// generated by rake generate:functions
// do not edit by hand

#include <torch/torch.h>
#include <rice/Module.hpp>
#include "templates.hpp"

void add_torch_functions(Module m) {
  m
  .define_singleton_method(
    "_abs",
    *[](const Tensor &self) {
      return torch::abs(self);
    })
  .define_singleton_method(
    "_abs_",
    *[](Tensor &self) {
      return torch::abs_(self);
    })
  .define_singleton_method(
    "_abs_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::abs_out(out, self);
    })
  .define_singleton_method(
    "_acos",
    *[](const Tensor &self) {
      return torch::acos(self);
    })
  .define_singleton_method(
    "_acos_",
    *[](Tensor &self) {
      return torch::acos_(self);
    })
  .define_singleton_method(
    "_acos_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::acos_out(out, self);
    })
  .define_singleton_method(
    "_adaptive_avg_pool1d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::adaptive_avg_pool1d(self, output_size);
    })
  .define_singleton_method(
    "_adaptive_max_pool1d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return wrap(torch::adaptive_max_pool1d(self, output_size));
    })
  .define_singleton_method(
    "_add_out",
    *[](const Tensor &self, const Tensor &other, Scalar alpha, Tensor &out) {
      return torch::add_out(out, self, other, alpha);
    })
  .define_singleton_method(
    "_add_scalar",
    *[](const Tensor &self, Scalar other, Scalar alpha) {
      return torch::add(self, other, alpha);
    })
  .define_singleton_method(
    "_add_tensor",
    *[](const Tensor &self, const Tensor &other, Scalar alpha) {
      return torch::add(self, other, alpha);
    })
  .define_singleton_method(
    "_addbmm",
    *[](const Tensor &self, const Tensor &batch1, const Tensor &batch2, Scalar beta, Scalar alpha) {
      return torch::addbmm(self, batch1, batch2, beta, alpha);
    })
  .define_singleton_method(
    "_addbmm_out",
    *[](const Tensor &self, const Tensor &batch1, const Tensor &batch2, Scalar beta, Scalar alpha, Tensor &out) {
      return torch::addbmm_out(out, self, batch1, batch2, beta, alpha);
    })
  .define_singleton_method(
    "_addcdiv",
    *[](const Tensor &self, const Tensor &tensor1, const Tensor &tensor2, Scalar value) {
      return torch::addcdiv(self, tensor1, tensor2, value);
    })
  .define_singleton_method(
    "_addcdiv_out",
    *[](const Tensor &self, const Tensor &tensor1, const Tensor &tensor2, Scalar value, Tensor &out) {
      return torch::addcdiv_out(out, self, tensor1, tensor2, value);
    })
  .define_singleton_method(
    "_addcmul",
    *[](const Tensor &self, const Tensor &tensor1, const Tensor &tensor2, Scalar value) {
      return torch::addcmul(self, tensor1, tensor2, value);
    })
  .define_singleton_method(
    "_addcmul_out",
    *[](const Tensor &self, const Tensor &tensor1, const Tensor &tensor2, Scalar value, Tensor &out) {
      return torch::addcmul_out(out, self, tensor1, tensor2, value);
    })
  .define_singleton_method(
    "_addmm",
    *[](const Tensor &self, const Tensor &mat1, const Tensor &mat2, Scalar beta, Scalar alpha) {
      return torch::addmm(self, mat1, mat2, beta, alpha);
    })
  .define_singleton_method(
    "_addmm_out",
    *[](const Tensor &self, const Tensor &mat1, const Tensor &mat2, Scalar beta, Scalar alpha, Tensor &out) {
      return torch::addmm_out(out, self, mat1, mat2, beta, alpha);
    })
  .define_singleton_method(
    "_addmv",
    *[](const Tensor &self, const Tensor &mat, const Tensor &vec, Scalar beta, Scalar alpha) {
      return torch::addmv(self, mat, vec, beta, alpha);
    })
  .define_singleton_method(
    "_addmv_",
    *[](Tensor &self, const Tensor &mat, const Tensor &vec, Scalar beta, Scalar alpha) {
      return torch::addmv_(self, mat, vec, beta, alpha);
    })
  .define_singleton_method(
    "_addmv_out",
    *[](const Tensor &self, const Tensor &mat, const Tensor &vec, Scalar beta, Scalar alpha, Tensor &out) {
      return torch::addmv_out(out, self, mat, vec, beta, alpha);
    })
  .define_singleton_method(
    "_addr",
    *[](const Tensor &self, const Tensor &vec1, const Tensor &vec2, Scalar beta, Scalar alpha) {
      return torch::addr(self, vec1, vec2, beta, alpha);
    })
  .define_singleton_method(
    "_addr_out",
    *[](const Tensor &self, const Tensor &vec1, const Tensor &vec2, Scalar beta, Scalar alpha, Tensor &out) {
      return torch::addr_out(out, self, vec1, vec2, beta, alpha);
    })
  .define_singleton_method(
    "_affine_grid_generator",
    *[](const Tensor &theta, IntArrayRef size, bool align_corners) {
      return torch::affine_grid_generator(theta, size, align_corners);
    })
  .define_singleton_method(
    "_alias",
    *[](Tensor &self) {
      return torch::alias(self);
    })
  .define_singleton_method(
    "_align_tensors",
    *[](TensorList tensors) {
      return torch::align_tensors(tensors);
    })
  .define_singleton_method(
    "_all",
    *[](const Tensor &self) {
      return torch::all(self);
    })
  .define_singleton_method(
    "_all_dim",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return torch::all(self, dim, keepdim);
    })
  .define_singleton_method(
    "_all_out",
    *[](const Tensor &self, int64_t dim, bool keepdim, Tensor &out) {
      return torch::all_out(out, self, dim, keepdim);
    })
  .define_singleton_method(
    "_allclose",
    *[](const Tensor &self, const Tensor &other, double rtol, double atol, bool equal_nan) {
      return torch::allclose(self, other, rtol, atol, equal_nan);
    })
  .define_singleton_method(
    "_alpha_dropout",
    *[](const Tensor &input, double p, bool train) {
      return torch::alpha_dropout(input, p, train);
    })
  .define_singleton_method(
    "_alpha_dropout_",
    *[](Tensor &self, double p, bool train) {
      return torch::alpha_dropout_(self, p, train);
    })
  .define_singleton_method(
    "_angle",
    *[](const Tensor &self) {
      return torch::angle(self);
    })
  .define_singleton_method(
    "_angle_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::angle_out(out, self);
    })
  .define_singleton_method(
    "_any",
    *[](const Tensor &self) {
      return torch::any(self);
    })
  .define_singleton_method(
    "_any_dim",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return torch::any(self, dim, keepdim);
    })
  .define_singleton_method(
    "_any_out",
    *[](const Tensor &self, int64_t dim, bool keepdim, Tensor &out) {
      return torch::any_out(out, self, dim, keepdim);
    })
  .define_singleton_method(
    "_arange_out",
    *[](Scalar end, Tensor &out) {
      return torch::arange_out(out, end);
    })
  .define_singleton_method(
    "_arange_start_out",
    *[](Scalar start, Scalar end, Scalar step, Tensor &out) {
      return torch::arange_out(out, start, end, step);
    })
  .define_singleton_method(
    "_argmax",
    *[](const Tensor &self) {
      return torch::argmax(self);
    })
  .define_singleton_method(
    "_argmax_dim",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return torch::argmax(self, dim, keepdim);
    })
  .define_singleton_method(
    "_argmin",
    *[](const Tensor &self) {
      return torch::argmin(self);
    })
  .define_singleton_method(
    "_argmin_dim",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return torch::argmin(self, dim, keepdim);
    })
  .define_singleton_method(
    "_argsort",
    *[](const Tensor &self, int64_t dim, bool descending) {
      return torch::argsort(self, dim, descending);
    })
  .define_singleton_method(
    "_as_strided",
    *[](Tensor &self, IntArrayRef size, IntArrayRef stride) {
      return torch::as_strided(self, size, stride);
    })
  .define_singleton_method(
    "_as_strided_",
    *[](Tensor &self, IntArrayRef size, IntArrayRef stride) {
      return torch::as_strided_(self, size, stride);
    })
  .define_singleton_method(
    "_as_strided__storage_offset",
    *[](Tensor &self, IntArrayRef size, IntArrayRef stride, int64_t storage_offset) {
      return torch::as_strided_(self, size, stride, storage_offset);
    })
  .define_singleton_method(
    "_as_strided_storage_offset",
    *[](Tensor &self, IntArrayRef size, IntArrayRef stride, int64_t storage_offset) {
      return torch::as_strided(self, size, stride, storage_offset);
    })
  .define_singleton_method(
    "_asin",
    *[](const Tensor &self) {
      return torch::asin(self);
    })
  .define_singleton_method(
    "_asin_",
    *[](Tensor &self) {
      return torch::asin_(self);
    })
  .define_singleton_method(
    "_asin_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::asin_out(out, self);
    })
  .define_singleton_method(
    "_atan",
    *[](const Tensor &self) {
      return torch::atan(self);
    })
  .define_singleton_method(
    "_atan2",
    *[](const Tensor &self, const Tensor &other) {
      return torch::atan2(self, other);
    })
  .define_singleton_method(
    "_atan2_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::atan2_out(out, self, other);
    })
  .define_singleton_method(
    "_atan_",
    *[](Tensor &self) {
      return torch::atan_(self);
    })
  .define_singleton_method(
    "_atan_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::atan_out(out, self);
    })
  .define_singleton_method(
    "_avg_pool1d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, bool ceil_mode, bool count_include_pad) {
      return torch::avg_pool1d(self, kernel_size, stride, padding, ceil_mode, count_include_pad);
    })
  .define_singleton_method(
    "_baddbmm",
    *[](const Tensor &self, const Tensor &batch1, const Tensor &batch2, Scalar beta, Scalar alpha) {
      return torch::baddbmm(self, batch1, batch2, beta, alpha);
    })
  .define_singleton_method(
    "_baddbmm_out",
    *[](const Tensor &self, const Tensor &batch1, const Tensor &batch2, Scalar beta, Scalar alpha, Tensor &out) {
      return torch::baddbmm_out(out, self, batch1, batch2, beta, alpha);
    })
  .define_singleton_method(
    "_batch_norm",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, OptionalTensor running_mean, OptionalTensor running_var, bool training, double momentum, double eps, bool cudnn_enabled) {
      return torch::batch_norm(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled);
    })
  .define_singleton_method(
    "_batch_norm_backward_elemt",
    *[](const Tensor &grad_out, const Tensor &input, const Tensor &mean, const Tensor &invstd, OptionalTensor weight, const Tensor &mean_dy, const Tensor &mean_dy_xmu) {
      return torch::batch_norm_backward_elemt(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu);
    })
  .define_singleton_method(
    "_batch_norm_backward_reduce",
    *[](const Tensor &grad_out, const Tensor &input, const Tensor &mean, const Tensor &invstd, OptionalTensor weight, bool input_g, bool weight_g, bool bias_g) {
      return wrap(torch::batch_norm_backward_reduce(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g));
    })
  .define_singleton_method(
    "_batch_norm_elemt",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, const Tensor &mean, const Tensor &invstd, double eps) {
      return torch::batch_norm_elemt(input, weight, bias, mean, invstd, eps);
    })
  .define_singleton_method(
    "_batch_norm_elemt_out",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, const Tensor &mean, const Tensor &invstd, double eps, Tensor &out) {
      return torch::batch_norm_elemt_out(out, input, weight, bias, mean, invstd, eps);
    })
  .define_singleton_method(
    "_batch_norm_gather_stats",
    *[](const Tensor &input, const Tensor &mean, const Tensor &invstd, OptionalTensor running_mean, OptionalTensor running_var, double momentum, double eps, int64_t count) {
      return wrap(torch::batch_norm_gather_stats(input, mean, invstd, running_mean, running_var, momentum, eps, count));
    })
  .define_singleton_method(
    "_batch_norm_gather_stats_with_counts",
    *[](const Tensor &input, const Tensor &mean, const Tensor &invstd, OptionalTensor running_mean, OptionalTensor running_var, double momentum, double eps, IntArrayRef counts) {
      return wrap(torch::batch_norm_gather_stats_with_counts(input, mean, invstd, running_mean, running_var, momentum, eps, counts));
    })
  .define_singleton_method(
    "_batch_norm_stats",
    *[](const Tensor &input, double eps) {
      return wrap(torch::batch_norm_stats(input, eps));
    })
  .define_singleton_method(
    "_batch_norm_update_stats",
    *[](const Tensor &input, OptionalTensor running_mean, OptionalTensor running_var, double momentum) {
      return wrap(torch::batch_norm_update_stats(input, running_mean, running_var, momentum));
    })
  .define_singleton_method(
    "_bernoulli",
    *[](const Tensor &self) {
      return torch::bernoulli(self);
    })
  .define_singleton_method(
    "_bernoulli_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::bernoulli_out(out, self);
    })
  .define_singleton_method(
    "_bernoulli_p",
    *[](const Tensor &self, double p) {
      return torch::bernoulli(self, p);
    })
  .define_singleton_method(
    "_bilinear",
    *[](const Tensor &input1, const Tensor &input2, const Tensor &weight, OptionalTensor bias) {
      return torch::bilinear(input1, input2, weight, bias);
    })
  .define_singleton_method(
    "_binary_cross_entropy_with_logits",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, OptionalTensor pos_weight, MyReduction reduction) {
      return torch::binary_cross_entropy_with_logits(self, target, weight, pos_weight, reduction);
    })
  .define_singleton_method(
    "_bincount",
    *[](const Tensor &self, OptionalTensor weights, int64_t minlength) {
      return torch::bincount(self, weights, minlength);
    })
  .define_singleton_method(
    "_bitwise_and_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::bitwise_and(self, other);
    })
  .define_singleton_method(
    "_bitwise_and_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::bitwise_and_out(out, self, other);
    })
  .define_singleton_method(
    "_bitwise_and_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::bitwise_and(self, other);
    })
  .define_singleton_method(
    "_bitwise_and_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::bitwise_and_out(out, self, other);
    })
  .define_singleton_method(
    "_bitwise_not",
    *[](const Tensor &self) {
      return torch::bitwise_not(self);
    })
  .define_singleton_method(
    "_bitwise_not_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::bitwise_not_out(out, self);
    })
  .define_singleton_method(
    "_bitwise_or_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::bitwise_or(self, other);
    })
  .define_singleton_method(
    "_bitwise_or_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::bitwise_or_out(out, self, other);
    })
  .define_singleton_method(
    "_bitwise_or_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::bitwise_or(self, other);
    })
  .define_singleton_method(
    "_bitwise_or_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::bitwise_or_out(out, self, other);
    })
  .define_singleton_method(
    "_bitwise_xor_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::bitwise_xor(self, other);
    })
  .define_singleton_method(
    "_bitwise_xor_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::bitwise_xor_out(out, self, other);
    })
  .define_singleton_method(
    "_bitwise_xor_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::bitwise_xor(self, other);
    })
  .define_singleton_method(
    "_bitwise_xor_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::bitwise_xor_out(out, self, other);
    })
  .define_singleton_method(
    "_bmm",
    *[](const Tensor &self, const Tensor &mat2) {
      return torch::bmm(self, mat2);
    })
  .define_singleton_method(
    "_bmm_out",
    *[](const Tensor &self, const Tensor &mat2, Tensor &out) {
      return torch::bmm_out(out, self, mat2);
    })
  .define_singleton_method(
    "_broadcast_tensors",
    *[](TensorList tensors) {
      return torch::broadcast_tensors(tensors);
    })
  .define_singleton_method(
    "_can_cast",
    *[](ScalarType from, ScalarType to) {
      return torch::can_cast(from, to);
    })
  .define_singleton_method(
    "_cartesian_prod",
    *[](TensorList tensors) {
      return torch::cartesian_prod(tensors);
    })
  .define_singleton_method(
    "_cat",
    *[](TensorList tensors, int64_t dim) {
      return torch::cat(tensors, dim);
    })
  .define_singleton_method(
    "_cat_out",
    *[](TensorList tensors, int64_t dim, Tensor &out) {
      return torch::cat_out(out, tensors, dim);
    })
  .define_singleton_method(
    "_cdist",
    *[](const Tensor &x1, const Tensor &x2, double p) {
      return torch::cdist(x1, x2, p);
    })
  .define_singleton_method(
    "_cdist_compute_mode",
    *[](const Tensor &x1, const Tensor &x2, double p, int64_t compute_mode) {
      return torch::cdist(x1, x2, p, compute_mode);
    })
  .define_singleton_method(
    "_ceil",
    *[](const Tensor &self) {
      return torch::ceil(self);
    })
  .define_singleton_method(
    "_ceil_",
    *[](Tensor &self) {
      return torch::ceil_(self);
    })
  .define_singleton_method(
    "_ceil_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::ceil_out(out, self);
    })
  .define_singleton_method(
    "_celu",
    *[](const Tensor &self, Scalar alpha) {
      return torch::celu(self, alpha);
    })
  .define_singleton_method(
    "_celu_",
    *[](Tensor &self, Scalar alpha) {
      return torch::celu_(self, alpha);
    })
  .define_singleton_method(
    "_chain_matmul",
    *[](TensorList matrices) {
      return torch::chain_matmul(matrices);
    })
  .define_singleton_method(
    "_cholesky",
    *[](const Tensor &self, bool upper) {
      return torch::cholesky(self, upper);
    })
  .define_singleton_method(
    "_cholesky_inverse",
    *[](const Tensor &self, bool upper) {
      return torch::cholesky_inverse(self, upper);
    })
  .define_singleton_method(
    "_cholesky_inverse_out",
    *[](const Tensor &self, bool upper, Tensor &out) {
      return torch::cholesky_inverse_out(out, self, upper);
    })
  .define_singleton_method(
    "_cholesky_out",
    *[](const Tensor &self, bool upper, Tensor &out) {
      return torch::cholesky_out(out, self, upper);
    })
  .define_singleton_method(
    "_cholesky_solve",
    *[](const Tensor &self, const Tensor &input2, bool upper) {
      return torch::cholesky_solve(self, input2, upper);
    })
  .define_singleton_method(
    "_cholesky_solve_out",
    *[](const Tensor &self, const Tensor &input2, bool upper, Tensor &out) {
      return torch::cholesky_solve_out(out, self, input2, upper);
    })
  .define_singleton_method(
    "_chunk",
    *[](Tensor &self, int64_t chunks, int64_t dim) {
      return torch::chunk(self, chunks, dim);
    })
  .define_singleton_method(
    "_clamp_max",
    *[](const Tensor &self, Scalar max) {
      return torch::clamp_max(self, max);
    })
  .define_singleton_method(
    "_clamp_max_",
    *[](Tensor &self, Scalar max) {
      return torch::clamp_max_(self, max);
    })
  .define_singleton_method(
    "_clamp_max_out",
    *[](const Tensor &self, Scalar max, Tensor &out) {
      return torch::clamp_max_out(out, self, max);
    })
  .define_singleton_method(
    "_clamp_min",
    *[](const Tensor &self, Scalar min) {
      return torch::clamp_min(self, min);
    })
  .define_singleton_method(
    "_clamp_min_",
    *[](Tensor &self, Scalar min) {
      return torch::clamp_min_(self, min);
    })
  .define_singleton_method(
    "_clamp_min_out",
    *[](const Tensor &self, Scalar min, Tensor &out) {
      return torch::clamp_min_out(out, self, min);
    })
  .define_singleton_method(
    "_clone",
    *[](const Tensor &self) {
      return torch::clone(self);
    })
  .define_singleton_method(
    "_combinations",
    *[](const Tensor &self, int64_t r, bool with_replacement) {
      return torch::combinations(self, r, with_replacement);
    })
  .define_singleton_method(
    "_conj",
    *[](const Tensor &self) {
      return torch::conj(self);
    })
  .define_singleton_method(
    "_conj_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::conj_out(out, self);
    })
  .define_singleton_method(
    "_constant_pad_nd",
    *[](const Tensor &self, IntArrayRef pad, Scalar value) {
      return torch::constant_pad_nd(self, pad, value);
    })
  .define_singleton_method(
    "_conv1d",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups) {
      return torch::conv1d(input, weight, bias, stride, padding, dilation, groups);
    })
  .define_singleton_method(
    "_conv2d",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups) {
      return torch::conv2d(input, weight, bias, stride, padding, dilation, groups);
    })
  .define_singleton_method(
    "_conv3d",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, int64_t groups) {
      return torch::conv3d(input, weight, bias, stride, padding, dilation, groups);
    })
  .define_singleton_method(
    "_conv_tbc",
    *[](const Tensor &self, const Tensor &weight, const Tensor &bias, int64_t pad) {
      return torch::conv_tbc(self, weight, bias, pad);
    })
  .define_singleton_method(
    "_conv_transpose1d",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation) {
      return torch::conv_transpose1d(input, weight, bias, stride, padding, output_padding, groups, dilation);
    })
  .define_singleton_method(
    "_conv_transpose2d_input",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation) {
      return torch::conv_transpose2d(input, weight, bias, stride, padding, output_padding, groups, dilation);
    })
  .define_singleton_method(
    "_conv_transpose3d_input",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, int64_t groups, IntArrayRef dilation) {
      return torch::conv_transpose3d(input, weight, bias, stride, padding, output_padding, groups, dilation);
    })
  .define_singleton_method(
    "_convolution",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups) {
      return torch::convolution(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups);
    })
  .define_singleton_method(
    "_convolution_overrideable",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool transposed, IntArrayRef output_padding, int64_t groups) {
      return torch::convolution_overrideable(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups);
    })
  .define_singleton_method(
    "_copy_sparse_to_sparse_",
    *[](Tensor &self, const Tensor &src, bool non_blocking) {
      return torch::copy_sparse_to_sparse_(self, src, non_blocking);
    })
  .define_singleton_method(
    "_cos",
    *[](const Tensor &self) {
      return torch::cos(self);
    })
  .define_singleton_method(
    "_cos_",
    *[](Tensor &self) {
      return torch::cos_(self);
    })
  .define_singleton_method(
    "_cos_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::cos_out(out, self);
    })
  .define_singleton_method(
    "_cosh",
    *[](const Tensor &self) {
      return torch::cosh(self);
    })
  .define_singleton_method(
    "_cosh_",
    *[](Tensor &self) {
      return torch::cosh_(self);
    })
  .define_singleton_method(
    "_cosh_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::cosh_out(out, self);
    })
  .define_singleton_method(
    "_cosine_embedding_loss",
    *[](const Tensor &input1, const Tensor &input2, const Tensor &target, double margin, MyReduction reduction) {
      return torch::cosine_embedding_loss(input1, input2, target, margin, reduction);
    })
  .define_singleton_method(
    "_cosine_similarity",
    *[](const Tensor &x1, const Tensor &x2, int64_t dim, double eps) {
      return torch::cosine_similarity(x1, x2, dim, eps);
    })
  .define_singleton_method(
    "_ctc_loss_intlist",
    *[](const Tensor &log_probs, const Tensor &targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, MyReduction reduction, bool zero_infinity) {
      return torch::ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity);
    })
  .define_singleton_method(
    "_ctc_loss_tensor",
    *[](const Tensor &log_probs, const Tensor &targets, const Tensor &input_lengths, const Tensor &target_lengths, int64_t blank, MyReduction reduction, bool zero_infinity) {
      return torch::ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity);
    })
  .define_singleton_method(
    "_cudnn_affine_grid_generator",
    *[](const Tensor &theta, int64_t N, int64_t C, int64_t H, int64_t W) {
      return torch::cudnn_affine_grid_generator(theta, N, C, H, W);
    })
  .define_singleton_method(
    "_cudnn_batch_norm",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, OptionalTensor running_mean, OptionalTensor running_var, bool training, double exponential_average_factor, double epsilon) {
      return wrap(torch::cudnn_batch_norm(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon));
    })
  .define_singleton_method(
    "_cudnn_convolution",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution(self, weight, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_convolution_backward_input",
    *[](IntArrayRef self_size, const Tensor &grad_output, const Tensor &weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution_backward_input(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_convolution_backward_weight",
    *[](IntArrayRef weight_size, const Tensor &grad_output, const Tensor &self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution_backward_weight(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_convolution_deprecated",
    *[](const Tensor &self, const Tensor &weight, OptionalTensor bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_convolution_transpose",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution_transpose(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_convolution_transpose_backward_input",
    *[](const Tensor &grad_output, const Tensor &weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution_transpose_backward_input(grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_convolution_transpose_backward_weight",
    *[](IntArrayRef weight_size, const Tensor &grad_output, const Tensor &self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution_transpose_backward_weight(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_convolution_transpose_deprecated",
    *[](const Tensor &self, const Tensor &weight, OptionalTensor bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::cudnn_convolution_transpose(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_cudnn_grid_sampler",
    *[](const Tensor &self, const Tensor &grid) {
      return torch::cudnn_grid_sampler(self, grid);
    })
  .define_singleton_method(
    "_cudnn_is_acceptable",
    *[](const Tensor &self) {
      return torch::cudnn_is_acceptable(self);
    })
  .define_singleton_method(
    "_cummax",
    *[](const Tensor &self, int64_t dim) {
      return wrap(torch::cummax(self, dim));
    })
  .define_singleton_method(
    "_cummax_out",
    *[](const Tensor &self, int64_t dim, Tensor &values, Tensor &indices) {
      return wrap(torch::cummax_out(values, indices, self, dim));
    })
  .define_singleton_method(
    "_cummin",
    *[](const Tensor &self, int64_t dim) {
      return wrap(torch::cummin(self, dim));
    })
  .define_singleton_method(
    "_cummin_out",
    *[](const Tensor &self, int64_t dim, Tensor &values, Tensor &indices) {
      return wrap(torch::cummin_out(values, indices, self, dim));
    })
  .define_singleton_method(
    "_cumprod",
    *[](const Tensor &self, int64_t dim, OptionalScalarType dtype) {
      return torch::cumprod(self, dim, dtype);
    })
  .define_singleton_method(
    "_cumprod_out",
    *[](const Tensor &self, int64_t dim, OptionalScalarType dtype, Tensor &out) {
      return torch::cumprod_out(out, self, dim, dtype);
    })
  .define_singleton_method(
    "_cumsum",
    *[](const Tensor &self, int64_t dim, OptionalScalarType dtype) {
      return torch::cumsum(self, dim, dtype);
    })
  .define_singleton_method(
    "_cumsum_out",
    *[](const Tensor &self, int64_t dim, OptionalScalarType dtype, Tensor &out) {
      return torch::cumsum_out(out, self, dim, dtype);
    })
  .define_singleton_method(
    "_dequantize",
    *[](const Tensor &self) {
      return torch::dequantize(self);
    })
  .define_singleton_method(
    "_det",
    *[](const Tensor &self) {
      return torch::det(self);
    })
  .define_singleton_method(
    "_detach",
    *[](const Tensor &self) {
      return torch::detach(self);
    })
  .define_singleton_method(
    "_detach_",
    *[](Tensor &self) {
      return torch::detach_(self);
    })
  .define_singleton_method(
    "_diag",
    *[](const Tensor &self, int64_t diagonal) {
      return torch::diag(self, diagonal);
    })
  .define_singleton_method(
    "_diag_embed",
    *[](const Tensor &self, int64_t offset, int64_t dim1, int64_t dim2) {
      return torch::diag_embed(self, offset, dim1, dim2);
    })
  .define_singleton_method(
    "_diag_out",
    *[](const Tensor &self, int64_t diagonal, Tensor &out) {
      return torch::diag_out(out, self, diagonal);
    })
  .define_singleton_method(
    "_diagflat",
    *[](const Tensor &self, int64_t offset) {
      return torch::diagflat(self, offset);
    })
  .define_singleton_method(
    "_diagonal",
    *[](Tensor &self, int64_t offset, int64_t dim1, int64_t dim2) {
      return torch::diagonal(self, offset, dim1, dim2);
    })
  .define_singleton_method(
    "_digamma",
    *[](const Tensor &self) {
      return torch::digamma(self);
    })
  .define_singleton_method(
    "_digamma_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::digamma_out(out, self);
    })
  .define_singleton_method(
    "_dist",
    *[](const Tensor &self, const Tensor &other, Scalar p) {
      return torch::dist(self, other, p);
    })
  .define_singleton_method(
    "_div_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::div_out(out, self, other);
    })
  .define_singleton_method(
    "_div_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::div(self, other);
    })
  .define_singleton_method(
    "_div_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::div(self, other);
    })
  .define_singleton_method(
    "_dot",
    *[](const Tensor &self, const Tensor &tensor) {
      return torch::dot(self, tensor);
    })
  .define_singleton_method(
    "_dot_out",
    *[](const Tensor &self, const Tensor &tensor, Tensor &out) {
      return torch::dot_out(out, self, tensor);
    })
  .define_singleton_method(
    "_dropout",
    *[](const Tensor &input, double p, bool train) {
      return torch::dropout(input, p, train);
    })
  .define_singleton_method(
    "_dropout_",
    *[](Tensor &self, double p, bool train) {
      return torch::dropout_(self, p, train);
    })
  .define_singleton_method(
    "_eig",
    *[](const Tensor &self, bool eigenvectors) {
      return wrap(torch::eig(self, eigenvectors));
    })
  .define_singleton_method(
    "_eig_e",
    *[](const Tensor &self, bool eigenvectors, Tensor &e, Tensor &v) {
      return wrap(torch::eig_out(e, v, self, eigenvectors));
    })
  .define_singleton_method(
    "_einsum",
    *[](std::string equation, TensorList tensors) {
      return torch::einsum(equation, tensors);
    })
  .define_singleton_method(
    "_embedding",
    *[](const Tensor &weight, const Tensor &indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) {
      return torch::embedding(weight, indices, padding_idx, scale_grad_by_freq, sparse);
    })
  .define_singleton_method(
    "_embedding_bag",
    *[](const Tensor &weight, const Tensor &indices, const Tensor &offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, OptionalTensor per_sample_weights, bool include_last_offset) {
      return wrap(torch::embedding_bag(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset));
    })
  .define_singleton_method(
    "_embedding_renorm_",
    *[](Tensor &self, const Tensor &indices, double max_norm, double norm_type) {
      return torch::embedding_renorm_(self, indices, max_norm, norm_type);
    })
  .define_singleton_method(
    "_empty_out",
    *[](IntArrayRef size, Tensor &out) {
      return torch::empty_out(out, size);
    })
  .define_singleton_method(
    "_eq_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::eq(self, other);
    })
  .define_singleton_method(
    "_eq_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::eq_out(out, self, other);
    })
  .define_singleton_method(
    "_eq_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::eq(self, other);
    })
  .define_singleton_method(
    "_eq_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::eq_out(out, self, other);
    })
  .define_singleton_method(
    "_equal",
    *[](const Tensor &self, const Tensor &other) {
      return torch::equal(self, other);
    })
  .define_singleton_method(
    "_erf",
    *[](const Tensor &self) {
      return torch::erf(self);
    })
  .define_singleton_method(
    "_erf_",
    *[](Tensor &self) {
      return torch::erf_(self);
    })
  .define_singleton_method(
    "_erf_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::erf_out(out, self);
    })
  .define_singleton_method(
    "_erfc",
    *[](const Tensor &self) {
      return torch::erfc(self);
    })
  .define_singleton_method(
    "_erfc_",
    *[](Tensor &self) {
      return torch::erfc_(self);
    })
  .define_singleton_method(
    "_erfc_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::erfc_out(out, self);
    })
  .define_singleton_method(
    "_erfinv",
    *[](const Tensor &self) {
      return torch::erfinv(self);
    })
  .define_singleton_method(
    "_erfinv_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::erfinv_out(out, self);
    })
  .define_singleton_method(
    "_exp",
    *[](const Tensor &self) {
      return torch::exp(self);
    })
  .define_singleton_method(
    "_exp_",
    *[](Tensor &self) {
      return torch::exp_(self);
    })
  .define_singleton_method(
    "_exp_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::exp_out(out, self);
    })
  .define_singleton_method(
    "_expm1",
    *[](const Tensor &self) {
      return torch::expm1(self);
    })
  .define_singleton_method(
    "_expm1_",
    *[](Tensor &self) {
      return torch::expm1_(self);
    })
  .define_singleton_method(
    "_expm1_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::expm1_out(out, self);
    })
  .define_singleton_method(
    "_eye_m_out",
    *[](int64_t n, int64_t m, Tensor &out) {
      return torch::eye_out(out, n, m);
    })
  .define_singleton_method(
    "_eye_out",
    *[](int64_t n, Tensor &out) {
      return torch::eye_out(out, n);
    })
  .define_singleton_method(
    "_fake_quantize_per_channel_affine",
    *[](const Tensor &self, const Tensor &scale, const Tensor &zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) {
      return torch::fake_quantize_per_channel_affine(self, scale, zero_point, axis, quant_min, quant_max);
    })
  .define_singleton_method(
    "_fake_quantize_per_tensor_affine",
    *[](const Tensor &self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) {
      return torch::fake_quantize_per_tensor_affine(self, scale, zero_point, quant_min, quant_max);
    })
  .define_singleton_method(
    "_fbgemm_linear_fp16_weight",
    *[](const Tensor &input, const Tensor &packed_weight, const Tensor &bias) {
      return torch::fbgemm_linear_fp16_weight(input, packed_weight, bias);
    })
  .define_singleton_method(
    "_fbgemm_linear_fp16_weight_fp32_activation",
    *[](const Tensor &input, const Tensor &packed_weight, const Tensor &bias) {
      return torch::fbgemm_linear_fp16_weight_fp32_activation(input, packed_weight, bias);
    })
  .define_singleton_method(
    "_fbgemm_linear_int8_weight",
    *[](const Tensor &input, const Tensor &weight, const Tensor &packed, const Tensor &col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor &bias) {
      return torch::fbgemm_linear_int8_weight(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias);
    })
  .define_singleton_method(
    "_fbgemm_linear_int8_weight_fp32_activation",
    *[](const Tensor &input, const Tensor &weight, const Tensor &packed, const Tensor &col_offsets, Scalar weight_scale, Scalar weight_zero_point, const Tensor &bias) {
      return torch::fbgemm_linear_int8_weight_fp32_activation(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias);
    })
  .define_singleton_method(
    "_fbgemm_linear_quantize_weight",
    *[](const Tensor &input) {
      return wrap(torch::fbgemm_linear_quantize_weight(input));
    })
  .define_singleton_method(
    "_fbgemm_pack_gemm_matrix_fp16",
    *[](const Tensor &input) {
      return torch::fbgemm_pack_gemm_matrix_fp16(input);
    })
  .define_singleton_method(
    "_fbgemm_pack_quantized_matrix",
    *[](const Tensor &input) {
      return torch::fbgemm_pack_quantized_matrix(input);
    })
  .define_singleton_method(
    "_fbgemm_pack_quantized_matrix_kn",
    *[](const Tensor &input, int64_t K, int64_t N) {
      return torch::fbgemm_pack_quantized_matrix(input, K, N);
    })
  .define_singleton_method(
    "_feature_alpha_dropout",
    *[](const Tensor &input, double p, bool train) {
      return torch::feature_alpha_dropout(input, p, train);
    })
  .define_singleton_method(
    "_feature_alpha_dropout_",
    *[](Tensor &self, double p, bool train) {
      return torch::feature_alpha_dropout_(self, p, train);
    })
  .define_singleton_method(
    "_feature_dropout",
    *[](const Tensor &input, double p, bool train) {
      return torch::feature_dropout(input, p, train);
    })
  .define_singleton_method(
    "_feature_dropout_",
    *[](Tensor &self, double p, bool train) {
      return torch::feature_dropout_(self, p, train);
    })
  .define_singleton_method(
    "_fft",
    *[](const Tensor &self, int64_t signal_ndim, bool normalized) {
      return torch::fft(self, signal_ndim, normalized);
    })
  .define_singleton_method(
    "_fill__scalar",
    *[](Tensor &self, Scalar value) {
      return torch::fill_(self, value);
    })
  .define_singleton_method(
    "_fill__tensor",
    *[](Tensor &self, const Tensor &value) {
      return torch::fill_(self, value);
    })
  .define_singleton_method(
    "_flatten_using_ints",
    *[](const Tensor &self, int64_t start_dim, int64_t end_dim) {
      return torch::flatten(self, start_dim, end_dim);
    })
  .define_singleton_method(
    "_flip",
    *[](const Tensor &self, IntArrayRef dims) {
      return torch::flip(self, dims);
    })
  .define_singleton_method(
    "_floor",
    *[](const Tensor &self) {
      return torch::floor(self);
    })
  .define_singleton_method(
    "_floor_",
    *[](Tensor &self) {
      return torch::floor_(self);
    })
  .define_singleton_method(
    "_floor_divide",
    *[](const Tensor &self, const Tensor &other) {
      return torch::floor_divide(self, other);
    })
  .define_singleton_method(
    "_floor_divide_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::floor_divide_out(out, self, other);
    })
  .define_singleton_method(
    "_floor_divide_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::floor_divide(self, other);
    })
  .define_singleton_method(
    "_floor_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::floor_out(out, self);
    })
  .define_singleton_method(
    "_fmod_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::fmod(self, other);
    })
  .define_singleton_method(
    "_fmod_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::fmod_out(out, self, other);
    })
  .define_singleton_method(
    "_fmod_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::fmod(self, other);
    })
  .define_singleton_method(
    "_fmod_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::fmod_out(out, self, other);
    })
  .define_singleton_method(
    "_frac",
    *[](const Tensor &self) {
      return torch::frac(self);
    })
  .define_singleton_method(
    "_frac_",
    *[](Tensor &self) {
      return torch::frac_(self);
    })
  .define_singleton_method(
    "_frac_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::frac_out(out, self);
    })
  .define_singleton_method(
    "_frobenius_norm",
    *[](const Tensor &self) {
      return torch::frobenius_norm(self);
    })
  .define_singleton_method(
    "_frobenius_norm_dim",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim) {
      return torch::frobenius_norm(self, dim, keepdim);
    })
  .define_singleton_method(
    "_frobenius_norm_out",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim, Tensor &out) {
      return torch::frobenius_norm_out(out, self, dim, keepdim);
    })
  .define_singleton_method(
    "_full_out",
    *[](IntArrayRef size, Scalar fill_value, Tensor &out) {
      return torch::full_out(out, size, fill_value);
    })
  .define_singleton_method(
    "_gather",
    *[](const Tensor &self, int64_t dim, const Tensor &index, bool sparse_grad) {
      return torch::gather(self, dim, index, sparse_grad);
    })
  .define_singleton_method(
    "_gather_out",
    *[](const Tensor &self, int64_t dim, const Tensor &index, bool sparse_grad, Tensor &out) {
      return torch::gather_out(out, self, dim, index, sparse_grad);
    })
  .define_singleton_method(
    "_ge_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::ge(self, other);
    })
  .define_singleton_method(
    "_ge_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::ge_out(out, self, other);
    })
  .define_singleton_method(
    "_ge_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::ge(self, other);
    })
  .define_singleton_method(
    "_ge_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::ge_out(out, self, other);
    })
  .define_singleton_method(
    "_geqrf",
    *[](const Tensor &self) {
      return wrap(torch::geqrf(self));
    })
  .define_singleton_method(
    "_geqrf_a",
    *[](const Tensor &self, Tensor &a, Tensor &tau) {
      return wrap(torch::geqrf_out(a, tau, self));
    })
  .define_singleton_method(
    "_ger",
    *[](const Tensor &self, const Tensor &vec2) {
      return torch::ger(self, vec2);
    })
  .define_singleton_method(
    "_ger_out",
    *[](const Tensor &self, const Tensor &vec2, Tensor &out) {
      return torch::ger_out(out, self, vec2);
    })
  .define_singleton_method(
    "_grid_sampler",
    *[](const Tensor &input, const Tensor &grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
      return torch::grid_sampler(input, grid, interpolation_mode, padding_mode, align_corners);
    })
  .define_singleton_method(
    "_grid_sampler_2d",
    *[](const Tensor &input, const Tensor &grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
      return torch::grid_sampler_2d(input, grid, interpolation_mode, padding_mode, align_corners);
    })
  .define_singleton_method(
    "_grid_sampler_3d",
    *[](const Tensor &input, const Tensor &grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
      return torch::grid_sampler_3d(input, grid, interpolation_mode, padding_mode, align_corners);
    })
  .define_singleton_method(
    "_group_norm",
    *[](const Tensor &input, int64_t num_groups, OptionalTensor weight, OptionalTensor bias, double eps, bool cudnn_enabled) {
      return torch::group_norm(input, num_groups, weight, bias, eps, cudnn_enabled);
    })
  .define_singleton_method(
    "_gru_cell",
    *[](const Tensor &input, const Tensor &hx, const Tensor &w_ih, const Tensor &w_hh, OptionalTensor b_ih, OptionalTensor b_hh) {
      return torch::gru_cell(input, hx, w_ih, w_hh, b_ih, b_hh);
    })
  .define_singleton_method(
    "_gru_data",
    *[](const Tensor &data, const Tensor &batch_sizes, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
      return wrap(torch::gru(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional));
    })
  .define_singleton_method(
    "_gru_input",
    *[](const Tensor &input, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
      return wrap(torch::gru(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first));
    })
  .define_singleton_method(
    "_gt_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::gt(self, other);
    })
  .define_singleton_method(
    "_gt_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::gt_out(out, self, other);
    })
  .define_singleton_method(
    "_gt_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::gt(self, other);
    })
  .define_singleton_method(
    "_gt_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::gt_out(out, self, other);
    })
  .define_singleton_method(
    "_hardshrink",
    *[](const Tensor &self, Scalar lambd) {
      return torch::hardshrink(self, lambd);
    })
  .define_singleton_method(
    "_hinge_embedding_loss",
    *[](const Tensor &self, const Tensor &target, double margin, MyReduction reduction) {
      return torch::hinge_embedding_loss(self, target, margin, reduction);
    })
  .define_singleton_method(
    "_histc",
    *[](const Tensor &self, int64_t bins, Scalar min, Scalar max) {
      return torch::histc(self, bins, min, max);
    })
  .define_singleton_method(
    "_histc_out",
    *[](const Tensor &self, int64_t bins, Scalar min, Scalar max, Tensor &out) {
      return torch::histc_out(out, self, bins, min, max);
    })
  .define_singleton_method(
    "_hspmm",
    *[](const Tensor &mat1, const Tensor &mat2) {
      return torch::hspmm(mat1, mat2);
    })
  .define_singleton_method(
    "_hspmm_out",
    *[](const Tensor &mat1, const Tensor &mat2, Tensor &out) {
      return torch::hspmm_out(out, mat1, mat2);
    })
  .define_singleton_method(
    "_ifft",
    *[](const Tensor &self, int64_t signal_ndim, bool normalized) {
      return torch::ifft(self, signal_ndim, normalized);
    })
  .define_singleton_method(
    "_imag",
    *[](const Tensor &self) {
      return torch::imag(self);
    })
  .define_singleton_method(
    "_index_add",
    *[](const Tensor &self, int64_t dim, const Tensor &index, const Tensor &source) {
      return torch::index_add(self, dim, index, source);
    })
  .define_singleton_method(
    "_index_copy",
    *[](const Tensor &self, int64_t dim, const Tensor &index, const Tensor &source) {
      return torch::index_copy(self, dim, index, source);
    })
  .define_singleton_method(
    "_index_fill_int_scalar",
    *[](const Tensor &self, int64_t dim, const Tensor &index, Scalar value) {
      return torch::index_fill(self, dim, index, value);
    })
  .define_singleton_method(
    "_index_fill_int_tensor",
    *[](const Tensor &self, int64_t dim, const Tensor &index, const Tensor &value) {
      return torch::index_fill(self, dim, index, value);
    })
  .define_singleton_method(
    "_index_select",
    *[](const Tensor &self, int64_t dim, const Tensor &index) {
      return torch::index_select(self, dim, index);
    })
  .define_singleton_method(
    "_index_select_out",
    *[](const Tensor &self, int64_t dim, const Tensor &index, Tensor &out) {
      return torch::index_select_out(out, self, dim, index);
    })
  .define_singleton_method(
    "_instance_norm",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, OptionalTensor running_mean, OptionalTensor running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) {
      return torch::instance_norm(input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled);
    })
  .define_singleton_method(
    "_int_repr",
    *[](const Tensor &self) {
      return torch::int_repr(self);
    })
  .define_singleton_method(
    "_inverse",
    *[](const Tensor &self) {
      return torch::inverse(self);
    })
  .define_singleton_method(
    "_inverse_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::inverse_out(out, self);
    })
  .define_singleton_method(
    "_irfft",
    *[](const Tensor &self, int64_t signal_ndim, bool normalized, bool onesided, IntArrayRef signal_sizes) {
      return torch::irfft(self, signal_ndim, normalized, onesided, signal_sizes);
    })
  .define_singleton_method(
    "_is_complex",
    *[](const Tensor &self) {
      return torch::is_complex(self);
    })
  .define_singleton_method(
    "_is_distributed",
    *[](const Tensor &self) {
      return torch::is_distributed(self);
    })
  .define_singleton_method(
    "_is_floating_point",
    *[](const Tensor &self) {
      return torch::is_floating_point(self);
    })
  .define_singleton_method(
    "_is_nonzero",
    *[](const Tensor &self) {
      return torch::is_nonzero(self);
    })
  .define_singleton_method(
    "_is_same_size",
    *[](const Tensor &self, const Tensor &other) {
      return torch::is_same_size(self, other);
    })
  .define_singleton_method(
    "_is_signed",
    *[](const Tensor &self) {
      return torch::is_signed(self);
    })
  .define_singleton_method(
    "_isclose",
    *[](const Tensor &self, const Tensor &other, double rtol, double atol, bool equal_nan) {
      return torch::isclose(self, other, rtol, atol, equal_nan);
    })
  .define_singleton_method(
    "_isfinite",
    *[](const Tensor &self) {
      return torch::isfinite(self);
    })
  .define_singleton_method(
    "_isinf",
    *[](const Tensor &self) {
      return torch::isinf(self);
    })
  .define_singleton_method(
    "_isnan",
    *[](const Tensor &self) {
      return torch::isnan(self);
    })
  .define_singleton_method(
    "_kl_div",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction) {
      return torch::kl_div(self, target, reduction);
    })
  .define_singleton_method(
    "_kthvalue",
    *[](const Tensor &self, int64_t k, int64_t dim, bool keepdim) {
      return wrap(torch::kthvalue(self, k, dim, keepdim));
    })
  .define_singleton_method(
    "_kthvalue_values",
    *[](const Tensor &self, int64_t k, int64_t dim, bool keepdim, Tensor &values, Tensor &indices) {
      return wrap(torch::kthvalue_out(values, indices, self, k, dim, keepdim));
    })
  .define_singleton_method(
    "_layer_norm",
    *[](const Tensor &input, IntArrayRef normalized_shape, OptionalTensor weight, OptionalTensor bias, double eps, bool cudnn_enable) {
      return torch::layer_norm(input, normalized_shape, weight, bias, eps, cudnn_enable);
    })
  .define_singleton_method(
    "_le_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::le(self, other);
    })
  .define_singleton_method(
    "_le_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::le_out(out, self, other);
    })
  .define_singleton_method(
    "_le_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::le(self, other);
    })
  .define_singleton_method(
    "_le_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::le_out(out, self, other);
    })
  .define_singleton_method(
    "_lerp_scalar",
    *[](const Tensor &self, const Tensor &end, Scalar weight) {
      return torch::lerp(self, end, weight);
    })
  .define_singleton_method(
    "_lerp_scalar_out",
    *[](const Tensor &self, const Tensor &end, Scalar weight, Tensor &out) {
      return torch::lerp_out(out, self, end, weight);
    })
  .define_singleton_method(
    "_lerp_tensor",
    *[](const Tensor &self, const Tensor &end, const Tensor &weight) {
      return torch::lerp(self, end, weight);
    })
  .define_singleton_method(
    "_lerp_tensor_out",
    *[](const Tensor &self, const Tensor &end, const Tensor &weight, Tensor &out) {
      return torch::lerp_out(out, self, end, weight);
    })
  .define_singleton_method(
    "_lgamma",
    *[](const Tensor &self) {
      return torch::lgamma(self);
    })
  .define_singleton_method(
    "_lgamma_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::lgamma_out(out, self);
    })
  .define_singleton_method(
    "_linspace_out",
    *[](Scalar start, Scalar end, int64_t steps, Tensor &out) {
      return torch::linspace_out(out, start, end, steps);
    })
  .define_singleton_method(
    "_log",
    *[](const Tensor &self) {
      return torch::log(self);
    })
  .define_singleton_method(
    "_log10",
    *[](const Tensor &self) {
      return torch::log10(self);
    })
  .define_singleton_method(
    "_log10_",
    *[](Tensor &self) {
      return torch::log10_(self);
    })
  .define_singleton_method(
    "_log10_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::log10_out(out, self);
    })
  .define_singleton_method(
    "_log1p",
    *[](const Tensor &self) {
      return torch::log1p(self);
    })
  .define_singleton_method(
    "_log1p_",
    *[](Tensor &self) {
      return torch::log1p_(self);
    })
  .define_singleton_method(
    "_log1p_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::log1p_out(out, self);
    })
  .define_singleton_method(
    "_log2",
    *[](const Tensor &self) {
      return torch::log2(self);
    })
  .define_singleton_method(
    "_log2_",
    *[](Tensor &self) {
      return torch::log2_(self);
    })
  .define_singleton_method(
    "_log2_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::log2_out(out, self);
    })
  .define_singleton_method(
    "_log_",
    *[](Tensor &self) {
      return torch::log_(self);
    })
  .define_singleton_method(
    "_log_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::log_out(out, self);
    })
  .define_singleton_method(
    "_log_softmax_int",
    *[](const Tensor &self, int64_t dim, OptionalScalarType dtype) {
      return torch::log_softmax(self, dim, dtype);
    })
  .define_singleton_method(
    "_logdet",
    *[](const Tensor &self) {
      return torch::logdet(self);
    })
  .define_singleton_method(
    "_logical_and",
    *[](const Tensor &self, const Tensor &other) {
      return torch::logical_and(self, other);
    })
  .define_singleton_method(
    "_logical_and_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::logical_and_out(out, self, other);
    })
  .define_singleton_method(
    "_logical_not",
    *[](const Tensor &self) {
      return torch::logical_not(self);
    })
  .define_singleton_method(
    "_logical_not_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::logical_not_out(out, self);
    })
  .define_singleton_method(
    "_logical_or",
    *[](const Tensor &self, const Tensor &other) {
      return torch::logical_or(self, other);
    })
  .define_singleton_method(
    "_logical_or_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::logical_or_out(out, self, other);
    })
  .define_singleton_method(
    "_logical_xor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::logical_xor(self, other);
    })
  .define_singleton_method(
    "_logical_xor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::logical_xor_out(out, self, other);
    })
  .define_singleton_method(
    "_logspace_out",
    *[](Scalar start, Scalar end, int64_t steps, double base, Tensor &out) {
      return torch::logspace_out(out, start, end, steps, base);
    })
  .define_singleton_method(
    "_logsumexp",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim) {
      return torch::logsumexp(self, dim, keepdim);
    })
  .define_singleton_method(
    "_logsumexp_out",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim, Tensor &out) {
      return torch::logsumexp_out(out, self, dim, keepdim);
    })
  .define_singleton_method(
    "_lstm_cell",
    *[](const Tensor &input, TensorList hx, const Tensor &w_ih, const Tensor &w_hh, OptionalTensor b_ih, OptionalTensor b_hh) {
      return wrap(torch::lstm_cell(input, hx, w_ih, w_hh, b_ih, b_hh));
    })
  .define_singleton_method(
    "_lstm_data",
    *[](const Tensor &data, const Tensor &batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
      return wrap(torch::lstm(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional));
    })
  .define_singleton_method(
    "_lstm_input",
    *[](const Tensor &input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
      return wrap(torch::lstm(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first));
    })
  .define_singleton_method(
    "_lstsq",
    *[](const Tensor &self, const Tensor &A) {
      return wrap(torch::lstsq(self, A));
    })
  .define_singleton_method(
    "_lstsq_x",
    *[](const Tensor &self, const Tensor &A, Tensor &X, Tensor &qr) {
      return wrap(torch::lstsq_out(X, qr, self, A));
    })
  .define_singleton_method(
    "_lt_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::lt(self, other);
    })
  .define_singleton_method(
    "_lt_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::lt_out(out, self, other);
    })
  .define_singleton_method(
    "_lt_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::lt(self, other);
    })
  .define_singleton_method(
    "_lt_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::lt_out(out, self, other);
    })
  .define_singleton_method(
    "_lu_solve",
    *[](const Tensor &self, const Tensor &LU_data, const Tensor &LU_pivots) {
      return torch::lu_solve(self, LU_data, LU_pivots);
    })
  .define_singleton_method(
    "_lu_solve_out",
    *[](const Tensor &self, const Tensor &LU_data, const Tensor &LU_pivots, Tensor &out) {
      return torch::lu_solve_out(out, self, LU_data, LU_pivots);
    })
  .define_singleton_method(
    "_margin_ranking_loss",
    *[](const Tensor &input1, const Tensor &input2, const Tensor &target, double margin, MyReduction reduction) {
      return torch::margin_ranking_loss(input1, input2, target, margin, reduction);
    })
  .define_singleton_method(
    "_masked_fill_scalar",
    *[](const Tensor &self, const Tensor &mask, Scalar value) {
      return torch::masked_fill(self, mask, value);
    })
  .define_singleton_method(
    "_masked_fill_tensor",
    *[](const Tensor &self, const Tensor &mask, const Tensor &value) {
      return torch::masked_fill(self, mask, value);
    })
  .define_singleton_method(
    "_masked_scatter",
    *[](const Tensor &self, const Tensor &mask, const Tensor &source) {
      return torch::masked_scatter(self, mask, source);
    })
  .define_singleton_method(
    "_masked_select",
    *[](const Tensor &self, const Tensor &mask) {
      return torch::masked_select(self, mask);
    })
  .define_singleton_method(
    "_masked_select_out",
    *[](const Tensor &self, const Tensor &mask, Tensor &out) {
      return torch::masked_select_out(out, self, mask);
    })
  .define_singleton_method(
    "_matmul",
    *[](const Tensor &self, const Tensor &other) {
      return torch::matmul(self, other);
    })
  .define_singleton_method(
    "_matmul_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::matmul_out(out, self, other);
    })
  .define_singleton_method(
    "_matrix_power",
    *[](const Tensor &self, int64_t n) {
      return torch::matrix_power(self, n);
    })
  .define_singleton_method(
    "_matrix_rank",
    *[](const Tensor &self, bool symmetric) {
      return torch::matrix_rank(self, symmetric);
    })
  .define_singleton_method(
    "_matrix_rank_tol",
    *[](const Tensor &self, double tol, bool symmetric) {
      return torch::matrix_rank(self, tol, symmetric);
    })
  .define_singleton_method(
    "_max",
    *[](const Tensor &self) {
      return torch::max(self);
    })
  .define_singleton_method(
    "_max_dim",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return wrap(torch::max(self, dim, keepdim));
    })
  .define_singleton_method(
    "_max_dim_max",
    *[](const Tensor &self, int64_t dim, bool keepdim, Tensor &max, Tensor &max_values) {
      return wrap(torch::max_out(max, max_values, self, dim, keepdim));
    })
  .define_singleton_method(
    "_max_other",
    *[](const Tensor &self, const Tensor &other) {
      return torch::max(self, other);
    })
  .define_singleton_method(
    "_max_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::max_out(out, self, other);
    })
  .define_singleton_method(
    "_max_pool1d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return torch::max_pool1d(self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_max_pool1d_with_indices",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return wrap(torch::max_pool1d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode));
    })
  .define_singleton_method(
    "_max_pool2d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return torch::max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_max_pool3d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return torch::max_pool3d(self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_max_values",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim) {
      return torch::max_values(self, dim, keepdim);
    })
  .define_singleton_method(
    "_mean",
    *[](const Tensor &self, OptionalScalarType dtype) {
      return torch::mean(self, dtype);
    })
  .define_singleton_method(
    "_mean_dim",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim, OptionalScalarType dtype) {
      return torch::mean(self, dim, keepdim, dtype);
    })
  .define_singleton_method(
    "_mean_out",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim, OptionalScalarType dtype, Tensor &out) {
      return torch::mean_out(out, self, dim, keepdim, dtype);
    })
  .define_singleton_method(
    "_median",
    *[](const Tensor &self) {
      return torch::median(self);
    })
  .define_singleton_method(
    "_median_dim",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return wrap(torch::median(self, dim, keepdim));
    })
  .define_singleton_method(
    "_median_dim_values",
    *[](const Tensor &self, int64_t dim, bool keepdim, Tensor &values, Tensor &indices) {
      return wrap(torch::median_out(values, indices, self, dim, keepdim));
    })
  .define_singleton_method(
    "_meshgrid",
    *[](TensorList tensors) {
      return torch::meshgrid(tensors);
    })
  .define_singleton_method(
    "_min",
    *[](const Tensor &self) {
      return torch::min(self);
    })
  .define_singleton_method(
    "_min_dim",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return wrap(torch::min(self, dim, keepdim));
    })
  .define_singleton_method(
    "_min_dim_min",
    *[](const Tensor &self, int64_t dim, bool keepdim, Tensor &min, Tensor &min_indices) {
      return wrap(torch::min_out(min, min_indices, self, dim, keepdim));
    })
  .define_singleton_method(
    "_min_other",
    *[](const Tensor &self, const Tensor &other) {
      return torch::min(self, other);
    })
  .define_singleton_method(
    "_min_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::min_out(out, self, other);
    })
  .define_singleton_method(
    "_min_values",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim) {
      return torch::min_values(self, dim, keepdim);
    })
  .define_singleton_method(
    "_miopen_batch_norm",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias, OptionalTensor running_mean, OptionalTensor running_var, bool training, double exponential_average_factor, double epsilon) {
      return wrap(torch::miopen_batch_norm(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon));
    })
  .define_singleton_method(
    "_miopen_convolution",
    *[](const Tensor &self, const Tensor &weight, OptionalTensor bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_convolution(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_convolution_backward_bias",
    *[](const Tensor &grad_output) {
      return torch::miopen_convolution_backward_bias(grad_output);
    })
  .define_singleton_method(
    "_miopen_convolution_backward_input",
    *[](IntArrayRef self_size, const Tensor &grad_output, const Tensor &weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_convolution_backward_input(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_convolution_backward_weight",
    *[](IntArrayRef weight_size, const Tensor &grad_output, const Tensor &self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_convolution_backward_weight(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_convolution_transpose",
    *[](const Tensor &self, const Tensor &weight, OptionalTensor bias, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_convolution_transpose(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_convolution_transpose_backward_input",
    *[](const Tensor &grad_output, const Tensor &weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_convolution_transpose_backward_input(grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_convolution_transpose_backward_weight",
    *[](IntArrayRef weight_size, const Tensor &grad_output, const Tensor &self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_convolution_transpose_backward_weight(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_depthwise_convolution",
    *[](const Tensor &self, const Tensor &weight, OptionalTensor bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_depthwise_convolution(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_depthwise_convolution_backward_input",
    *[](IntArrayRef self_size, const Tensor &grad_output, const Tensor &weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_depthwise_convolution_backward_input(self_size, grad_output, weight, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_depthwise_convolution_backward_weight",
    *[](IntArrayRef weight_size, const Tensor &grad_output, const Tensor &self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
      return torch::miopen_depthwise_convolution_backward_weight(weight_size, grad_output, self, padding, stride, dilation, groups, benchmark, deterministic);
    })
  .define_singleton_method(
    "_miopen_rnn",
    *[](const Tensor &input, TensorList weight, int64_t weight_stride0, const Tensor &hx, OptionalTensor cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, OptionalTensor dropout_state) {
      return wrap(torch::miopen_rnn(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state));
    })
  .define_singleton_method(
    "_mkldnn_adaptive_avg_pool2d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::mkldnn_adaptive_avg_pool2d(self, output_size);
    })
  .define_singleton_method(
    "_mkldnn_convolution",
    *[](const Tensor &self, const Tensor &weight, OptionalTensor bias, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups) {
      return torch::mkldnn_convolution(self, weight, bias, padding, stride, dilation, groups);
    })
  .define_singleton_method(
    "_mkldnn_convolution_backward_input",
    *[](IntArrayRef self_size, const Tensor &grad_output, const Tensor &weight, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined) {
      return torch::mkldnn_convolution_backward_input(self_size, grad_output, weight, padding, stride, dilation, groups, bias_defined);
    })
  .define_singleton_method(
    "_mkldnn_convolution_backward_weights",
    *[](IntArrayRef weight_size, const Tensor &grad_output, const Tensor &self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, bool bias_defined) {
      return wrap(torch::mkldnn_convolution_backward_weights(weight_size, grad_output, self, padding, stride, dilation, groups, bias_defined));
    })
  .define_singleton_method(
    "_mkldnn_max_pool2d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return torch::mkldnn_max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_mm",
    *[](const Tensor &self, const Tensor &mat2) {
      return torch::mm(self, mat2);
    })
  .define_singleton_method(
    "_mm_out",
    *[](const Tensor &self, const Tensor &mat2, Tensor &out) {
      return torch::mm_out(out, self, mat2);
    })
  .define_singleton_method(
    "_mode",
    *[](const Tensor &self, int64_t dim, bool keepdim) {
      return wrap(torch::mode(self, dim, keepdim));
    })
  .define_singleton_method(
    "_mode_values",
    *[](const Tensor &self, int64_t dim, bool keepdim, Tensor &values, Tensor &indices) {
      return wrap(torch::mode_out(values, indices, self, dim, keepdim));
    })
  .define_singleton_method(
    "_mul_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::mul_out(out, self, other);
    })
  .define_singleton_method(
    "_mul_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::mul(self, other);
    })
  .define_singleton_method(
    "_mul_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::mul(self, other);
    })
  .define_singleton_method(
    "_multinomial",
    *[](const Tensor &self, int64_t num_samples, bool replacement) {
      return torch::multinomial(self, num_samples, replacement);
    })
  .define_singleton_method(
    "_multinomial_out",
    *[](const Tensor &self, int64_t num_samples, bool replacement, Tensor &out) {
      return torch::multinomial_out(out, self, num_samples, replacement);
    })
  .define_singleton_method(
    "_mv",
    *[](const Tensor &self, const Tensor &vec) {
      return torch::mv(self, vec);
    })
  .define_singleton_method(
    "_mv_out",
    *[](const Tensor &self, const Tensor &vec, Tensor &out) {
      return torch::mv_out(out, self, vec);
    })
  .define_singleton_method(
    "_mvlgamma",
    *[](const Tensor &self, int64_t p) {
      return torch::mvlgamma(self, p);
    })
  .define_singleton_method(
    "_narrow",
    *[](Tensor &self, int64_t dim, int64_t start, int64_t length) {
      return torch::narrow(self, dim, start, length);
    })
  .define_singleton_method(
    "_narrow_tensor",
    *[](Tensor &self, int64_t dim, const Tensor &start, int64_t length) {
      return torch::narrow(self, dim, start, length);
    })
  .define_singleton_method(
    "_native_batch_norm",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, OptionalTensor running_mean, OptionalTensor running_var, bool training, double momentum, double eps) {
      return wrap(torch::native_batch_norm(input, weight, bias, running_mean, running_var, training, momentum, eps));
    })
  .define_singleton_method(
    "_native_batch_norm_out",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, OptionalTensor running_mean, OptionalTensor running_var, bool training, double momentum, double eps, Tensor &out, Tensor &save_mean, Tensor &save_invstd) {
      return wrap(torch::native_batch_norm_out(out, save_mean, save_invstd, input, weight, bias, running_mean, running_var, training, momentum, eps));
    })
  .define_singleton_method(
    "_native_layer_norm",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, int64_t M, int64_t N, double eps) {
      return wrap(torch::native_layer_norm(input, weight, bias, M, N, eps));
    })
  .define_singleton_method(
    "_native_norm",
    *[](const Tensor &self, Scalar p) {
      return torch::native_norm(self, p);
    })
  .define_singleton_method(
    "_ne_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::ne(self, other);
    })
  .define_singleton_method(
    "_ne_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::ne_out(out, self, other);
    })
  .define_singleton_method(
    "_ne_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::ne(self, other);
    })
  .define_singleton_method(
    "_ne_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::ne_out(out, self, other);
    })
  .define_singleton_method(
    "_neg",
    *[](const Tensor &self) {
      return torch::neg(self);
    })
  .define_singleton_method(
    "_neg_",
    *[](Tensor &self) {
      return torch::neg_(self);
    })
  .define_singleton_method(
    "_neg_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::neg_out(out, self);
    })
  .define_singleton_method(
    "_nonzero",
    *[](const Tensor &self) {
      return torch::nonzero(self);
    })
  .define_singleton_method(
    "_nonzero_numpy",
    *[](const Tensor &self) {
      return torch::nonzero_numpy(self);
    })
  .define_singleton_method(
    "_nonzero_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::nonzero_out(out, self);
    })
  .define_singleton_method(
    "_norm_except_dim",
    *[](const Tensor &v, int64_t pow, int64_t dim) {
      return torch::norm_except_dim(v, pow, dim);
    })
  .define_singleton_method(
    "_norm_scalar",
    *[](const Tensor &self, Scalar p) {
      return torch::norm(self, p);
    })
  .define_singleton_method(
    "_normal_float_float_out",
    *[](double mean, double std, IntArrayRef size, Tensor &out) {
      return torch::normal_out(out, mean, std, size);
    })
  .define_singleton_method(
    "_normal_float_tensor_out",
    *[](double mean, const Tensor &std, Tensor &out) {
      return torch::normal_out(out, mean, std);
    })
  .define_singleton_method(
    "_normal_tensor_float_out",
    *[](const Tensor &mean, double std, Tensor &out) {
      return torch::normal_out(out, mean, std);
    })
  .define_singleton_method(
    "_normal_tensor_tensor_out",
    *[](const Tensor &mean, const Tensor &std, Tensor &out) {
      return torch::normal_out(out, mean, std);
    })
  .define_singleton_method(
    "_nuclear_norm",
    *[](const Tensor &self, bool keepdim) {
      return torch::nuclear_norm(self, keepdim);
    })
  .define_singleton_method(
    "_nuclear_norm_dim",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim) {
      return torch::nuclear_norm(self, dim, keepdim);
    })
  .define_singleton_method(
    "_nuclear_norm_dim_out",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim, Tensor &out) {
      return torch::nuclear_norm_out(out, self, dim, keepdim);
    })
  .define_singleton_method(
    "_nuclear_norm_out",
    *[](const Tensor &self, bool keepdim, Tensor &out) {
      return torch::nuclear_norm_out(out, self, keepdim);
    })
  .define_singleton_method(
    "_ones_out",
    *[](IntArrayRef size, Tensor &out) {
      return torch::ones_out(out, size);
    })
  .define_singleton_method(
    "_orgqr",
    *[](const Tensor &self, const Tensor &input2) {
      return torch::orgqr(self, input2);
    })
  .define_singleton_method(
    "_orgqr_out",
    *[](const Tensor &self, const Tensor &input2, Tensor &out) {
      return torch::orgqr_out(out, self, input2);
    })
  .define_singleton_method(
    "_ormqr",
    *[](const Tensor &self, const Tensor &input2, const Tensor &input3, bool left, bool transpose) {
      return torch::ormqr(self, input2, input3, left, transpose);
    })
  .define_singleton_method(
    "_ormqr_out",
    *[](const Tensor &self, const Tensor &input2, const Tensor &input3, bool left, bool transpose, Tensor &out) {
      return torch::ormqr_out(out, self, input2, input3, left, transpose);
    })
  .define_singleton_method(
    "_pairwise_distance",
    *[](const Tensor &x1, const Tensor &x2, double p, double eps, bool keepdim) {
      return torch::pairwise_distance(x1, x2, p, eps, keepdim);
    })
  .define_singleton_method(
    "_pdist",
    *[](const Tensor &self, double p) {
      return torch::pdist(self, p);
    })
  .define_singleton_method(
    "_pinverse",
    *[](const Tensor &self, double rcond) {
      return torch::pinverse(self, rcond);
    })
  .define_singleton_method(
    "_pixel_shuffle",
    *[](const Tensor &self, int64_t upscale_factor) {
      return torch::pixel_shuffle(self, upscale_factor);
    })
  .define_singleton_method(
    "_poisson",
    *[](const Tensor &self) {
      return torch::poisson(self);
    })
  .define_singleton_method(
    "_poisson_nll_loss",
    *[](const Tensor &input, const Tensor &target, bool log_input, bool full, double eps, MyReduction reduction) {
      return torch::poisson_nll_loss(input, target, log_input, full, eps, reduction);
    })
  .define_singleton_method(
    "_polygamma",
    *[](int64_t n, const Tensor &self) {
      return torch::polygamma(n, self);
    })
  .define_singleton_method(
    "_polygamma_out",
    *[](int64_t n, const Tensor &self, Tensor &out) {
      return torch::polygamma_out(out, n, self);
    })
  .define_singleton_method(
    "_pow_scalar",
    *[](Scalar self, const Tensor &exponent) {
      return torch::pow(self, exponent);
    })
  .define_singleton_method(
    "_pow_scalar_out",
    *[](Scalar self, const Tensor &exponent, Tensor &out) {
      return torch::pow_out(out, self, exponent);
    })
  .define_singleton_method(
    "_pow_tensor_scalar",
    *[](const Tensor &self, Scalar exponent) {
      return torch::pow(self, exponent);
    })
  .define_singleton_method(
    "_pow_tensor_scalar_out",
    *[](const Tensor &self, Scalar exponent, Tensor &out) {
      return torch::pow_out(out, self, exponent);
    })
  .define_singleton_method(
    "_pow_tensor_tensor",
    *[](const Tensor &self, const Tensor &exponent) {
      return torch::pow(self, exponent);
    })
  .define_singleton_method(
    "_pow_tensor_tensor_out",
    *[](const Tensor &self, const Tensor &exponent, Tensor &out) {
      return torch::pow_out(out, self, exponent);
    })
  .define_singleton_method(
    "_prelu",
    *[](const Tensor &self, const Tensor &weight) {
      return torch::prelu(self, weight);
    })
  .define_singleton_method(
    "_prod",
    *[](const Tensor &self, OptionalScalarType dtype) {
      return torch::prod(self, dtype);
    })
  .define_singleton_method(
    "_prod_dim_int",
    *[](const Tensor &self, int64_t dim, bool keepdim, OptionalScalarType dtype) {
      return torch::prod(self, dim, keepdim, dtype);
    })
  .define_singleton_method(
    "_prod_int_out",
    *[](const Tensor &self, int64_t dim, bool keepdim, OptionalScalarType dtype, Tensor &out) {
      return torch::prod_out(out, self, dim, keepdim, dtype);
    })
  .define_singleton_method(
    "_promote_types",
    *[](ScalarType type1, ScalarType type2) {
      return torch::promote_types(type1, type2);
    })
  .define_singleton_method(
    "_q_per_channel_axis",
    *[](const Tensor &self) {
      return torch::q_per_channel_axis(self);
    })
  .define_singleton_method(
    "_q_per_channel_scales",
    *[](const Tensor &self) {
      return torch::q_per_channel_scales(self);
    })
  .define_singleton_method(
    "_q_per_channel_zero_points",
    *[](const Tensor &self) {
      return torch::q_per_channel_zero_points(self);
    })
  .define_singleton_method(
    "_q_scale",
    *[](const Tensor &self) {
      return torch::q_scale(self);
    })
  .define_singleton_method(
    "_q_zero_point",
    *[](const Tensor &self) {
      return torch::q_zero_point(self);
    })
  .define_singleton_method(
    "_qr",
    *[](const Tensor &self, bool some) {
      return wrap(torch::qr(self, some));
    })
  .define_singleton_method(
    "_qr_q",
    *[](const Tensor &self, bool some, Tensor &Q, Tensor &R) {
      return wrap(torch::qr_out(Q, R, self, some));
    })
  .define_singleton_method(
    "_quantize_per_channel",
    *[](const Tensor &self, const Tensor &scales, const Tensor &zero_points, int64_t axis, ScalarType dtype) {
      return torch::quantize_per_channel(self, scales, zero_points, axis, dtype);
    })
  .define_singleton_method(
    "_quantize_per_tensor",
    *[](const Tensor &self, double scale, int64_t zero_point, ScalarType dtype) {
      return torch::quantize_per_tensor(self, scale, zero_point, dtype);
    })
  .define_singleton_method(
    "_quantized_batch_norm",
    *[](const Tensor &input, OptionalTensor weight, OptionalTensor bias, const Tensor &mean, const Tensor &var, double eps, double output_scale, int64_t output_zero_point) {
      return torch::quantized_batch_norm(input, weight, bias, mean, var, eps, output_scale, output_zero_point);
    })
  .define_singleton_method(
    "_quantized_gru_cell",
    *[](const Tensor &input, const Tensor &hx, const Tensor &w_ih, const Tensor &w_hh, const Tensor &b_ih, const Tensor &b_hh, const Tensor &packed_ih, const Tensor &packed_hh, const Tensor &col_offsets_ih, const Tensor &col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) {
      return torch::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);
    })
  .define_singleton_method(
    "_quantized_gru_data",
    *[](const Tensor &data, const Tensor &batch_sizes, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
      return wrap(torch::quantized_gru(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional));
    })
  .define_singleton_method(
    "_quantized_gru_input",
    *[](const Tensor &input, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
      return wrap(torch::quantized_gru(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first));
    })
  .define_singleton_method(
    "_quantized_lstm",
    *[](const Tensor &input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, OptionalScalarType dtype, bool use_dynamic) {
      return wrap(torch::quantized_lstm(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, dtype, use_dynamic));
    })
  .define_singleton_method(
    "_quantized_lstm_cell",
    *[](const Tensor &input, TensorList hx, const Tensor &w_ih, const Tensor &w_hh, const Tensor &b_ih, const Tensor &b_hh, const Tensor &packed_ih, const Tensor &packed_hh, const Tensor &col_offsets_ih, const Tensor &col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) {
      return wrap(torch::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));
    })
  .define_singleton_method(
    "_quantized_lstm_data",
    *[](const Tensor &data, const Tensor &batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, OptionalScalarType dtype, bool use_dynamic) {
      return wrap(torch::quantized_lstm(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional, dtype, use_dynamic));
    })
  .define_singleton_method(
    "_quantized_max_pool2d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return torch::quantized_max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_quantized_rnn_relu_cell",
    *[](const Tensor &input, const Tensor &hx, const Tensor &w_ih, const Tensor &w_hh, const Tensor &b_ih, const Tensor &b_hh, const Tensor &packed_ih, const Tensor &packed_hh, const Tensor &col_offsets_ih, const Tensor &col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) {
      return torch::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);
    })
  .define_singleton_method(
    "_quantized_rnn_tanh_cell",
    *[](const Tensor &input, const Tensor &hx, const Tensor &w_ih, const Tensor &w_hh, const Tensor &b_ih, const Tensor &b_hh, const Tensor &packed_ih, const Tensor &packed_hh, const Tensor &col_offsets_ih, const Tensor &col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) {
      return torch::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);
    })
  .define_singleton_method(
    "_rand_generator_out",
    *[](IntArrayRef size, Tensor &out) {
      return torch::rand_out(out, size);
    })
  .define_singleton_method(
    "_rand_out",
    *[](IntArrayRef size, Tensor &out) {
      return torch::rand_out(out, size);
    })
  .define_singleton_method(
    "_randint_generator_out",
    *[](int64_t high, IntArrayRef size, Tensor &out) {
      return torch::randint_out(out, high, size);
    })
  .define_singleton_method(
    "_randint_low_generator_out",
    *[](int64_t low, int64_t high, IntArrayRef size, Tensor &out) {
      return torch::randint_out(out, low, high, size);
    })
  .define_singleton_method(
    "_randint_low_out",
    *[](int64_t low, int64_t high, IntArrayRef size, Tensor &out) {
      return torch::randint_out(out, low, high, size);
    })
  .define_singleton_method(
    "_randint_out",
    *[](int64_t high, IntArrayRef size, Tensor &out) {
      return torch::randint_out(out, high, size);
    })
  .define_singleton_method(
    "_randn_generator_out",
    *[](IntArrayRef size, Tensor &out) {
      return torch::randn_out(out, size);
    })
  .define_singleton_method(
    "_randn_out",
    *[](IntArrayRef size, Tensor &out) {
      return torch::randn_out(out, size);
    })
  .define_singleton_method(
    "_randperm_generator_out",
    *[](int64_t n, Tensor &out) {
      return torch::randperm_out(out, n);
    })
  .define_singleton_method(
    "_randperm_out",
    *[](int64_t n, Tensor &out) {
      return torch::randperm_out(out, n);
    })
  .define_singleton_method(
    "_range_out",
    *[](Scalar start, Scalar end, Scalar step, Tensor &out) {
      return torch::range_out(out, start, end, step);
    })
  .define_singleton_method(
    "_real",
    *[](const Tensor &self) {
      return torch::real(self);
    })
  .define_singleton_method(
    "_reciprocal",
    *[](const Tensor &self) {
      return torch::reciprocal(self);
    })
  .define_singleton_method(
    "_reciprocal_",
    *[](Tensor &self) {
      return torch::reciprocal_(self);
    })
  .define_singleton_method(
    "_reciprocal_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::reciprocal_out(out, self);
    })
  .define_singleton_method(
    "_relu",
    *[](const Tensor &self) {
      return torch::relu(self);
    })
  .define_singleton_method(
    "_relu_",
    *[](Tensor &self) {
      return torch::relu_(self);
    })
  .define_singleton_method(
    "_remainder_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::remainder(self, other);
    })
  .define_singleton_method(
    "_remainder_scalar_out",
    *[](const Tensor &self, Scalar other, Tensor &out) {
      return torch::remainder_out(out, self, other);
    })
  .define_singleton_method(
    "_remainder_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::remainder(self, other);
    })
  .define_singleton_method(
    "_remainder_tensor_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::remainder_out(out, self, other);
    })
  .define_singleton_method(
    "_renorm",
    *[](const Tensor &self, Scalar p, int64_t dim, Scalar maxnorm) {
      return torch::renorm(self, p, dim, maxnorm);
    })
  .define_singleton_method(
    "_renorm_out",
    *[](const Tensor &self, Scalar p, int64_t dim, Scalar maxnorm, Tensor &out) {
      return torch::renorm_out(out, self, p, dim, maxnorm);
    })
  .define_singleton_method(
    "_repeat_interleave_self_int",
    *[](const Tensor &self, int64_t repeats) {
      return torch::repeat_interleave(self, repeats);
    })
  .define_singleton_method(
    "_repeat_interleave_self_int_dim",
    *[](const Tensor &self, int64_t repeats, int64_t dim) {
      return torch::repeat_interleave(self, repeats, dim);
    })
  .define_singleton_method(
    "_repeat_interleave_self_tensor",
    *[](const Tensor &self, const Tensor &repeats) {
      return torch::repeat_interleave(self, repeats);
    })
  .define_singleton_method(
    "_repeat_interleave_self_tensor_dim",
    *[](const Tensor &self, const Tensor &repeats, int64_t dim) {
      return torch::repeat_interleave(self, repeats, dim);
    })
  .define_singleton_method(
    "_repeat_interleave_tensor",
    *[](const Tensor &repeats) {
      return torch::repeat_interleave(repeats);
    })
  .define_singleton_method(
    "_reshape",
    *[](const Tensor &self, IntArrayRef shape) {
      return torch::reshape(self, shape);
    })
  .define_singleton_method(
    "_resize_as_",
    *[](Tensor &self, const Tensor &the_template) {
      return torch::resize_as_(self, the_template);
    })
  .define_singleton_method(
    "_result_type_scalar",
    *[](const Tensor &tensor, Scalar other) {
      return torch::result_type(tensor, other);
    })
  .define_singleton_method(
    "_result_type_scalar_scalar",
    *[](Scalar scalar1, Scalar scalar2) {
      return torch::result_type(scalar1, scalar2);
    })
  .define_singleton_method(
    "_result_type_scalar_tensor",
    *[](Scalar scalar, const Tensor &tensor) {
      return torch::result_type(scalar, tensor);
    })
  .define_singleton_method(
    "_result_type_tensor",
    *[](const Tensor &tensor, const Tensor &other) {
      return torch::result_type(tensor, other);
    })
  .define_singleton_method(
    "_rfft",
    *[](const Tensor &self, int64_t signal_ndim, bool normalized, bool onesided) {
      return torch::rfft(self, signal_ndim, normalized, onesided);
    })
  .define_singleton_method(
    "_rnn_relu_cell",
    *[](const Tensor &input, const Tensor &hx, const Tensor &w_ih, const Tensor &w_hh, OptionalTensor b_ih, OptionalTensor b_hh) {
      return torch::rnn_relu_cell(input, hx, w_ih, w_hh, b_ih, b_hh);
    })
  .define_singleton_method(
    "_rnn_relu_data",
    *[](const Tensor &data, const Tensor &batch_sizes, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
      return wrap(torch::rnn_relu(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional));
    })
  .define_singleton_method(
    "_rnn_relu_input",
    *[](const Tensor &input, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
      return wrap(torch::rnn_relu(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first));
    })
  .define_singleton_method(
    "_rnn_tanh_cell",
    *[](const Tensor &input, const Tensor &hx, const Tensor &w_ih, const Tensor &w_hh, OptionalTensor b_ih, OptionalTensor b_hh) {
      return torch::rnn_tanh_cell(input, hx, w_ih, w_hh, b_ih, b_hh);
    })
  .define_singleton_method(
    "_rnn_tanh_data",
    *[](const Tensor &data, const Tensor &batch_sizes, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
      return wrap(torch::rnn_tanh(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional));
    })
  .define_singleton_method(
    "_rnn_tanh_input",
    *[](const Tensor &input, const Tensor &hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
      return wrap(torch::rnn_tanh(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first));
    })
  .define_singleton_method(
    "_roll",
    *[](const Tensor &self, IntArrayRef shifts, IntArrayRef dims) {
      return torch::roll(self, shifts, dims);
    })
  .define_singleton_method(
    "_rot90",
    *[](const Tensor &self, int64_t k, IntArrayRef dims) {
      return torch::rot90(self, k, dims);
    })
  .define_singleton_method(
    "_round",
    *[](const Tensor &self) {
      return torch::round(self);
    })
  .define_singleton_method(
    "_round_",
    *[](Tensor &self) {
      return torch::round_(self);
    })
  .define_singleton_method(
    "_round_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::round_out(out, self);
    })
  .define_singleton_method(
    "_rrelu",
    *[](const Tensor &self, Scalar lower, Scalar upper, bool training) {
      return torch::rrelu(self, lower, upper, training);
    })
  .define_singleton_method(
    "_rrelu_",
    *[](Tensor &self, Scalar lower, Scalar upper, bool training) {
      return torch::rrelu_(self, lower, upper, training);
    })
  .define_singleton_method(
    "_rsqrt",
    *[](const Tensor &self) {
      return torch::rsqrt(self);
    })
  .define_singleton_method(
    "_rsqrt_",
    *[](Tensor &self) {
      return torch::rsqrt_(self);
    })
  .define_singleton_method(
    "_rsqrt_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::rsqrt_out(out, self);
    })
  .define_singleton_method(
    "_rsub_scalar",
    *[](const Tensor &self, Scalar other, Scalar alpha) {
      return torch::rsub(self, other, alpha);
    })
  .define_singleton_method(
    "_rsub_tensor",
    *[](const Tensor &self, const Tensor &other, Scalar alpha) {
      return torch::rsub(self, other, alpha);
    })
  .define_singleton_method(
    "_scatter_add",
    *[](const Tensor &self, int64_t dim, const Tensor &index, const Tensor &src) {
      return torch::scatter_add(self, dim, index, src);
    })
  .define_singleton_method(
    "_scatter_src",
    *[](const Tensor &self, int64_t dim, const Tensor &index, const Tensor &src) {
      return torch::scatter(self, dim, index, src);
    })
  .define_singleton_method(
    "_scatter_value",
    *[](const Tensor &self, int64_t dim, const Tensor &index, Scalar value) {
      return torch::scatter(self, dim, index, value);
    })
  .define_singleton_method(
    "_select_int",
    *[](Tensor &self, int64_t dim, int64_t index) {
      return torch::select(self, dim, index);
    })
  .define_singleton_method(
    "_selu",
    *[](const Tensor &self) {
      return torch::selu(self);
    })
  .define_singleton_method(
    "_selu_",
    *[](Tensor &self) {
      return torch::selu_(self);
    })
  .define_singleton_method(
    "_sigmoid",
    *[](const Tensor &self) {
      return torch::sigmoid(self);
    })
  .define_singleton_method(
    "_sigmoid_",
    *[](Tensor &self) {
      return torch::sigmoid_(self);
    })
  .define_singleton_method(
    "_sigmoid_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::sigmoid_out(out, self);
    })
  .define_singleton_method(
    "_sign",
    *[](const Tensor &self) {
      return torch::sign(self);
    })
  .define_singleton_method(
    "_sign_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::sign_out(out, self);
    })
  .define_singleton_method(
    "_sin",
    *[](const Tensor &self) {
      return torch::sin(self);
    })
  .define_singleton_method(
    "_sin_",
    *[](Tensor &self) {
      return torch::sin_(self);
    })
  .define_singleton_method(
    "_sin_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::sin_out(out, self);
    })
  .define_singleton_method(
    "_sinh",
    *[](const Tensor &self) {
      return torch::sinh(self);
    })
  .define_singleton_method(
    "_sinh_",
    *[](Tensor &self) {
      return torch::sinh_(self);
    })
  .define_singleton_method(
    "_sinh_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::sinh_out(out, self);
    })
  .define_singleton_method(
    "_size_int",
    *[](const Tensor &self, int64_t dim) {
      return torch::size(self, dim);
    })
  .define_singleton_method(
    "_slice_tensor",
    *[](Tensor &self, int64_t dim, int64_t start, int64_t end, int64_t step) {
      return torch::slice(self, dim, start, end, step);
    })
  .define_singleton_method(
    "_slogdet",
    *[](const Tensor &self) {
      return wrap(torch::slogdet(self));
    })
  .define_singleton_method(
    "_smm",
    *[](const Tensor &self, const Tensor &mat2) {
      return torch::smm(self, mat2);
    })
  .define_singleton_method(
    "_softmax_int",
    *[](const Tensor &self, int64_t dim, OptionalScalarType dtype) {
      return torch::softmax(self, dim, dtype);
    })
  .define_singleton_method(
    "_solve",
    *[](const Tensor &self, const Tensor &A) {
      return wrap(torch::solve(self, A));
    })
  .define_singleton_method(
    "_solve_solution",
    *[](const Tensor &self, const Tensor &A, Tensor &solution, Tensor &lu) {
      return wrap(torch::solve_out(solution, lu, self, A));
    })
  .define_singleton_method(
    "_sort",
    *[](const Tensor &self, int64_t dim, bool descending) {
      return wrap(torch::sort(self, dim, descending));
    })
  .define_singleton_method(
    "_sort_values",
    *[](const Tensor &self, int64_t dim, bool descending, Tensor &values, Tensor &indices) {
      return wrap(torch::sort_out(values, indices, self, dim, descending));
    })
  .define_singleton_method(
    "_split_tensor",
    *[](Tensor &self, int64_t split_size, int64_t dim) {
      return torch::split(self, split_size, dim);
    })
  .define_singleton_method(
    "_split_with_sizes",
    *[](const Tensor &self, IntArrayRef split_sizes, int64_t dim) {
      return torch::split_with_sizes(self, split_sizes, dim);
    })
  .define_singleton_method(
    "_sqrt",
    *[](const Tensor &self) {
      return torch::sqrt(self);
    })
  .define_singleton_method(
    "_sqrt_",
    *[](Tensor &self) {
      return torch::sqrt_(self);
    })
  .define_singleton_method(
    "_sqrt_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::sqrt_out(out, self);
    })
  .define_singleton_method(
    "_square",
    *[](const Tensor &self) {
      return torch::square(self);
    })
  .define_singleton_method(
    "_square_",
    *[](Tensor &self) {
      return torch::square_(self);
    })
  .define_singleton_method(
    "_squeeze",
    *[](Tensor &self) {
      return torch::squeeze(self);
    })
  .define_singleton_method(
    "_squeeze_dim",
    *[](Tensor &self, int64_t dim) {
      return torch::squeeze(self, dim);
    })
  .define_singleton_method(
    "_sspaddmm",
    *[](const Tensor &self, const Tensor &mat1, const Tensor &mat2, Scalar beta, Scalar alpha) {
      return torch::sspaddmm(self, mat1, mat2, beta, alpha);
    })
  .define_singleton_method(
    "_sspaddmm_out",
    *[](const Tensor &self, const Tensor &mat1, const Tensor &mat2, Scalar beta, Scalar alpha, Tensor &out) {
      return torch::sspaddmm_out(out, self, mat1, mat2, beta, alpha);
    })
  .define_singleton_method(
    "_stack",
    *[](TensorList tensors, int64_t dim) {
      return torch::stack(tensors, dim);
    })
  .define_singleton_method(
    "_stack_out",
    *[](TensorList tensors, int64_t dim, Tensor &out) {
      return torch::stack_out(out, tensors, dim);
    })
  .define_singleton_method(
    "_std",
    *[](const Tensor &self, bool unbiased) {
      return torch::std(self, unbiased);
    })
  .define_singleton_method(
    "_std_dim",
    *[](const Tensor &self, IntArrayRef dim, bool unbiased, bool keepdim) {
      return torch::std(self, dim, unbiased, keepdim);
    })
  .define_singleton_method(
    "_std_mean",
    *[](const Tensor &self, bool unbiased) {
      return wrap(torch::std_mean(self, unbiased));
    })
  .define_singleton_method(
    "_std_mean_dim",
    *[](const Tensor &self, IntArrayRef dim, bool unbiased, bool keepdim) {
      return wrap(torch::std_mean(self, dim, unbiased, keepdim));
    })
  .define_singleton_method(
    "_std_out",
    *[](const Tensor &self, IntArrayRef dim, bool unbiased, bool keepdim, Tensor &out) {
      return torch::std_out(out, self, dim, unbiased, keepdim);
    })
  .define_singleton_method(
    "_stride_int",
    *[](const Tensor &self, int64_t dim) {
      return torch::stride(self, dim);
    })
  .define_singleton_method(
    "_sub_out",
    *[](const Tensor &self, const Tensor &other, Scalar alpha, Tensor &out) {
      return torch::sub_out(out, self, other, alpha);
    })
  .define_singleton_method(
    "_sub_scalar",
    *[](const Tensor &self, Scalar other, Scalar alpha) {
      return torch::sub(self, other, alpha);
    })
  .define_singleton_method(
    "_sub_tensor",
    *[](const Tensor &self, const Tensor &other, Scalar alpha) {
      return torch::sub(self, other, alpha);
    })
  .define_singleton_method(
    "_sum",
    *[](const Tensor &self, OptionalScalarType dtype) {
      return torch::sum(self, dtype);
    })
  .define_singleton_method(
    "_sum_dim_intlist",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim, OptionalScalarType dtype) {
      return torch::sum(self, dim, keepdim, dtype);
    })
  .define_singleton_method(
    "_sum_intlist_out",
    *[](const Tensor &self, IntArrayRef dim, bool keepdim, OptionalScalarType dtype, Tensor &out) {
      return torch::sum_out(out, self, dim, keepdim, dtype);
    })
  .define_singleton_method(
    "_svd",
    *[](const Tensor &self, bool some, bool compute_uv) {
      return wrap(torch::svd(self, some, compute_uv));
    })
  .define_singleton_method(
    "_svd_u",
    *[](const Tensor &self, bool some, bool compute_uv, Tensor &U, Tensor &S, Tensor &V) {
      return wrap(torch::svd_out(U, S, V, self, some, compute_uv));
    })
  .define_singleton_method(
    "_symeig",
    *[](const Tensor &self, bool eigenvectors, bool upper) {
      return wrap(torch::symeig(self, eigenvectors, upper));
    })
  .define_singleton_method(
    "_symeig_e",
    *[](const Tensor &self, bool eigenvectors, bool upper, Tensor &e, Tensor &V) {
      return wrap(torch::symeig_out(e, V, self, eigenvectors, upper));
    })
  .define_singleton_method(
    "_t",
    *[](Tensor &self) {
      return torch::t(self);
    })
  .define_singleton_method(
    "_take",
    *[](const Tensor &self, const Tensor &index) {
      return torch::take(self, index);
    })
  .define_singleton_method(
    "_take_out",
    *[](const Tensor &self, const Tensor &index, Tensor &out) {
      return torch::take_out(out, self, index);
    })
  .define_singleton_method(
    "_tan",
    *[](const Tensor &self) {
      return torch::tan(self);
    })
  .define_singleton_method(
    "_tan_",
    *[](Tensor &self) {
      return torch::tan_(self);
    })
  .define_singleton_method(
    "_tan_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::tan_out(out, self);
    })
  .define_singleton_method(
    "_tanh",
    *[](const Tensor &self) {
      return torch::tanh(self);
    })
  .define_singleton_method(
    "_tanh_",
    *[](Tensor &self) {
      return torch::tanh_(self);
    })
  .define_singleton_method(
    "_tanh_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::tanh_out(out, self);
    })
  .define_singleton_method(
    "_tensordot",
    *[](const Tensor &self, const Tensor &other, IntArrayRef dims_self, IntArrayRef dims_other) {
      return torch::tensordot(self, other, dims_self, dims_other);
    })
  .define_singleton_method(
    "_threshold",
    *[](const Tensor &self, Scalar threshold, Scalar value) {
      return torch::threshold(self, threshold, value);
    })
  .define_singleton_method(
    "_threshold_",
    *[](Tensor &self, Scalar threshold, Scalar value) {
      return torch::threshold_(self, threshold, value);
    })
  .define_singleton_method(
    "_threshold_out",
    *[](const Tensor &self, Scalar threshold, Scalar value, Tensor &out) {
      return torch::threshold_out(out, self, threshold, value);
    })
  .define_singleton_method(
    "_topk",
    *[](const Tensor &self, int64_t k, int64_t dim, bool largest, bool sorted) {
      return wrap(torch::topk(self, k, dim, largest, sorted));
    })
  .define_singleton_method(
    "_topk_values",
    *[](const Tensor &self, int64_t k, int64_t dim, bool largest, bool sorted, Tensor &values, Tensor &indices) {
      return wrap(torch::topk_out(values, indices, self, k, dim, largest, sorted));
    })
  .define_singleton_method(
    "_trace",
    *[](const Tensor &self) {
      return torch::trace(self);
    })
  .define_singleton_method(
    "_transpose_int",
    *[](Tensor &self, int64_t dim0, int64_t dim1) {
      return torch::transpose(self, dim0, dim1);
    })
  .define_singleton_method(
    "_trapz_dx",
    *[](const Tensor &y, double dx, int64_t dim) {
      return torch::trapz(y, dx, dim);
    })
  .define_singleton_method(
    "_trapz_x",
    *[](const Tensor &y, const Tensor &x, int64_t dim) {
      return torch::trapz(y, x, dim);
    })
  .define_singleton_method(
    "_triangular_solve",
    *[](const Tensor &self, const Tensor &A, bool upper, bool transpose, bool unitriangular) {
      return wrap(torch::triangular_solve(self, A, upper, transpose, unitriangular));
    })
  .define_singleton_method(
    "_triangular_solve_x",
    *[](const Tensor &self, const Tensor &A, bool upper, bool transpose, bool unitriangular, Tensor &X, Tensor &M) {
      return wrap(torch::triangular_solve_out(X, M, self, A, upper, transpose, unitriangular));
    })
  .define_singleton_method(
    "_tril",
    *[](const Tensor &self, int64_t diagonal) {
      return torch::tril(self, diagonal);
    })
  .define_singleton_method(
    "_tril_out",
    *[](const Tensor &self, int64_t diagonal, Tensor &out) {
      return torch::tril_out(out, self, diagonal);
    })
  .define_singleton_method(
    "_triplet_margin_loss",
    *[](const Tensor &anchor, const Tensor &positive, const Tensor &negative, double margin, double p, double eps, bool swap, MyReduction reduction) {
      return torch::triplet_margin_loss(anchor, positive, negative, margin, p, eps, swap, reduction);
    })
  .define_singleton_method(
    "_triu",
    *[](const Tensor &self, int64_t diagonal) {
      return torch::triu(self, diagonal);
    })
  .define_singleton_method(
    "_triu_out",
    *[](const Tensor &self, int64_t diagonal, Tensor &out) {
      return torch::triu_out(out, self, diagonal);
    })
  .define_singleton_method(
    "_true_divide_out",
    *[](const Tensor &self, const Tensor &other, Tensor &out) {
      return torch::true_divide_out(out, self, other);
    })
  .define_singleton_method(
    "_true_divide_scalar",
    *[](const Tensor &self, Scalar other) {
      return torch::true_divide(self, other);
    })
  .define_singleton_method(
    "_true_divide_tensor",
    *[](const Tensor &self, const Tensor &other) {
      return torch::true_divide(self, other);
    })
  .define_singleton_method(
    "_trunc",
    *[](const Tensor &self) {
      return torch::trunc(self);
    })
  .define_singleton_method(
    "_trunc_",
    *[](Tensor &self) {
      return torch::trunc_(self);
    })
  .define_singleton_method(
    "_trunc_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::trunc_out(out, self);
    })
  .define_singleton_method(
    "_unbind_int",
    *[](Tensor &self, int64_t dim) {
      return torch::unbind(self, dim);
    })
  .define_singleton_method(
    "_unique_consecutive",
    *[](const Tensor &self, bool return_inverse, bool return_counts) {
      return wrap(torch::unique_consecutive(self, return_inverse, return_counts));
    })
  .define_singleton_method(
    "_unique_consecutive_dim",
    *[](const Tensor &self, bool return_inverse, bool return_counts, int64_t dim) {
      return wrap(torch::unique_consecutive(self, return_inverse, return_counts, dim));
    })
  .define_singleton_method(
    "_unique_dim",
    *[](const Tensor &self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) {
      return wrap(torch::unique_dim(self, dim, sorted, return_inverse, return_counts));
    })
  .define_singleton_method(
    "_unique_dim_consecutive",
    *[](const Tensor &self, int64_t dim, bool return_inverse, bool return_counts) {
      return wrap(torch::unique_dim_consecutive(self, dim, return_inverse, return_counts));
    })
  .define_singleton_method(
    "_unsqueeze",
    *[](Tensor &self, int64_t dim) {
      return torch::unsqueeze(self, dim);
    })
  .define_singleton_method(
    "_var",
    *[](const Tensor &self, bool unbiased) {
      return torch::var(self, unbiased);
    })
  .define_singleton_method(
    "_var_dim",
    *[](const Tensor &self, IntArrayRef dim, bool unbiased, bool keepdim) {
      return torch::var(self, dim, unbiased, keepdim);
    })
  .define_singleton_method(
    "_var_mean",
    *[](const Tensor &self, bool unbiased) {
      return wrap(torch::var_mean(self, unbiased));
    })
  .define_singleton_method(
    "_var_mean_dim",
    *[](const Tensor &self, IntArrayRef dim, bool unbiased, bool keepdim) {
      return wrap(torch::var_mean(self, dim, unbiased, keepdim));
    })
  .define_singleton_method(
    "_var_out",
    *[](const Tensor &self, IntArrayRef dim, bool unbiased, bool keepdim, Tensor &out) {
      return torch::var_out(out, self, dim, unbiased, keepdim);
    })
  .define_singleton_method(
    "_where",
    *[](const Tensor &condition) {
      return torch::where(condition);
    })
  .define_singleton_method(
    "_where_self",
    *[](const Tensor &condition, const Tensor &self, const Tensor &other) {
      return torch::where(condition, self, other);
    })
  .define_singleton_method(
    "_zero_",
    *[](Tensor &self) {
      return torch::zero_(self);
    })
  .define_singleton_method(
    "_zeros_out",
    *[](IntArrayRef size, Tensor &out) {
      return torch::zeros_out(out, size);
    });
}