package torch
PyTorch bindings for OCaml
Install
Dune Dependency
Authors
Maintainers
Sources
0.4.tar.gz
md5=9547e9e025dacd52e405ff699539c582
sha512=23fd9bef6f5f11c55171f2383a2f7ca57330511af6521a4579410e002d8667a91e764aecc2deb1cf8d7bf3b0e988cd3020850fa4a5a1ec713dfb110ec7352892
doc/src/torch.core/wrapper_generated.ml.html
Source file wrapper_generated.ml
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6422 6423 6424 6425 6426 6427 6428 6429 6430 6431 6432 6433 6434 6435 6436 6437 6438 6439 6440 6441 6442 6443 6444 6445 6446 6447 6448 6449 6450 6451 6452 6453 6454 6455 6456 6457 6458 6459 6460 6461 6462 6463 6464 6465 6466 6467 6468 6469 6470 6471 6472 6473 6474 6475 6476 6477 6478 6479 6480 6481 6482 6483 6484 6485 6486 6487 6488 6489 6490 6491 6492 6493 6494 6495 6496 6497 6498 6499 6500 6501 6502 6503 6504 6505 6506 6507 6508 6509 6510 6511 6512 6513 6514 6515 6516 6517 6518 6519 6520 6521 6522 6523 6524 6525 6526 6527 6528 6529 6530 6531 6532 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 6543 6544 6545 6546 6547 6548 6549 6550 6551 6552 6553 6554 6555 6556 6557(* THIS FILE IS AUTOMATICALLY GENERATED, DO NOT EDIT BY HAND! *) open Ctypes module C = Torch_bindings.C(Torch_generated) open C.TensorG let to_tensor_list ptr = let rec loop ptr acc = let tensor = !@ptr in if is_null tensor then acc else begin Gc.finalise C.Tensor.free tensor; loop (ptr +@ 1) (tensor :: acc) end in let result = loop ptr [] in C.free (to_voidp ptr); List.rev result let abs self = let out__ = CArray.make t 1 in stubs_abs (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let abs_ self = let out__ = CArray.make t 1 in stubs_abs_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let abs_out ~out self = let out__ = CArray.make t 1 in stubs_abs_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let acos self = let out__ = CArray.make t 1 in stubs_acos (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let acos_ self = let out__ = CArray.make t 1 in stubs_acos_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let acos_out ~out self = let out__ = CArray.make t 1 in stubs_acos_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_avg_pool1d self ~output_size = let out__ = CArray.make t 1 in stubs_adaptive_avg_pool1d (CArray.start out__) self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_avg_pool2d self ~output_size = let out__ = CArray.make t 1 in stubs_adaptive_avg_pool2d (CArray.start out__) self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_avg_pool2d_out ~out self ~output_size = let out__ = CArray.make t 1 in stubs_adaptive_avg_pool2d_out (CArray.start out__) out self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_avg_pool3d self ~output_size = let out__ = CArray.make t 1 in stubs_adaptive_avg_pool3d (CArray.start out__) self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_avg_pool3d_backward ~grad_output self = let out__ = CArray.make t 1 in stubs_adaptive_avg_pool3d_backward (CArray.start out__) grad_output self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_avg_pool3d_backward_out ~grad_input ~grad_output self = let out__ = CArray.make t 1 in stubs_adaptive_avg_pool3d_backward_out (CArray.start out__) grad_input grad_output self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_avg_pool3d_out ~out self ~output_size = let out__ = CArray.make t 1 in stubs_adaptive_avg_pool3d_out (CArray.start out__) out self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_max_pool1d self ~output_size = let out__ = CArray.make t 2 in stubs_adaptive_max_pool1d (CArray.start out__) self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); 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 adaptive_max_pool2d self ~output_size = let out__ = CArray.make t 2 in stubs_adaptive_max_pool2d (CArray.start out__) self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); 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 adaptive_max_pool2d_backward ~grad_output self ~indices = let out__ = CArray.make t 1 in stubs_adaptive_max_pool2d_backward (CArray.start out__) grad_output self indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_max_pool2d_backward_out ~grad_input ~grad_output self ~indices = let out__ = CArray.make t 1 in stubs_adaptive_max_pool2d_backward_out (CArray.start out__) grad_input grad_output self indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_max_pool2d_out ~out ~indices self ~output_size = let out__ = CArray.make t 2 in stubs_adaptive_max_pool2d_out (CArray.start out__) out indices self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); 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 adaptive_max_pool3d self ~output_size = let out__ = CArray.make t 2 in stubs_adaptive_max_pool3d (CArray.start out__) self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); 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 adaptive_max_pool3d_backward ~grad_output self ~indices = let out__ = CArray.make t 1 in stubs_adaptive_max_pool3d_backward (CArray.start out__) grad_output self indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_max_pool3d_backward_out ~grad_input ~grad_output self ~indices = let out__ = CArray.make t 1 in stubs_adaptive_max_pool3d_backward_out (CArray.start out__) grad_input grad_output self indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let adaptive_max_pool3d_out ~out ~indices self ~output_size = let out__ = CArray.make t 2 in stubs_adaptive_max_pool3d_out (CArray.start out__) out indices self (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); 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 add self other = let out__ = CArray.make t 1 in stubs_add (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let add1 self other = let out__ = CArray.make t 1 in stubs_add1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let add_ self other = let out__ = CArray.make t 1 in stubs_add_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let add_1 self other = let out__ = CArray.make t 1 in stubs_add_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let add_out ~out self other = let out__ = CArray.make t 1 in stubs_add_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addbmm self ~batch1 ~batch2 = let out__ = CArray.make t 1 in stubs_addbmm (CArray.start out__) self batch1 batch2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addbmm_ self ~batch1 ~batch2 = let out__ = CArray.make t 1 in stubs_addbmm_ (CArray.start out__) self batch1 batch2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addbmm_out ~out self ~batch1 ~batch2 = let out__ = CArray.make t 1 in stubs_addbmm_out (CArray.start out__) out self batch1 batch2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addcdiv self ~tensor1 ~tensor2 = let out__ = CArray.make t 1 in stubs_addcdiv (CArray.start out__) self tensor1 tensor2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addcdiv_ self ~tensor1 ~tensor2 = let out__ = CArray.make t 1 in stubs_addcdiv_ (CArray.start out__) self tensor1 tensor2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addcdiv_out ~out self ~tensor1 ~tensor2 = let out__ = CArray.make t 1 in stubs_addcdiv_out (CArray.start out__) out self tensor1 tensor2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addcmul self ~tensor1 ~tensor2 = let out__ = CArray.make t 1 in stubs_addcmul (CArray.start out__) self tensor1 tensor2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addcmul_ self ~tensor1 ~tensor2 = let out__ = CArray.make t 1 in stubs_addcmul_ (CArray.start out__) self tensor1 tensor2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addcmul_out ~out self ~tensor1 ~tensor2 = let out__ = CArray.make t 1 in stubs_addcmul_out (CArray.start out__) out self tensor1 tensor2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addmm self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_addmm (CArray.start out__) self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addmm_ self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_addmm_ (CArray.start out__) self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addmm_out ~out self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_addmm_out (CArray.start out__) out self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addmv self ~mat ~vec = let out__ = CArray.make t 1 in stubs_addmv (CArray.start out__) self mat vec; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addmv_ self ~mat ~vec = let out__ = CArray.make t 1 in stubs_addmv_ (CArray.start out__) self mat vec; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addmv_out ~out self ~mat ~vec = let out__ = CArray.make t 1 in stubs_addmv_out (CArray.start out__) out self mat vec; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addr self ~vec1 ~vec2 = let out__ = CArray.make t 1 in stubs_addr (CArray.start out__) self vec1 vec2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addr_ self ~vec1 ~vec2 = let out__ = CArray.make t 1 in stubs_addr_ (CArray.start out__) self vec1 vec2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let addr_out ~out self ~vec1 ~vec2 = let out__ = CArray.make t 1 in stubs_addr_out (CArray.start out__) out self vec1 vec2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let affine_grid_generator ~theta ~size = let out__ = CArray.make t 1 in stubs_affine_grid_generator (CArray.start out__) theta (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 affine_grid_generator_backward ~grad ~size = let out__ = CArray.make t 1 in stubs_affine_grid_generator_backward (CArray.start out__) grad (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 alias self = let out__ = CArray.make t 1 in stubs_alias (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let all self = let out__ = CArray.make t 1 in stubs_all (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let all1 self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_all1 (CArray.start out__) self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let all_out ~out self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_all_out (CArray.start out__) out self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let alpha_dropout input ~p ~train = let out__ = CArray.make t 1 in stubs_alpha_dropout (CArray.start out__) input p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let alpha_dropout_ self ~p ~train = let out__ = CArray.make t 1 in stubs_alpha_dropout_ (CArray.start out__) self p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let any self = let out__ = CArray.make t 1 in stubs_any (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let any1 self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_any1 (CArray.start out__) self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let any_out ~out self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_any_out (CArray.start out__) out self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let arange ~end_ ~options = let out__ = CArray.make t 1 in stubs_arange (CArray.start out__) end_ (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let arange1 ~start ~end_ ~options = let out__ = CArray.make t 1 in stubs_arange1 (CArray.start out__) start end_ (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let arange2 ~start ~end_ ~step ~options = let out__ = CArray.make t 1 in stubs_arange2 (CArray.start out__) start end_ step (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let arange_out ~out ~end_ = let out__ = CArray.make t 1 in stubs_arange_out (CArray.start out__) out end_; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let arange_out1 ~out ~start ~end_ = let out__ = CArray.make t 1 in stubs_arange_out1 (CArray.start out__) out start end_; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let argmax self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_argmax (CArray.start out__) self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let argmin self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_argmin (CArray.start out__) self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let argsort self ~dim ~descending = let out__ = CArray.make t 1 in stubs_argsort (CArray.start out__) self (Int64.of_int dim) (if descending then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let as_strided self ~size ~stride ~storage_offset = let out__ = CArray.make t 1 in stubs_as_strided (CArray.start out__) self (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (Int64.of_int storage_offset); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let as_strided_ self ~size ~stride ~storage_offset = let out__ = CArray.make t 1 in stubs_as_strided_ (CArray.start out__) self (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (Int64.of_int storage_offset); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let asin self = let out__ = CArray.make t 1 in stubs_asin (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let asin_ self = let out__ = CArray.make t 1 in stubs_asin_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let asin_out ~out self = let out__ = CArray.make t 1 in stubs_asin_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let atan self = let out__ = CArray.make t 1 in stubs_atan (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let atan2 self other = let out__ = CArray.make t 1 in stubs_atan2 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let atan2_ self other = let out__ = CArray.make t 1 in stubs_atan2_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let atan2_out ~out self other = let out__ = CArray.make t 1 in stubs_atan2_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let atan_ self = let out__ = CArray.make t 1 in stubs_atan_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let atan_out ~out self = let out__ = CArray.make t 1 in stubs_atan_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool1d self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool1d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool2d self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool2d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool2d_backward ~grad_output self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool2d_backward (CArray.start out__) grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool2d_backward_out ~grad_input ~grad_output self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool2d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool2d_out ~out self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool2d_out (CArray.start out__) out self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool3d self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool3d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool3d_backward ~grad_output self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool3d_backward (CArray.start out__) grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool3d_backward_out ~grad_input ~grad_output self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool3d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let avg_pool3d_out ~out self ~kernel_size ~stride ~padding ~ceil_mode ~count_include_pad = let out__ = CArray.make t 1 in stubs_avg_pool3d_out (CArray.start out__) out self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (if ceil_mode then 1 else 0) (if count_include_pad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let baddbmm self ~batch1 ~batch2 = let out__ = CArray.make t 1 in stubs_baddbmm (CArray.start out__) self batch1 batch2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let baddbmm_ self ~batch1 ~batch2 = let out__ = CArray.make t 1 in stubs_baddbmm_ (CArray.start out__) self batch1 batch2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let baddbmm_out ~out self ~batch1 ~batch2 = let out__ = CArray.make t 1 in stubs_baddbmm_out (CArray.start out__) out self batch1 batch2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bartlett_window ~window_length ~options = let out__ = CArray.make t 1 in stubs_bartlett_window (CArray.start out__) (Int64.of_int window_length) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bartlett_window1 ~window_length ~periodic ~options = let out__ = CArray.make t 1 in stubs_bartlett_window1 (CArray.start out__) (Int64.of_int window_length) (if periodic then 1 else 0) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let batch_norm input ~weight ~bias ~running_mean ~running_var ~training ~momentum ~eps ~cudnn_enabled = let out__ = CArray.make t 1 in stubs_batch_norm (CArray.start out__) input (match weight with | Some v -> v | None -> null) (match bias with | Some v -> v | None -> null) (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) (if training then 1 else 0) momentum eps (if cudnn_enabled then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let batch_norm_backward_elemt ~grad_out input ~mean ~invstd ~weight ~mean_dy ~mean_dy_xmu = let out__ = CArray.make t 1 in stubs_batch_norm_backward_elemt (CArray.start out__) grad_out input mean invstd (match weight with | Some v -> v | None -> null) mean_dy mean_dy_xmu; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let batch_norm_backward_reduce ~grad_out input ~mean ~invstd ~input_g ~weight_g ~bias_g = let out__ = CArray.make t 4 in stubs_batch_norm_backward_reduce (CArray.start out__) grad_out input mean invstd (if input_g then 1 else 0) (if weight_g then 1 else 0) (if bias_g 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; let t3 = CArray.get out__ 3 in Gc.finalise C.Tensor.free t3; t0, t1, t2, t3 let batch_norm_elemt input ~weight ~bias ~mean ~invstd ~eps = let out__ = CArray.make t 1 in stubs_batch_norm_elemt (CArray.start out__) input (match weight with | Some v -> v | None -> null) (match bias with | Some v -> v | None -> null) mean invstd eps; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let batch_norm_gather_stats input ~mean ~invstd ~running_mean ~running_var ~momentum ~eps ~count = let out__ = CArray.make t 2 in stubs_batch_norm_gather_stats (CArray.start out__) input mean invstd (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) momentum eps (Int64.of_int count); 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 batch_norm_stats input ~eps = let out__ = CArray.make t 2 in stubs_batch_norm_stats (CArray.start out__) input eps; 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 batch_norm_update_stats input ~running_mean ~running_var ~momentum = let out__ = CArray.make t 2 in stubs_batch_norm_update_stats (CArray.start out__) input (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) momentum; 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 bernoulli self = let out__ = CArray.make t 1 in stubs_bernoulli (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bernoulli1 self ~p = let out__ = CArray.make t 1 in stubs_bernoulli1 (CArray.start out__) self p; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bernoulli_ self ~p = let out__ = CArray.make t 1 in stubs_bernoulli_ (CArray.start out__) self p; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bernoulli_1 self ~p = let out__ = CArray.make t 1 in stubs_bernoulli_1 (CArray.start out__) self p; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bernoulli_out ~out self = let out__ = CArray.make t 1 in stubs_bernoulli_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bilinear ~input1 ~input2 ~weight ~bias = let out__ = CArray.make t 1 in stubs_bilinear (CArray.start out__) input1 input2 weight (match bias with | Some v -> v | None -> null); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let binary_cross_entropy self ~target ~weight ~reduction = let out__ = CArray.make t 1 in stubs_binary_cross_entropy (CArray.start out__) self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let binary_cross_entropy_backward ~grad_output self ~target ~weight ~reduction = let out__ = CArray.make t 1 in stubs_binary_cross_entropy_backward (CArray.start out__) grad_output self target weight (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let binary_cross_entropy_backward_out ~grad_input ~grad_output self ~target ~weight ~reduction = let out__ = CArray.make t 1 in stubs_binary_cross_entropy_backward_out (CArray.start out__) grad_input grad_output self target weight (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let binary_cross_entropy_out ~out self ~target ~weight ~reduction = let out__ = CArray.make t 1 in stubs_binary_cross_entropy_out (CArray.start out__) out self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let binary_cross_entropy_with_logits self ~target ~weight ~pos_weight ~reduction = let out__ = CArray.make t 1 in stubs_binary_cross_entropy_with_logits (CArray.start out__) self target (match weight with | Some v -> v | None -> null) (match pos_weight with | Some v -> v | None -> null) (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let binary_cross_entropy_with_logits_backward ~grad_output self ~target ~weight ~pos_weight ~reduction = let out__ = CArray.make t 1 in stubs_binary_cross_entropy_with_logits_backward (CArray.start out__) grad_output self target (match weight with | Some v -> v | None -> null) (match pos_weight with | Some v -> v | None -> null) (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bincount self ~weights ~minlength = let out__ = CArray.make t 1 in stubs_bincount (CArray.start out__) self (match weights with | Some v -> v | None -> null) (Int64.of_int minlength); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let blackman_window ~window_length ~options = let out__ = CArray.make t 1 in stubs_blackman_window (CArray.start out__) (Int64.of_int window_length) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let blackman_window1 ~window_length ~periodic ~options = let out__ = CArray.make t 1 in stubs_blackman_window1 (CArray.start out__) (Int64.of_int window_length) (if periodic then 1 else 0) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bmm self ~mat2 = let out__ = CArray.make t 1 in stubs_bmm (CArray.start out__) self mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let bmm_out ~out self ~mat2 = let out__ = CArray.make t 1 in stubs_bmm_out (CArray.start out__) out self mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let broadcast_tensors tensors = stubs_broadcast_tensors (CArray.of_list t tensors |> CArray.start) (List.length tensors) |> to_tensor_list let cartesian_prod tensors = let out__ = CArray.make t 1 in stubs_cartesian_prod (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 cat tensors ~dim = let out__ = CArray.make t 1 in stubs_cat (CArray.start out__) (CArray.of_list t tensors |> CArray.start) (List.length tensors) (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cat_out ~out tensors ~dim = let out__ = CArray.make t 1 in stubs_cat_out (CArray.start out__) out (CArray.of_list t tensors |> CArray.start) (List.length tensors) (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cauchy_ self ~median ~sigma = let out__ = CArray.make t 1 in stubs_cauchy_ (CArray.start out__) self median sigma; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cdist ~x1 ~x2 ~p = let out__ = CArray.make t 1 in stubs_cdist (CArray.start out__) x1 x2 p; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ceil self = let out__ = CArray.make t 1 in stubs_ceil (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ceil_ self = let out__ = CArray.make t 1 in stubs_ceil_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ceil_out ~out self = let out__ = CArray.make t 1 in stubs_ceil_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let celu self = let out__ = CArray.make t 1 in stubs_celu (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let celu_ self = let out__ = CArray.make t 1 in stubs_celu_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let chain_matmul ~matrices = let out__ = CArray.make t 1 in stubs_chain_matmul (CArray.start out__) (CArray.of_list t matrices |> CArray.start) (List.length matrices); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cholesky self ~upper = let out__ = CArray.make t 1 in stubs_cholesky (CArray.start out__) self (if upper then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cholesky_inverse self ~upper = let out__ = CArray.make t 1 in stubs_cholesky_inverse (CArray.start out__) self (if upper then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cholesky_inverse_out ~out self ~upper = let out__ = CArray.make t 1 in stubs_cholesky_inverse_out (CArray.start out__) out self (if upper then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cholesky_out ~out self ~upper = let out__ = CArray.make t 1 in stubs_cholesky_out (CArray.start out__) out self (if upper then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cholesky_solve self ~input2 ~upper = let out__ = CArray.make t 1 in stubs_cholesky_solve (CArray.start out__) self input2 (if upper then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cholesky_solve_out ~out self ~input2 ~upper = let out__ = CArray.make t 1 in stubs_cholesky_solve_out (CArray.start out__) out self input2 (if upper then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let chunk self ~chunks ~dim = stubs_chunk self (Int64.of_int chunks) (Int64.of_int dim) |> to_tensor_list let clamp self ~min ~max = let out__ = CArray.make t 1 in stubs_clamp (CArray.start out__) self min max; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_ self ~min ~max = let out__ = CArray.make t 1 in stubs_clamp_ (CArray.start out__) self min max; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_max self ~max = let out__ = CArray.make t 1 in stubs_clamp_max (CArray.start out__) self max; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_max_ self ~max = let out__ = CArray.make t 1 in stubs_clamp_max_ (CArray.start out__) self max; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_max_out ~out self ~max = let out__ = CArray.make t 1 in stubs_clamp_max_out (CArray.start out__) out self max; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_min self ~min = let out__ = CArray.make t 1 in stubs_clamp_min (CArray.start out__) self min; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_min_ self ~min = let out__ = CArray.make t 1 in stubs_clamp_min_ (CArray.start out__) self min; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_min_out ~out self ~min = let out__ = CArray.make t 1 in stubs_clamp_min_out (CArray.start out__) out self min; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clamp_out ~out self ~min ~max = let out__ = CArray.make t 1 in stubs_clamp_out (CArray.start out__) out self min max; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let clone self = let out__ = CArray.make t 1 in stubs_clone (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let coalesce self = let out__ = CArray.make t 1 in stubs_coalesce (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let combinations self ~r ~with_replacement = let out__ = CArray.make t 1 in stubs_combinations (CArray.start out__) self (Int64.of_int r) (if with_replacement then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let constant_pad_nd self ~pad = let out__ = CArray.make t 1 in stubs_constant_pad_nd (CArray.start out__) self (List.map Int64.of_int pad |> CArray.of_list int64_t |> CArray.start) (List.length pad); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let contiguous self = let out__ = CArray.make t 1 in stubs_contiguous (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let conv1d input ~weight ~bias ~stride ~padding ~dilation ~groups = let out__ = CArray.make t 1 in stubs_conv1d (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let conv2d input ~weight ~bias ~stride ~padding ~dilation ~groups = let out__ = CArray.make t 1 in stubs_conv2d (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let conv3d input ~weight ~bias ~stride ~padding ~dilation ~groups = let out__ = CArray.make t 1 in stubs_conv3d (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let conv_tbc self ~weight ~bias ~pad = let out__ = CArray.make t 1 in stubs_conv_tbc (CArray.start out__) self weight bias (Int64.of_int pad); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let conv_tbc_backward self input ~weight ~bias ~pad = let out__ = CArray.make t 3 in stubs_conv_tbc_backward (CArray.start out__) self input weight bias (Int64.of_int pad); 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 conv_transpose1d input ~weight ~bias ~stride ~padding ~output_padding ~groups ~dilation = let out__ = CArray.make t 1 in stubs_conv_transpose1d (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int output_padding |> CArray.of_list int64_t |> CArray.start) (List.length output_padding) (Int64.of_int groups) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let conv_transpose2d input ~weight ~bias ~stride ~padding ~output_padding ~groups ~dilation = let out__ = CArray.make t 1 in stubs_conv_transpose2d (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int output_padding |> CArray.of_list int64_t |> CArray.start) (List.length output_padding) (Int64.of_int groups) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let conv_transpose3d input ~weight ~bias ~stride ~padding ~output_padding ~groups ~dilation = let out__ = CArray.make t 1 in stubs_conv_transpose3d (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int output_padding |> CArray.of_list int64_t |> CArray.start) (List.length output_padding) (Int64.of_int groups) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let convolution input ~weight ~bias ~stride ~padding ~dilation ~transposed ~output_padding ~groups = let out__ = CArray.make t 1 in stubs_convolution (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if transposed then 1 else 0) (List.map Int64.of_int output_padding |> CArray.of_list int64_t |> CArray.start) (List.length output_padding) (Int64.of_int groups); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let copy_sparse_to_sparse_ self ~src ~non_blocking = let out__ = CArray.make t 1 in stubs_copy_sparse_to_sparse_ (CArray.start out__) self src (if non_blocking then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cos self = let out__ = CArray.make t 1 in stubs_cos (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cos_ self = let out__ = CArray.make t 1 in stubs_cos_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cos_out ~out self = let out__ = CArray.make t 1 in stubs_cos_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cosh self = let out__ = CArray.make t 1 in stubs_cosh (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cosh_ self = let out__ = CArray.make t 1 in stubs_cosh_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cosh_out ~out self = let out__ = CArray.make t 1 in stubs_cosh_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cosine_embedding_loss ~input1 ~input2 ~target ~margin ~reduction = let out__ = CArray.make t 1 in stubs_cosine_embedding_loss (CArray.start out__) input1 input2 target margin (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cosine_similarity ~x1 ~x2 ~dim ~eps = let out__ = CArray.make t 1 in stubs_cosine_similarity (CArray.start out__) x1 x2 (Int64.of_int dim) eps; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cross self other ~dim = let out__ = CArray.make t 1 in stubs_cross (CArray.start out__) self other (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cross_out ~out self other ~dim = let out__ = CArray.make t 1 in stubs_cross_out (CArray.start out__) out self other (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ctc_loss ~log_probs ~targets ~input_lengths ~target_lengths ~blank ~reduction ~zero_infinity = let out__ = CArray.make t 1 in stubs_ctc_loss (CArray.start out__) log_probs targets (List.map Int64.of_int input_lengths |> CArray.of_list int64_t |> CArray.start) (List.length input_lengths) (List.map Int64.of_int target_lengths |> CArray.of_list int64_t |> CArray.start) (List.length target_lengths) (Int64.of_int blank) (Int64.of_int reduction) (if zero_infinity then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ctc_loss1 ~log_probs ~targets ~input_lengths ~target_lengths ~blank ~reduction ~zero_infinity = let out__ = CArray.make t 1 in stubs_ctc_loss1 (CArray.start out__) log_probs targets input_lengths target_lengths (Int64.of_int blank) (Int64.of_int reduction) (if zero_infinity then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_affine_grid_generator ~theta ~n ~c ~h ~w = let out__ = CArray.make t 1 in stubs_cudnn_affine_grid_generator (CArray.start out__) theta (Int64.of_int n) (Int64.of_int c) (Int64.of_int h) (Int64.of_int w); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_affine_grid_generator_backward ~grad ~n ~c ~h ~w = let out__ = CArray.make t 1 in stubs_cudnn_affine_grid_generator_backward (CArray.start out__) grad (Int64.of_int n) (Int64.of_int c) (Int64.of_int h) (Int64.of_int w); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_batch_norm input ~weight ~bias ~running_mean ~running_var ~training ~exponential_average_factor ~epsilon = let out__ = CArray.make t 3 in stubs_cudnn_batch_norm (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) (if training then 1 else 0) exponential_average_factor epsilon; 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 cudnn_batch_norm_backward input ~grad_output ~weight ~running_mean ~running_var ~save_mean ~save_var ~epsilon = let out__ = CArray.make t 3 in stubs_cudnn_batch_norm_backward (CArray.start out__) input grad_output weight (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) (match save_mean with | Some v -> v | None -> null) (match save_var with | Some v -> v | None -> null) epsilon; 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 cudnn_convolution self ~weight ~bias ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_cudnn_convolution (CArray.start out__) self weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_convolution_backward_bias ~grad_output = let out__ = CArray.make t 1 in stubs_cudnn_convolution_backward_bias (CArray.start out__) grad_output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_convolution_backward_input ~self_size ~grad_output ~weight ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_cudnn_convolution_backward_input (CArray.start out__) (List.map Int64.of_int self_size |> CArray.of_list int64_t |> CArray.start) (List.length self_size) grad_output weight (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_convolution_backward_weight ~weight_size ~grad_output self ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_cudnn_convolution_backward_weight (CArray.start out__) (List.map Int64.of_int weight_size |> CArray.of_list int64_t |> CArray.start) (List.length weight_size) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_convolution_transpose self ~weight ~bias ~padding ~output_padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_cudnn_convolution_transpose (CArray.start out__) self weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int output_padding |> CArray.of_list int64_t |> CArray.start) (List.length output_padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_convolution_transpose_backward_bias ~grad_output = let out__ = CArray.make t 1 in stubs_cudnn_convolution_transpose_backward_bias (CArray.start out__) grad_output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_convolution_transpose_backward_input ~grad_output ~weight ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_cudnn_convolution_transpose_backward_input (CArray.start out__) grad_output weight (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_convolution_transpose_backward_weight ~weight_size ~grad_output self ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_cudnn_convolution_transpose_backward_weight (CArray.start out__) (List.map Int64.of_int weight_size |> CArray.of_list int64_t |> CArray.start) (List.length weight_size) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_grid_sampler self ~grid = let out__ = CArray.make t 1 in stubs_cudnn_grid_sampler (CArray.start out__) self grid; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cudnn_grid_sampler_backward self ~grid ~grad_output = let out__ = CArray.make t 2 in stubs_cudnn_grid_sampler_backward (CArray.start out__) self grid grad_output; 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 cumprod self ~dim = let out__ = CArray.make t 1 in stubs_cumprod (CArray.start out__) self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cumprod1 self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_cumprod1 (CArray.start out__) self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cumprod_out ~out self ~dim = let out__ = CArray.make t 1 in stubs_cumprod_out (CArray.start out__) out self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cumprod_out1 ~out self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_cumprod_out1 (CArray.start out__) out self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cumsum self ~dim = let out__ = CArray.make t 1 in stubs_cumsum (CArray.start out__) self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cumsum1 self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_cumsum1 (CArray.start out__) self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cumsum_out ~out self ~dim = let out__ = CArray.make t 1 in stubs_cumsum_out (CArray.start out__) out self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let cumsum_out1 ~out self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_cumsum_out1 (CArray.start out__) out self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let dequantize self = let out__ = CArray.make t 1 in stubs_dequantize (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let det self = let out__ = CArray.make t 1 in stubs_det (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let detach self = let out__ = CArray.make t 1 in stubs_detach (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let detach_ self = let out__ = CArray.make t 1 in stubs_detach_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let diag self ~diagonal = let out__ = CArray.make t 1 in stubs_diag (CArray.start out__) self (Int64.of_int diagonal); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let diag_embed self ~offset ~dim1 ~dim2 = let out__ = CArray.make t 1 in stubs_diag_embed (CArray.start out__) self (Int64.of_int offset) (Int64.of_int dim1) (Int64.of_int dim2); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let diag_out ~out self ~diagonal = let out__ = CArray.make t 1 in stubs_diag_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 diagflat self ~offset = let out__ = CArray.make t 1 in stubs_diagflat (CArray.start out__) self (Int64.of_int offset); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let diagonal self ~offset ~dim1 ~dim2 = let out__ = CArray.make t 1 in stubs_diagonal (CArray.start out__) self (Int64.of_int offset) (Int64.of_int dim1) (Int64.of_int dim2); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let digamma self = let out__ = CArray.make t 1 in stubs_digamma (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let digamma_ self = let out__ = CArray.make t 1 in stubs_digamma_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let digamma_out ~out self = let out__ = CArray.make t 1 in stubs_digamma_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let dist self other = let out__ = CArray.make t 1 in stubs_dist (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let div self other = let out__ = CArray.make t 1 in stubs_div (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let div1 self other = let out__ = CArray.make t 1 in stubs_div1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let div_ self other = let out__ = CArray.make t 1 in stubs_div_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let div_1 self other = let out__ = CArray.make t 1 in stubs_div_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let div_out ~out self other = let out__ = CArray.make t 1 in stubs_div_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let dot self tensor = let out__ = CArray.make t 1 in stubs_dot (CArray.start out__) self tensor; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let dot_out ~out self tensor = let out__ = CArray.make t 1 in stubs_dot_out (CArray.start out__) out self tensor; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let dropout input ~p ~train = let out__ = CArray.make t 1 in stubs_dropout (CArray.start out__) input p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let dropout_ self ~p ~train = let out__ = CArray.make t 1 in stubs_dropout_ (CArray.start out__) self p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eig self ~eigenvectors = let out__ = CArray.make t 2 in stubs_eig (CArray.start out__) self (if eigenvectors 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 eig_out ~e ~v self ~eigenvectors = let out__ = CArray.make t 2 in stubs_eig_out (CArray.start out__) e v self (if eigenvectors 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 elu self = let out__ = CArray.make t 1 in stubs_elu (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let elu_ self = let out__ = CArray.make t 1 in stubs_elu_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let elu_backward ~grad_output ~alpha ~scale ~input_scale ~output = let out__ = CArray.make t 1 in stubs_elu_backward (CArray.start out__) grad_output alpha scale input_scale output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let elu_backward_out ~grad_input ~grad_output ~alpha ~scale ~input_scale ~output = let out__ = CArray.make t 1 in stubs_elu_backward_out (CArray.start out__) grad_input grad_output alpha scale input_scale output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let elu_out ~out self = let out__ = CArray.make t 1 in stubs_elu_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let embedding ~weight ~indices ~padding_idx ~scale_grad_by_freq ~sparse = let out__ = CArray.make t 1 in stubs_embedding (CArray.start out__) weight indices (Int64.of_int padding_idx) (if scale_grad_by_freq then 1 else 0) (if sparse then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let embedding_backward ~grad ~indices ~num_weights ~padding_idx ~scale_grad_by_freq ~sparse = let out__ = CArray.make t 1 in stubs_embedding_backward (CArray.start out__) grad indices (Int64.of_int num_weights) (Int64.of_int padding_idx) (if scale_grad_by_freq then 1 else 0) (if sparse then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let embedding_bag ~weight ~indices ~offsets ~scale_grad_by_freq ~mode ~sparse ~per_sample_weights = let out__ = CArray.make t 4 in stubs_embedding_bag (CArray.start out__) weight indices offsets (if scale_grad_by_freq then 1 else 0) (Int64.of_int mode) (if sparse then 1 else 0) (match per_sample_weights with | Some v -> v | None -> null); 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; let t3 = CArray.get out__ 3 in Gc.finalise C.Tensor.free t3; t0, t1, t2, t3 let embedding_dense_backward ~grad_output ~indices ~num_weights ~padding_idx ~scale_grad_by_freq = let out__ = CArray.make t 1 in stubs_embedding_dense_backward (CArray.start out__) grad_output indices (Int64.of_int num_weights) (Int64.of_int padding_idx) (if scale_grad_by_freq then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let embedding_renorm_ self ~indices ~max_norm ~norm_type = let out__ = CArray.make t 1 in stubs_embedding_renorm_ (CArray.start out__) self indices max_norm norm_type; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let embedding_sparse_backward ~grad ~indices ~num_weights ~padding_idx ~scale_grad_by_freq = let out__ = CArray.make t 1 in stubs_embedding_sparse_backward (CArray.start out__) grad indices (Int64.of_int num_weights) (Int64.of_int padding_idx) (if scale_grad_by_freq then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let empty ~size ~options = let out__ = CArray.make t 1 in stubs_empty (CArray.start out__) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let empty_like self = let out__ = CArray.make t 1 in stubs_empty_like (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let empty_like1 self ~options = let out__ = CArray.make t 1 in stubs_empty_like1 (CArray.start out__) self (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let empty_out ~out ~size = let out__ = CArray.make t 1 in stubs_empty_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 let empty_strided ~size ~stride ~options = let out__ = CArray.make t 1 in stubs_empty_strided (CArray.start out__) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eq self other = let out__ = CArray.make t 1 in stubs_eq (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eq1 self other = let out__ = CArray.make t 1 in stubs_eq1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eq_ self other = let out__ = CArray.make t 1 in stubs_eq_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eq_1 self other = let out__ = CArray.make t 1 in stubs_eq_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eq_out ~out self other = let out__ = CArray.make t 1 in stubs_eq_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eq_out1 ~out self other = let out__ = CArray.make t 1 in stubs_eq_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erf self = let out__ = CArray.make t 1 in stubs_erf (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erf_ self = let out__ = CArray.make t 1 in stubs_erf_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erf_out ~out self = let out__ = CArray.make t 1 in stubs_erf_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erfc self = let out__ = CArray.make t 1 in stubs_erfc (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erfc_ self = let out__ = CArray.make t 1 in stubs_erfc_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erfc_out ~out self = let out__ = CArray.make t 1 in stubs_erfc_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erfinv self = let out__ = CArray.make t 1 in stubs_erfinv (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erfinv_ self = let out__ = CArray.make t 1 in stubs_erfinv_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let erfinv_out ~out self = let out__ = CArray.make t 1 in stubs_erfinv_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let exp self = let out__ = CArray.make t 1 in stubs_exp (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let exp_ self = let out__ = CArray.make t 1 in stubs_exp_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let exp_out ~out self = let out__ = CArray.make t 1 in stubs_exp_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let expand self ~size ~implicit = let out__ = CArray.make t 1 in stubs_expand (CArray.start out__) self (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (if implicit then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let expand_as self other = let out__ = CArray.make t 1 in stubs_expand_as (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let expm1 self = let out__ = CArray.make t 1 in stubs_expm1 (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let expm1_ self = let out__ = CArray.make t 1 in stubs_expm1_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let expm1_out ~out self = let out__ = CArray.make t 1 in stubs_expm1_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let exponential_ self ~lambd = let out__ = CArray.make t 1 in stubs_exponential_ (CArray.start out__) self lambd; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eye ~n ~options = let out__ = CArray.make t 1 in stubs_eye (CArray.start out__) (Int64.of_int n) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eye1 ~n ~m ~options = let out__ = CArray.make t 1 in stubs_eye1 (CArray.start out__) (Int64.of_int n) (Int64.of_int m) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eye_out ~out ~n = let out__ = CArray.make t 1 in stubs_eye_out (CArray.start out__) out (Int64.of_int n); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let eye_out1 ~out ~n ~m = let out__ = CArray.make t 1 in stubs_eye_out1 (CArray.start out__) out (Int64.of_int n) (Int64.of_int m); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fbgemm_linear_int8_weight input ~weight ~packed ~col_offsets ~weight_scale ~weight_zero_point ~bias = let out__ = CArray.make t 1 in stubs_fbgemm_linear_int8_weight (CArray.start out__) input weight packed col_offsets weight_scale weight_zero_point bias; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fbgemm_pack_quantized_matrix input ~k ~n = let out__ = CArray.make t 1 in stubs_fbgemm_pack_quantized_matrix (CArray.start out__) input (Int64.of_int k) (Int64.of_int n); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let feature_alpha_dropout input ~p ~train = let out__ = CArray.make t 1 in stubs_feature_alpha_dropout (CArray.start out__) input p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let feature_alpha_dropout_ self ~p ~train = let out__ = CArray.make t 1 in stubs_feature_alpha_dropout_ (CArray.start out__) self p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let feature_dropout input ~p ~train = let out__ = CArray.make t 1 in stubs_feature_dropout (CArray.start out__) input p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let feature_dropout_ self ~p ~train = let out__ = CArray.make t 1 in stubs_feature_dropout_ (CArray.start out__) self p (if train then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fft self ~signal_ndim ~normalized = let out__ = CArray.make t 1 in stubs_fft (CArray.start out__) self (Int64.of_int signal_ndim) (if normalized then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fill_ self ~value = let out__ = CArray.make t 1 in stubs_fill_ (CArray.start out__) self value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fill_1 self ~value = let out__ = CArray.make t 1 in stubs_fill_1 (CArray.start out__) self value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let flatten self ~start_dim ~end_dim = let out__ = CArray.make t 1 in stubs_flatten (CArray.start out__) self (Int64.of_int start_dim) (Int64.of_int end_dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let flip self ~dims = let out__ = CArray.make t 1 in stubs_flip (CArray.start out__) self (List.map Int64.of_int dims |> CArray.of_list int64_t |> CArray.start) (List.length dims); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let floor self = let out__ = CArray.make t 1 in stubs_floor (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let floor_ self = let out__ = CArray.make t 1 in stubs_floor_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let floor_out ~out self = let out__ = CArray.make t 1 in stubs_floor_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fmod self other = let out__ = CArray.make t 1 in stubs_fmod (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fmod1 self other = let out__ = CArray.make t 1 in stubs_fmod1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fmod_ self other = let out__ = CArray.make t 1 in stubs_fmod_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fmod_1 self other = let out__ = CArray.make t 1 in stubs_fmod_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fmod_out ~out self other = let out__ = CArray.make t 1 in stubs_fmod_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fmod_out1 ~out self other = let out__ = CArray.make t 1 in stubs_fmod_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let frac self = let out__ = CArray.make t 1 in stubs_frac (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let frac_ self = let out__ = CArray.make t 1 in stubs_frac_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let frac_out ~out self = let out__ = CArray.make t 1 in stubs_frac_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fractional_max_pool2d self ~kernel_size ~output_size ~random_samples = let out__ = CArray.make t 2 in stubs_fractional_max_pool2d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) random_samples; 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 fractional_max_pool2d_backward ~grad_output self ~kernel_size ~output_size ~indices = let out__ = CArray.make t 1 in stubs_fractional_max_pool2d_backward (CArray.start out__) grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fractional_max_pool2d_backward_out ~grad_input ~grad_output self ~kernel_size ~output_size ~indices = let out__ = CArray.make t 1 in stubs_fractional_max_pool2d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fractional_max_pool2d_out ~output ~indices self ~kernel_size ~output_size ~random_samples = let out__ = CArray.make t 2 in stubs_fractional_max_pool2d_out (CArray.start out__) output indices self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) random_samples; 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 fractional_max_pool3d self ~kernel_size ~output_size ~random_samples = let out__ = CArray.make t 2 in stubs_fractional_max_pool3d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) random_samples; 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 fractional_max_pool3d_backward ~grad_output self ~kernel_size ~output_size ~indices = let out__ = CArray.make t 1 in stubs_fractional_max_pool3d_backward (CArray.start out__) grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fractional_max_pool3d_backward_out ~grad_input ~grad_output self ~kernel_size ~output_size ~indices = let out__ = CArray.make t 1 in stubs_fractional_max_pool3d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let fractional_max_pool3d_out ~output ~indices self ~kernel_size ~output_size ~random_samples = let out__ = CArray.make t 2 in stubs_fractional_max_pool3d_out (CArray.start out__) output indices self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) random_samples; 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 frobenius_norm self = let out__ = CArray.make t 1 in stubs_frobenius_norm (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let frobenius_norm1 self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_frobenius_norm1 (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let frobenius_norm_out ~out self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_frobenius_norm_out (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let full ~size ~fill_value ~options = let out__ = CArray.make t 1 in stubs_full (CArray.start out__) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) fill_value (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let full_like self ~fill_value = let out__ = CArray.make t 1 in stubs_full_like (CArray.start out__) self fill_value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let full_like1 self ~fill_value ~options = let out__ = CArray.make t 1 in stubs_full_like1 (CArray.start out__) self fill_value (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let full_out ~out ~size ~fill_value = let out__ = CArray.make t 1 in stubs_full_out (CArray.start out__) out (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) fill_value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gather self ~dim ~index ~sparse_grad = let out__ = CArray.make t 1 in stubs_gather (CArray.start out__) self (Int64.of_int dim) index (if sparse_grad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gather_out ~out self ~dim ~index ~sparse_grad = let out__ = CArray.make t 1 in stubs_gather_out (CArray.start out__) out self (Int64.of_int dim) index (if sparse_grad then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ge self other = let out__ = CArray.make t 1 in stubs_ge (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ge1 self other = let out__ = CArray.make t 1 in stubs_ge1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ge_ self other = let out__ = CArray.make t 1 in stubs_ge_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ge_1 self other = let out__ = CArray.make t 1 in stubs_ge_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ge_out ~out self other = let out__ = CArray.make t 1 in stubs_ge_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ge_out1 ~out self other = let out__ = CArray.make t 1 in stubs_ge_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gels self ~a = let out__ = CArray.make t 2 in stubs_gels (CArray.start out__) self a; 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 gels_out ~x ~qr self ~a = let out__ = CArray.make t 2 in stubs_gels_out (CArray.start out__) x qr self a; 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 geometric_ self ~p = let out__ = CArray.make t 1 in stubs_geometric_ (CArray.start out__) self p; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let geqrf self = let out__ = CArray.make t 2 in stubs_geqrf (CArray.start out__) self; 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 geqrf_out ~a ~tau self = let out__ = CArray.make t 2 in stubs_geqrf_out (CArray.start out__) a tau self; 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 ger self ~vec2 = let out__ = CArray.make t 1 in stubs_ger (CArray.start out__) self vec2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ger_out ~out self ~vec2 = let out__ = CArray.make t 1 in stubs_ger_out (CArray.start out__) out self vec2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let glu self ~dim = let out__ = CArray.make t 1 in stubs_glu (CArray.start out__) self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let glu_backward ~grad_output self ~dim = let out__ = CArray.make t 1 in stubs_glu_backward (CArray.start out__) grad_output self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let glu_backward_out ~grad_input ~grad_output self ~dim = let out__ = CArray.make t 1 in stubs_glu_backward_out (CArray.start out__) grad_input grad_output self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let glu_out ~out self ~dim = let out__ = CArray.make t 1 in stubs_glu_out (CArray.start out__) out self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let grad self = let out__ = CArray.make t 1 in stubs_grad (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let grid_sampler input ~grid ~interpolation_mode ~padding_mode = let out__ = CArray.make t 1 in stubs_grid_sampler (CArray.start out__) input grid (Int64.of_int interpolation_mode) (Int64.of_int padding_mode); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let grid_sampler_2d input ~grid ~interpolation_mode ~padding_mode = let out__ = CArray.make t 1 in stubs_grid_sampler_2d (CArray.start out__) input grid (Int64.of_int interpolation_mode) (Int64.of_int padding_mode); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let grid_sampler_2d_backward ~grad_output input ~grid ~interpolation_mode ~padding_mode = let out__ = CArray.make t 2 in stubs_grid_sampler_2d_backward (CArray.start out__) grad_output input grid (Int64.of_int interpolation_mode) (Int64.of_int padding_mode); 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 grid_sampler_3d input ~grid ~interpolation_mode ~padding_mode = let out__ = CArray.make t 1 in stubs_grid_sampler_3d (CArray.start out__) input grid (Int64.of_int interpolation_mode) (Int64.of_int padding_mode); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let grid_sampler_3d_backward ~grad_output input ~grid ~interpolation_mode ~padding_mode = let out__ = CArray.make t 2 in stubs_grid_sampler_3d_backward (CArray.start out__) grad_output input grid (Int64.of_int interpolation_mode) (Int64.of_int padding_mode); 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 group_norm input ~num_groups ~weight ~bias ~eps ~cudnn_enabled = let out__ = CArray.make t 1 in stubs_group_norm (CArray.start out__) input (Int64.of_int num_groups) (match weight with | Some v -> v | None -> null) (match bias with | Some v -> v | None -> null) eps (if cudnn_enabled then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gru input ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional ~batch_first = let out__ = CArray.make t 2 in stubs_gru (CArray.start out__) input hx (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional then 1 else 0) (if batch_first 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 gru1 ~data ~batch_sizes ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional = let out__ = CArray.make t 2 in stubs_gru1 (CArray.start out__) data batch_sizes hx (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional 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 gru_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh = let out__ = CArray.make t 1 in stubs_gru_cell (CArray.start out__) input hx w_ih w_hh (match b_ih with | Some v -> v | None -> null) (match b_hh with | Some v -> v | None -> null); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gt self other = let out__ = CArray.make t 1 in stubs_gt (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gt1 self other = let out__ = CArray.make t 1 in stubs_gt1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gt_ self other = let out__ = CArray.make t 1 in stubs_gt_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gt_1 self other = let out__ = CArray.make t 1 in stubs_gt_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gt_out ~out self other = let out__ = CArray.make t 1 in stubs_gt_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let gt_out1 ~out self other = let out__ = CArray.make t 1 in stubs_gt_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hamming_window ~window_length ~options = let out__ = CArray.make t 1 in stubs_hamming_window (CArray.start out__) (Int64.of_int window_length) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hamming_window1 ~window_length ~periodic ~options = let out__ = CArray.make t 1 in stubs_hamming_window1 (CArray.start out__) (Int64.of_int window_length) (if periodic then 1 else 0) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hamming_window2 ~window_length ~periodic ~alpha ~options = let out__ = CArray.make t 1 in stubs_hamming_window2 (CArray.start out__) (Int64.of_int window_length) (if periodic then 1 else 0) alpha (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hamming_window3 ~window_length ~periodic ~alpha ~beta ~options = let out__ = CArray.make t 1 in stubs_hamming_window3 (CArray.start out__) (Int64.of_int window_length) (if periodic then 1 else 0) alpha beta (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hann_window ~window_length ~options = let out__ = CArray.make t 1 in stubs_hann_window (CArray.start out__) (Int64.of_int window_length) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hann_window1 ~window_length ~periodic ~options = let out__ = CArray.make t 1 in stubs_hann_window1 (CArray.start out__) (Int64.of_int window_length) (if periodic then 1 else 0) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hardshrink self = let out__ = CArray.make t 1 in stubs_hardshrink (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hardshrink_backward ~grad_out self ~lambd = let out__ = CArray.make t 1 in stubs_hardshrink_backward (CArray.start out__) grad_out self lambd; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hardtanh self = let out__ = CArray.make t 1 in stubs_hardtanh (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hardtanh_ self = let out__ = CArray.make t 1 in stubs_hardtanh_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hardtanh_backward ~grad_output self ~min_val ~max_val = let out__ = CArray.make t 1 in stubs_hardtanh_backward (CArray.start out__) grad_output self min_val max_val; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hardtanh_backward_out ~grad_input ~grad_output self ~min_val ~max_val = let out__ = CArray.make t 1 in stubs_hardtanh_backward_out (CArray.start out__) grad_input grad_output self min_val max_val; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hardtanh_out ~out self = let out__ = CArray.make t 1 in stubs_hardtanh_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hinge_embedding_loss self ~target ~margin ~reduction = let out__ = CArray.make t 1 in stubs_hinge_embedding_loss (CArray.start out__) self target margin (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let histc self ~bins = let out__ = CArray.make t 1 in stubs_histc (CArray.start out__) self (Int64.of_int bins); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let histc_out ~out self ~bins = let out__ = CArray.make t 1 in stubs_histc_out (CArray.start out__) out self (Int64.of_int bins); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hspmm ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_hspmm (CArray.start out__) mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let hspmm_out ~out ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_hspmm_out (CArray.start out__) out mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ifft self ~signal_ndim ~normalized = let out__ = CArray.make t 1 in stubs_ifft (CArray.start out__) self (Int64.of_int signal_ndim) (if normalized then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index self ~indices = let out__ = CArray.make t 1 in stubs_index (CArray.start out__) self (CArray.of_list t indices |> CArray.start) (List.length indices); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_add self ~dim ~index ~source = let out__ = CArray.make t 1 in stubs_index_add (CArray.start out__) self (Int64.of_int dim) index source; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_add_ self ~dim ~index ~source = let out__ = CArray.make t 1 in stubs_index_add_ (CArray.start out__) self (Int64.of_int dim) index source; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_copy self ~dim ~index ~source = let out__ = CArray.make t 1 in stubs_index_copy (CArray.start out__) self (Int64.of_int dim) index source; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_copy_ self ~dim ~index ~source = let out__ = CArray.make t 1 in stubs_index_copy_ (CArray.start out__) self (Int64.of_int dim) index source; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_fill self ~dim ~index ~value = let out__ = CArray.make t 1 in stubs_index_fill (CArray.start out__) self (Int64.of_int dim) index value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_fill1 self ~dim ~index ~value = let out__ = CArray.make t 1 in stubs_index_fill1 (CArray.start out__) self (Int64.of_int dim) index value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_fill_ self ~dim ~index ~value = let out__ = CArray.make t 1 in stubs_index_fill_ (CArray.start out__) self (Int64.of_int dim) index value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_fill_1 self ~dim ~index ~value = let out__ = CArray.make t 1 in stubs_index_fill_1 (CArray.start out__) self (Int64.of_int dim) index value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_put self ~indices ~values ~accumulate = let out__ = CArray.make t 1 in stubs_index_put (CArray.start out__) self (CArray.of_list t indices |> CArray.start) (List.length indices) values (if accumulate then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_put_ self ~indices ~values ~accumulate = let out__ = CArray.make t 1 in stubs_index_put_ (CArray.start out__) self (CArray.of_list t indices |> CArray.start) (List.length indices) values (if accumulate then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_select self ~dim ~index = let out__ = CArray.make t 1 in stubs_index_select (CArray.start out__) self (Int64.of_int dim) index; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let index_select_out ~out self ~dim ~index = let out__ = CArray.make t 1 in stubs_index_select_out (CArray.start out__) out self (Int64.of_int dim) index; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let indices self = let out__ = CArray.make t 1 in stubs_indices (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let instance_norm input ~weight ~bias ~running_mean ~running_var ~use_input_stats ~momentum ~eps ~cudnn_enabled = let out__ = CArray.make t 1 in stubs_instance_norm (CArray.start out__) input (match weight with | Some v -> v | None -> null) (match bias with | Some v -> v | None -> null) (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) (if use_input_stats then 1 else 0) momentum eps (if cudnn_enabled then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let int_repr self = let out__ = CArray.make t 1 in stubs_int_repr (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let inverse self = let out__ = CArray.make t 1 in stubs_inverse (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let inverse_out ~out self = let out__ = CArray.make t 1 in stubs_inverse_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let irfft self ~signal_ndim ~normalized ~onesided ~signal_sizes = let out__ = CArray.make t 1 in stubs_irfft (CArray.start out__) self (Int64.of_int signal_ndim) (if normalized then 1 else 0) (if onesided then 1 else 0) (List.map Int64.of_int signal_sizes |> CArray.of_list int64_t |> CArray.start) (List.length signal_sizes); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let isclose self other ~rtol ~atol ~equal_nan = let out__ = CArray.make t 1 in stubs_isclose (CArray.start out__) self other rtol atol (if equal_nan then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let isnan self = let out__ = CArray.make t 1 in stubs_isnan (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let kl_div self ~target ~reduction = let out__ = CArray.make t 1 in stubs_kl_div (CArray.start out__) self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let kl_div_backward ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_kl_div_backward (CArray.start out__) grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let kthvalue self ~k ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_kthvalue (CArray.start out__) self (Int64.of_int k) (Int64.of_int dim) (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 kthvalue_out ~values ~indices self ~k ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_kthvalue_out (CArray.start out__) values indices self (Int64.of_int k) (Int64.of_int dim) (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 l1_loss self ~target ~reduction = let out__ = CArray.make t 1 in stubs_l1_loss (CArray.start out__) self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let l1_loss_backward ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_l1_loss_backward (CArray.start out__) grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let l1_loss_backward_out ~grad_input ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_l1_loss_backward_out (CArray.start out__) grad_input grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let l1_loss_out ~out self ~target ~reduction = let out__ = CArray.make t 1 in stubs_l1_loss_out (CArray.start out__) out self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let layer_norm input ~normalized_shape ~weight ~bias ~eps ~cudnn_enable = let out__ = CArray.make t 1 in stubs_layer_norm (CArray.start out__) input (List.map Int64.of_int normalized_shape |> CArray.of_list int64_t |> CArray.start) (List.length normalized_shape) (match weight with | Some v -> v | None -> null) (match bias with | Some v -> v | None -> null) eps (if cudnn_enable then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let le self other = let out__ = CArray.make t 1 in stubs_le (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let le1 self other = let out__ = CArray.make t 1 in stubs_le1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let le_ self other = let out__ = CArray.make t 1 in stubs_le_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let le_1 self other = let out__ = CArray.make t 1 in stubs_le_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let le_out ~out self other = let out__ = CArray.make t 1 in stubs_le_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let le_out1 ~out self other = let out__ = CArray.make t 1 in stubs_le_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let leaky_relu self = let out__ = CArray.make t 1 in stubs_leaky_relu (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let leaky_relu_ self = let out__ = CArray.make t 1 in stubs_leaky_relu_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let leaky_relu_backward ~grad_output self ~negative_slope = let out__ = CArray.make t 1 in stubs_leaky_relu_backward (CArray.start out__) grad_output self negative_slope; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let leaky_relu_backward_out ~grad_input ~grad_output self ~negative_slope = let out__ = CArray.make t 1 in stubs_leaky_relu_backward_out (CArray.start out__) grad_input grad_output self negative_slope; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let leaky_relu_out ~out self = let out__ = CArray.make t 1 in stubs_leaky_relu_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lerp self ~end_ ~weight = let out__ = CArray.make t 1 in stubs_lerp (CArray.start out__) self end_ weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lerp1 self ~end_ ~weight = let out__ = CArray.make t 1 in stubs_lerp1 (CArray.start out__) self end_ weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lerp_ self ~end_ ~weight = let out__ = CArray.make t 1 in stubs_lerp_ (CArray.start out__) self end_ weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lerp_1 self ~end_ ~weight = let out__ = CArray.make t 1 in stubs_lerp_1 (CArray.start out__) self end_ weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lerp_out ~out self ~end_ ~weight = let out__ = CArray.make t 1 in stubs_lerp_out (CArray.start out__) out self end_ weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lerp_out1 ~out self ~end_ ~weight = let out__ = CArray.make t 1 in stubs_lerp_out1 (CArray.start out__) out self end_ weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lgamma self = let out__ = CArray.make t 1 in stubs_lgamma (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lgamma_ self = let out__ = CArray.make t 1 in stubs_lgamma_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lgamma_out ~out self = let out__ = CArray.make t 1 in stubs_lgamma_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let linear input ~weight ~bias = let out__ = CArray.make t 1 in stubs_linear (CArray.start out__) input weight (match bias with | Some v -> v | None -> null); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let linspace ~start ~end_ ~steps ~options = let out__ = CArray.make t 1 in stubs_linspace (CArray.start out__) start end_ (Int64.of_int steps) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let linspace_out ~out ~start ~end_ ~steps = let out__ = CArray.make t 1 in stubs_linspace_out (CArray.start out__) out start end_ (Int64.of_int steps); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log self = let out__ = CArray.make t 1 in stubs_log (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log10 self = let out__ = CArray.make t 1 in stubs_log10 (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log10_ self = let out__ = CArray.make t 1 in stubs_log10_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log10_out ~out self = let out__ = CArray.make t 1 in stubs_log10_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log1p self = let out__ = CArray.make t 1 in stubs_log1p (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log1p_ self = let out__ = CArray.make t 1 in stubs_log1p_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log1p_out ~out self = let out__ = CArray.make t 1 in stubs_log1p_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log2 self = let out__ = CArray.make t 1 in stubs_log2 (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log2_ self = let out__ = CArray.make t 1 in stubs_log2_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log2_out ~out self = let out__ = CArray.make t 1 in stubs_log2_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_ self = let out__ = CArray.make t 1 in stubs_log_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_normal_ self ~mean ~std = let out__ = CArray.make t 1 in stubs_log_normal_ (CArray.start out__) self mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_out ~out self = let out__ = CArray.make t 1 in stubs_log_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_sigmoid self = let out__ = CArray.make t 1 in stubs_log_sigmoid (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_sigmoid_backward ~grad_output self ~buffer = let out__ = CArray.make t 1 in stubs_log_sigmoid_backward (CArray.start out__) grad_output self buffer; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_sigmoid_backward_out ~grad_input ~grad_output self ~buffer = let out__ = CArray.make t 1 in stubs_log_sigmoid_backward_out (CArray.start out__) grad_input grad_output self buffer; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_sigmoid_out ~out self = let out__ = CArray.make t 1 in stubs_log_sigmoid_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_softmax self ~dim = let out__ = CArray.make t 1 in stubs_log_softmax (CArray.start out__) self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let log_softmax1 self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_log_softmax1 (CArray.start out__) self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let logdet self = let out__ = CArray.make t 1 in stubs_logdet (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let logspace ~start ~end_ ~steps ~base ~options = let out__ = CArray.make t 1 in stubs_logspace (CArray.start out__) start end_ (Int64.of_int steps) base (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let logspace_out ~out ~start ~end_ ~steps ~base = let out__ = CArray.make t 1 in stubs_logspace_out (CArray.start out__) out start end_ (Int64.of_int steps) base; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let logsumexp self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_logsumexp (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let logsumexp_out ~out self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_logsumexp_out (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lstm input ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional ~batch_first = let out__ = CArray.make t 3 in stubs_lstm (CArray.start out__) input (CArray.of_list t hx |> CArray.start) (List.length hx) (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional then 1 else 0) (if batch_first 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 lstm1 ~data ~batch_sizes ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional = let out__ = CArray.make t 3 in stubs_lstm1 (CArray.start out__) data batch_sizes (CArray.of_list t hx |> CArray.start) (List.length hx) (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional 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 lstm_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh = let out__ = CArray.make t 2 in stubs_lstm_cell (CArray.start out__) input (CArray.of_list t hx |> CArray.start) (List.length hx) w_ih w_hh (match b_ih with | Some v -> v | None -> null) (match b_hh with | Some v -> v | None -> null); 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 lt self other = let out__ = CArray.make t 1 in stubs_lt (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lt1 self other = let out__ = CArray.make t 1 in stubs_lt1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lt_ self other = let out__ = CArray.make t 1 in stubs_lt_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lt_1 self other = let out__ = CArray.make t 1 in stubs_lt_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lt_out ~out self other = let out__ = CArray.make t 1 in stubs_lt_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lt_out1 ~out self other = let out__ = CArray.make t 1 in stubs_lt_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lu_solve self ~lu_data ~lu_pivots = let out__ = CArray.make t 1 in stubs_lu_solve (CArray.start out__) self lu_data lu_pivots; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let lu_solve_out ~out self ~lu_data ~lu_pivots = let out__ = CArray.make t 1 in stubs_lu_solve_out (CArray.start out__) out self lu_data lu_pivots; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let margin_ranking_loss ~input1 ~input2 ~target ~margin ~reduction = let out__ = CArray.make t 1 in stubs_margin_ranking_loss (CArray.start out__) input1 input2 target margin (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_fill self ~mask ~value = let out__ = CArray.make t 1 in stubs_masked_fill (CArray.start out__) self mask value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_fill1 self ~mask ~value = let out__ = CArray.make t 1 in stubs_masked_fill1 (CArray.start out__) self mask value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_fill_ self ~mask ~value = let out__ = CArray.make t 1 in stubs_masked_fill_ (CArray.start out__) self mask value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_fill_1 self ~mask ~value = let out__ = CArray.make t 1 in stubs_masked_fill_1 (CArray.start out__) self mask value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_scatter self ~mask ~source = let out__ = CArray.make t 1 in stubs_masked_scatter (CArray.start out__) self mask source; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_scatter_ self ~mask ~source = let out__ = CArray.make t 1 in stubs_masked_scatter_ (CArray.start out__) self mask source; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_select self ~mask = let out__ = CArray.make t 1 in stubs_masked_select (CArray.start out__) self mask; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let masked_select_out ~out self ~mask = let out__ = CArray.make t 1 in stubs_masked_select_out (CArray.start out__) out self mask; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let matmul self other = let out__ = CArray.make t 1 in stubs_matmul (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let matmul_out ~out self other = let out__ = CArray.make t 1 in stubs_matmul_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let matrix_power self ~n = let out__ = CArray.make t 1 in stubs_matrix_power (CArray.start out__) self (Int64.of_int n); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let matrix_rank self ~symmetric = let out__ = CArray.make t 1 in stubs_matrix_rank (CArray.start out__) self (if symmetric then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let matrix_rank1 self ~tol ~symmetric = let out__ = CArray.make t 1 in stubs_matrix_rank1 (CArray.start out__) self tol (if symmetric then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max self = let out__ = CArray.make t 1 in stubs_max (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max1 self other = let out__ = CArray.make t 1 in stubs_max1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max2 self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_max2 (CArray.start out__) self (Int64.of_int dim) (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 max_out ~out self other = let out__ = CArray.make t 1 in stubs_max_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_out1 ~max ~max_values self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_max_out1 (CArray.start out__) max max_values self (Int64.of_int dim) (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 max_pool1d self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 1 in stubs_max_pool1d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_pool1d_with_indices self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 2 in stubs_max_pool1d_with_indices (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode 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 max_pool2d self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 1 in stubs_max_pool2d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_pool2d_with_indices self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 2 in stubs_max_pool2d_with_indices (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode 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 max_pool2d_with_indices_backward ~grad_output self ~kernel_size ~stride ~padding ~dilation ~ceil_mode ~indices = let out__ = CArray.make t 1 in stubs_max_pool2d_with_indices_backward (CArray.start out__) grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_pool2d_with_indices_backward_out ~grad_input ~grad_output self ~kernel_size ~stride ~padding ~dilation ~ceil_mode ~indices = let out__ = CArray.make t 1 in stubs_max_pool2d_with_indices_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_pool2d_with_indices_out ~output ~indices self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 2 in stubs_max_pool2d_with_indices_out (CArray.start out__) output indices self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode 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 max_pool3d self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 1 in stubs_max_pool3d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_pool3d_with_indices self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 2 in stubs_max_pool3d_with_indices (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode 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 max_pool3d_with_indices_backward ~grad_output self ~kernel_size ~stride ~padding ~dilation ~ceil_mode ~indices = let out__ = CArray.make t 1 in stubs_max_pool3d_with_indices_backward (CArray.start out__) grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_pool3d_with_indices_backward_out ~grad_input ~grad_output self ~kernel_size ~stride ~padding ~dilation ~ceil_mode ~indices = let out__ = CArray.make t 1 in stubs_max_pool3d_with_indices_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0) indices; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_pool3d_with_indices_out ~output ~indices self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 2 in stubs_max_pool3d_with_indices_out (CArray.start out__) output indices self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode 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 max_unpool2d self ~indices ~output_size = let out__ = CArray.make t 1 in stubs_max_unpool2d (CArray.start out__) self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_unpool2d_backward ~grad_output self ~indices ~output_size = let out__ = CArray.make t 1 in stubs_max_unpool2d_backward (CArray.start out__) grad_output self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_unpool2d_backward_out ~grad_input ~grad_output self ~indices ~output_size = let out__ = CArray.make t 1 in stubs_max_unpool2d_backward_out (CArray.start out__) grad_input grad_output self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_unpool2d_out ~out self ~indices ~output_size = let out__ = CArray.make t 1 in stubs_max_unpool2d_out (CArray.start out__) out self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_unpool3d self ~indices ~output_size ~stride ~padding = let out__ = CArray.make t 1 in stubs_max_unpool3d (CArray.start out__) self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_unpool3d_backward ~grad_output self ~indices ~output_size ~stride ~padding = let out__ = CArray.make t 1 in stubs_max_unpool3d_backward (CArray.start out__) grad_output self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_unpool3d_backward_out ~grad_input ~grad_output self ~indices ~output_size ~stride ~padding = let out__ = CArray.make t 1 in stubs_max_unpool3d_backward_out (CArray.start out__) grad_input grad_output self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_unpool3d_out ~out self ~indices ~output_size ~stride ~padding = let out__ = CArray.make t 1 in stubs_max_unpool3d_out (CArray.start out__) out self indices (List.map Int64.of_int output_size |> CArray.of_list int64_t |> CArray.start) (List.length output_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let max_values self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_max_values (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean self = let out__ = CArray.make t 1 in stubs_mean (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean1 self ~dtype = let out__ = CArray.make t 1 in stubs_mean1 (CArray.start out__) self (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean2 self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_mean2 (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean3 self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_mean3 (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean4 self ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_mean4 (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean_out ~out self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_mean_out (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean_out1 ~out self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_mean_out1 (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mean_out2 ~out self ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_mean_out2 (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let median self = let out__ = CArray.make t 1 in stubs_median (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let median1 self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_median1 (CArray.start out__) self (Int64.of_int dim) (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 median_out ~values ~indices self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_median_out (CArray.start out__) values indices self (Int64.of_int dim) (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 meshgrid tensors = stubs_meshgrid (CArray.of_list t tensors |> CArray.start) (List.length tensors) |> to_tensor_list let min self = let out__ = CArray.make t 1 in stubs_min (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let min1 self other = let out__ = CArray.make t 1 in stubs_min1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let min2 self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_min2 (CArray.start out__) self (Int64.of_int dim) (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 min_out ~out self other = let out__ = CArray.make t 1 in stubs_min_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let min_out1 ~min ~min_indices self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_min_out1 (CArray.start out__) min min_indices self (Int64.of_int dim) (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 min_values self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_min_values (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_batch_norm input ~weight ~bias ~running_mean ~running_var ~training ~exponential_average_factor ~epsilon = let out__ = CArray.make t 3 in stubs_miopen_batch_norm (CArray.start out__) input weight (match bias with | Some v -> v | None -> null) (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) (if training then 1 else 0) exponential_average_factor epsilon; 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 miopen_batch_norm_backward input ~grad_output ~weight ~running_mean ~running_var ~save_mean ~save_var ~epsilon = let out__ = CArray.make t 3 in stubs_miopen_batch_norm_backward (CArray.start out__) input grad_output weight (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) (match save_mean with | Some v -> v | None -> null) (match save_var with | Some v -> v | None -> null) epsilon; 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 miopen_convolution self ~weight ~bias ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_convolution (CArray.start out__) self weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_convolution_backward_bias ~grad_output = let out__ = CArray.make t 1 in stubs_miopen_convolution_backward_bias (CArray.start out__) grad_output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_convolution_backward_input ~self_size ~grad_output ~weight ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_convolution_backward_input (CArray.start out__) (List.map Int64.of_int self_size |> CArray.of_list int64_t |> CArray.start) (List.length self_size) grad_output weight (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_convolution_backward_weight ~weight_size ~grad_output self ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_convolution_backward_weight (CArray.start out__) (List.map Int64.of_int weight_size |> CArray.of_list int64_t |> CArray.start) (List.length weight_size) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_convolution_transpose self ~weight ~bias ~padding ~output_padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_convolution_transpose (CArray.start out__) self weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int output_padding |> CArray.of_list int64_t |> CArray.start) (List.length output_padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_convolution_transpose_backward_input ~grad_output ~weight ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_convolution_transpose_backward_input (CArray.start out__) grad_output weight (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_convolution_transpose_backward_weight ~weight_size ~grad_output self ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_convolution_transpose_backward_weight (CArray.start out__) (List.map Int64.of_int weight_size |> CArray.of_list int64_t |> CArray.start) (List.length weight_size) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_depthwise_convolution self ~weight ~bias ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_depthwise_convolution (CArray.start out__) self weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_depthwise_convolution_backward_input ~self_size ~grad_output ~weight ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_depthwise_convolution_backward_input (CArray.start out__) (List.map Int64.of_int self_size |> CArray.of_list int64_t |> CArray.start) (List.length self_size) grad_output weight (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let miopen_depthwise_convolution_backward_weight ~weight_size ~grad_output self ~padding ~stride ~dilation ~groups ~benchmark ~deterministic = let out__ = CArray.make t 1 in stubs_miopen_depthwise_convolution_backward_weight (CArray.start out__) (List.map Int64.of_int weight_size |> CArray.of_list int64_t |> CArray.start) (List.length weight_size) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if benchmark then 1 else 0) (if deterministic then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mkldnn_convolution self ~weight ~bias ~padding ~stride ~dilation ~groups = let out__ = CArray.make t 1 in stubs_mkldnn_convolution (CArray.start out__) self weight (match bias with | Some v -> v | None -> null) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mkldnn_convolution_backward_input ~self_size ~grad_output ~weight ~padding ~stride ~dilation ~groups ~bias_defined = let out__ = CArray.make t 1 in stubs_mkldnn_convolution_backward_input (CArray.start out__) (List.map Int64.of_int self_size |> CArray.of_list int64_t |> CArray.start) (List.length self_size) grad_output weight (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if bias_defined then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mkldnn_convolution_backward_weights ~weight_size ~grad_output self ~padding ~stride ~dilation ~groups ~bias_defined = let out__ = CArray.make t 2 in stubs_mkldnn_convolution_backward_weights (CArray.start out__) (List.map Int64.of_int weight_size |> CArray.of_list int64_t |> CArray.start) (List.length weight_size) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups) (if bias_defined 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 mkldnn_linear input ~weight ~bias = let out__ = CArray.make t 1 in stubs_mkldnn_linear (CArray.start out__) input weight (match bias with | Some v -> v | None -> null); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mkldnn_max_pool2d self ~kernel_size ~stride ~padding ~dilation ~ceil_mode = let out__ = CArray.make t 1 in stubs_mkldnn_max_pool2d (CArray.start out__) self (List.map Int64.of_int kernel_size |> CArray.of_list int64_t |> CArray.start) (List.length kernel_size) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (if ceil_mode then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mkldnn_reorder_conv2d_weight self ~padding ~stride ~dilation ~groups = let out__ = CArray.make t 1 in stubs_mkldnn_reorder_conv2d_weight (CArray.start out__) self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding) (List.map Int64.of_int stride |> CArray.of_list int64_t |> CArray.start) (List.length stride) (List.map Int64.of_int dilation |> CArray.of_list int64_t |> CArray.start) (List.length dilation) (Int64.of_int groups); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mkldnn_reshape self ~shape = let out__ = CArray.make t 1 in stubs_mkldnn_reshape (CArray.start out__) self (List.map Int64.of_int shape |> CArray.of_list int64_t |> CArray.start) (List.length shape); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mm self ~mat2 = let out__ = CArray.make t 1 in stubs_mm (CArray.start out__) self mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mm_out ~out self ~mat2 = let out__ = CArray.make t 1 in stubs_mm_out (CArray.start out__) out self mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mode self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_mode (CArray.start out__) self (Int64.of_int dim) (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 mode_out ~values ~indices self ~dim ~keepdim = let out__ = CArray.make t 2 in stubs_mode_out (CArray.start out__) values indices self (Int64.of_int dim) (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 mse_loss self ~target ~reduction = let out__ = CArray.make t 1 in stubs_mse_loss (CArray.start out__) self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mse_loss_backward ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_mse_loss_backward (CArray.start out__) grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mse_loss_backward_out ~grad_input ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_mse_loss_backward_out (CArray.start out__) grad_input grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mse_loss_out ~out self ~target ~reduction = let out__ = CArray.make t 1 in stubs_mse_loss_out (CArray.start out__) out self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mul self other = let out__ = CArray.make t 1 in stubs_mul (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mul1 self other = let out__ = CArray.make t 1 in stubs_mul1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mul_ self other = let out__ = CArray.make t 1 in stubs_mul_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mul_1 self other = let out__ = CArray.make t 1 in stubs_mul_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mul_out ~out self other = let out__ = CArray.make t 1 in stubs_mul_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multi_margin_loss_backward ~grad_output self ~target ~p ~margin ~weight ~reduction = let out__ = CArray.make t 1 in stubs_multi_margin_loss_backward (CArray.start out__) grad_output self target p margin weight (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multi_margin_loss_backward_out ~grad_input ~grad_output self ~target ~p ~margin ~weight ~reduction = let out__ = CArray.make t 1 in stubs_multi_margin_loss_backward_out (CArray.start out__) grad_input grad_output self target p margin weight (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multilabel_margin_loss self ~target ~reduction = let out__ = CArray.make t 1 in stubs_multilabel_margin_loss (CArray.start out__) self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multilabel_margin_loss_backward ~grad_output self ~target ~reduction ~is_target = let out__ = CArray.make t 1 in stubs_multilabel_margin_loss_backward (CArray.start out__) grad_output self target (Int64.of_int reduction) is_target; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multilabel_margin_loss_backward_out ~grad_input ~grad_output self ~target ~reduction ~is_target = let out__ = CArray.make t 1 in stubs_multilabel_margin_loss_backward_out (CArray.start out__) grad_input grad_output self target (Int64.of_int reduction) is_target; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multilabel_margin_loss_out ~out self ~target ~reduction = let out__ = CArray.make t 1 in stubs_multilabel_margin_loss_out (CArray.start out__) out self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multinomial self ~num_samples ~replacement = let out__ = CArray.make t 1 in stubs_multinomial (CArray.start out__) self (Int64.of_int num_samples) (if replacement then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let multinomial_out ~out self ~num_samples ~replacement = let out__ = CArray.make t 1 in stubs_multinomial_out (CArray.start out__) out self (Int64.of_int num_samples) (if replacement then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mv self ~vec = let out__ = CArray.make t 1 in stubs_mv (CArray.start out__) self vec; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mv_out ~out self ~vec = let out__ = CArray.make t 1 in stubs_mv_out (CArray.start out__) out self vec; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mvlgamma self ~p = let out__ = CArray.make t 1 in stubs_mvlgamma (CArray.start out__) self (Int64.of_int p); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let mvlgamma_ self ~p = let out__ = CArray.make t 1 in stubs_mvlgamma_ (CArray.start out__) self (Int64.of_int p); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let narrow self ~dim ~start ~length = let out__ = CArray.make t 1 in stubs_narrow (CArray.start out__) self (Int64.of_int dim) (Int64.of_int start) (Int64.of_int length); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let narrow_copy self ~dim ~start ~length = let out__ = CArray.make t 1 in stubs_narrow_copy (CArray.start out__) self (Int64.of_int dim) (Int64.of_int start) (Int64.of_int length); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let native_batch_norm input ~weight ~bias ~running_mean ~running_var ~training ~momentum ~eps = let out__ = CArray.make t 3 in stubs_native_batch_norm (CArray.start out__) input (match weight with | Some v -> v | None -> null) (match bias with | Some v -> v | None -> null) (match running_mean with | Some v -> v | None -> null) (match running_var with | Some v -> v | None -> null) (if training then 1 else 0) momentum eps; 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 native_norm self = let out__ = CArray.make t 1 in stubs_native_norm (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ne self other = let out__ = CArray.make t 1 in stubs_ne (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ne1 self other = let out__ = CArray.make t 1 in stubs_ne1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ne_ self other = let out__ = CArray.make t 1 in stubs_ne_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ne_1 self other = let out__ = CArray.make t 1 in stubs_ne_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ne_out ~out self other = let out__ = CArray.make t 1 in stubs_ne_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ne_out1 ~out self other = let out__ = CArray.make t 1 in stubs_ne_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let neg self = let out__ = CArray.make t 1 in stubs_neg (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let neg_ self = let out__ = CArray.make t 1 in stubs_neg_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let neg_out ~out self = let out__ = CArray.make t 1 in stubs_neg_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss self ~target ~weight ~reduction ~ignore_index = let out__ = CArray.make t 1 in stubs_nll_loss (CArray.start out__) self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss2d self ~target ~weight ~reduction ~ignore_index = let out__ = CArray.make t 1 in stubs_nll_loss2d (CArray.start out__) self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss2d_backward ~grad_output self ~target ~weight ~reduction ~ignore_index ~total_weight = let out__ = CArray.make t 1 in stubs_nll_loss2d_backward (CArray.start out__) grad_output self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index) total_weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss2d_backward_out ~grad_input ~grad_output self ~target ~weight ~reduction ~ignore_index ~total_weight = let out__ = CArray.make t 1 in stubs_nll_loss2d_backward_out (CArray.start out__) grad_input grad_output self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index) total_weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss2d_out ~out self ~target ~weight ~reduction ~ignore_index = let out__ = CArray.make t 1 in stubs_nll_loss2d_out (CArray.start out__) out self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss_backward ~grad_output self ~target ~weight ~reduction ~ignore_index ~total_weight = let out__ = CArray.make t 1 in stubs_nll_loss_backward (CArray.start out__) grad_output self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index) total_weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss_backward_out ~grad_input ~grad_output self ~target ~weight ~reduction ~ignore_index ~total_weight = let out__ = CArray.make t 1 in stubs_nll_loss_backward_out (CArray.start out__) grad_input grad_output self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index) total_weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nll_loss_out ~out self ~target ~weight ~reduction ~ignore_index = let out__ = CArray.make t 1 in stubs_nll_loss_out (CArray.start out__) out self target (match weight with | Some v -> v | None -> null) (Int64.of_int reduction) (Int64.of_int ignore_index); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nonzero self = let out__ = CArray.make t 1 in stubs_nonzero (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nonzero_out ~out self = let out__ = CArray.make t 1 in stubs_nonzero_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let norm self = let out__ = CArray.make t 1 in stubs_norm (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let norm1 self ~p ~dtype = let out__ = CArray.make t 1 in stubs_norm1 (CArray.start out__) self p (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let norm2 self ~p ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_norm2 (CArray.start out__) self p (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let norm3 self ~p ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_norm3 (CArray.start out__) self p (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let norm_except_dim ~v ~pow ~dim = let out__ = CArray.make t 1 in stubs_norm_except_dim (CArray.start out__) v (Int64.of_int pow) (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let norm_out ~out self ~p ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_norm_out (CArray.start out__) out self p (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let norm_out1 ~out self ~p ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_norm_out1 (CArray.start out__) out self p (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let normal ~mean ~std = let out__ = CArray.make t 1 in stubs_normal (CArray.start out__) mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let normal1 ~mean ~std = let out__ = CArray.make t 1 in stubs_normal1 (CArray.start out__) mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let normal2 ~mean ~std = let out__ = CArray.make t 1 in stubs_normal2 (CArray.start out__) mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let normal_ self ~mean ~std = let out__ = CArray.make t 1 in stubs_normal_ (CArray.start out__) self mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let normal_out ~out ~mean ~std = let out__ = CArray.make t 1 in stubs_normal_out (CArray.start out__) out mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let normal_out1 ~out ~mean ~std = let out__ = CArray.make t 1 in stubs_normal_out1 (CArray.start out__) out mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let normal_out2 ~out ~mean ~std = let out__ = CArray.make t 1 in stubs_normal_out2 (CArray.start out__) out mean std; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nuclear_norm self ~keepdim = let out__ = CArray.make t 1 in stubs_nuclear_norm (CArray.start out__) self (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let nuclear_norm_out ~out self ~keepdim = let out__ = CArray.make t 1 in stubs_nuclear_norm_out (CArray.start out__) out self (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let one_hot self ~num_classes = let out__ = CArray.make t 1 in stubs_one_hot (CArray.start out__) self (Int64.of_int num_classes); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ones ~size ~options = let out__ = CArray.make t 1 in stubs_ones (CArray.start out__) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ones_like self = let out__ = CArray.make t 1 in stubs_ones_like (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ones_like1 self ~options = let out__ = CArray.make t 1 in stubs_ones_like1 (CArray.start out__) self (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ones_out ~out ~size = let out__ = CArray.make t 1 in stubs_ones_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 let orgqr self ~input2 = let out__ = CArray.make t 1 in stubs_orgqr (CArray.start out__) self input2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let orgqr_out ~out self ~input2 = let out__ = CArray.make t 1 in stubs_orgqr_out (CArray.start out__) out self input2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ormqr self ~input2 ~input3 ~left ~transpose = let out__ = CArray.make t 1 in stubs_ormqr (CArray.start out__) self input2 input3 (if left then 1 else 0) (if transpose then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let ormqr_out ~out self ~input2 ~input3 ~left ~transpose = let out__ = CArray.make t 1 in stubs_ormqr_out (CArray.start out__) out self input2 input3 (if left then 1 else 0) (if transpose then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pairwise_distance ~x1 ~x2 ~p ~eps ~keepdim = let out__ = CArray.make t 1 in stubs_pairwise_distance (CArray.start out__) x1 x2 p eps (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pdist self ~p = let out__ = CArray.make t 1 in stubs_pdist (CArray.start out__) self p; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let permute self ~dims = let out__ = CArray.make t 1 in stubs_permute (CArray.start out__) self (List.map Int64.of_int dims |> CArray.of_list int64_t |> CArray.start) (List.length dims); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pin_memory self = let out__ = CArray.make t 1 in stubs_pin_memory (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pinverse self ~rcond = let out__ = CArray.make t 1 in stubs_pinverse (CArray.start out__) self rcond; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pixel_shuffle self ~upscale_factor = let out__ = CArray.make t 1 in stubs_pixel_shuffle (CArray.start out__) self (Int64.of_int upscale_factor); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let poisson self = let out__ = CArray.make t 1 in stubs_poisson (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let polygamma ~n self = let out__ = CArray.make t 1 in stubs_polygamma (CArray.start out__) (Int64.of_int n) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let polygamma_ self ~n = let out__ = CArray.make t 1 in stubs_polygamma_ (CArray.start out__) self (Int64.of_int n); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let polygamma_out ~out ~n self = let out__ = CArray.make t 1 in stubs_polygamma_out (CArray.start out__) out (Int64.of_int n) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow self ~exponent = let out__ = CArray.make t 1 in stubs_pow (CArray.start out__) self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow1 self ~exponent = let out__ = CArray.make t 1 in stubs_pow1 (CArray.start out__) self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow2 self ~exponent = let out__ = CArray.make t 1 in stubs_pow2 (CArray.start out__) self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow_ self ~exponent = let out__ = CArray.make t 1 in stubs_pow_ (CArray.start out__) self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow_1 self ~exponent = let out__ = CArray.make t 1 in stubs_pow_1 (CArray.start out__) self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow_out ~out self ~exponent = let out__ = CArray.make t 1 in stubs_pow_out (CArray.start out__) out self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow_out1 ~out self ~exponent = let out__ = CArray.make t 1 in stubs_pow_out1 (CArray.start out__) out self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pow_out2 ~out self ~exponent = let out__ = CArray.make t 1 in stubs_pow_out2 (CArray.start out__) out self exponent; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prelu self ~weight = let out__ = CArray.make t 1 in stubs_prelu (CArray.start out__) self weight; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prelu_backward ~grad_output self ~weight = let out__ = CArray.make t 2 in stubs_prelu_backward (CArray.start out__) grad_output self weight; 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 prod self = let out__ = CArray.make t 1 in stubs_prod (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prod1 self ~dtype = let out__ = CArray.make t 1 in stubs_prod1 (CArray.start out__) self (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prod2 self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_prod2 (CArray.start out__) self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prod3 self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_prod3 (CArray.start out__) self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prod4 self ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_prod4 (CArray.start out__) self (Int64.of_int dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prod_out ~out self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_prod_out (CArray.start out__) out self (Int64.of_int dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prod_out1 ~out self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_prod_out1 (CArray.start out__) out self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let prod_out2 ~out self ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_prod_out2 (CArray.start out__) out self (Int64.of_int dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let pstrf self ~upper = let out__ = CArray.make t 2 in stubs_pstrf (CArray.start out__) self (if upper 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 pstrf_out ~u ~pivot self ~upper = let out__ = CArray.make t 2 in stubs_pstrf_out (CArray.start out__) u pivot self (if upper 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 put_ self ~index ~source ~accumulate = let out__ = CArray.make t 1 in stubs_put_ (CArray.start out__) self index source (if accumulate then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let qr self = let out__ = CArray.make t 2 in stubs_qr (CArray.start out__) self; 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 qr_out ~q ~r self = let out__ = CArray.make t 2 in stubs_qr_out (CArray.start out__) q r self; 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 quantize_linear self ~scale ~zero_point = let out__ = CArray.make t 1 in stubs_quantize_linear (CArray.start out__) self scale (Int64.of_int zero_point); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let quantized_gru_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh ~packed_ih ~packed_hh ~col_offsets_ih ~col_offsets_hh ~scale_ih ~scale_hh ~zero_point_ih ~zero_point_hh = let out__ = CArray.make t 1 in stubs_quantized_gru_cell (CArray.start out__) input hx w_ih w_hh b_ih b_hh packed_ih packed_hh col_offsets_ih col_offsets_hh scale_ih scale_hh zero_point_ih zero_point_hh; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let quantized_lstm input ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional ~batch_first = let out__ = CArray.make t 3 in stubs_quantized_lstm (CArray.start out__) input (CArray.of_list t hx |> CArray.start) (List.length hx) (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional then 1 else 0) (if batch_first 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 quantized_lstm_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh ~packed_ih ~packed_hh ~col_offsets_ih ~col_offsets_hh ~scale_ih ~scale_hh ~zero_point_ih ~zero_point_hh = let out__ = CArray.make t 2 in stubs_quantized_lstm_cell (CArray.start out__) input (CArray.of_list t hx |> CArray.start) (List.length hx) w_ih w_hh b_ih b_hh packed_ih packed_hh col_offsets_ih col_offsets_hh scale_ih scale_hh zero_point_ih zero_point_hh; 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 quantized_rnn_relu_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh ~packed_ih ~packed_hh ~col_offsets_ih ~col_offsets_hh ~scale_ih ~scale_hh ~zero_point_ih ~zero_point_hh = let out__ = CArray.make t 1 in stubs_quantized_rnn_relu_cell (CArray.start out__) input hx w_ih w_hh b_ih b_hh packed_ih packed_hh col_offsets_ih col_offsets_hh scale_ih scale_hh zero_point_ih zero_point_hh; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let quantized_rnn_tanh_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh ~packed_ih ~packed_hh ~col_offsets_ih ~col_offsets_hh ~scale_ih ~scale_hh ~zero_point_ih ~zero_point_hh = let out__ = CArray.make t 1 in stubs_quantized_rnn_tanh_cell (CArray.start out__) input hx w_ih w_hh b_ih b_hh packed_ih packed_hh col_offsets_ih col_offsets_hh scale_ih scale_hh zero_point_ih zero_point_hh; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rand ~size ~options = let out__ = CArray.make t 1 in stubs_rand (CArray.start out__) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rand_like self = let out__ = CArray.make t 1 in stubs_rand_like (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rand_like1 self ~options = let out__ = CArray.make t 1 in stubs_rand_like1 (CArray.start out__) self (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rand_out ~out ~size = let out__ = CArray.make t 1 in stubs_rand_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 let randint ~high ~size ~options = let out__ = CArray.make t 1 in stubs_randint (CArray.start out__) (Int64.of_int high) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randint1 ~low ~high ~size ~options = let out__ = CArray.make t 1 in stubs_randint1 (CArray.start out__) (Int64.of_int low) (Int64.of_int high) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randint_like self ~high = let out__ = CArray.make t 1 in stubs_randint_like (CArray.start out__) self (Int64.of_int high); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randint_like1 self ~low ~high = let out__ = CArray.make t 1 in stubs_randint_like1 (CArray.start out__) self (Int64.of_int low) (Int64.of_int high); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randint_like2 self ~high ~options = let out__ = CArray.make t 1 in stubs_randint_like2 (CArray.start out__) self (Int64.of_int high) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randint_like3 self ~low ~high ~options = let out__ = CArray.make t 1 in stubs_randint_like3 (CArray.start out__) self (Int64.of_int low) (Int64.of_int high) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randint_out ~out ~high ~size = let out__ = CArray.make t 1 in stubs_randint_out (CArray.start out__) out (Int64.of_int high) (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 randint_out1 ~out ~low ~high ~size = let out__ = CArray.make t 1 in stubs_randint_out1 (CArray.start out__) out (Int64.of_int low) (Int64.of_int high) (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 randn ~size ~options = let out__ = CArray.make t 1 in stubs_randn (CArray.start out__) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randn_like self = let out__ = CArray.make t 1 in stubs_randn_like (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randn_like1 self ~options = let out__ = CArray.make t 1 in stubs_randn_like1 (CArray.start out__) self (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randn_out ~out ~size = let out__ = CArray.make t 1 in stubs_randn_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 let random_ self = let out__ = CArray.make t 1 in stubs_random_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let random_1 self ~to_ = let out__ = CArray.make t 1 in stubs_random_1 (CArray.start out__) self (Int64.of_int to_); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let random_2 self ~from ~to_ = let out__ = CArray.make t 1 in stubs_random_2 (CArray.start out__) self (Int64.of_int from) (Int64.of_int to_); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randperm ~n ~options = let out__ = CArray.make t 1 in stubs_randperm (CArray.start out__) (Int64.of_int n) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let randperm_out ~out ~n = let out__ = CArray.make t 1 in stubs_randperm_out (CArray.start out__) out (Int64.of_int n); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let range ~start ~end_ ~options = let out__ = CArray.make t 1 in stubs_range (CArray.start out__) start end_ (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let range1 ~start ~end_ ~options = let out__ = CArray.make t 1 in stubs_range1 (CArray.start out__) start end_ (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let range_out ~out ~start ~end_ = let out__ = CArray.make t 1 in stubs_range_out (CArray.start out__) out start end_; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reciprocal self = let out__ = CArray.make t 1 in stubs_reciprocal (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reciprocal_ self = let out__ = CArray.make t 1 in stubs_reciprocal_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reciprocal_out ~out self = let out__ = CArray.make t 1 in stubs_reciprocal_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad1d self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad1d (CArray.start out__) self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad1d_backward ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad1d_backward (CArray.start out__) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad1d_backward_out ~grad_input ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad1d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad1d_out ~out self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad1d_out (CArray.start out__) out self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad2d self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad2d (CArray.start out__) self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad2d_backward ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad2d_backward (CArray.start out__) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad2d_backward_out ~grad_input ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad2d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reflection_pad2d_out ~out self ~padding = let out__ = CArray.make t 1 in stubs_reflection_pad2d_out (CArray.start out__) out self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let relu self = let out__ = CArray.make t 1 in stubs_relu (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let relu_ self = let out__ = CArray.make t 1 in stubs_relu_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let remainder self other = let out__ = CArray.make t 1 in stubs_remainder (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let remainder1 self other = let out__ = CArray.make t 1 in stubs_remainder1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let remainder_ self other = let out__ = CArray.make t 1 in stubs_remainder_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let remainder_1 self other = let out__ = CArray.make t 1 in stubs_remainder_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let remainder_out ~out self other = let out__ = CArray.make t 1 in stubs_remainder_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let remainder_out1 ~out self other = let out__ = CArray.make t 1 in stubs_remainder_out1 (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let renorm self ~p ~dim ~maxnorm = let out__ = CArray.make t 1 in stubs_renorm (CArray.start out__) self p (Int64.of_int dim) maxnorm; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let renorm_ self ~p ~dim ~maxnorm = let out__ = CArray.make t 1 in stubs_renorm_ (CArray.start out__) self p (Int64.of_int dim) maxnorm; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let renorm_out ~out self ~p ~dim ~maxnorm = let out__ = CArray.make t 1 in stubs_renorm_out (CArray.start out__) out self p (Int64.of_int dim) maxnorm; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let repeat self ~repeats = let out__ = CArray.make t 1 in stubs_repeat (CArray.start out__) self (List.map Int64.of_int repeats |> CArray.of_list int64_t |> CArray.start) (List.length repeats); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let repeat_interleave ~repeats = let out__ = CArray.make t 1 in stubs_repeat_interleave (CArray.start out__) repeats; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let repeat_interleave1 self ~repeats ~dim = let out__ = CArray.make t 1 in stubs_repeat_interleave1 (CArray.start out__) self repeats (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let repeat_interleave2 self ~repeats ~dim = let out__ = CArray.make t 1 in stubs_repeat_interleave2 (CArray.start out__) self (Int64.of_int repeats) (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad1d self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad1d (CArray.start out__) self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad1d_backward ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad1d_backward (CArray.start out__) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad1d_backward_out ~grad_input ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad1d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad1d_out ~out self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad1d_out (CArray.start out__) out self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad2d self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad2d (CArray.start out__) self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad2d_backward ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad2d_backward (CArray.start out__) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad2d_backward_out ~grad_input ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad2d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad2d_out ~out self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad2d_out (CArray.start out__) out self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad3d self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad3d (CArray.start out__) self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad3d_backward ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad3d_backward (CArray.start out__) grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad3d_backward_out ~grad_input ~grad_output self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad3d_backward_out (CArray.start out__) grad_input grad_output self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let replication_pad3d_out ~out self ~padding = let out__ = CArray.make t 1 in stubs_replication_pad3d_out (CArray.start out__) out self (List.map Int64.of_int padding |> CArray.of_list int64_t |> CArray.start) (List.length padding); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reshape self ~shape = let out__ = CArray.make t 1 in stubs_reshape (CArray.start out__) self (List.map Int64.of_int shape |> CArray.of_list int64_t |> CArray.start) (List.length shape); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let reshape_as self other = let out__ = CArray.make t 1 in stubs_reshape_as (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let resize_ self ~size = let out__ = CArray.make t 1 in stubs_resize_ (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 resize_as_ self ~the_template = let out__ = CArray.make t 1 in stubs_resize_as_ (CArray.start out__) self the_template; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rfft self ~signal_ndim ~normalized ~onesided = let out__ = CArray.make t 1 in stubs_rfft (CArray.start out__) self (Int64.of_int signal_ndim) (if normalized then 1 else 0) (if onesided then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rnn_relu input ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional ~batch_first = let out__ = CArray.make t 2 in stubs_rnn_relu (CArray.start out__) input hx (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional then 1 else 0) (if batch_first 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 rnn_relu1 ~data ~batch_sizes ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional = let out__ = CArray.make t 2 in stubs_rnn_relu1 (CArray.start out__) data batch_sizes hx (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional 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 rnn_relu_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh = let out__ = CArray.make t 1 in stubs_rnn_relu_cell (CArray.start out__) input hx w_ih w_hh (match b_ih with | Some v -> v | None -> null) (match b_hh with | Some v -> v | None -> null); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rnn_tanh input ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional ~batch_first = let out__ = CArray.make t 2 in stubs_rnn_tanh (CArray.start out__) input hx (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional then 1 else 0) (if batch_first 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 rnn_tanh1 ~data ~batch_sizes ~hx ~params ~has_biases ~num_layers ~dropout ~train ~bidirectional = let out__ = CArray.make t 2 in stubs_rnn_tanh1 (CArray.start out__) data batch_sizes hx (CArray.of_list t params |> CArray.start) (List.length params) (if has_biases then 1 else 0) (Int64.of_int num_layers) dropout (if train then 1 else 0) (if bidirectional 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 rnn_tanh_cell input ~hx ~w_ih ~w_hh ~b_ih ~b_hh = let out__ = CArray.make t 1 in stubs_rnn_tanh_cell (CArray.start out__) input hx w_ih w_hh (match b_ih with | Some v -> v | None -> null) (match b_hh with | Some v -> v | None -> null); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let roll self ~shifts ~dims = let out__ = CArray.make t 1 in stubs_roll (CArray.start out__) self (List.map Int64.of_int shifts |> CArray.of_list int64_t |> CArray.start) (List.length shifts) (List.map Int64.of_int dims |> CArray.of_list int64_t |> CArray.start) (List.length dims); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rot90 self ~k ~dims = let out__ = CArray.make t 1 in stubs_rot90 (CArray.start out__) self (Int64.of_int k) (List.map Int64.of_int dims |> CArray.of_list int64_t |> CArray.start) (List.length dims); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let round self = let out__ = CArray.make t 1 in stubs_round (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let round_ self = let out__ = CArray.make t 1 in stubs_round_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let round_out ~out self = let out__ = CArray.make t 1 in stubs_round_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rrelu self ~training = let out__ = CArray.make t 1 in stubs_rrelu (CArray.start out__) self (if training then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rrelu_ self ~training = let out__ = CArray.make t 1 in stubs_rrelu_ (CArray.start out__) self (if training then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rrelu_with_noise self ~noise ~training = let out__ = CArray.make t 1 in stubs_rrelu_with_noise (CArray.start out__) self noise (if training then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rrelu_with_noise_ self ~noise ~training = let out__ = CArray.make t 1 in stubs_rrelu_with_noise_ (CArray.start out__) self noise (if training then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rrelu_with_noise_backward ~grad_output self ~noise ~lower ~upper ~training = let out__ = CArray.make t 1 in stubs_rrelu_with_noise_backward (CArray.start out__) grad_output self noise lower upper (if training then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rrelu_with_noise_backward_out ~grad_input ~grad_output self ~noise ~lower ~upper ~training = let out__ = CArray.make t 1 in stubs_rrelu_with_noise_backward_out (CArray.start out__) grad_input grad_output self noise lower upper (if training then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rrelu_with_noise_out ~out self ~noise ~training = let out__ = CArray.make t 1 in stubs_rrelu_with_noise_out (CArray.start out__) out self noise (if training then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rsqrt self = let out__ = CArray.make t 1 in stubs_rsqrt (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rsqrt_ self = let out__ = CArray.make t 1 in stubs_rsqrt_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rsqrt_out ~out self = let out__ = CArray.make t 1 in stubs_rsqrt_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rsub self other = let out__ = CArray.make t 1 in stubs_rsub (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let rsub1 self other = let out__ = CArray.make t 1 in stubs_rsub1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let s_copy_ self ~src ~non_blocking = let out__ = CArray.make t 1 in stubs_s_copy_ (CArray.start out__) self src (if non_blocking then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let s_native_addmm self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_s_native_addmm (CArray.start out__) self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let s_native_addmm_ self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_s_native_addmm_ (CArray.start out__) self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let s_native_addmm_out ~out self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_s_native_addmm_out (CArray.start out__) out self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let scalar_tensor ~s ~options = let out__ = CArray.make t 1 in stubs_scalar_tensor (CArray.start out__) s (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let scatter self ~dim ~index ~src = let out__ = CArray.make t 1 in stubs_scatter (CArray.start out__) self (Int64.of_int dim) index src; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let scatter1 self ~dim ~index ~value = let out__ = CArray.make t 1 in stubs_scatter1 (CArray.start out__) self (Int64.of_int dim) index value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let scatter_ self ~dim ~index ~src = let out__ = CArray.make t 1 in stubs_scatter_ (CArray.start out__) self (Int64.of_int dim) index src; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let scatter_1 self ~dim ~index ~value = let out__ = CArray.make t 1 in stubs_scatter_1 (CArray.start out__) self (Int64.of_int dim) index value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let scatter_add self ~dim ~index ~src = let out__ = CArray.make t 1 in stubs_scatter_add (CArray.start out__) self (Int64.of_int dim) index src; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let scatter_add_ self ~dim ~index ~src = let out__ = CArray.make t 1 in stubs_scatter_add_ (CArray.start out__) self (Int64.of_int dim) index src; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let select self ~dim ~index = let out__ = CArray.make t 1 in stubs_select (CArray.start out__) self (Int64.of_int dim) (Int64.of_int index); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let selu self = let out__ = CArray.make t 1 in stubs_selu (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let selu_ self = let out__ = CArray.make t 1 in stubs_selu_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let set_ self = let out__ = CArray.make t 1 in stubs_set_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let set_1 self ~source = let out__ = CArray.make t 1 in stubs_set_1 (CArray.start out__) self source; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let set_requires_grad self ~r = let out__ = CArray.make t 1 in stubs_set_requires_grad (CArray.start out__) self (if r then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sigmoid self = let out__ = CArray.make t 1 in stubs_sigmoid (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sigmoid_ self = let out__ = CArray.make t 1 in stubs_sigmoid_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sigmoid_backward ~grad_output ~output = let out__ = CArray.make t 1 in stubs_sigmoid_backward (CArray.start out__) grad_output output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sigmoid_backward_out ~grad_input ~grad_output ~output = let out__ = CArray.make t 1 in stubs_sigmoid_backward_out (CArray.start out__) grad_input grad_output output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sigmoid_out ~out self = let out__ = CArray.make t 1 in stubs_sigmoid_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sign self = let out__ = CArray.make t 1 in stubs_sign (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sign_ self = let out__ = CArray.make t 1 in stubs_sign_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sign_out ~out self = let out__ = CArray.make t 1 in stubs_sign_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sin self = let out__ = CArray.make t 1 in stubs_sin (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sin_ self = let out__ = CArray.make t 1 in stubs_sin_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sin_out ~out self = let out__ = CArray.make t 1 in stubs_sin_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sinh self = let out__ = CArray.make t 1 in stubs_sinh (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sinh_ self = let out__ = CArray.make t 1 in stubs_sinh_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sinh_out ~out self = let out__ = CArray.make t 1 in stubs_sinh_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let slice self ~dim ~start ~end_ ~step = let out__ = CArray.make t 1 in stubs_slice (CArray.start out__) self (Int64.of_int dim) (Int64.of_int start) (Int64.of_int end_) (Int64.of_int step); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let slogdet self = let out__ = CArray.make t 2 in stubs_slogdet (CArray.start out__) self; 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 smm self ~mat2 = let out__ = CArray.make t 1 in stubs_smm (CArray.start out__) self mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let smooth_l1_loss self ~target ~reduction = let out__ = CArray.make t 1 in stubs_smooth_l1_loss (CArray.start out__) self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let smooth_l1_loss_backward ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_smooth_l1_loss_backward (CArray.start out__) grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let smooth_l1_loss_backward_out ~grad_input ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_smooth_l1_loss_backward_out (CArray.start out__) grad_input grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let smooth_l1_loss_out ~out self ~target ~reduction = let out__ = CArray.make t 1 in stubs_smooth_l1_loss_out (CArray.start out__) out self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let soft_margin_loss self ~target ~reduction = let out__ = CArray.make t 1 in stubs_soft_margin_loss (CArray.start out__) self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let soft_margin_loss_backward ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_soft_margin_loss_backward (CArray.start out__) grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let soft_margin_loss_backward_out ~grad_input ~grad_output self ~target ~reduction = let out__ = CArray.make t 1 in stubs_soft_margin_loss_backward_out (CArray.start out__) grad_input grad_output self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let soft_margin_loss_out ~out self ~target ~reduction = let out__ = CArray.make t 1 in stubs_soft_margin_loss_out (CArray.start out__) out self target (Int64.of_int reduction); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softmax self ~dim = let out__ = CArray.make t 1 in stubs_softmax (CArray.start out__) self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softmax1 self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_softmax1 (CArray.start out__) self (Int64.of_int dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softplus self = let out__ = CArray.make t 1 in stubs_softplus (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softplus_backward ~grad_output self ~beta ~threshold ~output = let out__ = CArray.make t 1 in stubs_softplus_backward (CArray.start out__) grad_output self beta threshold output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softplus_backward_out ~grad_input ~grad_output self ~beta ~threshold ~output = let out__ = CArray.make t 1 in stubs_softplus_backward_out (CArray.start out__) grad_input grad_output self beta threshold output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softplus_out ~out self = let out__ = CArray.make t 1 in stubs_softplus_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softshrink self = let out__ = CArray.make t 1 in stubs_softshrink (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softshrink_backward ~grad_output self ~lambd = let out__ = CArray.make t 1 in stubs_softshrink_backward (CArray.start out__) grad_output self lambd; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softshrink_backward_out ~grad_input ~grad_output self ~lambd = let out__ = CArray.make t 1 in stubs_softshrink_backward_out (CArray.start out__) grad_input grad_output self lambd; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let softshrink_out ~out self = let out__ = CArray.make t 1 in stubs_softshrink_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let solve self ~a = let out__ = CArray.make t 2 in stubs_solve (CArray.start out__) self a; 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 solve_out ~solution ~lu self ~a = let out__ = CArray.make t 2 in stubs_solve_out (CArray.start out__) solution lu self a; 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 sort self ~dim ~descending = let out__ = CArray.make t 2 in stubs_sort (CArray.start out__) self (Int64.of_int dim) (if descending 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 sort_out ~values ~indices self ~dim ~descending = let out__ = CArray.make t 2 in stubs_sort_out (CArray.start out__) values indices self (Int64.of_int dim) (if descending 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 sparse_coo_tensor ~size ~options = let out__ = CArray.make t 1 in stubs_sparse_coo_tensor (CArray.start out__) (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sparse_coo_tensor1 ~indices ~values ~options = let out__ = CArray.make t 1 in stubs_sparse_coo_tensor1 (CArray.start out__) indices values (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sparse_coo_tensor2 ~indices ~values ~size ~options = let out__ = CArray.make t 1 in stubs_sparse_coo_tensor2 (CArray.start out__) indices values (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Kind.to_int (fst options)) (Device.to_int (snd options)); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sparse_resize_ self ~size ~sparse_dim ~dense_dim = let out__ = CArray.make t 1 in stubs_sparse_resize_ (CArray.start out__) self (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Int64.of_int sparse_dim) (Int64.of_int dense_dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sparse_resize_and_clear_ self ~size ~sparse_dim ~dense_dim = let out__ = CArray.make t 1 in stubs_sparse_resize_and_clear_ (CArray.start out__) self (List.map Int64.of_int size |> CArray.of_list int64_t |> CArray.start) (List.length size) (Int64.of_int sparse_dim) (Int64.of_int dense_dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let split self ~split_size ~dim = stubs_split self (Int64.of_int split_size) (Int64.of_int dim) |> to_tensor_list let split_with_sizes self ~split_sizes ~dim = stubs_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 sqrt self = let out__ = CArray.make t 1 in stubs_sqrt (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sqrt_ self = let out__ = CArray.make t 1 in stubs_sqrt_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sqrt_out ~out self = let out__ = CArray.make t 1 in stubs_sqrt_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let squeeze self = let out__ = CArray.make t 1 in stubs_squeeze (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let squeeze1 self ~dim = let out__ = CArray.make t 1 in stubs_squeeze1 (CArray.start out__) self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let squeeze_ self = let out__ = CArray.make t 1 in stubs_squeeze_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let squeeze_1 self ~dim = let out__ = CArray.make t 1 in stubs_squeeze_1 (CArray.start out__) self (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sspaddmm self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_sspaddmm (CArray.start out__) self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sspaddmm_out ~out self ~mat1 ~mat2 = let out__ = CArray.make t 1 in stubs_sspaddmm_out (CArray.start out__) out self mat1 mat2; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let stack tensors ~dim = let out__ = CArray.make t 1 in stubs_stack (CArray.start out__) (CArray.of_list t tensors |> CArray.start) (List.length tensors) (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let stack_out ~out tensors ~dim = let out__ = CArray.make t 1 in stubs_stack_out (CArray.start out__) out (CArray.of_list t tensors |> CArray.start) (List.length tensors) (Int64.of_int dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let std self ~unbiased = let out__ = CArray.make t 1 in stubs_std (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 std1 self ~dim ~unbiased ~keepdim = let out__ = CArray.make t 1 in stubs_std1 (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 std_out ~out self ~dim ~unbiased ~keepdim = let out__ = CArray.make t 1 in stubs_std_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 stft self ~n_fft ~hop_length ~win_length ~window ~normalized ~onesided = let out__ = CArray.make t 1 in stubs_stft (CArray.start out__) self (Int64.of_int n_fft) (Int64.of_int hop_length) (Int64.of_int win_length) (match window with | Some v -> v | None -> null) (if normalized then 1 else 0) (if onesided then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sub self other = let out__ = CArray.make t 1 in stubs_sub (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sub1 self other = let out__ = CArray.make t 1 in stubs_sub1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sub_ self other = let out__ = CArray.make t 1 in stubs_sub_ (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sub_1 self other = let out__ = CArray.make t 1 in stubs_sub_1 (CArray.start out__) self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sub_out ~out self other = let out__ = CArray.make t 1 in stubs_sub_out (CArray.start out__) out self other; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum self = let out__ = CArray.make t 1 in stubs_sum (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum1 self ~dtype = let out__ = CArray.make t 1 in stubs_sum1 (CArray.start out__) self (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum2 self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_sum2 (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum3 self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_sum3 (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum4 self ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_sum4 (CArray.start out__) self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum_out ~out self ~dim ~keepdim = let out__ = CArray.make t 1 in stubs_sum_out (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum_out1 ~out self ~dim ~dtype = let out__ = CArray.make t 1 in stubs_sum_out1 (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum_out2 ~out self ~dim ~keepdim ~dtype = let out__ = CArray.make t 1 in stubs_sum_out2 (CArray.start out__) out self (List.map Int64.of_int dim |> CArray.of_list int64_t |> CArray.start) (List.length dim) (if keepdim then 1 else 0) (Kind.to_int dtype); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let sum_to_size self ~size = let out__ = CArray.make t 1 in stubs_sum_to_size (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 svd self ~some ~compute_uv = let out__ = CArray.make t 3 in stubs_svd (CArray.start out__) self (if some then 1 else 0) (if compute_uv 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 svd_out ~u ~s ~v self ~some ~compute_uv = let out__ = CArray.make t 3 in stubs_svd_out (CArray.start out__) u s v self (if some then 1 else 0) (if compute_uv 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 symeig self ~eigenvectors ~upper = let out__ = CArray.make t 2 in stubs_symeig (CArray.start out__) self (if eigenvectors then 1 else 0) (if upper 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 symeig_out ~e ~v self ~eigenvectors ~upper = let out__ = CArray.make t 2 in stubs_symeig_out (CArray.start out__) e v self (if eigenvectors then 1 else 0) (if upper 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 tr self = let out__ = CArray.make t 1 in stubs_tr (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let t_ self = let out__ = CArray.make t 1 in stubs_t_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let take self ~index = let out__ = CArray.make t 1 in stubs_take (CArray.start out__) self index; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let take_out ~out self ~index = let out__ = CArray.make t 1 in stubs_take_out (CArray.start out__) out self index; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tan self = let out__ = CArray.make t 1 in stubs_tan (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tan_ self = let out__ = CArray.make t 1 in stubs_tan_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tan_out ~out self = let out__ = CArray.make t 1 in stubs_tan_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tanh self = let out__ = CArray.make t 1 in stubs_tanh (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tanh_ self = let out__ = CArray.make t 1 in stubs_tanh_ (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tanh_backward ~grad_output ~output = let out__ = CArray.make t 1 in stubs_tanh_backward (CArray.start out__) grad_output output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tanh_backward_out ~grad_input ~grad_output ~output = let out__ = CArray.make t 1 in stubs_tanh_backward_out (CArray.start out__) grad_input grad_output output; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tanh_out ~out self = let out__ = CArray.make t 1 in stubs_tanh_out (CArray.start out__) out self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let tensordot self other ~dims_self ~dims_other = let out__ = CArray.make t 1 in stubs_tensordot (CArray.start out__) self other (List.map Int64.of_int dims_self |> CArray.of_list int64_t |> CArray.start) (List.length dims_self) (List.map Int64.of_int dims_other |> CArray.of_list int64_t |> CArray.start) (List.length dims_other); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let threshold self ~threshold ~value = let out__ = CArray.make t 1 in stubs_threshold (CArray.start out__) self threshold value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let threshold_ self ~threshold ~value = let out__ = CArray.make t 1 in stubs_threshold_ (CArray.start out__) self threshold value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let threshold_backward ~grad_output self ~threshold = let out__ = CArray.make t 1 in stubs_threshold_backward (CArray.start out__) grad_output self threshold; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let threshold_out ~out self ~threshold ~value = let out__ = CArray.make t 1 in stubs_threshold_out (CArray.start out__) out self threshold value; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to_ self ~device = let out__ = CArray.make t 1 in stubs_to_ (CArray.start out__) self (Device.to_int device); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to1 self ~options ~non_blocking ~copy = let out__ = CArray.make t 1 in stubs_to1 (CArray.start out__) self (Kind.to_int (fst options)) (Device.to_int (snd options)) (if non_blocking then 1 else 0) (if copy then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to2 self ~dtype ~non_blocking ~copy = let out__ = CArray.make t 1 in stubs_to2 (CArray.start out__) self (Kind.to_int dtype) (if non_blocking then 1 else 0) (if copy then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to3 self other ~non_blocking ~copy = let out__ = CArray.make t 1 in stubs_to3 (CArray.start out__) self other (if non_blocking then 1 else 0) (if copy then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to4 self ~device ~dtype ~non_blocking ~copy = let out__ = CArray.make t 1 in stubs_to4 (CArray.start out__) self (Device.to_int device) (Kind.to_int dtype) (if non_blocking then 1 else 0) (if copy then 1 else 0); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to_dense self = let out__ = CArray.make t 1 in stubs_to_dense (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to_dense_backward ~grad input = let out__ = CArray.make t 1 in stubs_to_dense_backward (CArray.start out__) grad input; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to_mkldnn self = let out__ = CArray.make t 1 in stubs_to_mkldnn (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to_mkldnn_backward ~grad input = let out__ = CArray.make t 1 in stubs_to_mkldnn_backward (CArray.start out__) grad input; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to_sparse self = let out__ = CArray.make t 1 in stubs_to_sparse (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let to_sparse1 self ~sparse_dim = let out__ = CArray.make t 1 in stubs_to_sparse1 (CArray.start out__) self (Int64.of_int sparse_dim); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let topk self ~k ~dim ~largest ~sorted = let out__ = CArray.make t 2 in stubs_topk (CArray.start out__) self (Int64.of_int k) (Int64.of_int dim) (if largest then 1 else 0) (if sorted 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 topk_out ~values ~indices self ~k ~dim ~largest ~sorted = let out__ = CArray.make t 2 in stubs_topk_out (CArray.start out__) values indices self (Int64.of_int k) (Int64.of_int dim) (if largest then 1 else 0) (if sorted 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 totype self ~scalar_type = let out__ = CArray.make t 1 in stubs_totype (CArray.start out__) self (Kind.to_int scalar_type); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let trace self = let out__ = CArray.make t 1 in stubs_trace (CArray.start out__) self; let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let transpose self ~dim0 ~dim1 = let out__ = CArray.make t 1 in stubs_transpose (CArray.start out__) self (Int64.of_int dim0) (Int64.of_int dim1); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let transpose_ self ~dim0 ~dim1 = let out__ = CArray.make t 1 in stubs_transpose_ (CArray.start out__) self (Int64.of_int dim0) (Int64.of_int dim1); 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_out ~x ~m self ~a ~upper ~transpose ~unitriangular = let out__ = CArray.make t 2 in stubs_triangular_solve_out (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.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) (Int64.of_int reduction); 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.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 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 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 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 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 = 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); 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 = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_bicubic2d_backward_out ~grad_input ~grad_output ~output_size ~input_size ~align_corners = let out__ = CArray.make t 1 in stubs_upsample_bicubic2d_backward_out (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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_bicubic2d_out ~out self ~output_size ~align_corners = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_bilinear2d self ~output_size ~align_corners = 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); 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 = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_bilinear2d_backward_out ~grad_input ~grad_output ~output_size ~input_size ~align_corners = let out__ = CArray.make t 1 in stubs_upsample_bilinear2d_backward_out (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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_bilinear2d_out ~out self ~output_size ~align_corners = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_linear1d self ~output_size ~align_corners = 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); 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 = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_linear1d_backward_out ~grad_input ~grad_output ~output_size ~input_size ~align_corners = let out__ = CArray.make t 1 in stubs_upsample_linear1d_backward_out (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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_linear1d_out ~out self ~output_size ~align_corners = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest1d self ~output_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest1d_backward ~grad_output ~output_size ~input_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest1d_backward_out ~grad_input ~grad_output ~output_size ~input_size = let out__ = CArray.make t 1 in stubs_upsample_nearest1d_backward_out (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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest1d_out ~out self ~output_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest2d self ~output_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest2d_backward ~grad_output ~output_size ~input_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest2d_backward_out ~grad_input ~grad_output ~output_size ~input_size = let out__ = CArray.make t 1 in stubs_upsample_nearest2d_backward_out (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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest2d_out ~out self ~output_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest3d self ~output_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest3d_backward ~grad_output ~output_size ~input_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest3d_backward_out ~grad_input ~grad_output ~output_size ~input_size = let out__ = CArray.make t 1 in stubs_upsample_nearest3d_backward_out (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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_nearest3d_out ~out self ~output_size = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_trilinear3d self ~output_size ~align_corners = 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); 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 = 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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_trilinear3d_backward_out ~grad_input ~grad_output ~output_size ~input_size ~align_corners = let out__ = CArray.make t 1 in stubs_upsample_trilinear3d_backward_out (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); let t0 = CArray.get out__ 0 in Gc.finalise C.Tensor.free t0; t0 let upsample_trilinear3d_out ~out self ~output_size ~align_corners = 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); 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 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 var1 self ~dim ~unbiased ~keepdim = let out__ = CArray.make t 1 in stubs_var1 (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_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 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 where ~condition self other = let out__ = CArray.make t 1 in stubs_where (CArray.start out__) condition 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.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_like1 self ~options = let out__ = CArray.make t 1 in stubs_zeros_like1 (CArray.start out__) self (Kind.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_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