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

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

void add_nn_functions(Module m) {
  m
  .define_singleton_method(
    "_adaptive_avg_pool2d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::adaptive_avg_pool2d(self, output_size);
    })
  .define_singleton_method(
    "_adaptive_avg_pool2d_out",
    *[](const Tensor &self, IntArrayRef output_size, Tensor &out) {
      return torch::adaptive_avg_pool2d_out(out, self, output_size);
    })
  .define_singleton_method(
    "_adaptive_avg_pool3d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::adaptive_avg_pool3d(self, output_size);
    })
  .define_singleton_method(
    "_adaptive_avg_pool3d_out",
    *[](const Tensor &self, IntArrayRef output_size, Tensor &out) {
      return torch::adaptive_avg_pool3d_out(out, self, output_size);
    })
  .define_singleton_method(
    "_adaptive_max_pool2d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::adaptive_max_pool2d(self, output_size);
    })
  .define_singleton_method(
    "_adaptive_max_pool2d_out",
    *[](const Tensor &self, IntArrayRef output_size, Tensor &out, Tensor &indices) {
      return torch::adaptive_max_pool2d_out(out, indices, self, output_size);
    })
  .define_singleton_method(
    "_adaptive_max_pool3d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::adaptive_max_pool3d(self, output_size);
    })
  .define_singleton_method(
    "_adaptive_max_pool3d_out",
    *[](const Tensor &self, IntArrayRef output_size, Tensor &out, Tensor &indices) {
      return torch::adaptive_max_pool3d_out(out, indices, self, output_size);
    })
  .define_singleton_method(
    "_binary_cross_entropy",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction) {
      return torch::binary_cross_entropy(self, target, weight, reduction);
    })
  .define_singleton_method(
    "_binary_cross_entropy_out",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, Tensor &out) {
      return torch::binary_cross_entropy_out(out, self, target, weight, reduction);
    })
  .define_singleton_method(
    "_col2im",
    *[](const Tensor &self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) {
      return torch::col2im(self, output_size, kernel_size, dilation, padding, stride);
    })
  .define_singleton_method(
    "_col2im_out",
    *[](const Tensor &self, IntArrayRef output_size, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride, Tensor &out) {
      return torch::col2im_out(out, self, output_size, kernel_size, dilation, padding, stride);
    })
  .define_singleton_method(
    "_elu",
    *[](const Tensor &self, Scalar alpha, Scalar scale, Scalar input_scale) {
      return torch::elu(self, alpha, scale, input_scale);
    })
  .define_singleton_method(
    "_elu_",
    *[](Tensor &self, Scalar alpha, Scalar scale, Scalar input_scale) {
      return torch::elu_(self, alpha, scale, input_scale);
    })
  .define_singleton_method(
    "_elu_out",
    *[](const Tensor &self, Scalar alpha, Scalar scale, Scalar input_scale, Tensor &out) {
      return torch::elu_out(out, self, alpha, scale, input_scale);
    })
  .define_singleton_method(
    "_fractional_max_pool2d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor &random_samples) {
      return torch::fractional_max_pool2d(self, kernel_size, output_size, random_samples);
    })
  .define_singleton_method(
    "_fractional_max_pool2d_output",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor &random_samples, Tensor &output, Tensor &indices) {
      return torch::fractional_max_pool2d_out(output, indices, self, kernel_size, output_size, random_samples);
    })
  .define_singleton_method(
    "_fractional_max_pool3d",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor &random_samples) {
      return torch::fractional_max_pool3d(self, kernel_size, output_size, random_samples);
    })
  .define_singleton_method(
    "_fractional_max_pool3d_output",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef output_size, const Tensor &random_samples, Tensor &output, Tensor &indices) {
      return torch::fractional_max_pool3d_out(output, indices, self, kernel_size, output_size, random_samples);
    })
  .define_singleton_method(
    "_gelu",
    *[](const Tensor &self) {
      return torch::gelu(self);
    })
  .define_singleton_method(
    "_glu",
    *[](const Tensor &self, int64_t dim) {
      return torch::glu(self, dim);
    })
  .define_singleton_method(
    "_glu_out",
    *[](const Tensor &self, int64_t dim, Tensor &out) {
      return torch::glu_out(out, self, dim);
    })
  .define_singleton_method(
    "_hardtanh",
    *[](const Tensor &self, Scalar min_val, Scalar max_val) {
      return torch::hardtanh(self, min_val, max_val);
    })
  .define_singleton_method(
    "_hardtanh_",
    *[](Tensor &self, Scalar min_val, Scalar max_val) {
      return torch::hardtanh_(self, min_val, max_val);
    })
  .define_singleton_method(
    "_hardtanh_out",
    *[](const Tensor &self, Scalar min_val, Scalar max_val, Tensor &out) {
      return torch::hardtanh_out(out, self, min_val, max_val);
    })
  .define_singleton_method(
    "_im2col",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride) {
      return torch::im2col(self, kernel_size, dilation, padding, stride);
    })
  .define_singleton_method(
    "_im2col_out",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef dilation, IntArrayRef padding, IntArrayRef stride, Tensor &out) {
      return torch::im2col_out(out, self, kernel_size, dilation, padding, stride);
    })
  .define_singleton_method(
    "_l1_loss",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction) {
      return torch::l1_loss(self, target, reduction);
    })
  .define_singleton_method(
    "_l1_loss_out",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction, Tensor &out) {
      return torch::l1_loss_out(out, self, target, reduction);
    })
  .define_singleton_method(
    "_leaky_relu",
    *[](const Tensor &self, Scalar negative_slope) {
      return torch::leaky_relu(self, negative_slope);
    })
  .define_singleton_method(
    "_leaky_relu_",
    *[](Tensor &self, Scalar negative_slope) {
      return torch::leaky_relu_(self, negative_slope);
    })
  .define_singleton_method(
    "_leaky_relu_out",
    *[](const Tensor &self, Scalar negative_slope, Tensor &out) {
      return torch::leaky_relu_out(out, self, negative_slope);
    })
  .define_singleton_method(
    "_linear",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias) {
      return torch::linear(input, weight, bias);
    })
  .define_singleton_method(
    "_log_sigmoid",
    *[](const Tensor &self) {
      return torch::log_sigmoid(self);
    })
  .define_singleton_method(
    "_log_sigmoid_forward",
    *[](const Tensor &self) {
      return torch::log_sigmoid_forward(self);
    })
  .define_singleton_method(
    "_log_sigmoid_forward_output",
    *[](const Tensor &self, Tensor &output, Tensor &buffer) {
      return torch::log_sigmoid_forward_out(output, buffer, self);
    })
  .define_singleton_method(
    "_log_sigmoid_out",
    *[](const Tensor &self, Tensor &out) {
      return torch::log_sigmoid_out(out, self);
    })
  .define_singleton_method(
    "_max_pool2d_with_indices",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return torch::max_pool2d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_max_pool2d_with_indices_out",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, Tensor &out, Tensor &indices) {
      return torch::max_pool2d_with_indices_out(out, indices, self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_max_pool3d_with_indices",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode) {
      return torch::max_pool3d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_max_pool3d_with_indices_out",
    *[](const Tensor &self, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, bool ceil_mode, Tensor &out, Tensor &indices) {
      return torch::max_pool3d_with_indices_out(out, indices, self, kernel_size, stride, padding, dilation, ceil_mode);
    })
  .define_singleton_method(
    "_max_unpool2d",
    *[](const Tensor &self, const Tensor &indices, IntArrayRef output_size) {
      return torch::max_unpool2d(self, indices, output_size);
    })
  .define_singleton_method(
    "_max_unpool2d_out",
    *[](const Tensor &self, const Tensor &indices, IntArrayRef output_size, Tensor &out) {
      return torch::max_unpool2d_out(out, self, indices, output_size);
    })
  .define_singleton_method(
    "_max_unpool3d",
    *[](const Tensor &self, const Tensor &indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding) {
      return torch::max_unpool3d(self, indices, output_size, stride, padding);
    })
  .define_singleton_method(
    "_max_unpool3d_out",
    *[](const Tensor &self, const Tensor &indices, IntArrayRef output_size, IntArrayRef stride, IntArrayRef padding, Tensor &out) {
      return torch::max_unpool3d_out(out, self, indices, output_size, stride, padding);
    })
  .define_singleton_method(
    "_mkldnn_linear",
    *[](const Tensor &input, const Tensor &weight, OptionalTensor bias) {
      return torch::mkldnn_linear(input, weight, bias);
    })
  .define_singleton_method(
    "_mkldnn_reorder_conv2d_weight",
    *[](const Tensor &self, IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups) {
      return torch::mkldnn_reorder_conv2d_weight(self, padding, stride, dilation, groups);
    })
  .define_singleton_method(
    "_mse_loss",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction) {
      return torch::mse_loss(self, target, reduction);
    })
  .define_singleton_method(
    "_mse_loss_out",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction, Tensor &out) {
      return torch::mse_loss_out(out, self, target, reduction);
    })
  .define_singleton_method(
    "_multi_margin_loss",
    *[](const Tensor &self, const Tensor &target, Scalar p, Scalar margin, OptionalTensor weight, MyReduction reduction) {
      return torch::multi_margin_loss(self, target, p, margin, weight, reduction);
    })
  .define_singleton_method(
    "_multi_margin_loss_out",
    *[](const Tensor &self, const Tensor &target, Scalar p, Scalar margin, OptionalTensor weight, MyReduction reduction, Tensor &out) {
      return torch::multi_margin_loss_out(out, self, target, p, margin, weight, reduction);
    })
  .define_singleton_method(
    "_multilabel_margin_loss",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction) {
      return torch::multilabel_margin_loss(self, target, reduction);
    })
  .define_singleton_method(
    "_multilabel_margin_loss_forward",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction) {
      return torch::multilabel_margin_loss_forward(self, target, reduction);
    })
  .define_singleton_method(
    "_multilabel_margin_loss_forward_output",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction, Tensor &output, Tensor &is_target) {
      return torch::multilabel_margin_loss_forward_out(output, is_target, self, target, reduction);
    })
  .define_singleton_method(
    "_multilabel_margin_loss_out",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction, Tensor &out) {
      return torch::multilabel_margin_loss_out(out, self, target, reduction);
    })
  .define_singleton_method(
    "_nll_loss",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index) {
      return torch::nll_loss(self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_nll_loss2d",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index) {
      return torch::nll_loss2d(self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_nll_loss2d_forward",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index) {
      return torch::nll_loss2d_forward(self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_nll_loss2d_forward_output",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index, Tensor &output, Tensor &total_weight) {
      return torch::nll_loss2d_forward_out(output, total_weight, self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_nll_loss2d_out",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index, Tensor &out) {
      return torch::nll_loss2d_out(out, self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_nll_loss_forward",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index) {
      return torch::nll_loss_forward(self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_nll_loss_forward_output",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index, Tensor &output, Tensor &total_weight) {
      return torch::nll_loss_forward_out(output, total_weight, self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_nll_loss_out",
    *[](const Tensor &self, const Tensor &target, OptionalTensor weight, MyReduction reduction, int64_t ignore_index, Tensor &out) {
      return torch::nll_loss_out(out, self, target, weight, reduction, ignore_index);
    })
  .define_singleton_method(
    "_one_hot",
    *[](const Tensor &self, int64_t num_classes) {
      return torch::one_hot(self, num_classes);
    })
  .define_singleton_method(
    "_reflection_pad1d",
    *[](const Tensor &self, IntArrayRef padding) {
      return torch::reflection_pad1d(self, padding);
    })
  .define_singleton_method(
    "_reflection_pad1d_out",
    *[](const Tensor &self, IntArrayRef padding, Tensor &out) {
      return torch::reflection_pad1d_out(out, self, padding);
    })
  .define_singleton_method(
    "_reflection_pad2d",
    *[](const Tensor &self, IntArrayRef padding) {
      return torch::reflection_pad2d(self, padding);
    })
  .define_singleton_method(
    "_reflection_pad2d_out",
    *[](const Tensor &self, IntArrayRef padding, Tensor &out) {
      return torch::reflection_pad2d_out(out, self, padding);
    })
  .define_singleton_method(
    "_replication_pad1d",
    *[](const Tensor &self, IntArrayRef padding) {
      return torch::replication_pad1d(self, padding);
    })
  .define_singleton_method(
    "_replication_pad1d_out",
    *[](const Tensor &self, IntArrayRef padding, Tensor &out) {
      return torch::replication_pad1d_out(out, self, padding);
    })
  .define_singleton_method(
    "_replication_pad2d",
    *[](const Tensor &self, IntArrayRef padding) {
      return torch::replication_pad2d(self, padding);
    })
  .define_singleton_method(
    "_replication_pad2d_out",
    *[](const Tensor &self, IntArrayRef padding, Tensor &out) {
      return torch::replication_pad2d_out(out, self, padding);
    })
  .define_singleton_method(
    "_replication_pad3d",
    *[](const Tensor &self, IntArrayRef padding) {
      return torch::replication_pad3d(self, padding);
    })
  .define_singleton_method(
    "_replication_pad3d_out",
    *[](const Tensor &self, IntArrayRef padding, Tensor &out) {
      return torch::replication_pad3d_out(out, self, padding);
    })
  .define_singleton_method(
    "_rrelu_with_noise",
    *[](const Tensor &self, const Tensor &noise, Scalar lower, Scalar upper, bool training) {
      return torch::rrelu_with_noise(self, noise, lower, upper, training);
    })
  .define_singleton_method(
    "_rrelu_with_noise_",
    *[](Tensor &self, const Tensor &noise, Scalar lower, Scalar upper, bool training) {
      return torch::rrelu_with_noise_(self, noise, lower, upper, training);
    })
  .define_singleton_method(
    "_rrelu_with_noise_out",
    *[](const Tensor &self, const Tensor &noise, Scalar lower, Scalar upper, bool training, Tensor &out) {
      return torch::rrelu_with_noise_out(out, self, noise, lower, upper, training);
    })
  .define_singleton_method(
    "_slow_conv_dilated2d",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) {
      return torch::slow_conv_dilated2d(self, weight, kernel_size, bias, stride, padding, dilation);
    })
  .define_singleton_method(
    "_slow_conv_dilated3d",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) {
      return torch::slow_conv_dilated3d(self, weight, kernel_size, bias, stride, padding, dilation);
    })
  .define_singleton_method(
    "_slow_conv_transpose2d",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) {
      return torch::slow_conv_transpose2d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
    })
  .define_singleton_method(
    "_slow_conv_transpose2d_out",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, Tensor &out) {
      return torch::slow_conv_transpose2d_out(out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
    })
  .define_singleton_method(
    "_slow_conv_transpose3d",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation) {
      return torch::slow_conv_transpose3d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
    })
  .define_singleton_method(
    "_slow_conv_transpose3d_out",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef output_padding, IntArrayRef dilation, Tensor &out) {
      return torch::slow_conv_transpose3d_out(out, self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
    })
  .define_singleton_method(
    "_smooth_l1_loss",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction) {
      return torch::smooth_l1_loss(self, target, reduction);
    })
  .define_singleton_method(
    "_smooth_l1_loss_out",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction, Tensor &out) {
      return torch::smooth_l1_loss_out(out, self, target, reduction);
    })
  .define_singleton_method(
    "_soft_margin_loss",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction) {
      return torch::soft_margin_loss(self, target, reduction);
    })
  .define_singleton_method(
    "_soft_margin_loss_out",
    *[](const Tensor &self, const Tensor &target, MyReduction reduction, Tensor &out) {
      return torch::soft_margin_loss_out(out, self, target, reduction);
    })
  .define_singleton_method(
    "_softplus",
    *[](const Tensor &self, Scalar beta, Scalar threshold) {
      return torch::softplus(self, beta, threshold);
    })
  .define_singleton_method(
    "_softplus_out",
    *[](const Tensor &self, Scalar beta, Scalar threshold, Tensor &out) {
      return torch::softplus_out(out, self, beta, threshold);
    })
  .define_singleton_method(
    "_softshrink",
    *[](const Tensor &self, Scalar lambd) {
      return torch::softshrink(self, lambd);
    })
  .define_singleton_method(
    "_softshrink_out",
    *[](const Tensor &self, Scalar lambd, Tensor &out) {
      return torch::softshrink_out(out, self, lambd);
    })
  .define_singleton_method(
    "_thnn_conv2d",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding) {
      return torch::thnn_conv2d(self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv2d_forward",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding) {
      return torch::thnn_conv2d_forward(self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv2d_forward_output",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, Tensor &output, Tensor &finput, Tensor &fgrad_input) {
      return torch::thnn_conv2d_forward_out(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv2d_out",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, Tensor &out) {
      return torch::thnn_conv2d_out(out, self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv3d",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding) {
      return torch::thnn_conv3d(self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv3d_forward",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding) {
      return torch::thnn_conv3d_forward(self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv3d_forward_output",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, Tensor &output, Tensor &finput, Tensor &fgrad_input) {
      return torch::thnn_conv3d_forward_out(output, finput, fgrad_input, self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv3d_out",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, Tensor &out) {
      return torch::thnn_conv3d_out(out, self, weight, kernel_size, bias, stride, padding);
    })
  .define_singleton_method(
    "_thnn_conv_depthwise2d",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) {
      return torch::thnn_conv_depthwise2d(self, weight, kernel_size, bias, stride, padding, dilation);
    })
  .define_singleton_method(
    "_thnn_conv_depthwise2d_forward",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation) {
      return torch::thnn_conv_depthwise2d_forward(self, weight, kernel_size, bias, stride, padding, dilation);
    })
  .define_singleton_method(
    "_thnn_conv_depthwise2d_forward_out",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, Tensor &out) {
      return torch::thnn_conv_depthwise2d_forward_out(out, self, weight, kernel_size, bias, stride, padding, dilation);
    })
  .define_singleton_method(
    "_thnn_conv_depthwise2d_out",
    *[](const Tensor &self, const Tensor &weight, IntArrayRef kernel_size, OptionalTensor bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, Tensor &out) {
      return torch::thnn_conv_depthwise2d_out(out, self, weight, kernel_size, bias, stride, padding, dilation);
    })
  .define_singleton_method(
    "_upsample_bicubic2d",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners) {
      return torch::upsample_bicubic2d(self, output_size, align_corners);
    })
  .define_singleton_method(
    "_upsample_bicubic2d_out",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners, Tensor &out) {
      return torch::upsample_bicubic2d_out(out, self, output_size, align_corners);
    })
  .define_singleton_method(
    "_upsample_bilinear2d",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners) {
      return torch::upsample_bilinear2d(self, output_size, align_corners);
    })
  .define_singleton_method(
    "_upsample_bilinear2d_out",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners, Tensor &out) {
      return torch::upsample_bilinear2d_out(out, self, output_size, align_corners);
    })
  .define_singleton_method(
    "_upsample_linear1d",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners) {
      return torch::upsample_linear1d(self, output_size, align_corners);
    })
  .define_singleton_method(
    "_upsample_linear1d_out",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners, Tensor &out) {
      return torch::upsample_linear1d_out(out, self, output_size, align_corners);
    })
  .define_singleton_method(
    "_upsample_nearest1d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::upsample_nearest1d(self, output_size);
    })
  .define_singleton_method(
    "_upsample_nearest1d_out",
    *[](const Tensor &self, IntArrayRef output_size, Tensor &out) {
      return torch::upsample_nearest1d_out(out, self, output_size);
    })
  .define_singleton_method(
    "_upsample_nearest2d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::upsample_nearest2d(self, output_size);
    })
  .define_singleton_method(
    "_upsample_nearest2d_out",
    *[](const Tensor &self, IntArrayRef output_size, Tensor &out) {
      return torch::upsample_nearest2d_out(out, self, output_size);
    })
  .define_singleton_method(
    "_upsample_nearest3d",
    *[](const Tensor &self, IntArrayRef output_size) {
      return torch::upsample_nearest3d(self, output_size);
    })
  .define_singleton_method(
    "_upsample_nearest3d_out",
    *[](const Tensor &self, IntArrayRef output_size, Tensor &out) {
      return torch::upsample_nearest3d_out(out, self, output_size);
    })
  .define_singleton_method(
    "_upsample_trilinear3d",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners) {
      return torch::upsample_trilinear3d(self, output_size, align_corners);
    })
  .define_singleton_method(
    "_upsample_trilinear3d_out",
    *[](const Tensor &self, IntArrayRef output_size, bool align_corners, Tensor &out) {
      return torch::upsample_trilinear3d_out(out, self, output_size, align_corners);
    });
}