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let trapezoid ~ y ~ dim = let out__ = CArray . make t 1 in stubs_trapezoid ( CArray . start out__ ) y ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trapezoid_x ~ y ~ x ~ dim = let out__ = CArray . make t 1 in stubs_trapezoid_x ( CArray . start out__ ) y x ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trapz ~ y ~ x ~ dim = let out__ = CArray . make t 1 in stubs_trapz ( CArray . start out__ ) y x ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trapz_dx ~ y ~ dx ~ dim = let out__ = CArray . make t 1 in stubs_trapz_dx ( CArray . start out__ ) y dx ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triangular_solve self ~ a ~ upper ~ transpose ~ unitriangular = let out__ = CArray . make t 2 in stubs_triangular_solve ( CArray . start out__ ) self a ( if upper then 1 else 0 ) ( if transpose then 1 else 0 ) ( if unitriangular 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 triangular_solve_x ~ x ~ m self ~ a ~ upper ~ transpose ~ unitriangular = let out__ = CArray . make t 2 in stubs_triangular_solve_x ( CArray . start out__ ) x m self a ( if upper then 1 else 0 ) ( if transpose then 1 else 0 ) ( if unitriangular 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 tril self ~ diagonal = let out__ = CArray . make t 1 in stubs_tril ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tril_ self ~ diagonal = let out__ = CArray . make t 1 in stubs_tril_ ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tril_indices ~ row ~ col ~ offset ~ options = let out__ = CArray . make t 1 in stubs_tril_indices ( CArray . start out__ ) ( Int64 . of_int row ) ( Int64 . of_int col ) ( Int64 . of_int offset ) ( 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 tril_out ~ out self ~ diagonal = let out__ = CArray . make t 1 in stubs_tril_out ( CArray . start out__ ) out self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triplet_margin_loss ~ anchor ~ positive ~ negative ~ margin ~ p ~ eps ~ swap ~ reduction = let out__ = CArray . make t 1 in stubs_triplet_margin_loss ( CArray . start out__ ) anchor positive negative margin p eps ( if swap then 1 else 0 ) ( Reduction . to_int reduction |> Int64 . of_int ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triu self ~ diagonal = let out__ = CArray . make t 1 in stubs_triu ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triu_ self ~ diagonal = let out__ = CArray . make t 1 in stubs_triu_ ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triu_indices ~ row ~ col ~ offset ~ options = let out__ = CArray . make t 1 in stubs_triu_indices ( CArray . start out__ ) ( Int64 . of_int row ) ( Int64 . of_int col ) ( Int64 . of_int offset ) ( 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 triu_out ~ out self ~ diagonal = let out__ = CArray . make t 1 in stubs_triu_out ( CArray . start out__ ) out self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide self other = let out__ = CArray . make t 1 in stubs_true_divide ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_ self other = let out__ = CArray . make t 1 in stubs_true_divide_ ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_out ~ out self other = let out__ = CArray . make t 1 in stubs_true_divide_out ( CArray . start out__ ) out self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_scalar self other = let out__ = CArray . make t 1 in stubs_true_divide_scalar ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_scalar_ self other = let out__ = CArray . make t 1 in stubs_true_divide_scalar_ ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trunc self = let out__ = CArray . make t 1 in stubs_trunc ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trunc_ self = let out__ = CArray . make t 1 in stubs_trunc_ ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trunc_out ~ out self = let out__ = CArray . make t 1 in stubs_trunc_out ( CArray . start out__ ) out self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let type_as self other = let out__ = CArray . make t 1 in stubs_type_as ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unbind self ~ dim = stubs_unbind self ( Int64 . of_int dim ) |> to_tensor_list |
let unflatten self ~ dim ~ sizes = let out__ = CArray . make t 1 in stubs_unflatten ( CArray . start out__ ) self ( Int64 . of_int dim ) ( List . map Int64 . of_int sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length sizes ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unflatten_dense_tensors ~ flat tensors = stubs_unflatten_dense_tensors flat ( CArray . of_list t tensors |> CArray . start ) ( List . length tensors ) |> to_tensor_list |
let unfold self ~ dimension ~ size ~ step = let out__ = CArray . make t 1 in stubs_unfold ( CArray . start out__ ) self ( Int64 . of_int dimension ) ( Int64 . of_int size ) ( Int64 . of_int step ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unfold_backward ~ grad_in ~ input_sizes ~ dim ~ size ~ step = let out__ = CArray . make t 1 in stubs_unfold_backward ( CArray . start out__ ) grad_in ( List . map Int64 . of_int input_sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length input_sizes ) ( Int64 . of_int dim ) ( Int64 . of_int size ) ( Int64 . of_int step ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let uniform_ self ~ from ~ to_ = let out__ = CArray . make t 1 in stubs_uniform_ ( CArray . start out__ ) self from to_ ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unique_consecutive self ~ return_inverse ~ return_counts ~ dim = let out__ = CArray . make t 3 in stubs_unique_consecutive ( CArray . start out__ ) self ( if return_inverse then 1 else 0 ) ( if return_counts then 1 else 0 ) ( Int64 . of_int dim ) ; 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 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let unique_dim self ~ dim ~ sorted ~ return_inverse ~ return_counts = let out__ = CArray . make t 3 in stubs_unique_dim ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if sorted then 1 else 0 ) ( if return_inverse then 1 else 0 ) ( if return_counts 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 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let unique_dim_consecutive self ~ dim ~ return_inverse ~ return_counts = let out__ = CArray . make t 3 in stubs_unique_dim_consecutive ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if return_inverse then 1 else 0 ) ( if return_counts 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 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let unsafe_chunk self ~ chunks ~ dim = stubs_unsafe_chunk self ( Int64 . of_int chunks ) ( Int64 . of_int dim ) |> to_tensor_list |
let unsafe_split self ~ split_size ~ dim = stubs_unsafe_split self ( Int64 . of_int split_size ) ( Int64 . of_int dim ) |> to_tensor_list |
let unsafe_split_with_sizes self ~ split_sizes ~ dim = stubs_unsafe_split_with_sizes self ( List . map Int64 . of_int split_sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length split_sizes ) ( Int64 . of_int dim ) |> to_tensor_list |
let unsqueeze self ~ dim = let out__ = CArray . make t 1 in stubs_unsqueeze ( CArray . start out__ ) self ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unsqueeze_ self ~ dim = let out__ = CArray . make t 1 in stubs_unsqueeze_ ( CArray . start out__ ) self ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d ( CArray . start 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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d_backward ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d_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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d_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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d_out ~ out self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d_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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d ( CArray . start 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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d_backward ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d_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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d_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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d_out ~ out self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d_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_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d self ~ output_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d ( CArray . start 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 ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d_backward ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d_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 ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d_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 ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d_out ~ out self ~ output_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d_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 ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d self ~ output_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d_backward ~ grad_output ~ output_size ~ input_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d_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 ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d_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 ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d_out ~ out self ~ output_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d self ~ output_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d_backward ~ grad_output ~ output_size ~ input_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d_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 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d_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 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d_out ~ out self ~ output_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d self ~ output_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d_backward ~ grad_output ~ output_size ~ input_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d_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 ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d_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 ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d_out ~ out self ~ output_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_trilinear3d self ~ output_size ~ align_corners ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_trilinear3d ( CArray . start 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 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 |
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