package torch

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include module type of Torch_core.Wrapper.Tensor with type t := t
include Torch_core.Wrapper_generated_intf.S with type t := t and type scalar := Torch_core.Wrapper.Scalar.t
val abs : t -> t
val abs_ : t -> t
val abs_out : out:t -> t -> t
val acos : t -> t
val acos_ : t -> t
val acos_out : out:t -> t -> t
val adaptive_avg_pool1d : t -> output_size:int list -> t
val adaptive_avg_pool2d : t -> output_size:int list -> t
val adaptive_avg_pool2d_out : out:t -> t -> output_size:int list -> t
val adaptive_avg_pool3d : t -> output_size:int list -> t
val adaptive_avg_pool3d_backward : grad_output:t -> t -> t
val adaptive_avg_pool3d_backward_out : grad_input:t -> grad_output:t -> t -> t
val adaptive_avg_pool3d_out : out:t -> t -> output_size:int list -> t
val adaptive_max_pool1d : t -> output_size:int list -> t * t
val adaptive_max_pool2d : t -> output_size:int list -> t * t
val adaptive_max_pool2d_backward : grad_output:t -> t -> indices:t -> t
val adaptive_max_pool2d_backward_out : grad_input:t -> grad_output:t -> t -> indices:t -> t
val adaptive_max_pool2d_out : out:t -> indices:t -> t -> output_size:int list -> t * t
val adaptive_max_pool3d : t -> output_size:int list -> t * t
val adaptive_max_pool3d_backward : grad_output:t -> t -> indices:t -> t
val adaptive_max_pool3d_backward_out : grad_input:t -> grad_output:t -> t -> indices:t -> t
val adaptive_max_pool3d_out : out:t -> indices:t -> t -> output_size:int list -> t * t
val add : t -> t -> t
val add_ : t -> t -> t
val add_1 : t -> Torch_core.Wrapper.Scalar.t -> t
val add_out : out:t -> t -> t -> t
val addbmm : t -> batch1:t -> batch2:t -> t
val addbmm_ : t -> batch1:t -> batch2:t -> t
val addbmm_out : out:t -> t -> batch1:t -> batch2:t -> t
val addcdiv : t -> tensor1:t -> tensor2:t -> t
val addcdiv_ : t -> tensor1:t -> tensor2:t -> t
val addcdiv_out : out:t -> t -> tensor1:t -> tensor2:t -> t
val addcmul : t -> tensor1:t -> tensor2:t -> t
val addcmul_ : t -> tensor1:t -> tensor2:t -> t
val addcmul_out : out:t -> t -> tensor1:t -> tensor2:t -> t
val addmm : t -> mat1:t -> mat2:t -> t
val addmm_ : t -> mat1:t -> mat2:t -> t
val addmm_out : out:t -> t -> mat1:t -> mat2:t -> t
val addmv : t -> mat:t -> vec:t -> t
val addmv_ : t -> mat:t -> vec:t -> t
val addmv_out : out:t -> t -> mat:t -> vec:t -> t
val addr : t -> vec1:t -> vec2:t -> t
val addr_ : t -> vec1:t -> vec2:t -> t
val addr_out : out:t -> t -> vec1:t -> vec2:t -> t
val affine_grid_generator : theta:t -> size:int list -> t
val affine_grid_generator_backward : grad:t -> size:int list -> t
val alias : t -> t
val all : t -> t
val all1 : t -> dim:int -> keepdim:bool -> t
val all_out : out:t -> t -> dim:int -> keepdim:bool -> t
val alpha_dropout : t -> p:float -> train:bool -> t
val alpha_dropout_ : t -> p:float -> train:bool -> t
val any : t -> t
val any1 : t -> dim:int -> keepdim:bool -> t
val any_out : out:t -> t -> dim:int -> keepdim:bool -> t
val arange_out : out:t -> end_:Torch_core.Wrapper.Scalar.t -> t
val arange_out1 : out:t -> start:Torch_core.Wrapper.Scalar.t -> end_:Torch_core.Wrapper.Scalar.t -> t
val argmin : t -> dim:int -> keepdim:bool -> t
val argsort : t -> dim:int -> descending:bool -> t
val as_strided : t -> size:int list -> stride:int list -> storage_offset:int -> t
val as_strided_ : t -> size:int list -> stride:int list -> storage_offset:int -> t
val asin : t -> t
val asin_ : t -> t
val asin_out : out:t -> t -> t
val atan : t -> t
val atan2 : t -> t -> t
val atan2_ : t -> t -> t
val atan2_out : out:t -> t -> t -> t
val atan_ : t -> t
val atan_out : out:t -> t -> t
val avg_pool1d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool2d_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool2d_backward_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool2d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool3d_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool3d_backward_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val avg_pool3d_out : out:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> ceil_mode:bool -> count_include_pad:bool -> t
val baddbmm : t -> batch1:t -> batch2:t -> t
val baddbmm_ : t -> batch1:t -> batch2:t -> t
val baddbmm_out : out:t -> t -> batch1:t -> batch2:t -> t
val bartlett_window : window_length:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val bartlett_window1 : window_length:int -> periodic:bool -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val batch_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> t
val batch_norm_backward_elemt : grad_out:t -> t -> mean:t -> invstd:t -> weight:t option -> mean_dy:t -> mean_dy_xmu:t -> t
val batch_norm_backward_reduce : grad_out:t -> t -> mean:t -> invstd:t -> input_g:bool -> weight_g:bool -> bias_g:bool -> t * t * t * t
val batch_norm_elemt : t -> weight:t option -> bias:t option -> mean:t -> invstd:t -> eps:float -> t
val batch_norm_gather_stats : t -> mean:t -> invstd:t -> running_mean:t option -> running_var:t option -> momentum:float -> eps:float -> count:int -> t * t
val batch_norm_stats : t -> eps:float -> t * t
val batch_norm_update_stats : t -> running_mean:t option -> running_var:t option -> momentum:float -> t * t
val bernoulli : t -> t
val bernoulli1 : t -> p:float -> t
val bernoulli_ : t -> p:t -> t
val bernoulli_1 : t -> p:float -> t
val bernoulli_out : out:t -> t -> t
val bilinear : input1:t -> input2:t -> weight:t -> bias:t option -> t
val binary_cross_entropy : t -> target:t -> weight:t option -> reduction:int -> t
val binary_cross_entropy_backward : grad_output:t -> t -> target:t -> weight:t -> reduction:int -> t
val binary_cross_entropy_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> weight:t -> reduction:int -> t
val binary_cross_entropy_out : out:t -> t -> target:t -> weight:t option -> reduction:int -> t
val binary_cross_entropy_with_logits : t -> target:t -> weight:t option -> pos_weight:t option -> reduction:int -> t
val binary_cross_entropy_with_logits_backward : grad_output:t -> t -> target:t -> weight:t option -> pos_weight:t option -> reduction:int -> t
val bincount : t -> weights:t option -> minlength:int -> t
val blackman_window : window_length:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val blackman_window1 : window_length:int -> periodic:bool -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val bmm : t -> mat2:t -> t
val bmm_out : out:t -> t -> mat2:t -> t
val broadcast_tensors : t list -> t list
val cartesian_prod : t list -> t
val cat : t list -> dim:int -> t
val cat_out : out:t -> t list -> dim:int -> t
val cauchy_ : t -> median:float -> sigma:float -> t
val cdist : x1:t -> x2:t -> p:float -> t
val ceil : t -> t
val ceil_ : t -> t
val ceil_out : out:t -> t -> t
val celu : t -> t
val celu_ : t -> t
val chain_matmul : matrices:t list -> t
val cholesky : t -> upper:bool -> t
val cholesky_inverse : t -> upper:bool -> t
val cholesky_inverse_out : out:t -> t -> upper:bool -> t
val cholesky_out : out:t -> t -> upper:bool -> t
val cholesky_solve : t -> input2:t -> upper:bool -> t
val cholesky_solve_out : out:t -> t -> input2:t -> upper:bool -> t
val chunk : t -> chunks:int -> dim:int -> t list
val clamp_ : t -> min:Torch_core.Wrapper.Scalar.t -> max:Torch_core.Wrapper.Scalar.t -> t
val clamp_max : t -> max:Torch_core.Wrapper.Scalar.t -> t
val clamp_max_ : t -> max:Torch_core.Wrapper.Scalar.t -> t
val clamp_max_out : out:t -> t -> max:Torch_core.Wrapper.Scalar.t -> t
val clamp_min : t -> min:Torch_core.Wrapper.Scalar.t -> t
val clamp_min_ : t -> min:Torch_core.Wrapper.Scalar.t -> t
val clamp_min_out : out:t -> t -> min:Torch_core.Wrapper.Scalar.t -> t
val clamp_out : out:t -> t -> min:Torch_core.Wrapper.Scalar.t -> max:Torch_core.Wrapper.Scalar.t -> t
val clone : t -> t
val coalesce : t -> t
val combinations : t -> r:int -> with_replacement:bool -> t
val constant_pad_nd : t -> pad:int list -> t
val contiguous : t -> t
val conv1d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
val conv3d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> groups:int -> t
val conv_tbc : t -> weight:t -> bias:t -> pad:int -> t
val conv_tbc_backward : t -> t -> weight:t -> bias:t -> pad:int -> t * t * t
val conv_transpose1d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
val conv_transpose3d : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> output_padding:int list -> groups:int -> dilation:int list -> t
val convolution : t -> weight:t -> bias:t option -> stride:int list -> padding:int list -> dilation:int list -> transposed:bool -> output_padding:int list -> groups:int -> t
val copy_sparse_to_sparse_ : t -> src:t -> non_blocking:bool -> t
val cos : t -> t
val cos_ : t -> t
val cos_out : out:t -> t -> t
val cosh : t -> t
val cosh_ : t -> t
val cosh_out : out:t -> t -> t
val cosine_embedding_loss : input1:t -> input2:t -> target:t -> margin:float -> reduction:int -> t
val cosine_similarity : x1:t -> x2:t -> dim:int -> eps:float -> t
val cross : t -> t -> dim:int -> t
val cross_out : out:t -> t -> t -> dim:int -> t
val ctc_loss : log_probs:t -> targets:t -> input_lengths:int list -> target_lengths:int list -> blank:int -> reduction:int -> zero_infinity:bool -> t
val ctc_loss1 : log_probs:t -> targets:t -> input_lengths:t -> target_lengths:t -> blank:int -> reduction:int -> zero_infinity:bool -> t
val cudnn_affine_grid_generator : theta:t -> n:int -> c:int -> h:int -> w:int -> t
val cudnn_affine_grid_generator_backward : grad:t -> n:int -> c:int -> h:int -> w:int -> t
val cudnn_batch_norm : t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t
val cudnn_batch_norm_backward : t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> t * t * t
val cudnn_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_backward_bias : grad_output:t -> t
val cudnn_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_transpose : t -> weight:t -> bias:t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_transpose_backward_bias : grad_output:t -> t
val cudnn_convolution_transpose_backward_input : grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_convolution_transpose_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val cudnn_grid_sampler : t -> grid:t -> t
val cudnn_grid_sampler_backward : t -> grid:t -> grad_output:t -> t * t
val cumprod : t -> dim:int -> t
val cumprod1 : t -> dim:int -> dtype:Torch_core.Kind.t -> t
val cumprod_out : out:t -> t -> dim:int -> t
val cumprod_out1 : out:t -> t -> dim:int -> dtype:Torch_core.Kind.t -> t
val cumsum : t -> dim:int -> t
val cumsum1 : t -> dim:int -> dtype:Torch_core.Kind.t -> t
val cumsum_out : out:t -> t -> dim:int -> t
val cumsum_out1 : out:t -> t -> dim:int -> dtype:Torch_core.Kind.t -> t
val dequantize : t -> t
val det : t -> t
val detach : t -> t
val detach_ : t -> t
val diag : t -> diagonal:int -> t
val diag_embed : t -> offset:int -> dim1:int -> dim2:int -> t
val diag_out : out:t -> t -> diagonal:int -> t
val diagflat : t -> offset:int -> t
val diagonal : t -> offset:int -> dim1:int -> dim2:int -> t
val digamma : t -> t
val digamma_ : t -> t
val digamma_out : out:t -> t -> t
val dist : t -> t -> t
val div : t -> t -> t
val div_ : t -> t -> t
val div_1 : t -> Torch_core.Wrapper.Scalar.t -> t
val div_out : out:t -> t -> t -> t
val dot : t -> t -> t
val dot_out : out:t -> t -> t -> t
val dropout_ : t -> p:float -> train:bool -> t
val eig : t -> eigenvectors:bool -> t * t
val eig_out : e:t -> v:t -> t -> eigenvectors:bool -> t * t
val elu : t -> t
val elu_ : t -> t
val elu_backward : grad_output:t -> alpha:Torch_core.Wrapper.Scalar.t -> scale:Torch_core.Wrapper.Scalar.t -> input_scale:Torch_core.Wrapper.Scalar.t -> output:t -> t
val elu_backward_out : grad_input:t -> grad_output:t -> alpha:Torch_core.Wrapper.Scalar.t -> scale:Torch_core.Wrapper.Scalar.t -> input_scale:Torch_core.Wrapper.Scalar.t -> output:t -> t
val elu_out : out:t -> t -> t
val embedding : weight:t -> indices:t -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> t
val embedding_backward : grad:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> sparse:bool -> t
val embedding_bag : weight:t -> indices:t -> offsets:t -> scale_grad_by_freq:bool -> mode:int -> sparse:bool -> per_sample_weights:t option -> t * t * t * t
val embedding_dense_backward : grad_output:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> t
val embedding_renorm_ : t -> indices:t -> max_norm:float -> norm_type:float -> t
val embedding_sparse_backward : grad:t -> indices:t -> num_weights:int -> padding_idx:int -> scale_grad_by_freq:bool -> t
val empty : size:int list -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val empty_like : t -> t
val empty_like1 : t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val empty_out : out:t -> size:int list -> t
val empty_strided : size:int list -> stride:int list -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val eq1 : t -> t -> t
val eq_ : t -> Torch_core.Wrapper.Scalar.t -> t
val eq_1 : t -> t -> t
val eq_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val eq_out1 : out:t -> t -> t -> t
val erf : t -> t
val erf_ : t -> t
val erf_out : out:t -> t -> t
val erfc : t -> t
val erfc_ : t -> t
val erfc_out : out:t -> t -> t
val erfinv : t -> t
val erfinv_ : t -> t
val erfinv_out : out:t -> t -> t
val exp : t -> t
val exp_ : t -> t
val exp_out : out:t -> t -> t
val expand : t -> size:int list -> implicit:bool -> t
val expand_as : t -> t -> t
val expm1 : t -> t
val expm1_ : t -> t
val expm1_out : out:t -> t -> t
val exponential_ : t -> lambd:float -> t
val eye : n:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val eye1 : n:int -> m:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val eye_out : out:t -> n:int -> t
val eye_out1 : out:t -> n:int -> m:int -> t
val fbgemm_linear_int8_weight : t -> weight:t -> packed:t -> col_offsets:t -> weight_scale:Torch_core.Wrapper.Scalar.t -> weight_zero_point:Torch_core.Wrapper.Scalar.t -> bias:t -> t
val fbgemm_pack_quantized_matrix : t -> k:int -> n:int -> t
val feature_alpha_dropout : t -> p:float -> train:bool -> t
val feature_alpha_dropout_ : t -> p:float -> train:bool -> t
val feature_dropout : t -> p:float -> train:bool -> t
val feature_dropout_ : t -> p:float -> train:bool -> t
val fft : t -> signal_ndim:int -> normalized:bool -> t
val fill_ : t -> value:Torch_core.Wrapper.Scalar.t -> t
val fill_1 : t -> value:t -> t
val flip : t -> dims:int list -> t
val floor : t -> t
val floor_ : t -> t
val floor_out : out:t -> t -> t
val fmod1 : t -> t -> t
val fmod_ : t -> Torch_core.Wrapper.Scalar.t -> t
val fmod_1 : t -> t -> t
val fmod_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val fmod_out1 : out:t -> t -> t -> t
val frac : t -> t
val frac_ : t -> t
val frac_out : out:t -> t -> t
val fractional_max_pool2d : t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val fractional_max_pool2d_backward : grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool2d_backward_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool2d_out : output:t -> indices:t -> t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val fractional_max_pool3d : t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val fractional_max_pool3d_backward : grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool3d_backward_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> output_size:int list -> indices:t -> t
val fractional_max_pool3d_out : output:t -> indices:t -> t -> kernel_size:int list -> output_size:int list -> random_samples:t -> t * t
val frobenius_norm : t -> t
val frobenius_norm1 : t -> dim:int list -> keepdim:bool -> t
val frobenius_norm_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val full : size:int list -> fill_value:Torch_core.Wrapper.Scalar.t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val full_like : t -> fill_value:Torch_core.Wrapper.Scalar.t -> t
val full_like1 : t -> fill_value:Torch_core.Wrapper.Scalar.t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val full_out : out:t -> size:int list -> fill_value:Torch_core.Wrapper.Scalar.t -> t
val gather : t -> dim:int -> index:t -> sparse_grad:bool -> t
val gather_out : out:t -> t -> dim:int -> index:t -> sparse_grad:bool -> t
val ge1 : t -> t -> t
val ge_ : t -> Torch_core.Wrapper.Scalar.t -> t
val ge_1 : t -> t -> t
val ge_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val ge_out1 : out:t -> t -> t -> t
val gels : t -> a:t -> t * t
val gels_out : x:t -> qr:t -> t -> a:t -> t * t
val geometric_ : t -> p:float -> t
val geqrf : t -> t * t
val geqrf_out : a:t -> tau:t -> t -> t * t
val ger : t -> vec2:t -> t
val ger_out : out:t -> t -> vec2:t -> t
val glu : t -> dim:int -> t
val glu_backward : grad_output:t -> t -> dim:int -> t
val glu_backward_out : grad_input:t -> grad_output:t -> t -> dim:int -> t
val glu_out : out:t -> t -> dim:int -> t
val grad : t -> t
val grid_sampler : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> t
val grid_sampler_2d : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> t
val grid_sampler_2d_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> t * t
val grid_sampler_3d : t -> grid:t -> interpolation_mode:int -> padding_mode:int -> t
val grid_sampler_3d_backward : grad_output:t -> t -> grid:t -> interpolation_mode:int -> padding_mode:int -> t * t
val group_norm : t -> num_groups:int -> weight:t option -> bias:t option -> eps:float -> cudnn_enabled:bool -> t
val gru : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
val gru1 : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
val gru_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
val gt1 : t -> t -> t
val gt_ : t -> Torch_core.Wrapper.Scalar.t -> t
val gt_1 : t -> t -> t
val gt_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val gt_out1 : out:t -> t -> t -> t
val hamming_window : window_length:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val hamming_window1 : window_length:int -> periodic:bool -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val hamming_window2 : window_length:int -> periodic:bool -> alpha:float -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val hamming_window3 : window_length:int -> periodic:bool -> alpha:float -> beta:float -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val hann_window : window_length:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val hann_window1 : window_length:int -> periodic:bool -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val hardshrink : t -> t
val hardshrink_backward : grad_out:t -> t -> lambd:Torch_core.Wrapper.Scalar.t -> t
val hardtanh : t -> t
val hardtanh_ : t -> t
val hardtanh_backward : grad_output:t -> t -> min_val:Torch_core.Wrapper.Scalar.t -> max_val:Torch_core.Wrapper.Scalar.t -> t
val hardtanh_backward_out : grad_input:t -> grad_output:t -> t -> min_val:Torch_core.Wrapper.Scalar.t -> max_val:Torch_core.Wrapper.Scalar.t -> t
val hardtanh_out : out:t -> t -> t
val hinge_embedding_loss : t -> target:t -> margin:float -> reduction:int -> t
val histc : t -> bins:int -> t
val histc_out : out:t -> t -> bins:int -> t
val hspmm : mat1:t -> mat2:t -> t
val hspmm_out : out:t -> mat1:t -> mat2:t -> t
val ifft : t -> signal_ndim:int -> normalized:bool -> t
val index : t -> indices:t list -> t
val index_add : t -> dim:int -> index:t -> source:t -> t
val index_add_ : t -> dim:int -> index:t -> source:t -> t
val index_copy : t -> dim:int -> index:t -> source:t -> t
val index_copy_ : t -> dim:int -> index:t -> source:t -> t
val index_fill : t -> dim:int -> index:t -> value:Torch_core.Wrapper.Scalar.t -> t
val index_fill1 : t -> dim:int -> index:t -> value:t -> t
val index_fill_ : t -> dim:int -> index:t -> value:Torch_core.Wrapper.Scalar.t -> t
val index_fill_1 : t -> dim:int -> index:t -> value:t -> t
val index_put : t -> indices:t list -> values:t -> accumulate:bool -> t
val index_put_ : t -> indices:t list -> values:t -> accumulate:bool -> t
val index_select : t -> dim:int -> index:t -> t
val index_select_out : out:t -> t -> dim:int -> index:t -> t
val indices : t -> t
val instance_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> use_input_stats:bool -> momentum:float -> eps:float -> cudnn_enabled:bool -> t
val int_repr : t -> t
val inverse : t -> t
val inverse_out : out:t -> t -> t
val irfft : t -> signal_ndim:int -> normalized:bool -> onesided:bool -> signal_sizes:int list -> t
val isclose : t -> t -> rtol:float -> atol:float -> equal_nan:bool -> t
val isnan : t -> t
val kl_div : t -> target:t -> reduction:int -> t
val kl_div_backward : grad_output:t -> t -> target:t -> reduction:int -> t
val kthvalue : t -> k:int -> dim:int -> keepdim:bool -> t * t
val kthvalue_out : values:t -> indices:t -> t -> k:int -> dim:int -> keepdim:bool -> t * t
val l1_loss : t -> target:t -> reduction:int -> t
val l1_loss_backward : grad_output:t -> t -> target:t -> reduction:int -> t
val l1_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:int -> t
val l1_loss_out : out:t -> t -> target:t -> reduction:int -> t
val layer_norm : t -> normalized_shape:int list -> weight:t option -> bias:t option -> eps:float -> cudnn_enable:bool -> t
val le1 : t -> t -> t
val le_ : t -> Torch_core.Wrapper.Scalar.t -> t
val le_1 : t -> t -> t
val le_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val le_out1 : out:t -> t -> t -> t
val leaky_relu : t -> t
val leaky_relu_ : t -> t
val leaky_relu_backward : grad_output:t -> t -> negative_slope:Torch_core.Wrapper.Scalar.t -> t
val leaky_relu_backward_out : grad_input:t -> grad_output:t -> t -> negative_slope:Torch_core.Wrapper.Scalar.t -> t
val leaky_relu_out : out:t -> t -> t
val lerp : t -> end_:t -> weight:Torch_core.Wrapper.Scalar.t -> t
val lerp1 : t -> end_:t -> weight:t -> t
val lerp_ : t -> end_:t -> weight:Torch_core.Wrapper.Scalar.t -> t
val lerp_1 : t -> end_:t -> weight:t -> t
val lerp_out : out:t -> t -> end_:t -> weight:Torch_core.Wrapper.Scalar.t -> t
val lerp_out1 : out:t -> t -> end_:t -> weight:t -> t
val lgamma : t -> t
val lgamma_ : t -> t
val lgamma_out : out:t -> t -> t
val linear : t -> weight:t -> bias:t option -> t
val linspace : start:Torch_core.Wrapper.Scalar.t -> end_:Torch_core.Wrapper.Scalar.t -> steps:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val linspace_out : out:t -> start:Torch_core.Wrapper.Scalar.t -> end_:Torch_core.Wrapper.Scalar.t -> steps:int -> t
val log : t -> t
val log10 : t -> t
val log10_ : t -> t
val log10_out : out:t -> t -> t
val log1p : t -> t
val log1p_ : t -> t
val log1p_out : out:t -> t -> t
val log2 : t -> t
val log2_ : t -> t
val log2_out : out:t -> t -> t
val log_ : t -> t
val log_normal_ : t -> mean:float -> std:float -> t
val log_out : out:t -> t -> t
val log_sigmoid : t -> t
val log_sigmoid_backward : grad_output:t -> t -> buffer:t -> t
val log_sigmoid_backward_out : grad_input:t -> grad_output:t -> t -> buffer:t -> t
val log_sigmoid_out : out:t -> t -> t
val log_softmax : t -> dim:int -> t
val log_softmax1 : t -> dim:int -> dtype:Torch_core.Kind.t -> t
val logdet : t -> t
val logspace : start:Torch_core.Wrapper.Scalar.t -> end_:Torch_core.Wrapper.Scalar.t -> steps:int -> base:float -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val logspace_out : out:t -> start:Torch_core.Wrapper.Scalar.t -> end_:Torch_core.Wrapper.Scalar.t -> steps:int -> base:float -> t
val logsumexp : t -> dim:int list -> keepdim:bool -> t
val logsumexp_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val lstm : t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t * t
val lstm1 : data:t -> batch_sizes:t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t * t
val lstm_cell : t -> hx:t list -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t * t
val lt1 : t -> t -> t
val lt_ : t -> Torch_core.Wrapper.Scalar.t -> t
val lt_1 : t -> t -> t
val lt_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val lt_out1 : out:t -> t -> t -> t
val lu_solve : t -> lu_data:t -> lu_pivots:t -> t
val lu_solve_out : out:t -> t -> lu_data:t -> lu_pivots:t -> t
val margin_ranking_loss : input1:t -> input2:t -> target:t -> margin:float -> reduction:int -> t
val masked_fill : t -> mask:t -> value:Torch_core.Wrapper.Scalar.t -> t
val masked_fill1 : t -> mask:t -> value:t -> t
val masked_fill_ : t -> mask:t -> value:Torch_core.Wrapper.Scalar.t -> t
val masked_fill_1 : t -> mask:t -> value:t -> t
val masked_scatter : t -> mask:t -> source:t -> t
val masked_scatter_ : t -> mask:t -> source:t -> t
val masked_select : t -> mask:t -> t
val masked_select_out : out:t -> t -> mask:t -> t
val matmul : t -> t -> t
val matmul_out : out:t -> t -> t -> t
val matrix_power : t -> n:int -> t
val matrix_rank : t -> symmetric:bool -> t
val matrix_rank1 : t -> tol:float -> symmetric:bool -> t
val max1 : t -> t -> t
val max2 : t -> dim:int -> keepdim:bool -> t * t
val max_out : out:t -> t -> t -> t
val max_out1 : max:t -> max_values:t -> t -> dim:int -> keepdim:bool -> t * t
val max_pool1d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val max_pool1d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool2d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool2d_with_indices_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool2d_with_indices_backward_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool2d_with_indices_out : output:t -> indices:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool3d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val max_pool3d_with_indices : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_pool3d_with_indices_backward : grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool3d_with_indices_backward_out : grad_input:t -> grad_output:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> indices:t -> t
val max_pool3d_with_indices_out : output:t -> indices:t -> t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t * t
val max_unpool2d : t -> indices:t -> output_size:int list -> t
val max_unpool2d_backward : grad_output:t -> t -> indices:t -> output_size:int list -> t
val max_unpool2d_backward_out : grad_input:t -> grad_output:t -> t -> indices:t -> output_size:int list -> t
val max_unpool2d_out : out:t -> t -> indices:t -> output_size:int list -> t
val max_unpool3d : t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_unpool3d_backward : grad_output:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_unpool3d_backward_out : grad_input:t -> grad_output:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_unpool3d_out : out:t -> t -> indices:t -> output_size:int list -> stride:int list -> padding:int list -> t
val max_values : t -> dim:int list -> keepdim:bool -> t
val mean1 : t -> dtype:Torch_core.Kind.t -> t
val mean2 : t -> dim:int list -> keepdim:bool -> t
val mean3 : t -> dim:int list -> dtype:Torch_core.Kind.t -> t
val mean4 : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val mean_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val mean_out1 : out:t -> t -> dim:int list -> dtype:Torch_core.Kind.t -> t
val mean_out2 : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val median : t -> t
val median1 : t -> dim:int -> keepdim:bool -> t * t
val median_out : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val meshgrid : t list -> t list
val min1 : t -> t -> t
val min2 : t -> dim:int -> keepdim:bool -> t * t
val min_out : out:t -> t -> t -> t
val min_out1 : min:t -> min_indices:t -> t -> dim:int -> keepdim:bool -> t * t
val min_values : t -> dim:int list -> keepdim:bool -> t
val miopen_batch_norm : t -> weight:t -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> exponential_average_factor:float -> epsilon:float -> t * t * t
val miopen_batch_norm_backward : t -> grad_output:t -> weight:t -> running_mean:t option -> running_var:t option -> save_mean:t option -> save_var:t option -> epsilon:float -> t * t * t
val miopen_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_backward_bias : grad_output:t -> t
val miopen_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_transpose : t -> weight:t -> bias:t option -> padding:int list -> output_padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_transpose_backward_input : grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_convolution_transpose_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_depthwise_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_depthwise_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val miopen_depthwise_convolution_backward_weight : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> benchmark:bool -> deterministic:bool -> t
val mkldnn_convolution : t -> weight:t -> bias:t option -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
val mkldnn_convolution_backward_input : self_size:int list -> grad_output:t -> weight:t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> t
val mkldnn_convolution_backward_weights : weight_size:int list -> grad_output:t -> t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> bias_defined:bool -> t * t
val mkldnn_linear : t -> weight:t -> bias:t option -> t
val mkldnn_max_pool2d : t -> kernel_size:int list -> stride:int list -> padding:int list -> dilation:int list -> ceil_mode:bool -> t
val mkldnn_reorder_conv2d_weight : t -> padding:int list -> stride:int list -> dilation:int list -> groups:int -> t
val mkldnn_reshape : t -> shape:int list -> t
val mm_out : out:t -> t -> mat2:t -> t
val mode : t -> dim:int -> keepdim:bool -> t * t
val mode_out : values:t -> indices:t -> t -> dim:int -> keepdim:bool -> t * t
val mse_loss_backward : grad_output:t -> t -> target:t -> reduction:int -> t
val mse_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:int -> t
val mse_loss_out : out:t -> t -> target:t -> reduction:int -> t
val mul : t -> t -> t
val mul_ : t -> t -> t
val mul_1 : t -> Torch_core.Wrapper.Scalar.t -> t
val mul_out : out:t -> t -> t -> t
val multi_margin_loss_backward : grad_output:t -> t -> target:t -> p:Torch_core.Wrapper.Scalar.t -> margin:Torch_core.Wrapper.Scalar.t -> weight:t -> reduction:int -> t
val multi_margin_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> p:Torch_core.Wrapper.Scalar.t -> margin:Torch_core.Wrapper.Scalar.t -> weight:t -> reduction:int -> t
val multilabel_margin_loss : t -> target:t -> reduction:int -> t
val multilabel_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:int -> is_target:t -> t
val multilabel_margin_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:int -> is_target:t -> t
val multilabel_margin_loss_out : out:t -> t -> target:t -> reduction:int -> t
val multinomial : t -> num_samples:int -> replacement:bool -> t
val multinomial_out : out:t -> t -> num_samples:int -> replacement:bool -> t
val mv : t -> vec:t -> t
val mv_out : out:t -> t -> vec:t -> t
val mvlgamma : t -> p:int -> t
val mvlgamma_ : t -> p:int -> t
val narrow : t -> dim:int -> start:int -> length:int -> t
val narrow_copy : t -> dim:int -> start:int -> length:int -> t
val native_batch_norm : t -> weight:t option -> bias:t option -> running_mean:t option -> running_var:t option -> training:bool -> momentum:float -> eps:float -> t * t * t
val native_norm : t -> t
val ne1 : t -> t -> t
val ne_ : t -> Torch_core.Wrapper.Scalar.t -> t
val ne_1 : t -> t -> t
val ne_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val ne_out1 : out:t -> t -> t -> t
val neg : t -> t
val neg_ : t -> t
val neg_out : out:t -> t -> t
val nll_loss2d : t -> target:t -> weight:t option -> reduction:int -> ignore_index:int -> t
val nll_loss2d_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:int -> ignore_index:int -> total_weight:t -> t
val nll_loss2d_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:int -> ignore_index:int -> total_weight:t -> t
val nll_loss2d_out : out:t -> t -> target:t -> weight:t option -> reduction:int -> ignore_index:int -> t
val nll_loss_backward : grad_output:t -> t -> target:t -> weight:t option -> reduction:int -> ignore_index:int -> total_weight:t -> t
val nll_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> weight:t option -> reduction:int -> ignore_index:int -> total_weight:t -> t
val nll_loss_out : out:t -> t -> target:t -> weight:t option -> reduction:int -> ignore_index:int -> t
val nonzero : t -> t
val nonzero_out : out:t -> t -> t
val norm : t -> t
val norm1 : t -> p:Torch_core.Wrapper.Scalar.t -> dtype:Torch_core.Kind.t -> t
val norm2 : t -> p:Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> t
val norm3 : t -> p:Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val norm_except_dim : v:t -> pow:int -> dim:int -> t
val norm_out : out:t -> t -> p:Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> t
val norm_out1 : out:t -> t -> p:Torch_core.Wrapper.Scalar.t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val normal : mean:t -> std:float -> t
val normal1 : mean:float -> std:t -> t
val normal2 : mean:t -> std:t -> t
val normal_ : t -> mean:float -> std:float -> t
val normal_out : out:t -> mean:t -> std:float -> t
val normal_out1 : out:t -> mean:float -> std:t -> t
val normal_out2 : out:t -> mean:t -> std:t -> t
val nuclear_norm : t -> keepdim:bool -> t
val nuclear_norm_out : out:t -> t -> keepdim:bool -> t
val one_hot : t -> num_classes:int -> t
val ones_like : t -> t
val ones_like1 : t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val ones_out : out:t -> size:int list -> t
val orgqr : t -> input2:t -> t
val orgqr_out : out:t -> t -> input2:t -> t
val ormqr : t -> input2:t -> input3:t -> left:bool -> transpose:bool -> t
val ormqr_out : out:t -> t -> input2:t -> input3:t -> left:bool -> transpose:bool -> t
val pairwise_distance : x1:t -> x2:t -> p:float -> eps:float -> keepdim:bool -> t
val pdist : t -> p:float -> t
val permute : t -> dims:int list -> t
val pin_memory : t -> t
val pinverse : t -> rcond:float -> t
val pixel_shuffle : t -> upscale_factor:int -> t
val poisson : t -> t
val polygamma : n:int -> t -> t
val polygamma_ : t -> n:int -> t
val polygamma_out : out:t -> n:int -> t -> t
val pow : t -> exponent:Torch_core.Wrapper.Scalar.t -> t
val pow1 : t -> exponent:t -> t
val pow2 : Torch_core.Wrapper.Scalar.t -> exponent:t -> t
val pow_ : t -> exponent:Torch_core.Wrapper.Scalar.t -> t
val pow_1 : t -> exponent:t -> t
val pow_out : out:t -> t -> exponent:Torch_core.Wrapper.Scalar.t -> t
val pow_out1 : out:t -> t -> exponent:t -> t
val pow_out2 : out:t -> Torch_core.Wrapper.Scalar.t -> exponent:t -> t
val prelu : t -> weight:t -> t
val prelu_backward : grad_output:t -> t -> weight:t -> t * t
val prod : t -> t
val prod1 : t -> dtype:Torch_core.Kind.t -> t
val prod2 : t -> dim:int -> keepdim:bool -> t
val prod3 : t -> dim:int -> dtype:Torch_core.Kind.t -> t
val prod4 : t -> dim:int -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val prod_out : out:t -> t -> dim:int -> keepdim:bool -> t
val prod_out1 : out:t -> t -> dim:int -> dtype:Torch_core.Kind.t -> t
val prod_out2 : out:t -> t -> dim:int -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val pstrf : t -> upper:bool -> t * t
val pstrf_out : u:t -> pivot:t -> t -> upper:bool -> t * t
val put_ : t -> index:t -> source:t -> accumulate:bool -> t
val qr : t -> t * t
val qr_out : q:t -> r:t -> t -> t * t
val quantize_linear : t -> scale:float -> zero_point:int -> t
val quantized_gru_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:Torch_core.Wrapper.Scalar.t -> scale_hh:Torch_core.Wrapper.Scalar.t -> zero_point_ih:Torch_core.Wrapper.Scalar.t -> zero_point_hh:Torch_core.Wrapper.Scalar.t -> t
val quantized_lstm : t -> hx:t list -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t * t
val quantized_lstm_cell : t -> hx:t list -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:Torch_core.Wrapper.Scalar.t -> scale_hh:Torch_core.Wrapper.Scalar.t -> zero_point_ih:Torch_core.Wrapper.Scalar.t -> zero_point_hh:Torch_core.Wrapper.Scalar.t -> t * t
val quantized_rnn_relu_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:Torch_core.Wrapper.Scalar.t -> scale_hh:Torch_core.Wrapper.Scalar.t -> zero_point_ih:Torch_core.Wrapper.Scalar.t -> zero_point_hh:Torch_core.Wrapper.Scalar.t -> t
val quantized_rnn_tanh_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t -> b_hh:t -> packed_ih:t -> packed_hh:t -> col_offsets_ih:t -> col_offsets_hh:t -> scale_ih:Torch_core.Wrapper.Scalar.t -> scale_hh:Torch_core.Wrapper.Scalar.t -> zero_point_ih:Torch_core.Wrapper.Scalar.t -> zero_point_hh:Torch_core.Wrapper.Scalar.t -> t
val rand_like : t -> t
val rand_like1 : t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val rand_out : out:t -> size:int list -> t
val randint : high:int -> size:int list -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val randint1 : low:int -> high:int -> size:int list -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val randint_like : t -> high:int -> t
val randint_like1 : t -> low:int -> high:int -> t
val randint_like2 : t -> high:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val randint_like3 : t -> low:int -> high:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val randint_out : out:t -> high:int -> size:int list -> t
val randint_out1 : out:t -> low:int -> high:int -> size:int list -> t
val randn_like : t -> t
val randn_like1 : t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val randn_out : out:t -> size:int list -> t
val random_ : t -> t
val random_1 : t -> to_:int -> t
val random_2 : t -> from:int -> to_:int -> t
val randperm : n:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val randperm_out : out:t -> n:int -> t
val range_out : out:t -> start:Torch_core.Wrapper.Scalar.t -> end_:Torch_core.Wrapper.Scalar.t -> t
val reciprocal : t -> t
val reciprocal_ : t -> t
val reciprocal_out : out:t -> t -> t
val reflection_pad1d : t -> padding:int list -> t
val reflection_pad1d_backward : grad_output:t -> t -> padding:int list -> t
val reflection_pad1d_backward_out : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val reflection_pad1d_out : out:t -> t -> padding:int list -> t
val reflection_pad2d : t -> padding:int list -> t
val reflection_pad2d_backward : grad_output:t -> t -> padding:int list -> t
val reflection_pad2d_backward_out : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val reflection_pad2d_out : out:t -> t -> padding:int list -> t
val relu : t -> t
val relu_ : t -> t
val remainder : t -> Torch_core.Wrapper.Scalar.t -> t
val remainder1 : t -> t -> t
val remainder_ : t -> Torch_core.Wrapper.Scalar.t -> t
val remainder_1 : t -> t -> t
val remainder_out : out:t -> t -> Torch_core.Wrapper.Scalar.t -> t
val remainder_out1 : out:t -> t -> t -> t
val renorm : t -> p:Torch_core.Wrapper.Scalar.t -> dim:int -> maxnorm:Torch_core.Wrapper.Scalar.t -> t
val renorm_ : t -> p:Torch_core.Wrapper.Scalar.t -> dim:int -> maxnorm:Torch_core.Wrapper.Scalar.t -> t
val renorm_out : out:t -> t -> p:Torch_core.Wrapper.Scalar.t -> dim:int -> maxnorm:Torch_core.Wrapper.Scalar.t -> t
val repeat : t -> repeats:int list -> t
val repeat_interleave : repeats:t -> t
val repeat_interleave1 : t -> repeats:t -> dim:int -> t
val repeat_interleave2 : t -> repeats:int -> dim:int -> t
val replication_pad1d : t -> padding:int list -> t
val replication_pad1d_backward : grad_output:t -> t -> padding:int list -> t
val replication_pad1d_backward_out : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val replication_pad1d_out : out:t -> t -> padding:int list -> t
val replication_pad2d : t -> padding:int list -> t
val replication_pad2d_backward : grad_output:t -> t -> padding:int list -> t
val replication_pad2d_backward_out : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val replication_pad2d_out : out:t -> t -> padding:int list -> t
val replication_pad3d : t -> padding:int list -> t
val replication_pad3d_backward : grad_output:t -> t -> padding:int list -> t
val replication_pad3d_backward_out : grad_input:t -> grad_output:t -> t -> padding:int list -> t
val replication_pad3d_out : out:t -> t -> padding:int list -> t
val reshape : t -> shape:int list -> t
val reshape_as : t -> t -> t
val resize_ : t -> size:int list -> t
val resize_as_ : t -> the_template:t -> t
val rfft : t -> signal_ndim:int -> normalized:bool -> onesided:bool -> t
val rnn_relu : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
val rnn_relu1 : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
val rnn_relu_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
val rnn_tanh : t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> batch_first:bool -> t * t
val rnn_tanh1 : data:t -> batch_sizes:t -> hx:t -> params:t list -> has_biases:bool -> num_layers:int -> dropout:float -> train:bool -> bidirectional:bool -> t * t
val rnn_tanh_cell : t -> hx:t -> w_ih:t -> w_hh:t -> b_ih:t option -> b_hh:t option -> t
val roll : t -> shifts:int list -> dims:int list -> t
val rot90 : t -> k:int -> dims:int list -> t
val round : t -> t
val round_ : t -> t
val round_out : out:t -> t -> t
val rrelu : t -> training:bool -> t
val rrelu_ : t -> training:bool -> t
val rrelu_with_noise : t -> noise:t -> training:bool -> t
val rrelu_with_noise_ : t -> noise:t -> training:bool -> t
val rrelu_with_noise_backward : grad_output:t -> t -> noise:t -> lower:Torch_core.Wrapper.Scalar.t -> upper:Torch_core.Wrapper.Scalar.t -> training:bool -> t
val rrelu_with_noise_backward_out : grad_input:t -> grad_output:t -> t -> noise:t -> lower:Torch_core.Wrapper.Scalar.t -> upper:Torch_core.Wrapper.Scalar.t -> training:bool -> t
val rrelu_with_noise_out : out:t -> t -> noise:t -> training:bool -> t
val rsqrt : t -> t
val rsqrt_ : t -> t
val rsqrt_out : out:t -> t -> t
val rsub : t -> t -> t
val rsub1 : t -> Torch_core.Wrapper.Scalar.t -> t
val s_copy_ : t -> src:t -> non_blocking:bool -> t
val s_native_addmm : t -> mat1:t -> mat2:t -> t
val s_native_addmm_ : t -> mat1:t -> mat2:t -> t
val s_native_addmm_out : out:t -> t -> mat1:t -> mat2:t -> t
val scalar_tensor : s:Torch_core.Wrapper.Scalar.t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val scatter : t -> dim:int -> index:t -> src:t -> t
val scatter1 : t -> dim:int -> index:t -> value:Torch_core.Wrapper.Scalar.t -> t
val scatter_ : t -> dim:int -> index:t -> src:t -> t
val scatter_1 : t -> dim:int -> index:t -> value:Torch_core.Wrapper.Scalar.t -> t
val scatter_add : t -> dim:int -> index:t -> src:t -> t
val scatter_add_ : t -> dim:int -> index:t -> src:t -> t
val selu : t -> t
val selu_ : t -> t
val set_ : t -> t
val set_1 : t -> source:t -> t
val set_requires_grad : t -> r:bool -> t
val sigmoid : t -> t
val sigmoid_ : t -> t
val sigmoid_backward : grad_output:t -> output:t -> t
val sigmoid_backward_out : grad_input:t -> grad_output:t -> output:t -> t
val sigmoid_out : out:t -> t -> t
val sign : t -> t
val sign_ : t -> t
val sign_out : out:t -> t -> t
val sin : t -> t
val sin_ : t -> t
val sin_out : out:t -> t -> t
val sinh : t -> t
val sinh_ : t -> t
val sinh_out : out:t -> t -> t
val slice : t -> dim:int -> start:int -> end_:int -> step:int -> t
val slogdet : t -> t * t
val smm : t -> mat2:t -> t
val smooth_l1_loss : t -> target:t -> reduction:int -> t
val smooth_l1_loss_backward : grad_output:t -> t -> target:t -> reduction:int -> t
val smooth_l1_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:int -> t
val smooth_l1_loss_out : out:t -> t -> target:t -> reduction:int -> t
val soft_margin_loss : t -> target:t -> reduction:int -> t
val soft_margin_loss_backward : grad_output:t -> t -> target:t -> reduction:int -> t
val soft_margin_loss_backward_out : grad_input:t -> grad_output:t -> t -> target:t -> reduction:int -> t
val soft_margin_loss_out : out:t -> t -> target:t -> reduction:int -> t
val softmax : t -> dim:int -> t
val softmax1 : t -> dim:int -> dtype:Torch_core.Kind.t -> t
val softplus : t -> t
val softplus_backward : grad_output:t -> t -> beta:Torch_core.Wrapper.Scalar.t -> threshold:Torch_core.Wrapper.Scalar.t -> output:t -> t
val softplus_backward_out : grad_input:t -> grad_output:t -> t -> beta:Torch_core.Wrapper.Scalar.t -> threshold:Torch_core.Wrapper.Scalar.t -> output:t -> t
val softplus_out : out:t -> t -> t
val softshrink : t -> t
val softshrink_backward : grad_output:t -> t -> lambd:Torch_core.Wrapper.Scalar.t -> t
val softshrink_backward_out : grad_input:t -> grad_output:t -> t -> lambd:Torch_core.Wrapper.Scalar.t -> t
val softshrink_out : out:t -> t -> t
val solve : t -> a:t -> t * t
val solve_out : solution:t -> lu:t -> t -> a:t -> t * t
val sort : t -> dim:int -> descending:bool -> t * t
val sort_out : values:t -> indices:t -> t -> dim:int -> descending:bool -> t * t
val sparse_coo_tensor : size:int list -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val sparse_coo_tensor1 : indices:t -> values:t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val sparse_coo_tensor2 : indices:t -> values:t -> size:int list -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val sparse_resize_ : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
val sparse_resize_and_clear_ : t -> size:int list -> sparse_dim:int -> dense_dim:int -> t
val split : t -> split_size:int -> dim:int -> t list
val split_with_sizes : t -> split_sizes:int list -> dim:int -> t list
val sqrt : t -> t
val sqrt_ : t -> t
val sqrt_out : out:t -> t -> t
val squeeze : t -> t
val squeeze1 : t -> dim:int -> t
val squeeze_ : t -> t
val squeeze_1 : t -> dim:int -> t
val sspaddmm : t -> mat1:t -> mat2:t -> t
val sspaddmm_out : out:t -> t -> mat1:t -> mat2:t -> t
val stack : t list -> dim:int -> t
val stack_out : out:t -> t list -> dim:int -> t
val std : t -> unbiased:bool -> t
val std1 : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val std_out : out:t -> t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val stft : t -> n_fft:int -> hop_length:int -> win_length:int -> window:t option -> normalized:bool -> onesided:bool -> t
val sub : t -> t -> t
val sub_ : t -> t -> t
val sub_1 : t -> Torch_core.Wrapper.Scalar.t -> t
val sub_out : out:t -> t -> t -> t
val sum1 : t -> dtype:Torch_core.Kind.t -> t
val sum2 : t -> dim:int list -> keepdim:bool -> t
val sum3 : t -> dim:int list -> dtype:Torch_core.Kind.t -> t
val sum4 : t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val sum_out : out:t -> t -> dim:int list -> keepdim:bool -> t
val sum_out1 : out:t -> t -> dim:int list -> dtype:Torch_core.Kind.t -> t
val sum_out2 : out:t -> t -> dim:int list -> keepdim:bool -> dtype:Torch_core.Kind.t -> t
val sum_to_size : t -> size:int list -> t
val svd : t -> some:bool -> compute_uv:bool -> t * t * t
val svd_out : u:t -> s:t -> v:t -> t -> some:bool -> compute_uv:bool -> t * t * t
val symeig : t -> eigenvectors:bool -> upper:bool -> t * t
val symeig_out : e:t -> v:t -> t -> eigenvectors:bool -> upper:bool -> t * t
val tr : t -> t
val t_ : t -> t
val take : t -> index:t -> t
val take_out : out:t -> t -> index:t -> t
val tan : t -> t
val tan_ : t -> t
val tan_out : out:t -> t -> t
val tanh : t -> t
val tanh_ : t -> t
val tanh_backward : grad_output:t -> output:t -> t
val tanh_backward_out : grad_input:t -> grad_output:t -> output:t -> t
val tanh_out : out:t -> t -> t
val tensordot : t -> t -> dims_self:int list -> dims_other:int list -> t
val threshold : t -> threshold:Torch_core.Wrapper.Scalar.t -> value:Torch_core.Wrapper.Scalar.t -> t
val threshold_ : t -> threshold:Torch_core.Wrapper.Scalar.t -> value:Torch_core.Wrapper.Scalar.t -> t
val threshold_backward : grad_output:t -> t -> threshold:Torch_core.Wrapper.Scalar.t -> t
val threshold_out : out:t -> t -> threshold:Torch_core.Wrapper.Scalar.t -> value:Torch_core.Wrapper.Scalar.t -> t
val to_ : t -> device:Torch_core.Device.t -> t
val to1 : t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> non_blocking:bool -> copy:bool -> t
val to2 : t -> dtype:Torch_core.Kind.t -> non_blocking:bool -> copy:bool -> t
val to3 : t -> t -> non_blocking:bool -> copy:bool -> t
val to4 : t -> device:Torch_core.Device.t -> dtype:Torch_core.Kind.t -> non_blocking:bool -> copy:bool -> t
val to_dense : t -> t
val to_dense_backward : grad:t -> t -> t
val to_mkldnn : t -> t
val to_mkldnn_backward : grad:t -> t -> t
val to_sparse : t -> t
val to_sparse1 : t -> sparse_dim:int -> t
val topk : t -> k:int -> dim:int -> largest:bool -> sorted:bool -> t * t
val topk_out : values:t -> indices:t -> t -> k:int -> dim:int -> largest:bool -> sorted:bool -> t * t
val totype : t -> scalar_type:Torch_core.Kind.t -> t
val trace : t -> t
val transpose : t -> dim0:int -> dim1:int -> t
val transpose_ : t -> dim0:int -> dim1:int -> t
val triangular_solve : t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
val triangular_solve_out : x:t -> m:t -> t -> a:t -> upper:bool -> transpose:bool -> unitriangular:bool -> t * t
val tril : t -> diagonal:int -> t
val tril_ : t -> diagonal:int -> t
val tril_indices : row:int -> col:int -> offset:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val tril_out : out:t -> t -> diagonal:int -> t
val triplet_margin_loss : anchor:t -> positive:t -> negative:t -> margin:float -> p:float -> eps:float -> swap:bool -> reduction:int -> t
val triu : t -> diagonal:int -> t
val triu_ : t -> diagonal:int -> t
val triu_indices : row:int -> col:int -> offset:int -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val triu_out : out:t -> t -> diagonal:int -> t
val trunc : t -> t
val trunc_ : t -> t
val trunc_out : out:t -> t -> t
val type_as : t -> t -> t
val unbind : t -> dim:int -> t list
val unfold : t -> dimension:int -> size:int -> step:int -> t
val uniform_ : t -> from:float -> to_:float -> t
val unique_consecutive : t -> return_inverse:bool -> return_counts:bool -> dim:int -> t * t * t
val unique_dim : t -> dim:int -> sorted:bool -> return_inverse:bool -> return_counts:bool -> t * t * t
val unique_dim_consecutive : t -> dim:int -> return_inverse:bool -> return_counts:bool -> t * t * t
val unsqueeze : t -> dim:int -> t
val unsqueeze_ : t -> dim:int -> t
val upsample_bicubic2d : t -> output_size:int list -> align_corners:bool -> t
val upsample_bicubic2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_bicubic2d_backward_out : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_bicubic2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> t
val upsample_bilinear2d : t -> output_size:int list -> align_corners:bool -> t
val upsample_bilinear2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_bilinear2d_backward_out : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_bilinear2d_out : out:t -> t -> output_size:int list -> align_corners:bool -> t
val upsample_linear1d : t -> output_size:int list -> align_corners:bool -> t
val upsample_linear1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_linear1d_backward_out : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_linear1d_out : out:t -> t -> output_size:int list -> align_corners:bool -> t
val upsample_nearest1d : t -> output_size:int list -> t
val upsample_nearest1d_backward : grad_output:t -> output_size:int list -> input_size:int list -> t
val upsample_nearest1d_backward_out : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> t
val upsample_nearest1d_out : out:t -> t -> output_size:int list -> t
val upsample_nearest2d : t -> output_size:int list -> t
val upsample_nearest2d_backward : grad_output:t -> output_size:int list -> input_size:int list -> t
val upsample_nearest2d_backward_out : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> t
val upsample_nearest2d_out : out:t -> t -> output_size:int list -> t
val upsample_nearest3d : t -> output_size:int list -> t
val upsample_nearest3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> t
val upsample_nearest3d_backward_out : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> t
val upsample_nearest3d_out : out:t -> t -> output_size:int list -> t
val upsample_trilinear3d : t -> output_size:int list -> align_corners:bool -> t
val upsample_trilinear3d_backward : grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_trilinear3d_backward_out : grad_input:t -> grad_output:t -> output_size:int list -> input_size:int list -> align_corners:bool -> t
val upsample_trilinear3d_out : out:t -> t -> output_size:int list -> align_corners:bool -> t
val values : t -> t
val var : t -> unbiased:bool -> t
val var1 : t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val var_out : out:t -> t -> dim:int list -> unbiased:bool -> keepdim:bool -> t
val view : t -> size:int list -> t
val view_as : t -> t -> t
val where : condition:t -> t -> t -> t
val zero_ : t -> t
val zeros_like : t -> t
val zeros_like1 : t -> options:(Torch_core.Kind.t * Torch_core.Device.t) -> t
val zeros_out : out:t -> size:int list -> t
val new_tensor : unit -> t
val int_vec : ?kind:[ `int | `int16 | `int64 | `int8 | `uint8 ] -> int list -> t
val shape : t -> int list
val shape1_exn : t -> int
val shape2_exn : t -> int * int
val shape3_exn : t -> int * int * int
val shape4_exn : t -> int * int * int * int
val kind : t -> Torch_core.Kind.t
val requires_grad : t -> bool
val grad_set_enabled : bool -> bool
val get : t -> int -> t
val select : t -> dim:int -> index:int -> t
val float_value : t -> float
val int_value : t -> int
val float_get : t -> int list -> float
val int_get : t -> int list -> int
val float_set : t -> int list -> float -> unit
val int_set : t -> int list -> int -> unit
val fill_float : t -> float -> unit
val fill_int : t -> int -> unit
val backward : ?keep_graph:bool -> ?create_graph:bool -> t -> unit
val run_backward : ?keep_graph:bool -> ?create_graph:bool -> t list -> t list -> t list
val print : t -> unit
val to_string : t -> line_size:int -> string
val sum : t -> t
val mean : t -> t
val argmax : ?dim:int -> ?keepdim:bool -> t -> t
val defined : t -> bool
val copy_ : t -> src:t -> unit
val max : t -> t -> t
val min : t -> t -> t
val set_float2 : t -> int -> int -> float -> unit
val set_float1 : t -> int -> float -> unit
val set_int2 : t -> int -> int -> int -> unit
val set_int1 : t -> int -> int -> unit
val get_float2 : t -> int -> int -> float
val get_float1 : t -> int -> float
val get_int2 : t -> int -> int -> int
val get_int1 : t -> int -> int
val (.%{}) : t -> int list -> int
val (.%{}<-) : t -> int list -> int -> unit
val (.%.{}) : t -> int list -> float
val (.%.{}<-) : t -> int list -> float -> unit
val (.%[]) : t -> int -> int
val (.%[]<-) : t -> int -> int -> unit
val (.%.[]) : t -> int -> float
val (.%.[]<-) : t -> int -> float -> unit
val no_grad_ : t -> f:(t -> 'a) -> 'a
val no_grad : (unit -> 'a) -> 'a
val zero_grad : t -> unit
val (+) : t -> t -> t
val (-) : t -> t -> t
val (*) : t -> t -> t
val (/) : t -> t -> t
val (+=) : t -> t -> unit
val (-=) : t -> t -> unit
val (*=) : t -> t -> unit
val (/=) : t -> t -> unit
val (~-) : t -> t
val (=) : t -> t -> t
val mm : t -> t -> t
val f : float -> t
type create = ?requires_grad:bool -> ?kind:Torch_core.Kind.t -> ?device:Torch_core.Device.t -> ?scale:float -> int list -> t
val zeros : create
val ones : create
val rand : create
val randn : create
val float_vec : ?kind:[ `double | `float | `half ] -> ?device:Torch_core.Device.t -> float list -> t
val to_type : t -> type_:Torch_core.Kind.t -> t
val type_ : t -> Torch_core.Kind.t
val to_device : ?device:Torch_core.Device.t -> t -> t
val to_float0 : t -> float option
val to_float1 : t -> float array option
val to_float2 : t -> float array array option
val to_float3 : t -> float array array array option
val to_float0_exn : t -> float
val to_float1_exn : t -> float array
val to_float2_exn : t -> float array array
val to_float3_exn : t -> float array array array
val to_int0 : t -> int option
val to_int1 : t -> int array option
val to_int2 : t -> int array array option
val to_int3 : t -> int array array array option
val to_int0_exn : t -> int
val to_int1_exn : t -> int array
val to_int2_exn : t -> int array array
val to_int3_exn : t -> int array array array
val of_float0 : float -> t
val of_float1 : float array -> t
val of_float2 : float array array -> t
val of_float3 : float array array array -> t
val of_int0 : int -> t
val of_int1 : int array -> t
val of_int2 : int array array -> t
val of_int3 : int array array array -> t
val conv2d : ?padding:(int * int) -> ?dilation:(int * int) -> ?groups:int -> t -> t -> t option -> stride:(int * int) -> t
val conv_transpose2d : ?output_padding:(int * int) -> ?padding:(int * int) -> ?dilation:(int * int) -> ?groups:int -> t -> t -> t option -> stride:(int * int) -> t
val max_pool2d : ?padding:(int * int) -> ?dilation:(int * int) -> ?ceil_mode:bool -> ?stride:(int * int) -> t -> ksize:(int * int) -> t
val avg_pool2d : ?padding:(int * int) -> ?count_include_pad:bool -> ?ceil_mode:bool -> ?stride:(int * int) -> t -> ksize:(int * int) -> t
val const_batch_norm : ?momentum:float -> ?eps:float -> t -> t
val of_bigarray : ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t -> t
val copy_to_bigarray : t -> ('b, 'a, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t -> unit
val to_bigarray : t -> kind:('a, 'b) Stdlib.Bigarray.kind -> ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
val cross_entropy_for_logits : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
val dropout : t -> p:float -> is_training:bool -> t
val nll_loss : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
val bce_loss : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
val bce_loss_with_logits : ?reduction:Torch_core.Reduction.t -> t -> targets:t -> t
val mse_loss : ?reduction:Torch_core.Reduction.t -> t -> t -> t
val huber_loss : ?reduction:Torch_core.Reduction.t -> t -> t -> t
val pp : Stdlib.Format.formatter -> t -> unit
val copy : t -> t
val shape_str : t -> string
val print_shape : ?name:string -> t -> unit
val minimum : t -> t
val maximum : t -> t
val flatten : t -> t
val squeeze_last : t -> t
val scale : t -> float -> t
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