^ suffix , [ Identifier " v ] ) ) ] " in [ wrapper_field ] wrapper_field , get_wrapper_idx_meth , get_wrapper_meth , wrapper_meth , wrap_meth |
let make_wrapper_elements_type_parameter class_name suffix type_parameters = let open JavaAST in let sub_wrapper_types = List . map ( fun ( id , _ ) _ -> Reference ( id , [ ] ) ) type_parameters in let wrapper_type = Reference ( " Wrapper " , [ Reference ( " ? extends " ^ ( checked_name class_name ) class_name , sub_wrapper_types ) sub_wrapper_types ] sub_wrapper_types ) sub_wrapper_types in let wrapper_type ' 0 = Reference ( class_name , sub_wrapper_types ) sub_wrapper_types in let wrapper_type ' = Reference ( " ComposedWrapper " , [ wrapper_type ' 0 ] wrapper_type ' 0 ) wrapper_type ' 0 in let fields = List . map ( fun ( id , _ ) _ -> let type_ = Reference ( " Wrapper " , [ Reference ( id , [ ] ) ] ) in let name = " w " ^ id in [ Public ; Final ] Final , type_ , name , None ) None type_parameters in let get_wrapper_meth = method_ [ Public ] Public ~ return ( : Some wrapper_type ) wrapper_type " getWrapper " [ return ( Static_call ( class_name , " wrapper " , List . map ( fun ( _ , _ , n , _ ) _ -> Identifier ( " this . " ^ n ) n ) n fields ) fields ) fields ] fields in let get_wrapper_idx_meth = method_ [ Public ] Public ~ return ( : Some ( Reference ( " Wrapper " , [ Reference ( " ? extends OCamlValue " , [ ] ) ] ) ) ) " getWrapper " ~ parameters [ : Int , " idx ] " [ Switch ( Identifier " idx " , ( List . mapi ( fun i ( _ , _ , n , _ ) _ -> Int32 . of_int i , return ( Identifier ( " this . " ^ n ) n ) n ) n fields ) fields , Some ( return ( Identifier " OCamlUnit . WRAPPER ) ) ) ] " in let class_type = Reference ( class_name , [ ] ) in let ident_v = Identifier " v " in let wrap_meth = let formal , effective = List . split ( List . map ( fun ( _ , ty , name , _ ) _ -> ( ty , name ) name , Identifier name ) name fields ) fields in let generics = List . map ( fun ( x , _ ) _ -> x ^ " extends OCamlValue ) " type_parameters in method_ ~ generics ~ annotations [ " :@ SuppressWarnings ( SuppressWarnings " \ unchecked ) ] " " \ [ Public ; Static ] Static ~ return ( : Some class_type ) class_type " wrap " ~ parameters ( : formal @ [ type_Value , " v ] ) " [ return ( New ( class_name ^ suffix , effective @ [ ident_v ] ident_v ) ident_v ) ident_v ] ident_v in let wrapper_meth = method_ ~ annotations [ " :@ SuppressWarnings ( SuppressWarnings " \ unchecked ) ] " " \ ~ generics ( : List . map ( fun ( x , _ ) _ -> x ^ " extends OCamlValue ) " type_parameters ) type_parameters [ Public ; Static ] Static ~ return ( : Some wrapper_type ) wrapper_type " wrapper " ~ parameters ( : List . map ( fun ( _ , ty , name , _ ) _ -> ty , name ) name fields ) fields [ return ( Anonymous_class ( wrapper_type ' , ( List . map ( fun ( x , _ ) _ -> Identifier ( " w " ^ x ) x ) x type_parameters ) type_parameters , wrapper_type ' 0 , " wrap " , [ type_Value , " v ] " , ( New ( class_name ^ suffix , ( List . map ( fun ( _ , _ , name , _ ) _ -> Identifier name ) name fields ) fields @ [ Identifier " v ] ) ) ) ) ] " in fields , get_wrapper_meth , get_wrapper_idx_meth , wrapper_meth , wrap_meth |
let make_wrapper_elements class_name ( ? suffix = ) " " type_parameters = if type_parameters = [ ] then make_wrapper_elements_no_type_parameter class_name suffix else make_wrapper_elements_type_parameter class_name suffix type_parameters |
let make_wrapper_cstr_elements wrapper_fields type_parameters = let open JavaAST in if type_parameters = [ ] then [ ] , [ ] else List . split ( List . map ( fun ( _ , ty , name , _ ) _ -> ( ty , name ) name , Assign ( " this . " ^ name , Identifier name ) name ) name wrapper_fields ) wrapper_fields |
let primitive_type = function | JavaAST . Reference _ -> false | _ -> true |
let is_arrow type_expr = let open Types in match type_expr . desc with | Tarrow _ -> true | _ -> false |
let flatten_arrow type_expr = let open Types in let rec flatten acc = function | { desc = Types . Tarrow ( _ , t1 , t2 , _ ) _ ; _ } -> flatten ( t1 :: acc ) acc t2 | t -> t , List . rev acc in match type_expr . desc with | Tarrow ( _ , { desc = ( Ttuple l ) l ; _ } , t2 , _ ) _ when not ( is_arrow t2 ) t2 -> t2 , l | _ -> flatten [ ] type_expr |
let flatten_arrow_not_tuple type_expr = let open Types in let rec flatten acc = function | { desc = Types . Tarrow ( _ , t1 , t2 , _ ) _ ; _ } -> flatten ( t1 :: acc ) acc t2 | t -> t , List . rev acc in flatten [ ] type_expr |
let same_item x y = let open Types in match x , y with | Sig_type ( id , { type_kind = Type_abstract ; _ } , _ ) _ , Sig_type ( id ' , { type_kind = Type_abstract ; type_manifest = Some { desc = Tconstr ( path , _ , _ ) _ ; _ } ; _ } , _ ) _ when ( Ident . name id ) id = ( Ident . name id ' ) id ' -> true , [ id , path ] path | Sig_value ( id , _ ) _ , Sig_value ( id ' , _ ) _ | Sig_type ( id , _ , _ ) _ , Sig_type ( id ' , _ , _ ) _ | Sig_exception ( id , _ ) _ , Sig_exception ( id ' , _ ) _ | Sig_module ( id , _ , _ ) _ , Sig_module ( id ' , _ , _ ) _ | Sig_modtype ( id , _ ) _ , Sig_modtype ( id ' , _ ) _ | Sig_class ( id , _ , _ ) _ , Sig_class ( id ' , _ , _ ) _ | Sig_class_type ( id , _ , _ ) _ , Sig_class_type ( id ' , _ , _ ) _ -> ( ( Ident . name id ) id = ( Ident . name id ' ) id ' ) id ' , [ ] | _ -> false , [ ] |
let flatten_functor module_types module_type = let open Types in let rec flatten acc = function | Mty_functor ( id , Mty_ident path , tl ) tl -> flatten ( ( id , path ) path :: acc ) acc tl | Mty_functor _ -> fail Cannot_determine_functor_signature | Mty_ident path -> path , List . rev acc , [ ] | Mty_signature s -> let same x y = if ( List . length x ) x = ( List . length y ) y then List . fold_left2 ( fun ( acc_same , acc_eq ) acc_eq elem1 elem2 -> let same , eq = same_item elem1 elem2 in acc_same && same , eq :: acc_eq ) acc_eq ( true , [ ] ) x y else false , [ ] in let candidates = List . map ( fun ( id , sign , _ ) _ -> let same , eqs = same sign s in if same then Some ( id , List . flatten eqs ) eqs else None ) None module_types in let candidates = map_option candidates in match candidates with | [ id , eqs ] eqs -> let path = Path . Pident id in let eqs = List . map ( fun ( id , path ) path -> match path with | Path . Pdot ( Path . Pident module_id , type_id , _ ) _ -> if List . exists ( fun ( id , _ ) _ -> ( Ident . name id ) id = ( Ident . name module_id ) module_id ) module_id acc then Ident . name id , Ident . name module_id , type_id else fail Cannot_determine_functor_signature | _ -> fail Cannot_determine_functor_signature ) Cannot_determine_functor_signature eqs in path , List . rev acc , eqs | _ :: _ :: _ -> fail Cannot_determine_functor_signature | [ ] -> fail Cannot_determine_functor_signature in flatten [ ] module_type |
let is_unit t = Ctype . moregeneral ! State . environment false Predef . type_unit t |
let cast_if_needed t e = let open JavaAST in match e with | Cast _ -> e | _ -> begin match t with | Reference ( _ , _ :: _ ) _ -> Cast ( t , e ) e | _ -> e end |
let not_an_object = function | Some type_expr -> let open Types in begin match type_expr . desc with | Tobject _ -> false | _ -> true end | None -> true |
let make_basic_object_methods name field reference_comparison = let open JavaAST in let ident_this_value = if field <> " " then Identifier ( " this . " ^ field ) field else Identifier " super " in let ident_that_value = if field <> " " then Identifier ( " that . " ^ field ) field else Identifier " that " in let hash_code_meth = method_ ~ annotations [ " :@ Override ] " [ Public ] Public ~ return ( : Some Int ) Int " hashCode " [ return ( Call ( ident_this_value , " hashCode " , [ ] ) ) ] in let equals_meth = let comp = if reference_comparison then Infix ( " " , == ident_this_value , ident_that_value ) ident_that_value else Call ( ident_this_value , " equals " , [ ident_that_value ] ident_that_value ) ident_that_value in method_ ~ annotations [ " :@ Override ] " [ Public ] Public ~ return ( : Some Boolean ) Boolean " equals " ~ parameters [ : type_Object , " obj ] " ( make_equals_body_expr_list name " obj " [ comp ] comp ) comp in let to_string_meth = method_ ~ annotations [ " :@ Override ] " [ Public ] Public ~ return ( : Some type_String ) type_String " toString " [ return ( String_literal ( name ^ ( ) ) ) ] " . . . " in hash_code_meth , equals_meth , to_string_meth |
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