aten_hooks.cpp

// Autogenerated file by gen.py. Do not edit directly!

#include "aten_xla_bridge.h"

#include <ATen/Context.h>
#include <ATen/CPUGenerator.h>
#include <ATen/TypeDefault.h>

namespace torch_xla {

class XLATypeBase : public at::TypeDefault {
 public:
  XLATypeBase(at::TensorTypeId type_id, bool is_variable, bool is_undefined)
    : at::TypeDefault(type_id, is_variable, is_undefined) {}

  caffe2::TypeMeta typeMeta() const override {
    return scalarTypeToTypeMeta(scalarType());
  }

  at::Backend backend() const override {
    return at::Backend::HIP;
  }

  at::Allocator * allocator() const override {
    return at::getCPUAllocator();
  }

  c10::Device getDeviceFromPtr(void * data) const override {
    return at::DeviceType::HIP;
  }

  std::unique_ptr<at::Generator> generator() const override {
    return std::unique_ptr<at::Generator>(new at::CPUGenerator(&at::globalContext()));
  }

  at::TypeID ID() const override {
    return at::TypeID::Undefined;
  }

  at::Tensor & _th_set_(at::Tensor & self, at::Storage source) const override;
  at::Tensor & _th_set_(at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntList size, at::IntList stride) const override;
  at::Tensor & _th_set_(at::Tensor & self, const at::Tensor & source) const override;
  at::Tensor & _th_set_(at::Tensor & self) const override;
  at::Tensor & _th_fill_(at::Tensor & self, at::Scalar value) const override;
  at::Tensor & _th_fill_(at::Tensor & self, const at::Tensor & value) const override;
  bool _th_is_set_to(const at::Tensor & self, const at::Tensor & tensor) const override;
  at::Tensor & _th_masked_fill_(at::Tensor & self, const at::Tensor & mask, at::Scalar value) const override;
  at::Tensor & s__th_masked_fill_(at::Tensor & self, const at::Tensor & mask, at::Scalar value) const override;
  at::Tensor & _th_masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) const override;
  at::Tensor & s__th_masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) const override;
  at::Tensor & _th_masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) const override;
  at::Tensor & s__th_masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) const override;
  at::Tensor & _th_masked_select_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mask) const override;
  at::Tensor & s__th_masked_select_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mask) const override;
  at::Tensor _th_masked_select(const at::Tensor & self, const at::Tensor & mask) const override;
  at::Tensor s__th_masked_select(const at::Tensor & self, const at::Tensor & mask) const override;
  at::Tensor & _th_nonzero_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_nonzero(const at::Tensor & self) const override;
  at::Tensor _th_clone(const at::Tensor & self) const override;
  at::Tensor _th_view(const at::Tensor & self, at::IntList size) const override;
  at::Tensor & _th_resize_as_(at::Tensor & self, const at::Tensor & the_template) const override;
  at::Tensor & _th_index_select_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  at::Tensor _th_index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  at::Tensor & _th_index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const override;
  at::Tensor & _th_take_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & index) const override;
  at::Tensor _th_take(const at::Tensor & self, const at::Tensor & index) const override;
  at::Tensor & _th_put_(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) const override;
  at::Tensor & _th_index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const override;
  at::Tensor & _th_index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const override;
  at::Tensor & _th_index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) const override;
  at::Tensor & _th_unfold_out(at::Tensor & result, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) const override;
  at::Tensor _th_unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) const override;
  at::Tensor & _th_scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const override;
  at::Tensor & _th_scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const override;
  at::Tensor & _th_scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const override;
  at::Tensor & _th_gather_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  at::Tensor _th_gather(const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  bool _th_equal(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_and_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_and(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_and_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_and_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_and(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_and(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_iand_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_iand_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_iand_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_or_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_or(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_or_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_or_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_or(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_or(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_ior_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_ior_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_ior_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_xor_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_xor(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_xor_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_xor_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_xor(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_xor(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_ixor_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_ixor_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_ixor_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_lshift_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_lshift(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_lshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_lshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_lshift(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_lshift(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_ilshift_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_ilshift_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_ilshift_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_rshift_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_rshift(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_rshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_rshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_rshift(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_rshift(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_irshift_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_irshift_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_irshift_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_lt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_lt(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_lt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_lt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_lt(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_lt(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_lt_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_lt_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_lt_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_gt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_gt(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_gt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_gt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_gt(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_gt(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_gt_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_gt_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_gt_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_le_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_le(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_le_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_le_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_le(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_le(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_le_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_le_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_le_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_ge_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_ge(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_ge_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_ge_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_ge(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_ge(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_ge_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_ge_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_ge_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_eq_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_eq(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_eq_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_eq_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_eq(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_eq(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_eq_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_eq_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_eq_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_ne_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_ne(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_ne_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_ne_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_ne(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_ne(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_ne_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_ne_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_ne_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_min_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_min_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_min(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_min(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_min(const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor,at::Tensor> _th_min(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor & _th_max_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_max_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_max(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_max(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_max(const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_max_out(at::Tensor & max, at::Tensor & max_indices, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor,at::Tensor> _th_max(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_kthvalue_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor,at::Tensor> _th_kthvalue(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_mode_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor,at::Tensor> _th_mode(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor _th_median(const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_median_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor,at::Tensor> _th_median(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool descending) const override;
  std::tuple<at::Tensor,at::Tensor> _th_sort(const at::Tensor & self, int64_t dim, bool descending) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const override;
  std::tuple<at::Tensor,at::Tensor> _th_topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const override;
  at::Tensor _th_any(const at::Tensor & self) const override;
  at::Tensor & _th_any_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor _th_any(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor & _th_abs_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_abs(const at::Tensor & self) const override;
  at::Tensor & _th_sigmoid_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_sigmoid(const at::Tensor & self) const override;
  at::Tensor & _th_log_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_log(const at::Tensor & self) const override;
  at::Tensor & _th_log10_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_log10(const at::Tensor & self) const override;
  at::Tensor & _th_log1p_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_log1p(const at::Tensor & self) const override;
  at::Tensor & _th_log2_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_log2(const at::Tensor & self) const override;
  at::Tensor & _th_lgamma_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_lgamma(const at::Tensor & self) const override;
  at::Tensor & _th_lgamma_(at::Tensor & self) const override;
  at::Tensor & _th_digamma_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_digamma(const at::Tensor & self) const override;
  at::Tensor & _th_digamma_(at::Tensor & self) const override;
  at::Tensor & _th_polygamma_out(at::Tensor & result, int64_t n, const at::Tensor & self) const override;
  at::Tensor _th_polygamma(int64_t n, const at::Tensor & self) const override;
  at::Tensor & _th_polygamma_(at::Tensor & self, int64_t n) const override;
  at::Tensor & _th_exp_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_exp(const at::Tensor & self) const override;
  at::Tensor & _th_expm1_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_expm1(const at::Tensor & self) const override;
  at::Tensor & _th_cos_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_cos(const at::Tensor & self) const override;
  at::Tensor & _th_acos_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_acos(const at::Tensor & self) const override;
  at::Tensor & _th_cosh_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_cosh(const at::Tensor & self) const override;
  at::Tensor & _th_sin_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_sin(const at::Tensor & self) const override;
  at::Tensor & _th_asin_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_asin(const at::Tensor & self) const override;
  at::Tensor & _th_sinh_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_sinh(const at::Tensor & self) const override;
  at::Tensor & _th_tan_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_tan(const at::Tensor & self) const override;
  at::Tensor & _th_atan_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_atan(const at::Tensor & self) const override;
  at::Tensor & _th_tanh_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_tanh(const at::Tensor & self) const override;
  at::Tensor & _th_erf_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_erf(const at::Tensor & self) const override;
  at::Tensor & _th_erfc_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_erfc(const at::Tensor & self) const override;
  at::Tensor & _th_erfinv_(at::Tensor & self) const override;
  at::Tensor & _th_erfinv_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_erfinv(const at::Tensor & self) const override;
  at::Tensor & _th_sqrt_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_sqrt(const at::Tensor & self) const override;
  at::Tensor & _th_rsqrt_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_rsqrt(const at::Tensor & self) const override;
  at::Tensor & _th_ceil_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_ceil(const at::Tensor & self) const override;
  at::Tensor & _th_floor_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_floor(const at::Tensor & self) const override;
  at::Tensor & _th_round_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_round(const at::Tensor & self) const override;
  at::Tensor & _th_trunc_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_trunc(const at::Tensor & self) const override;
  at::Tensor & _th_frac_(at::Tensor & self) const override;
  at::Tensor & _th_frac_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_frac(const at::Tensor & self) const override;
  at::Tensor & _th_var_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const override;
  at::Tensor _th_var(const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const override;
  at::Tensor _th_var(const at::Tensor & self, bool unbiased) const override;
  at::Tensor & _th_std_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const override;
  at::Tensor _th_std(const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const override;
  at::Tensor _th_std(const at::Tensor & self, bool unbiased) const override;
  at::Tensor & _th_renorm_out(at::Tensor & result, const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const override;
  at::Tensor _th_renorm(const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const override;
  at::Tensor & _th_renorm_(at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const override;
  at::Tensor _th_dist(const at::Tensor & self, const at::Tensor & other, at::Scalar p) const override;
  at::Tensor s__th_dist(const at::Tensor & self, const at::Tensor & other, at::Scalar p) const override;
  at::Tensor & _th_reciprocal_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_reciprocal(const at::Tensor & self) const override;
  at::Tensor & _th_reciprocal_(at::Tensor & self) const override;
  at::Tensor & _th_neg_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_neg(const at::Tensor & self) const override;
  at::Tensor & _th_neg_(at::Tensor & self) const override;
  at::Tensor & _th_atan2_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_atan2_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_atan2(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_atan2(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_atan2_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_atan2_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_pow_out(at::Tensor & result, const at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor _th_pow(const at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor & _th_pow_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor & s__th_pow_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor _th_pow(const at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor s__th_pow(const at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor & _th_pow_out(at::Tensor & result, at::Scalar self, const at::Tensor & exponent) const override;
  at::Tensor _th_pow(at::Scalar self, const at::Tensor & exponent) const override;
  at::Tensor & _th_pow_(at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor & _th_pow_(at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor & s__th_pow_(at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor & _th_lerp_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor & s__th_lerp_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor _th_lerp(const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor s__th_lerp(const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor & _th_lerp_(at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor & s__th_lerp_(at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor & _th_histc_out(at::Tensor & result, const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const override;
  at::Tensor _th_histc(const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const override;
  at::Tensor & _th_zero_(at::Tensor & self) const override;
  at::Tensor & _th_cumsum_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const override;
  at::Tensor _th_cumsum(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & _th_cumprod_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const override;
  at::Tensor _th_cumprod(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & _th_sign_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _th_sign(const at::Tensor & self) const override;
  at::Tensor & _th_sign_(at::Tensor & self) const override;
  at::Tensor _th_trace(const at::Tensor & self) const override;
  at::Tensor & _th_fmod_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_fmod(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_fmod_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_fmod_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_fmod(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_fmod(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_fmod_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_fmod_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_fmod_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_remainder_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor _th_remainder(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_remainder_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_remainder_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _th_remainder(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor s__th_remainder(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_remainder_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _th_remainder_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & s__th_remainder_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _th_clamp_out(at::Tensor & result, const at::Tensor & self, at::Scalar min, at::Scalar max) const override;
  at::Tensor _th_clamp(const at::Tensor & self, at::Scalar min, at::Scalar max) const override;
  at::Tensor & _th_clamp_min_out(at::Tensor & result, const at::Tensor & self, at::Scalar min) const override;
  at::Tensor _th_clamp_min(const at::Tensor & self, at::Scalar min) const override;
  at::Tensor & _th_clamp_max_out(at::Tensor & result, const at::Tensor & self, at::Scalar max) const override;
  at::Tensor _th_clamp_max(const at::Tensor & self, at::Scalar max) const override;
  at::Tensor _th_dot(const at::Tensor & self, const at::Tensor & tensor) const override;
  at::Tensor & _th_cross_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, int64_t dim) const override;
  at::Tensor _th_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) const override;
  at::Tensor & _th_diag_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor _th_diag(const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor & _th_addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & s__th_addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor _th_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor s__th_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_addmv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & s__th_addmv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor _th_addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor s__th_addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_addr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & s__th_addr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor _th_addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor s__th_addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_addr_(at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_ger_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec2) const override;
  at::Tensor _th_ger(const at::Tensor & self, const at::Tensor & vec2) const override;
  at::Tensor & _th_mv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec) const override;
  at::Tensor _th_mv(const at::Tensor & self, const at::Tensor & vec) const override;
  at::Tensor & _th_mm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor _th_mm(const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor & _th_bmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor _th_bmm(const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor & _th_addbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & s__th_addbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor _th_addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor s__th_addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_addbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_baddbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & s__th_baddbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor _th_baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor s__th_baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _th_addcmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & s__th_addcmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor _th_addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor s__th_addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & _th_addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & s__th_addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & _th_addcdiv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & s__th_addcdiv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor _th_addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor s__th_addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & _th_addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & s__th_addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_gels_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, const at::Tensor & A) const override;
  std::tuple<at::Tensor,at::Tensor> _th_gels(const at::Tensor & self, const at::Tensor & A) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_trtrs_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const override;
  std::tuple<at::Tensor,at::Tensor> _th_trtrs(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_symeig_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, bool eigenvectors, bool upper) const override;
  std::tuple<at::Tensor,at::Tensor> _th_symeig(const at::Tensor & self, bool eigenvectors, bool upper) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_eig_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, bool eigenvectors) const override;
  std::tuple<at::Tensor,at::Tensor> _th_eig(const at::Tensor & self, bool eigenvectors) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _th_svd_out(at::Tensor & res1, at::Tensor & res2, at::Tensor & res3, const at::Tensor & self, bool some, bool compute_uv) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _th_svd(const at::Tensor & self, bool some, bool compute_uv) const override;
  at::Tensor & _th_getri_single_out(at::Tensor & output, const at::Tensor & self) const override;
  at::Tensor _th_getri_single(const at::Tensor & self) const override;
  at::Tensor & _th_potri_out(at::Tensor & output, const at::Tensor & self, bool upper) const override;
  at::Tensor _th_potri(const at::Tensor & self, bool upper) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_pstrf_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, bool upper, at::Scalar tol) const override;
  std::tuple<at::Tensor,at::Tensor> _th_pstrf(const at::Tensor & self, bool upper, at::Scalar tol) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_qr_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self) const override;
  std::tuple<at::Tensor,at::Tensor> _th_qr(const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_geqrf_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self) const override;
  std::tuple<at::Tensor,at::Tensor> _th_geqrf(const at::Tensor & self) const override;
  at::Tensor & _th_orgqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2) const override;
  at::Tensor _th_orgqr(const at::Tensor & self, const at::Tensor & input2) const override;
  at::Tensor & _th_ormqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const override;
  at::Tensor _th_ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const override;
  std::tuple<at::Tensor &,at::Tensor &> _th_btrifact_out(at::Tensor & result, at::Tensor & pivots, const at::Tensor & self, bool pivot) const override;
  std::tuple<at::Tensor,at::Tensor> _th_btrifact(const at::Tensor & self, bool pivot) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _th_btrifact_with_info_out(at::Tensor & result, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool pivot) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _th_btrifact_with_info(const at::Tensor & self, bool pivot) const override;
  at::Tensor & _th_btrisolve_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const override;
  at::Tensor _th_btrisolve(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const override;
  at::Tensor & _th_random_(at::Tensor & self, int64_t from, int64_t to, at::Generator * generator) const override;
  at::Tensor & _th_random_(at::Tensor & self, int64_t to, at::Generator * generator) const override;
  at::Tensor & _th_random_(at::Tensor & self, at::Generator * generator) const override;
  at::Tensor & _th_multinomial_out(at::Tensor & result, const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const override;
  at::Tensor _th_multinomial(const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const override;
  at::Tensor & _th_uniform_(at::Tensor & self, double from, double to, at::Generator * generator) const override;
  at::Tensor & _th_normal_out(at::Tensor & output, const at::Tensor & mean, double std, at::Generator * generator) const override;
  at::Tensor _th_normal(const at::Tensor & mean, double std, at::Generator * generator) const override;
  at::Tensor & _th_normal_out(at::Tensor & output, double mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor _th_normal(double mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor & _th_normal_out(at::Tensor & output, const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor _th_normal(const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor & _th_normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const override;
  at::Tensor & _th_cauchy_(at::Tensor & self, double median, double sigma, at::Generator * generator) const override;
  at::Tensor & _th_log_normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const override;
  at::Tensor & _th_exponential_(at::Tensor & self, double lambd, at::Generator * generator) const override;
  at::Tensor & _th_geometric_(at::Tensor & self, double p, at::Generator * generator) const override;
  at::Tensor & _th_dirichlet_grad_out(at::Tensor & output, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const override;
  at::Tensor _th_dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const override;
  at::Tensor _th_alias(const at::Tensor & self) const override;
  at::Tensor & _th_copy_ignoring_overlaps_(at::Tensor & self, const at::Tensor & src) const override;
  at::Tensor & _th_cat_out(at::Tensor & self, at::TensorList tensors, int64_t dim) const override;
  at::Tensor _th_cat(at::TensorList tensors, int64_t dim) const override;
  at::Tensor & _thnn_binary_cross_entropy_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor _thnn_binary_cross_entropy_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor & _thnn_binary_cross_entropy_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor _thnn_binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor & _thnn_l1_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_l1_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_mse_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_mse_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_mse_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_multi_margin_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor _thnn_multi_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor & _thnn_multi_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor _thnn_multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_multilabel_margin_loss_forward_out(at::Tensor & output, at::Tensor & is_target, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_multilabel_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const override;
  at::Tensor _thnn_multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  at::Tensor & _thnn_nll_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  at::Tensor _thnn_nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  at::Tensor & _thnn_nll_loss2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  at::Tensor _thnn_nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  at::Tensor & _thnn_smooth_l1_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_smooth_l1_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_smooth_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_smooth_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_soft_margin_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_soft_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_soft_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor _thnn_soft_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & _thnn_elu_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const override;
  at::Tensor _thnn_elu_forward(const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const override;
  at::Tensor & _thnn_elu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const override;
  at::Tensor _thnn_elu_backward(const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const override;
  at::Tensor & _thnn_elu_(at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const override;
  at::Tensor & _thnn_elu_forward_(at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const override;
  at::Tensor & _thnn_glu_forward_out(at::Tensor & output, const at::Tensor & self, int64_t dim) const override;
  at::Tensor _thnn_glu_forward(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & _thnn_glu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const override;
  at::Tensor _thnn_glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const override;
  at::Tensor & _thnn_hardtanh_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor _thnn_hardtanh_forward(const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor & _thnn_hardtanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor _thnn_hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor & _thnn_hardtanh_(at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor & _thnn_hardtanh_forward_(at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor & _thnn_leaky_relu_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor _thnn_leaky_relu_forward(const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor & _thnn_leaky_relu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor _thnn_leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor & _thnn_leaky_relu_(at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor & _thnn_leaky_relu_forward_(at::Tensor & self, at::Scalar negative_slope) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_log_sigmoid_forward_out(at::Tensor & output, at::Tensor & buffer, const at::Tensor & self) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_log_sigmoid_forward(const at::Tensor & self) const override;
  at::Tensor & _thnn_log_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const override;
  at::Tensor _thnn_log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const override;
  at::Tensor & _thnn_rrelu_with_noise_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor _thnn_rrelu_with_noise_forward(const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor & _thnn_rrelu_with_noise_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const override;
  at::Tensor _thnn_rrelu_with_noise_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const override;
  at::Tensor & _thnn_rrelu_with_noise_(at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor & _thnn_rrelu_with_noise_forward_(at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor & _thnn_softplus_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const override;
  at::Tensor _thnn_softplus_forward(const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const override;
  at::Tensor & _thnn_softplus_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const override;
  at::Tensor _thnn_softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const override;
  at::Tensor & _thnn_softshrink_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor _thnn_softshrink_forward(const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor & _thnn_softshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor _thnn_softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor & _thnn_adaptive_avg_pool3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor _thnn_adaptive_avg_pool3d_forward(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & _thnn_adaptive_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) const override;
  at::Tensor _thnn_adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_adaptive_max_pool2d_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_adaptive_max_pool2d_forward(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & _thnn_adaptive_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  at::Tensor _thnn_adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_adaptive_max_pool3d_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_adaptive_max_pool3d_forward(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & _thnn_adaptive_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  at::Tensor _thnn_adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  at::Tensor & _thnn_avg_pool2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor _thnn_avg_pool2d_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor & _thnn_avg_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor _thnn_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor & _thnn_avg_pool3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor _thnn_avg_pool3d_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor & _thnn_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor _thnn_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_max_pool2d_with_indices_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_max_pool2d_with_indices_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor & _thnn_max_pool2d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  at::Tensor _thnn_max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_max_pool3d_with_indices_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_max_pool3d_with_indices_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor & _thnn_max_pool3d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  at::Tensor _thnn_max_pool3d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  at::Tensor & _thnn_max_unpool2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor _thnn_max_unpool2d_forward(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor & _thnn_max_unpool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor _thnn_max_unpool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor & _thnn_max_unpool3d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor _thnn_max_unpool3d_forward(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor & _thnn_max_unpool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor _thnn_max_unpool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor & _thnn_upsample_linear1d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_linear1d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_linear1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_bilinear2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_bilinear2d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_bicubic2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_bicubic2d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_trilinear3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_trilinear3d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor _thnn_upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & _thnn_upsample_nearest1d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor _thnn_upsample_nearest1d_forward(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & _thnn_upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor _thnn_upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor & _thnn_upsample_nearest2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor _thnn_upsample_nearest2d_forward(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & _thnn_upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor _thnn_upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor & _thnn_upsample_nearest3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor _thnn_upsample_nearest3d_forward(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & _thnn_upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor _thnn_upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor & _thnn_sigmoid_forward_out(at::Tensor & output, const at::Tensor & self) const override;
  at::Tensor _thnn_sigmoid_forward(const at::Tensor & self) const override;
  at::Tensor & _thnn_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const override;
  at::Tensor _thnn_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) const override;
  at::Tensor & _thnn_tanh_forward_out(at::Tensor & output, const at::Tensor & self) const override;
  at::Tensor _thnn_tanh_forward(const at::Tensor & self) const override;
  at::Tensor & _thnn_tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const override;
  at::Tensor _thnn_tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_transpose2d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_transpose2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_transpose2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_transpose2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_transpose3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_transpose3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_transpose3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_transpose3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv2d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const override;
  at::Tensor & _thnn_conv_depthwise2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  at::Tensor _thnn_conv_depthwise2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &> _thnn_conv_depthwise2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_conv_depthwise2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, std::array<bool,2> output_mask) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_dilated2d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_dilated2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_dilated2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_dilated2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_dilated3d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_dilated3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_conv_dilated3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_conv_dilated3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const override;
  at::Tensor & _thnn_col2im_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor _thnn_col2im_forward(const at::Tensor & self, at::IntList output_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor & _thnn_col2im_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor _thnn_col2im_backward(const at::Tensor & grad_output, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor & _thnn_im2col_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor _thnn_im2col_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor & _thnn_im2col_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList input_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor _thnn_im2col_backward(const at::Tensor & grad_output, at::IntList input_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor _cast_Byte(const at::Tensor & self, bool non_blocking) const override;
  at::Tensor _cast_Char(const at::Tensor & self, bool non_blocking) const override;
  at::Tensor _cast_Double(const at::Tensor & self, bool non_blocking) const override;
  at::Tensor _cast_Float(const at::Tensor & self, bool non_blocking) const override;
  at::Tensor _cast_Int(const at::Tensor & self, bool non_blocking) const override;
  at::Tensor _cast_Long(const at::Tensor & self, bool non_blocking) const override;
  at::Tensor _cast_Short(const at::Tensor & self, bool non_blocking) const override;
  at::Tensor _cast_Half(const at::Tensor & self, bool non_blocking) const override;
  std::tuple<at::Tensor,at::Tensor> _fused_dropout(const at::Tensor & self, double p, at::Generator * generator) const override;
  at::Tensor _masked_scale(const at::Tensor & self, const at::Tensor & mask, double scale) const override;
  at::Tensor _reshape_from_tensor(const at::Tensor & self, const at::Tensor & shape) const override;
  at::Tensor _shape_as_tensor(const at::Tensor & self) const override;
  at::Tensor dropout(const at::Tensor & input, double p, bool train) const override;
  at::Tensor & dropout_(at::Tensor & self, double p, bool train) const override;
  at::Tensor feature_dropout(const at::Tensor & input, double p, bool train) const override;
  at::Tensor & feature_dropout_(at::Tensor & self, double p, bool train) const override;
  at::Tensor alpha_dropout(const at::Tensor & input, double p, bool train) const override;
  at::Tensor & alpha_dropout_(at::Tensor & self, double p, bool train) const override;
  at::Tensor feature_alpha_dropout(const at::Tensor & input, double p, bool train) const override;
  at::Tensor & feature_alpha_dropout_(at::Tensor & self, double p, bool train) const override;
  at::Tensor abs(const at::Tensor & self) const override;
  at::Tensor & abs_(at::Tensor & self) const override;
  at::Tensor & abs_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor acos(const at::Tensor & self) const override;
  at::Tensor & acos_(at::Tensor & self) const override;
  at::Tensor & acos_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor avg_pool1d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor adaptive_avg_pool1d(const at::Tensor & self, at::IntList output_size) const override;
  std::tuple<at::Tensor,at::Tensor> adaptive_max_pool1d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor add(const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor & add_(at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor & add_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor add(const at::Tensor & self, at::Scalar other, at::Scalar alpha) const override;
  at::Tensor & add_(at::Tensor & self, at::Scalar other, at::Scalar alpha) const override;
  at::Tensor addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addmv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addr_(at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor affine_grid_generator(const at::Tensor & theta, at::IntList size) const override;
  at::Tensor affine_grid_generator_backward(const at::Tensor & grad, at::IntList size) const override;
  at::Tensor all(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor & all_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  bool allclose(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan) const override;
  at::Tensor any(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor & any_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor & arange_out(at::Tensor & result, at::Scalar end) const override;
  at::Tensor & arange_out(at::Tensor & result, at::Scalar start, at::Scalar end, at::Scalar step) const override;
  at::Tensor _dim_arange(const at::Tensor & like, int64_t dim) const override;
  at::Tensor argmax(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor argmax(const at::Tensor & self) const override;
  at::Tensor _argmax(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor argmin(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor argmin(const at::Tensor & self) const override;
  at::Tensor _argmin(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor as_strided(const at::Tensor & self, at::IntList size, at::IntList stride, c10::optional<int64_t> storage_offset) const override;
  at::Tensor & as_strided_(at::Tensor & self, at::IntList size, at::IntList stride, c10::optional<int64_t> storage_offset) const override;
  at::Tensor asin(const at::Tensor & self) const override;
  at::Tensor & asin_(at::Tensor & self) const override;
  at::Tensor & asin_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor atan(const at::Tensor & self) const override;
  at::Tensor & atan_(at::Tensor & self) const override;
  at::Tensor & atan_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & baddbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & _baddbmm_mkl_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & baddbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor batch_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled) const override;
  at::Tensor bernoulli(const at::Tensor & self, at::Generator * generator) const override;
  at::Tensor & bernoulli_out(at::Tensor & result, const at::Tensor & self, at::Generator * generator) const override;
  at::Tensor & bernoulli_(at::Tensor & self, const at::Tensor & p, at::Generator * generator) const override;
  at::Tensor & bernoulli_(at::Tensor & self, double p, at::Generator * generator) const override;
  at::Tensor bernoulli(const at::Tensor & self, double p, at::Generator * generator) const override;
  at::Tensor bilinear(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const at::Tensor & bias) const override;
  at::Tensor binary_cross_entropy_with_logits(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, const at::Tensor & pos_weight, int64_t reduction) const override;
  at::Tensor binary_cross_entropy_with_logits_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, const at::Tensor & pos_weight, int64_t reduction) const override;
  at::Tensor bincount(const at::Tensor & self, const at::Tensor & weights, int64_t minlength) const override;
  at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor & bmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const override;
  std::vector<at::Tensor> broadcast_tensors(at::TensorList tensors) const override;
  at::Tensor cat(at::TensorList tensors, int64_t dim) const override;
  at::Tensor & cat_out(at::Tensor & result, at::TensorList tensors, int64_t dim) const override;
  at::Tensor ceil(const at::Tensor & self) const override;
  at::Tensor & ceil_(at::Tensor & self) const override;
  at::Tensor & ceil_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor chain_matmul(at::TensorList matrices) const override;
  std::vector<at::Tensor> chunk(const at::Tensor & self, int64_t chunks, int64_t dim) const override;
  at::Tensor clamp(const at::Tensor & self, c10::optional<at::Scalar> min, c10::optional<at::Scalar> max) const override;
  at::Tensor & clamp_(at::Tensor & self, c10::optional<at::Scalar> min, c10::optional<at::Scalar> max) const override;
  at::Tensor & clamp_out(at::Tensor & result, const at::Tensor & self, c10::optional<at::Scalar> min, c10::optional<at::Scalar> max) const override;
  at::Tensor clamp_max(const at::Tensor & self, at::Scalar max) const override;
  at::Tensor & clamp_max_(at::Tensor & self, at::Scalar max) const override;
  at::Tensor & clamp_max_out(at::Tensor & result, const at::Tensor & self, at::Scalar max) const override;
  at::Tensor clamp_min(const at::Tensor & self, at::Scalar min) const override;
  at::Tensor & clamp_min_(at::Tensor & self, at::Scalar min) const override;
  at::Tensor & clamp_min_out(at::Tensor & result, const at::Tensor & self, at::Scalar min) const override;
  at::Tensor constant_pad_nd(const at::Tensor & self, at::IntList pad, at::Scalar value) const override;
  at::Tensor contiguous(const at::Tensor & self) const override;
  at::Tensor convolution(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding, int64_t groups) const override;
  at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) const override;
  at::Tensor _convolution_nogroup(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const at::Tensor & ggI, const at::Tensor & ggW, const at::Tensor & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array<bool,3> output_mask) const override;
  at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, int64_t groups) const override;
  at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, int64_t groups) const override;
  at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, int64_t groups) const override;
  at::Tensor conv_tbc(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> conv_tbc_backward(const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) const override;
  at::Tensor conv_transpose1d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, int64_t groups, at::IntList dilation) const override;
  at::Tensor conv_transpose2d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, int64_t groups, at::IntList dilation) const override;
  at::Tensor conv_transpose3d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, int64_t groups, at::IntList dilation) const override;
  at::Tensor & s_copy_(at::Tensor & self, const at::Tensor & src, bool non_blocking) const override;
  at::Tensor _s_copy_from(const at::Tensor & self, const at::Tensor & dst, bool non_blocking) const override;
  void _copy_same_type_(at::Tensor & self, const at::Tensor & src) const override;
  at::Tensor cos(const at::Tensor & self) const override;
  at::Tensor & cos_(at::Tensor & self) const override;
  at::Tensor & cos_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor cosh(const at::Tensor & self) const override;
  at::Tensor & cosh_(at::Tensor & self) const override;
  at::Tensor & cosh_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor cosine_embedding_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) const override;
  at::Tensor cumsum(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor cumsum(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & cumsum_out(at::Tensor & result, const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor & cumsum_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const override;
  at::Tensor cumprod(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor cumprod(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & cumprod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor & cumprod_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const override;
  at::Tensor ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntList input_lengths, at::IntList target_lengths, int64_t blank, int64_t reduction) const override;
  at::Tensor ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, int64_t reduction) const override;
  std::tuple<at::Tensor,at::Tensor> _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntList input_lengths, at::IntList target_lengths, int64_t blank) const override;
  at::Tensor _ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntList input_lengths, at::IntList target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank) const override;
  at::Tensor det(const at::Tensor & self) const override;
  at::Tensor diag_embed(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) const override;
  at::Tensor diagflat(const at::Tensor & self, int64_t offset) const override;
  at::Tensor diagonal(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) const override;
  at::Tensor div(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & div_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & div_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor div(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & div_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor dot(const at::Tensor & self, const at::Tensor & tensor) const override;
  at::Tensor & dot_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor) const override;
  at::Tensor einsum(std::string equation, at::TensorList tensors) const override;
  at::Tensor embedding(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) const override;
  at::Tensor embedding_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) const override;
  at::Tensor embedding_dense_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) const override;
  at::Tensor & embedding_renorm_(at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) const override;
  at::Tensor embedding_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse) const override;
  at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse) const override;
  at::Tensor _embedding_bag_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) const override;
  at::Tensor _embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) const override;
  at::Tensor empty(at::IntList size, const at::TensorOptions & options) const override;
  at::Tensor & resize_(at::Tensor & self, at::IntList size) const override;
  at::Tensor & empty_out(at::Tensor & result, at::IntList size) const override;
  at::Tensor empty_like(const at::Tensor & self) const override;
  at::Tensor empty_strided(at::IntList size, at::IntList stride, const at::TensorOptions & options) const override;
  at::Tensor erf(const at::Tensor & self) const override;
  at::Tensor & erf_(at::Tensor & self) const override;
  at::Tensor & erf_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor erfc(const at::Tensor & self) const override;
  at::Tensor & erfc_(at::Tensor & self) const override;
  at::Tensor & erfc_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor exp(const at::Tensor & self) const override;
  at::Tensor & exp_(at::Tensor & self) const override;
  at::Tensor & exp_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor expm1(const at::Tensor & self) const override;
  at::Tensor & expm1_(at::Tensor & self) const override;
  at::Tensor & expm1_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor expand(const at::Tensor & self, at::IntList size, bool implicit) const override;
  at::Tensor expand_as(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & eye_out(at::Tensor & result, int64_t n) const override;
  at::Tensor & eye_out(at::Tensor & result, int64_t n, int64_t m) const override;
  at::Tensor flatten(const at::Tensor & self, int64_t start_dim, int64_t end_dim) const override;
  at::Tensor & fill_(at::Tensor & self, at::Scalar value) const override;
  at::Tensor & fill_(at::Tensor & self, const at::Tensor & value) const override;
  at::Tensor floor(const at::Tensor & self) const override;
  at::Tensor & floor_(at::Tensor & self) const override;
  at::Tensor & floor_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor & full_out(at::Tensor & result, at::IntList size, at::Scalar fill_value) const override;
  at::Tensor full_like(const at::Tensor & self, at::Scalar fill_value) const override;
  at::Tensor grid_sampler(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const override;
  at::Tensor grid_sampler_2d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const override;
  std::tuple<at::Tensor,at::Tensor> grid_sampler_2d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const override;
  at::Tensor grid_sampler_3d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const override;
  std::tuple<at::Tensor,at::Tensor> grid_sampler_3d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const override;
  at::Tensor hinge_embedding_loss(const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction) const override;
  at::Tensor ger(const at::Tensor & self, const at::Tensor & vec2) const override;
  at::Tensor & ger_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec2) const override;
  std::tuple<at::Tensor,at::Tensor> gesv(const at::Tensor & self, const at::Tensor & A) const override;
  std::tuple<at::Tensor &,at::Tensor &> gesv_out(at::Tensor & solution, at::Tensor & lu, const at::Tensor & self, const at::Tensor & A) const override;
  std::tuple<at::Tensor,at::Tensor> _gesv_helper(const at::Tensor & self, const at::Tensor & A) const override;
  at::Tensor group_norm(const at::Tensor & input, int64_t num_groups, const at::Tensor & weight, const at::Tensor & bias, double eps, bool cudnn_enabled) const override;
  at::Tensor fft(const at::Tensor & self, int64_t signal_ndim, bool normalized) const override;
  at::Tensor ifft(const at::Tensor & self, int64_t signal_ndim, bool normalized) const override;
  at::Tensor rfft(const at::Tensor & self, int64_t signal_ndim, bool normalized, bool onesided) const override;
  at::Tensor irfft(const at::Tensor & self, int64_t signal_ndim, bool normalized, bool onesided, at::IntList signal_sizes) const override;
  at::Tensor _fft_with_size(const at::Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, at::IntList checked_signal_sizes, bool normalized, bool onesided, at::IntList output_sizes) const override;
  void _cufft_set_plan_cache_max_size(int64_t max_size) const override;
  at::Tensor index(const at::Tensor & self, at::TensorList indices) const override;
  at::Tensor & index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const override;
  at::Tensor index_put(const at::Tensor & self, at::TensorList indices, const at::Tensor & values, bool accumulate) const override;
  at::Tensor & index_put_(at::Tensor & self, at::TensorList indices, const at::Tensor & values, bool accumulate) const override;
  at::Tensor instance_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) const override;
  at::Tensor inverse(const at::Tensor & self) const override;
  at::Tensor & inverse_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor _inverse_helper(const at::Tensor & self) const override;
  at::Tensor isclose(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan) const override;
  at::Tensor isnan(const at::Tensor & self) const override;
  bool is_distributed(const at::Tensor & self) const override;
  bool is_floating_point(const at::Tensor & self) const override;
  bool is_complex(const at::Tensor & self) const override;
  bool is_nonzero(const at::Tensor & self) const override;
  bool is_same_size(const at::Tensor & self, const at::Tensor & other) const override;
  bool is_signed(const at::Tensor & self) const override;
  at::Tensor kl_div(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor kl_div_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  std::tuple<at::Tensor,at::Tensor> kthvalue(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> kthvalue_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const override;
  at::Tensor layer_norm(const at::Tensor & input, at::IntList normalized_shape, const at::Tensor & weight, const at::Tensor & bias, double eps, bool cudnn_enable) const override;
  at::Tensor linear(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias) const override;
  at::Tensor fbgemm_linear_int8_weight(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, at::Scalar weight_scale, at::Scalar weight_zero_point, const at::Tensor & bias) const override;
  std::tuple<at::Tensor,at::Tensor,double,int64_t> fbgemm_linear_quantize_weight(const at::Tensor & input) const override;
  at::Tensor fbgemm_pack_quantized_matrix(const at::Tensor & input, int64_t K, int64_t N) const override;
  at::Tensor & linspace_out(at::Tensor & result, at::Scalar start, at::Scalar end, int64_t steps) const override;
  at::Tensor log(const at::Tensor & self) const override;
  at::Tensor & log_(at::Tensor & self) const override;
  at::Tensor & log_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor log10(const at::Tensor & self) const override;
  at::Tensor & log10_(at::Tensor & self) const override;
  at::Tensor & log10_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor log1p(const at::Tensor & self) const override;
  at::Tensor & log1p_(at::Tensor & self) const override;
  at::Tensor & log1p_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor log2(const at::Tensor & self) const override;
  at::Tensor & log2_(at::Tensor & self) const override;
  at::Tensor & log2_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor logdet(const at::Tensor & self) const override;
  at::Tensor & logspace_out(at::Tensor & result, at::Scalar start, at::Scalar end, int64_t steps) const override;
  at::Tensor log_softmax(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor log_softmax(const at::Tensor & self, int64_t dim) const override;
  at::Tensor _log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) const override;
  at::Tensor _log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) const override;
  at::Tensor logsumexp(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor & logsumexp_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor margin_ranking_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) const override;
  at::Tensor matmul(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & matmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor matrix_rank(const at::Tensor & self, double tol, bool symmetric) const override;
  at::Tensor matrix_rank(const at::Tensor & self, bool symmetric) const override;
  at::Tensor matrix_power(const at::Tensor & self, int64_t n) const override;
  std::tuple<at::Tensor,at::Tensor> max(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> max_out(at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor max_values(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor,at::Tensor> max_pool1d_with_indices(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor max_pool1d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor max_pool2d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor max_pool3d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor mean(const at::Tensor & self, at::ScalarType dtype) const override;
  at::Tensor mean(const at::Tensor & self) const override;
  at::Tensor mean(const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor mean(const at::Tensor & self, at::IntList dim, bool keepdim) const override;
  at::Tensor mean(const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const override;
  at::Tensor & mean_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor & mean_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim) const override;
  at::Tensor & mean_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const override;
  std::tuple<at::Tensor,at::Tensor> median(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> median_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor min_values(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor mkldnn_convolution(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups) const override;
  at::Tensor mkldnn_convolution_backward_input(at::IntList self_size, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool bias_defined) const override;
  std::tuple<at::Tensor,at::Tensor> mkldnn_convolution_backward_weights(at::IntList weight_size, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool bias_defined) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> mkldnn_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, std::array<bool,3> output_mask) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_batch_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double exponential_average_factor, double epsilon) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_batch_norm_backward(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const at::Tensor & running_mean, const at::Tensor & running_var, const at::Tensor & save_mean, const at::Tensor & save_var, double epsilon) const override;
  at::Tensor miopen_convolution(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const override;
  at::Tensor miopen_convolution_backward_input(at::IntList self_size, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) const override;
  at::Tensor miopen_convolution_backward_bias(const at::Tensor & grad_output) const override;
  at::Tensor miopen_convolution_backward_weight(at::IntList weight_size, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const override;
  at::Tensor miopen_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, at::IntList padding, at::IntList output_padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_convolution_transpose_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList output_padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) const override;
  at::Tensor miopen_convolution_transpose_backward_input(const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const override;
  at::Tensor miopen_convolution_transpose_backward_weight(at::IntList weight_size, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const override;
  at::Tensor mm(const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor & mm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor _sparse_mm(const at::Tensor & sparse, const at::Tensor & dense) const override;
  std::tuple<at::Tensor,at::Tensor> mode(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  std::tuple<at::Tensor &,at::Tensor &> mode_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor mul(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & mul_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & mul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor mul(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & mul_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor mv(const at::Tensor & self, const at::Tensor & vec) const override;
  at::Tensor & mv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec) const override;
  at::Tensor mvlgamma(const at::Tensor & self, int64_t p) const override;
  at::Tensor & mvlgamma_(at::Tensor & self, int64_t p) const override;
  at::Tensor narrow_copy(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) const override;
  at::Tensor narrow(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & running_mean, const at::Tensor & running_var, const at::Tensor & save_mean, const at::Tensor & save_invstd, bool train, double eps, std::array<bool,3> output_mask) const override;
  std::tuple<at::Tensor,at::Tensor> batch_norm_update_stats(const at::Tensor & input, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum) const override;
  at::Tensor & ones_out(at::Tensor & result, at::IntList size) const override;
  at::Tensor ones_like(const at::Tensor & self) const override;
  at::Tensor pairwise_distance(const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim) const override;
  at::Tensor pdist(const at::Tensor & self, double p) const override;
  at::Tensor _pdist_forward(const at::Tensor & self, double p) const override;
  at::Tensor _pdist_backward(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) const override;
  at::Tensor cosine_similarity(const at::Tensor & x1, const at::Tensor & x2, int64_t dim, double eps) const override;
  at::Tensor permute(const at::Tensor & self, at::IntList dims) const override;
  at::Tensor pixel_shuffle(const at::Tensor & self, int64_t upscale_factor) const override;
  at::Tensor pin_memory(const at::Tensor & self) const override;
  at::Tensor pinverse(const at::Tensor & self, double rcond) const override;
  at::Tensor & rand_out(at::Tensor & result, at::IntList size) const override;
  at::Tensor & rand_out(at::Tensor & result, at::IntList size, at::Generator * generator) const override;
  at::Tensor rand_like(const at::Tensor & self) const override;
  at::Tensor & randint_out(at::Tensor & result, int64_t high, at::IntList size) const override;
  at::Tensor & randint_out(at::Tensor & result, int64_t high, at::IntList size, at::Generator * generator) const override;
  at::Tensor & randint_out(at::Tensor & result, int64_t low, int64_t high, at::IntList size) const override;
  at::Tensor & randint_out(at::Tensor & result, int64_t low, int64_t high, at::IntList size, at::Generator * generator) const override;
  at::Tensor randint_like(const at::Tensor & self, int64_t high) const override;
  at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high) const override;
  at::Tensor & randn_out(at::Tensor & result, at::IntList size) const override;
  at::Tensor & randn_out(at::Tensor & result, at::IntList size, at::Generator * generator) const override;
  at::Tensor randn_like(const at::Tensor & self) const override;
  at::Tensor & randperm_out(at::Tensor & result, int64_t n) const override;
  at::Tensor & randperm_out(at::Tensor & result, int64_t n, at::Generator * generator) const override;
  at::Tensor & range_out(at::Tensor & result, at::Scalar start, at::Scalar end, at::Scalar step) const override;
  at::Tensor repeat(const at::Tensor & self, at::IntList repeats) const override;
  at::Tensor reshape(const at::Tensor & self, at::IntList shape) const override;
  at::Tensor reshape_as(const at::Tensor & self, const at::Tensor & other) const override;
  std::tuple<at::Tensor,at::Tensor> RoiPooling2d_forward(const at::Tensor & input, const at::Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale) const override;
  at::Tensor RoiPooling2d_backward(const at::Tensor & input, const at::Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale, const at::Tensor & gradOutput, const at::Tensor & argmaxes) const override;
  at::Tensor round(const at::Tensor & self) const override;
  at::Tensor & round_(at::Tensor & self) const override;
  at::Tensor & round_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor rrelu(const at::Tensor & self, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor & rrelu_(at::Tensor & self, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor relu(const at::Tensor & self) const override;
  at::Tensor & relu_(at::Tensor & self) const override;
  at::Tensor prelu(const at::Tensor & self, const at::Tensor & weight) const override;
  std::tuple<at::Tensor,at::Tensor> prelu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight) const override;
  at::Tensor hardshrink(const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor rsqrt(const at::Tensor & self) const override;
  at::Tensor & rsqrt_(at::Tensor & self) const override;
  at::Tensor & rsqrt_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor select(const at::Tensor & self, int64_t dim, int64_t index) const override;
  at::Tensor selu(const at::Tensor & self) const override;
  at::Tensor & selu_(at::Tensor & self) const override;
  at::Tensor celu(const at::Tensor & self, at::Scalar alpha) const override;
  at::Tensor & celu_(at::Tensor & self, at::Scalar alpha) const override;
  at::Tensor sigmoid(const at::Tensor & self) const override;
  at::Tensor & sigmoid_(at::Tensor & self) const override;
  at::Tensor & sigmoid_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor sin(const at::Tensor & self) const override;
  at::Tensor & sin_(at::Tensor & self) const override;
  at::Tensor & sin_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor sinh(const at::Tensor & self) const override;
  at::Tensor & sinh_(at::Tensor & self) const override;
  at::Tensor & sinh_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor detach(const at::Tensor & self) const override;
  at::Tensor & detach_(at::Tensor & self) const override;
  int64_t size(const at::Tensor & self, int64_t dim) const override;
  at::Tensor slice(const at::Tensor & self, int64_t dim, int64_t start, int64_t end, int64_t step) const override;
  std::tuple<at::Tensor,at::Tensor> slogdet(const at::Tensor & self) const override;
  at::Tensor smm(const at::Tensor & self, const at::Tensor & mat2) const override;
  at::Tensor softmax(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor softmax(const at::Tensor & self, int64_t dim) const override;
  at::Tensor _softmax(const at::Tensor & self, int64_t dim, bool half_to_float) const override;
  at::Tensor _softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) const override;
  at::Tensor & _sparse_add_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor & _sparse_dense_add_out(at::Tensor & result, const at::Tensor & self, at::SparseTensorRef other, at::Scalar alpha) const override;
  at::Tensor & _sparse_div_zerodim_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _sparse_div_scalar_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & _sparse_mul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _sparse_mul_zerodim_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & _sparse_mul_scalar_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  std::vector<at::Tensor> split(const at::Tensor & self, int64_t split_size, int64_t dim) const override;
  std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, at::IntList split_sizes, int64_t dim) const override;
  at::Tensor squeeze(const at::Tensor & self) const override;
  at::Tensor squeeze(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & squeeze_(at::Tensor & self) const override;
  at::Tensor & squeeze_(at::Tensor & self, int64_t dim) const override;
  at::Tensor sspaddmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & sspaddmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor stack(at::TensorList tensors, int64_t dim) const override;
  at::Tensor & stack_out(at::Tensor & result, at::TensorList tensors, int64_t dim) const override;
  at::Tensor stft(const at::Tensor & self, int64_t n_fft, c10::optional<int64_t> hop_length, c10::optional<int64_t> win_length, const at::Tensor & window, bool normalized, bool onesided) const override;
  int64_t stride(const at::Tensor & self, int64_t dim) const override;
  at::Tensor sum(const at::Tensor & self, at::ScalarType dtype) const override;
  at::Tensor sum(const at::Tensor & self) const override;
  at::Tensor sum(const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor sum(const at::Tensor & self, at::IntList dim, bool keepdim) const override;
  at::Tensor sum(const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const override;
  at::Tensor & sum_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor & sum_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim) const override;
  at::Tensor & sum_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const override;
  at::Tensor sum_to_size(const at::Tensor & self, at::IntList size) const override;
  at::Tensor sqrt(const at::Tensor & self) const override;
  at::Tensor & sqrt_(at::Tensor & self) const override;
  at::Tensor & sqrt_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor std(const at::Tensor & self, bool unbiased) const override;
  at::Tensor std(const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const override;
  at::Tensor & std_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const override;
  at::Tensor prod(const at::Tensor & self, at::ScalarType dtype) const override;
  at::Tensor prod(const at::Tensor & self) const override;
  at::Tensor prod(const at::Tensor & self, int64_t dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor prod(const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor prod(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor & prod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor & prod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const override;
  at::Tensor & prod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, at::ScalarType dtype) const override;
  at::Tensor t(const at::Tensor & self) const override;
  at::Tensor & t_(at::Tensor & self) const override;
  at::Tensor tan(const at::Tensor & self) const override;
  at::Tensor & tan_(at::Tensor & self) const override;
  at::Tensor & tan_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor tanh(const at::Tensor & self) const override;
  at::Tensor & tanh_(at::Tensor & self) const override;
  at::Tensor & tanh_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor tensordot(const at::Tensor & self, const at::Tensor & other, at::IntList dims_self, at::IntList dims_other) const override;
  at::Tensor threshold(const at::Tensor & self, at::Scalar threshold, at::Scalar value) const override;
  at::Tensor & threshold_(at::Tensor & self, at::Scalar threshold, at::Scalar value) const override;
  at::Tensor & threshold_out(at::Tensor & result, const at::Tensor & self, at::Scalar threshold, at::Scalar value) const override;
  at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar threshold) const override;
  at::Tensor transpose(const at::Tensor & self, int64_t dim0, int64_t dim1) const override;
  at::Tensor & transpose_(at::Tensor & self, int64_t dim0, int64_t dim1) const override;
  at::Tensor one_hot(const at::Tensor & self, int64_t num_classes) const override;
  at::Tensor flip(const at::Tensor & self, at::IntList dims) const override;
  at::Tensor roll(const at::Tensor & self, at::IntList shifts, at::IntList dims) const override;
  at::Tensor rot90(const at::Tensor & self, int64_t k, at::IntList dims) const override;
  at::Tensor _trilinear(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntList expand1, at::IntList expand2, at::IntList expand3, at::IntList sumdim, int64_t unroll_dim) const override;
  at::Tensor triplet_margin_loss(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin, double p, double eps, bool swap, int64_t reduction) const override;
  at::Tensor trunc(const at::Tensor & self) const override;
  at::Tensor & trunc_(at::Tensor & self) const override;
  at::Tensor & trunc_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor type_as(const at::Tensor & self, const at::Tensor & other) const override;
  std::tuple<at::Tensor,at::Tensor> _unique(const at::Tensor & self, bool sorted, bool return_inverse) const override;
  std::tuple<at::Tensor,at::Tensor> _unique_dim(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse) const override;
  at::Tensor _unsafe_view(const at::Tensor & self, at::IntList size) const override;
  at::Tensor unsqueeze(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & unsqueeze_(at::Tensor & self, int64_t dim) const override;
  at::Tensor var(const at::Tensor & self, bool unbiased) const override;
  at::Tensor var(const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const override;
  at::Tensor & var_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const override;
  at::Tensor view_as(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor _s_where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor norm_except_dim(const at::Tensor & v, int64_t pow, int64_t dim) const override;
  at::Tensor _weight_norm(const at::Tensor & v, const at::Tensor & g, int64_t dim) const override;
  std::tuple<at::Tensor,at::Tensor> _weight_norm_cuda_interface(const at::Tensor & v, const at::Tensor & g, int64_t dim) const override;
  std::tuple<at::Tensor,at::Tensor> _weight_norm_cuda_interface_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) const override;
  std::tuple<at::Tensor,at::Tensor> _weight_norm_differentiable_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) const override;
  at::Tensor & zeros_out(at::Tensor & result, at::IntList size) const override;
  at::Tensor zeros_like(const at::Tensor & self) const override;
  at::Tensor _standard_gamma_grad(const at::Tensor & self, const at::Tensor & output) const override;
  at::Tensor _standard_gamma(const at::Tensor & self, at::Generator * generator) const override;
  at::Tensor poisson(const at::Tensor & self, at::Generator * generator) const override;
  at::Tensor native_norm(const at::Tensor & self, at::Scalar p) const override;
  at::Tensor _sparse_sum(const at::Tensor & self) const override;
  at::Tensor _sparse_sum(const at::Tensor & self, at::ScalarType dtype) const override;
  at::Tensor _sparse_sum(const at::Tensor & self, at::IntList dim) const override;
  at::Tensor _sparse_sum(const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const override;
  at::Tensor _sparse_sum_backward(const at::Tensor & grad, const at::Tensor & self, at::IntList dim) const override;
  at::Tensor norm(const at::Tensor & self, c10::optional<at::Scalar> p, at::ScalarType dtype) const override;
  at::Tensor norm(const at::Tensor & self, at::Scalar p) const override;
  at::Tensor norm(const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor norm(const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim) const override;
  at::Tensor & norm_out(at::Tensor & result, const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim, at::ScalarType dtype) const override;
  at::Tensor & norm_out(at::Tensor & result, const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim) const override;
  at::Tensor frobenius_norm(const at::Tensor & self) const override;
  at::Tensor frobenius_norm(const at::Tensor & self, at::IntList dim, bool keepdim) const override;
  at::Tensor & frobenius_norm_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim) const override;
  at::Tensor nuclear_norm(const at::Tensor & self, bool keepdim) const override;
  at::Tensor & nuclear_norm_out(at::Tensor & result, const at::Tensor & self, bool keepdim) const override;
  at::Tensor native_clone(const at::Tensor & self) const override;
  at::Tensor clone(const at::Tensor & self) const override;
  at::Tensor & native_resize_as_(at::Tensor & self, const at::Tensor & the_template) const override;
  at::Tensor & resize_as_(at::Tensor & self, const at::Tensor & the_template) const override;
  at::Tensor & native_pow_out(at::Tensor & result, const at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor native_pow(const at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor & pow_out(at::Tensor & result, const at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor pow(const at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor & native_zero_(at::Tensor & self) const override;
  at::Tensor & zero_(at::Tensor & self) const override;
  at::Tensor & sub_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor sub(const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor sub(const at::Tensor & self, at::Scalar other, at::Scalar alpha) const override;
  at::Tensor & sub_(at::Tensor & self, at::Scalar other, at::Scalar alpha) const override;
  at::Tensor rsub(const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const override;
  at::Tensor rsub(const at::Tensor & self, at::Scalar other, at::Scalar alpha) const override;
  at::Tensor & s_native_addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor s_native_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & s_native_addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor _sparse_addmm(const at::Tensor & self, const at::Tensor & sparse, const at::Tensor & dense, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, at::IntList size, const at::TensorOptions & options) const override;
  at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, at::IntList size, const at::Tensor & indices, const at::Tensor & values, const at::TensorOptions & options) const override;
  at::Tensor & sparse_resize_(at::Tensor & self, at::IntList size, int64_t sparse_dim, int64_t dense_dim) const override;
  at::Tensor & sparse_resize_and_clear_(at::Tensor & self, at::IntList size, int64_t sparse_dim, int64_t dense_dim) const override;
  at::Tensor sparse_mask(const at::Tensor & self, at::SparseTensorRef mask) const override;
  at::Tensor to_dense(const at::Tensor & self) const override;
  int64_t sparse_dim(const at::Tensor & self) const override;
  int64_t _dimI(const at::Tensor & self) const override;
  int64_t dense_dim(const at::Tensor & self) const override;
  int64_t _dimV(const at::Tensor & self) const override;
  int64_t _nnz(const at::Tensor & self) const override;
  at::Tensor coalesce(const at::Tensor & self) const override;
  bool is_coalesced(const at::Tensor & self) const override;
  at::Tensor _indices(const at::Tensor & self) const override;
  at::Tensor _values(const at::Tensor & self) const override;
  at::Tensor & _coalesced_(at::Tensor & self, bool coalesced) const override;
  at::Tensor indices(const at::Tensor & self) const override;
  at::Tensor values(const at::Tensor & self) const override;
  at::Tensor & hspmm_out(at::Tensor & result, const at::Tensor & mat1, const at::Tensor & mat2) const override;
  at::Tensor hspmm(const at::Tensor & mat1, const at::Tensor & mat2) const override;
  at::Tensor & copy_sparse_to_sparse_(at::Tensor & self, const at::Tensor & src, bool non_blocking) const override;
  int64_t numel(const at::Tensor & self) const override;
  std::vector<at::Tensor> unbind(const at::Tensor & self, int64_t dim) const override;
  at::Tensor to_sparse(const at::Tensor & self, int64_t sparse_dim) const override;
  at::Tensor to_sparse(const at::Tensor & self) const override;
  at::Tensor to(const at::Tensor & self, const at::TensorOptions & options, bool non_blocking, bool copy) const override;
  at::Tensor to(const at::Tensor & self, c10::Device device, at::ScalarType dtype, bool non_blocking, bool copy) const override;
  at::Tensor to(const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy) const override;
  at::Tensor to(const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy) const override;
  std::vector<at::Tensor> meshgrid(at::TensorList tensors) const override;
  at::Tensor cartesian_prod(at::TensorList tensors) const override;
  at::Tensor combinations(const at::Tensor & self, int64_t r, bool with_replacement) const override;
  at::Scalar item(const at::Tensor & self) const override;
  at::Scalar _local_scalar_dense(const at::Tensor & self) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const at::Tensor & input_bias, const at::Tensor & hidden_bias) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward(const at::Tensor & grad_hy, const at::Tensor & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) const override;
  std::tuple<at::Tensor,at::Tensor> _thnn_fused_gru_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const at::Tensor & input_bias, const at::Tensor & hidden_bias) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_gru_cell_backward(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm(const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const override;
  std::tuple<at::Tensor,at::Tensor> gru(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const override;
  std::tuple<at::Tensor,at::Tensor> gru(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const override;
  std::tuple<at::Tensor,at::Tensor> rnn_tanh(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const override;
  std::tuple<at::Tensor,at::Tensor> rnn_tanh(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const override;
  std::tuple<at::Tensor,at::Tensor> rnn_relu(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const override;
  std::tuple<at::Tensor,at::Tensor> rnn_relu(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const override;
  std::tuple<at::Tensor,at::Tensor> lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const override;
  at::Tensor gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const override;
  at::Tensor rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const override;
  at::Tensor rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> quantized_lstm(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const override;
  std::tuple<at::Tensor,at::Tensor> quantized_lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const override;
  at::Tensor quantized_gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const override;
  at::Tensor quantized_rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const override;
  at::Tensor quantized_rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const override;
  std::tuple<at::Tensor,at::Tensor> _pack_padded_sequence(const at::Tensor & input, const at::Tensor & lengths, bool batch_first) const override;
  at::Tensor _pack_padded_sequence_backward(const at::Tensor & grad, at::IntList input_size, const at::Tensor & batch_sizes, bool batch_first) const override;
  std::tuple<at::Tensor,at::Tensor> _pad_packed_sequence(const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, at::Scalar padding_value, int64_t total_length) const override;
  void* data_ptr(const at::Tensor & self) const override;
  at::Tensor & set_(at::Tensor & self, at::Storage source) const override;
  at::Tensor & set_(at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntList size, at::IntList stride) const override;
  at::Tensor & set_(at::Tensor & self, const at::Tensor & source) const override;
  at::Tensor & set_(at::Tensor & self) const override;
  bool is_set_to(const at::Tensor & self, const at::Tensor & tensor) const override;
  at::Tensor & masked_fill_(at::Tensor & self, const at::Tensor & mask, at::Scalar value) const override;
  at::Tensor & masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) const override;
  at::Tensor & masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) const override;
  at::Tensor view(const at::Tensor & self, at::IntList size) const override;
  at::Tensor & put_(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) const override;
  at::Tensor & index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const override;
  at::Tensor & index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const override;
  at::Tensor & index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) const override;
  at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const override;
  at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const override;
  at::Tensor & scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const override;
  at::Tensor & lt_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & lt_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & gt_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & gt_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & le_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & le_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & ge_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & ge_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & eq_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & eq_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & ne_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & ne_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor __and__(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor __and__(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & __iand__(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & __iand__(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor __or__(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor __or__(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & __ior__(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & __ior__(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor __xor__(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & __ixor__(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & __ixor__(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor __lshift__(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor __lshift__(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & __ilshift__(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & __ilshift__(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor __rshift__(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor __rshift__(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & __irshift__(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & __irshift__(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & lgamma_(at::Tensor & self) const override;
  at::Tensor & atan2_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & tril_(at::Tensor & self, int64_t diagonal) const override;
  at::Tensor & triu_(at::Tensor & self, int64_t diagonal) const override;
  at::Tensor & digamma_(at::Tensor & self) const override;
  at::Tensor & polygamma_(at::Tensor & self, int64_t n) const override;
  at::Tensor & erfinv_(at::Tensor & self) const override;
  at::Tensor & frac_(at::Tensor & self) const override;
  at::Tensor & renorm_(at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const override;
  at::Tensor & reciprocal_(at::Tensor & self) const override;
  at::Tensor & neg_(at::Tensor & self) const override;
  at::Tensor & pow_(at::Tensor & self, at::Scalar exponent) const override;
  at::Tensor & pow_(at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor & lerp_(at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor & sign_(at::Tensor & self) const override;
  at::Tensor & fmod_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & fmod_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & remainder_(at::Tensor & self, at::Scalar other) const override;
  at::Tensor & remainder_(at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & addbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const override;
  at::Tensor & addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & random_(at::Tensor & self, int64_t from, int64_t to, at::Generator * generator) const override;
  at::Tensor & random_(at::Tensor & self, int64_t to, at::Generator * generator) const override;
  at::Tensor & random_(at::Tensor & self, at::Generator * generator) const override;
  at::Tensor & uniform_(at::Tensor & self, double from, double to, at::Generator * generator) const override;
  at::Tensor & normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const override;
  at::Tensor & cauchy_(at::Tensor & self, double median, double sigma, at::Generator * generator) const override;
  at::Tensor & log_normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const override;
  at::Tensor & exponential_(at::Tensor & self, double lambd, at::Generator * generator) const override;
  at::Tensor & geometric_(at::Tensor & self, double p, at::Generator * generator) const override;
  at::Tensor & diag_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor diag(const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor & cross_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, int64_t dim) const override;
  at::Tensor cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) const override;
  at::Tensor & triu_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor triu(const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor & tril_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor tril(const at::Tensor & self, int64_t diagonal) const override;
  at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, const at::TensorOptions & options) const override;
  at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, const at::TensorOptions & options) const override;
  at::Tensor trace(const at::Tensor & self) const override;
  at::Tensor & ne_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor ne(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & ne_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor ne(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & eq_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor eq(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & eq_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor eq(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & ge_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor ge(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & ge_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor ge(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & le_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor le(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & le_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor le(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & gt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor gt(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & gt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor gt(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & lt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor lt(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & lt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor lt(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & take_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & index) const override;
  at::Tensor take(const at::Tensor & self, const at::Tensor & index) const override;
  at::Tensor & index_select_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  at::Tensor index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  at::Tensor & masked_select_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mask) const override;
  at::Tensor masked_select(const at::Tensor & self, const at::Tensor & mask) const override;
  at::Tensor & nonzero_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor nonzero(const at::Tensor & self) const override;
  at::Tensor & gather_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  at::Tensor gather(const at::Tensor & self, int64_t dim, const at::Tensor & index) const override;
  at::Tensor & addcmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor & addcdiv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  at::Tensor addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const override;
  std::tuple<at::Tensor &,at::Tensor &> gels_out(at::Tensor & X, at::Tensor & qr, const at::Tensor & self, const at::Tensor & A) const override;
  std::tuple<at::Tensor,at::Tensor> gels(const at::Tensor & self, const at::Tensor & A) const override;
  std::tuple<at::Tensor &,at::Tensor &> trtrs_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const override;
  std::tuple<at::Tensor,at::Tensor> trtrs(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const override;
  std::tuple<at::Tensor &,at::Tensor &> symeig_out(at::Tensor & e, at::Tensor & V, const at::Tensor & self, bool eigenvectors, bool upper) const override;
  std::tuple<at::Tensor,at::Tensor> symeig(const at::Tensor & self, bool eigenvectors, bool upper) const override;
  std::tuple<at::Tensor &,at::Tensor &> eig_out(at::Tensor & e, at::Tensor & v, const at::Tensor & self, bool eigenvectors) const override;
  std::tuple<at::Tensor,at::Tensor> eig(const at::Tensor & self, bool eigenvectors) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some, bool compute_uv) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some, bool compute_uv) const override;
  at::Tensor & cholesky_out(at::Tensor & result, const at::Tensor & self, bool upper) const override;
  at::Tensor cholesky(const at::Tensor & self, bool upper) const override;
  at::Tensor _cholesky_helper(const at::Tensor & self, bool upper) const override;
  at::Tensor & cholesky_solve_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2, bool upper) const override;
  at::Tensor cholesky_solve(const at::Tensor & self, const at::Tensor & input2, bool upper) const override;
  at::Tensor _cholesky_solve_helper(const at::Tensor & self, const at::Tensor & A, bool upper) const override;
  at::Tensor & potri_out(at::Tensor & result, const at::Tensor & self, bool upper) const override;
  at::Tensor potri(const at::Tensor & self, bool upper) const override;
  std::tuple<at::Tensor &,at::Tensor &> pstrf_out(at::Tensor & u, at::Tensor & piv, const at::Tensor & self, bool upper, at::Scalar tol) const override;
  std::tuple<at::Tensor,at::Tensor> pstrf(const at::Tensor & self, bool upper, at::Scalar tol) const override;
  std::tuple<at::Tensor &,at::Tensor &> qr_out(at::Tensor & Q, at::Tensor & R, const at::Tensor & self) const override;
  std::tuple<at::Tensor,at::Tensor> qr(const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> geqrf_out(at::Tensor & result0, at::Tensor & result1, const at::Tensor & self) const override;
  std::tuple<at::Tensor,at::Tensor> geqrf(const at::Tensor & self) const override;
  at::Tensor & orgqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2) const override;
  at::Tensor orgqr(const at::Tensor & self, const at::Tensor & input2) const override;
  at::Tensor & ormqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const override;
  at::Tensor ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const override;
  std::tuple<at::Tensor &,at::Tensor &> btrifact_out(at::Tensor & A_LU, at::Tensor & pivots, const at::Tensor & self, bool pivot) const override;
  std::tuple<at::Tensor,at::Tensor> btrifact(const at::Tensor & self, bool pivot) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> btrifact_with_info_out(at::Tensor & A_LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool pivot) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> btrifact_with_info(const at::Tensor & self, bool pivot) const override;
  at::Tensor & btrisolve_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const override;
  at::Tensor btrisolve(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const override;
  at::Tensor & multinomial_out(at::Tensor & result, const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const override;
  at::Tensor multinomial(const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const override;
  at::Tensor & lgamma_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor lgamma(const at::Tensor & self) const override;
  at::Tensor & digamma_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor digamma(const at::Tensor & self) const override;
  at::Tensor & polygamma_out(at::Tensor & result, int64_t n, const at::Tensor & self) const override;
  at::Tensor polygamma(int64_t n, const at::Tensor & self) const override;
  at::Tensor & erfinv_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor erfinv(const at::Tensor & self) const override;
  at::Tensor & frac_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor frac(const at::Tensor & self) const override;
  at::Tensor dist(const at::Tensor & self, const at::Tensor & other, at::Scalar p) const override;
  at::Tensor & reciprocal_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor reciprocal(const at::Tensor & self) const override;
  at::Tensor & neg_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor neg(const at::Tensor & self) const override;
  at::Tensor & atan2_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor atan2(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & lerp_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const override;
  at::Tensor & histc_out(at::Tensor & result, const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const override;
  at::Tensor histc(const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const override;
  at::Tensor & sign_out(at::Tensor & result, const at::Tensor & self) const override;
  at::Tensor sign(const at::Tensor & self) const override;
  at::Tensor & fmod_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor fmod(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & fmod_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor fmod(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & remainder_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const override;
  at::Tensor remainder(const at::Tensor & self, at::Scalar other) const override;
  at::Tensor & remainder_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor remainder(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & min_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor min(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor min(const at::Tensor & self) const override;
  at::Tensor & max_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor max(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor max(const at::Tensor & self) const override;
  at::Tensor median(const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool descending) const override;
  std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, int64_t dim, bool descending) const override;
  std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const override;
  std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const override;
  at::Tensor all(const at::Tensor & self) const override;
  at::Tensor any(const at::Tensor & self) const override;
  at::Tensor & renorm_out(at::Tensor & result, const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const override;
  at::Tensor renorm(const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const override;
  at::Tensor unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) const override;
  bool equal(const at::Tensor & self, const at::Tensor & other) const override;
  at::Tensor & pow_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor pow(const at::Tensor & self, const at::Tensor & exponent) const override;
  at::Tensor & pow_out(at::Tensor & result, at::Scalar self, const at::Tensor & exponent) const override;
  at::Tensor pow(at::Scalar self, const at::Tensor & exponent) const override;
  at::Tensor & normal_out(at::Tensor & output, const at::Tensor & mean, double std, at::Generator * generator) const override;
  at::Tensor normal(const at::Tensor & mean, double std, at::Generator * generator) const override;
  at::Tensor & normal_out(at::Tensor & output, double mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor normal(double mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor & normal_out(at::Tensor & output, const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor normal(const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const override;
  at::Tensor alias(const at::Tensor & self) const override;
  at::Tensor & _dirichlet_grad_out(at::Tensor & output, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const override;
  at::Tensor _dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const override;
  at::Tensor & binary_cross_entropy_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor binary_cross_entropy(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor & binary_cross_entropy_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor & mse_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & mse_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & l1_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & multi_margin_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor multi_margin_loss(const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor & multi_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const override;
  at::Tensor & multilabel_margin_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor multilabel_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  std::tuple<at::Tensor &,at::Tensor &> multilabel_margin_loss_forward_out(at::Tensor & output, at::Tensor & is_target, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  std::tuple<at::Tensor,at::Tensor> multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & multilabel_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const override;
  at::Tensor multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const override;
  at::Tensor & nll_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  at::Tensor nll_loss(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  std::tuple<at::Tensor,at::Tensor> nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  at::Tensor & nll_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  at::Tensor nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  at::Tensor & nll_loss2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  at::Tensor nll_loss2d(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const override;
  at::Tensor & nll_loss2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  at::Tensor nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const override;
  at::Tensor & smooth_l1_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & smooth_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor smooth_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & soft_margin_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor soft_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & soft_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor soft_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const override;
  at::Tensor & elu_out(at::Tensor & output, const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const override;
  at::Tensor elu(const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const override;
  at::Tensor & elu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const override;
  at::Tensor elu_backward(const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const override;
  at::Tensor & elu_(at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const override;
  at::Tensor & glu_out(at::Tensor & output, const at::Tensor & self, int64_t dim) const override;
  at::Tensor glu(const at::Tensor & self, int64_t dim) const override;
  at::Tensor & glu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const override;
  at::Tensor glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const override;
  at::Tensor & hardtanh_out(at::Tensor & output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor hardtanh(const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor & hardtanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor & hardtanh_(at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const override;
  at::Tensor & leaky_relu_out(at::Tensor & output, const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor leaky_relu(const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor & leaky_relu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor & leaky_relu_(at::Tensor & self, at::Scalar negative_slope) const override;
  at::Tensor & log_sigmoid_out(at::Tensor & output, const at::Tensor & self) const override;
  at::Tensor log_sigmoid(const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_out(at::Tensor & output, at::Tensor & buffer, const at::Tensor & self) const override;
  std::tuple<at::Tensor,at::Tensor> log_sigmoid_forward(const at::Tensor & self) const override;
  at::Tensor & log_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const override;
  at::Tensor log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const override;
  at::Tensor & rrelu_with_noise_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor rrelu_with_noise(const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor & rrelu_with_noise_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const override;
  at::Tensor rrelu_with_noise_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const override;
  at::Tensor & rrelu_with_noise_(at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const override;
  at::Tensor & softplus_out(at::Tensor & output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const override;
  at::Tensor softplus(const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const override;
  at::Tensor & softplus_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const override;
  at::Tensor softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const override;
  at::Tensor & softshrink_out(at::Tensor & output, const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor softshrink(const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor & softshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const override;
  at::Tensor & adaptive_avg_pool2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor adaptive_avg_pool2d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & adaptive_avg_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) const override;
  at::Tensor adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self) const override;
  at::Tensor & adaptive_avg_pool3d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor adaptive_avg_pool3d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & adaptive_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) const override;
  at::Tensor adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) const override;
  std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const override;
  std::tuple<at::Tensor,at::Tensor> adaptive_max_pool2d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & adaptive_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  at::Tensor adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const override;
  std::tuple<at::Tensor,at::Tensor> adaptive_max_pool3d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & adaptive_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  at::Tensor adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const override;
  at::Tensor & avg_pool2d_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor avg_pool2d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor & avg_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor & avg_pool3d_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor avg_pool3d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor & avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  at::Tensor avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const override;
  std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const override;
  std::tuple<at::Tensor,at::Tensor> fractional_max_pool2d(const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const override;
  at::Tensor & fractional_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const override;
  at::Tensor fractional_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const override;
  std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const override;
  std::tuple<at::Tensor,at::Tensor> fractional_max_pool3d(const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const override;
  at::Tensor & fractional_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const override;
  at::Tensor fractional_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const override;
  std::tuple<at::Tensor &,at::Tensor &> max_pool2d_with_indices_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  std::tuple<at::Tensor,at::Tensor> max_pool2d_with_indices(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor & max_pool2d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  at::Tensor max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  std::tuple<at::Tensor &,at::Tensor &> max_pool3d_with_indices_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  std::tuple<at::Tensor,at::Tensor> max_pool3d_with_indices(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const override;
  at::Tensor & max_pool3d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  at::Tensor max_pool3d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const override;
  at::Tensor & max_unpool2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor max_unpool2d(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor & max_unpool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor max_unpool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const override;
  at::Tensor & max_unpool3d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor max_unpool3d(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor & max_unpool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor max_unpool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const override;
  at::Tensor & reflection_pad1d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor reflection_pad1d(const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & reflection_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & reflection_pad2d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor reflection_pad2d(const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & reflection_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & replication_pad1d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor replication_pad1d(const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & replication_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & replication_pad2d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor replication_pad2d(const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & replication_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & replication_pad3d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor replication_pad3d(const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & replication_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const override;
  at::Tensor & upsample_linear1d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor upsample_linear1d(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & upsample_bilinear2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & upsample_bicubic2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & upsample_trilinear3d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntList output_size, bool align_corners) const override;
  at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const override;
  at::Tensor & upsample_nearest1d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor & upsample_nearest2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor & upsample_nearest3d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntList output_size) const override;
  at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const override;
  at::Tensor & sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const override;
  at::Tensor sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) const override;
  at::Tensor & tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const override;
  at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) const override;
  at::Tensor & thnn_conv_transpose2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  at::Tensor thnn_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_transpose2d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_transpose2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_transpose2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_transpose2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const override;
  at::Tensor & thnn_conv_transpose3d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  at::Tensor thnn_conv_transpose3d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_transpose3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_transpose3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_transpose3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_transpose3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const override;
  at::Tensor & thnn_conv2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv2d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const override;
  at::Tensor & thnn_conv_depthwise2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  at::Tensor thnn_conv_depthwise2d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  at::Tensor & thnn_conv_depthwise2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  at::Tensor thnn_conv_depthwise2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &> thnn_conv_depthwise2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor> thnn_conv_depthwise2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, std::array<bool,2> output_mask) const override;
  at::Tensor & thnn_conv3d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  at::Tensor thnn_conv3d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const override;
  at::Tensor & thnn_conv_dilated2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  at::Tensor thnn_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_dilated2d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_dilated2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_dilated2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_dilated2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const override;
  at::Tensor & thnn_conv_dilated3d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  at::Tensor thnn_conv_dilated3d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_dilated3d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_dilated3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const override;
  std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> thnn_conv_dilated3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const override;
  std::tuple<at::Tensor,at::Tensor,at::Tensor> thnn_conv_dilated3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const override;
  at::Tensor thnn_col2im(const at::Tensor & self, at::IntList output_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor thnn_col2im_backward(const at::Tensor & grad_output, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor thnn_im2col(const at::Tensor & self, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;
  at::Tensor thnn_im2col_backward(const at::Tensor & grad_output, at::IntList input_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const override;

};

at::Tensor & XLATypeBase::_th_set_(at::Tensor & self, at::Storage source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_set_(w_self, source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_set_(at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntList size, at::IntList stride) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_set_(w_self, source, storage_offset, size, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_set_(at::Tensor & self, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::_th_set_(w_self, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_set_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_set_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_fill_(at::Tensor & self, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_fill_(w_self, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_fill_(at::Tensor & self, const at::Tensor & value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_value = bridge::XlaToAtenTensor(value);
  auto&& x_result = at::_th_fill_(w_self, r_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

bool XLATypeBase::_th_is_set_to(const at::Tensor & self, const at::Tensor & tensor) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor = bridge::XlaToAtenTensor(tensor);
  auto&& x_result = at::_th_is_set_to(r_self, r_tensor);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor & XLATypeBase::_th_masked_fill_(at::Tensor & self, const at::Tensor & mask, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::_th_masked_fill_(w_self, r_mask, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_masked_fill_(at::Tensor & self, const at::Tensor & mask, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::detail::infer_type(w_self).s__th_masked_fill_(w_self, r_mask, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto r_value = bridge::XlaToAtenTensor(value);
  auto&& x_result = at::_th_masked_fill_(w_self, r_mask, r_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto r_value = bridge::XlaToAtenTensor(value);
  auto&& x_result = at::detail::infer_type(w_self).s__th_masked_fill_(w_self, r_mask, r_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::_th_masked_scatter_(w_self, r_mask, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::detail::infer_type(w_self).s__th_masked_scatter_(w_self, r_mask, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_masked_select_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mask) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::_th_masked_select_out(w_result, r_self, r_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_masked_select_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mask) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::detail::infer_type(r_self).s__th_masked_select_out(w_result, r_self, r_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_masked_select(const at::Tensor & self, const at::Tensor & mask) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::_th_masked_select(r_self, r_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_masked_select(const at::Tensor & self, const at::Tensor & mask) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::detail::infer_type(r_self).s__th_masked_select(r_self, r_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_nonzero_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_nonzero_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_nonzero(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_nonzero(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_th_clone(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_clone(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_th_view(const at::Tensor & self, at::IntList size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_view(r_self, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_resize_as_(at::Tensor & self, const at::Tensor & the_template) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_the_template = bridge::XlaToAtenTensor(the_template);
  auto&& x_result = at::_th_resize_as_(w_self, r_the_template);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_index_select_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_index_select_out(w_result, r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_index_select(r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::_th_index_copy_(w_self, dim, r_index, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_take_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & index) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_take_out(w_result, r_self, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_take(const at::Tensor & self, const at::Tensor & index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_take(r_self, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_put_(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::_th_put_(w_self, r_index, r_source, accumulate);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::_th_index_add_(w_self, dim, r_index, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_index_fill_(w_self, dim, r_index, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_value = bridge::XlaToAtenTensor(value);
  auto&& x_result = at::_th_index_fill_(w_self, dim, r_index, r_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_unfold_out(at::Tensor & result, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_unfold_out(w_result, r_self, dimension, size, step);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_unfold(r_self, dimension, size, step);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_src = bridge::XlaToAtenTensor(src);
  auto&& x_result = at::_th_scatter_(w_self, dim, r_index, r_src);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_scatter_(w_self, dim, r_index, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_src = bridge::XlaToAtenTensor(src);
  auto&& x_result = at::_th_scatter_add_(w_self, dim, r_index, r_src);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_gather_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_gather_out(w_result, r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_gather(const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::_th_gather(r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

bool XLATypeBase::_th_equal(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_equal(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor & XLATypeBase::_th_and_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_and_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_and(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_and(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_and_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_and_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_and_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_and_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_and(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_and(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_and(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_and(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_iand_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_iand_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_iand_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_iand_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_iand_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_iand_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_or_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_or_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_or(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_or(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_or_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_or_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_or_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_or_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_or(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_or(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_or(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_or(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ior_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_ior_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ior_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ior_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_ior_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_ior_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_xor_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_xor_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_xor(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_xor(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_xor_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_xor_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_xor_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_xor_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_xor(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_xor(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_xor(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_xor(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ixor_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_ixor_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ixor_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ixor_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_ixor_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_ixor_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_lshift_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_lshift_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_lshift(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_lshift(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_lshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_lshift_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_lshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_lshift_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_lshift(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_lshift(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_lshift(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_lshift(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ilshift_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_ilshift_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ilshift_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ilshift_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_ilshift_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_ilshift_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_rshift_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_rshift_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_rshift(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_rshift(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_rshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_rshift_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_rshift_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_rshift_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_rshift(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_rshift(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_rshift(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_rshift(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_irshift_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_irshift_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_irshift_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_irshift_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_irshift_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_irshift_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_lt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_lt_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_lt(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_lt(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_lt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_lt_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_lt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_lt_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_lt(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_lt(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_lt(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_lt(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_lt_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_lt_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_lt_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_lt_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_lt_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_lt_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_gt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_gt_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_gt(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_gt(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_gt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_gt_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_gt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_gt_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_gt(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_gt(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_gt(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_gt(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_gt_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_gt_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_gt_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_gt_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_gt_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_gt_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_le_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_le_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_le(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_le(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_le_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_le_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_le_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_le_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_le(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_le(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_le(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_le(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_le_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_le_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_le_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_le_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_le_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_le_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ge_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_ge_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_ge(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_ge(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ge_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ge_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_ge_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_ge_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_ge(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ge(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_ge(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_ge(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ge_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_ge_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ge_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ge_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_ge_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_ge_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_eq_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_eq_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_eq(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_eq(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_eq_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_eq_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_eq_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_eq_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_eq(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_eq(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_eq(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_eq(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_eq_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_eq_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_eq_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_eq_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_eq_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_eq_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ne_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_ne_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_ne(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_ne(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ne_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ne_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_ne_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_ne_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_ne(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ne(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_ne(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_ne(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ne_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_ne_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ne_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_ne_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_ne_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_ne_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_min_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_min_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_min_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_min_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_min(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_min(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_min(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_min(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_th_min(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_min(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_min = bridge::XlaToAtenMutableTensor(min);
  auto w_min_indices = bridge::XlaToAtenMutableTensor(min_indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_min_out(w_min, w_min_indices, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(min, min_indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_min(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_min(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_th_max_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_max_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_max_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_max_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_max(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_max(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_max(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_max(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_th_max(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_max(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_max_out(at::Tensor & max, at::Tensor & max_indices, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_max = bridge::XlaToAtenMutableTensor(max);
  auto w_max_indices = bridge::XlaToAtenMutableTensor(max_indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_max_out(w_max, w_max_indices, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(max, max_indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_max(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_max(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_kthvalue_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_kthvalue_out(w_values, w_indices, r_self, k, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_kthvalue(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_kthvalue(r_self, k, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_mode_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_mode_out(w_values, w_indices, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_mode(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_mode(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::_th_median(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_median(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_median_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_median_out(w_values, w_indices, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_median(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_median(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool descending) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sort_out(w_values, w_indices, r_self, dim, descending);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_sort(const at::Tensor & self, int64_t dim, bool descending) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sort(r_self, dim, descending);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_topk_out(w_values, w_indices, r_self, k, dim, largest, sorted);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_topk(r_self, k, dim, largest, sorted);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::_th_any(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_any(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_any_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_any_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_any(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_any(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_abs_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_abs_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_abs(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_abs(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_sigmoid_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sigmoid_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_sigmoid(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sigmoid(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_log_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_log(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_log10_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log10_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_log10(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log10(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_log1p_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log1p_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_log1p(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log1p(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_log2_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log2_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_log2(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_log2(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_lgamma_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_lgamma_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_lgamma(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_lgamma(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_lgamma_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_lgamma_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_digamma_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_digamma_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_digamma(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_digamma(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_digamma_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_digamma_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_polygamma_out(at::Tensor & result, int64_t n, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_polygamma_out(w_result, n, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_polygamma(int64_t n, const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_polygamma(n, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_polygamma_(at::Tensor & self, int64_t n) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_polygamma_(w_self, n);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_exp_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_exp_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_exp(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_exp(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_expm1_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_expm1_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_expm1(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_expm1(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_cos_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cos_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_cos(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cos(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_acos_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_acos_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_acos(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_acos(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_cosh_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cosh_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_cosh(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cosh(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_sin_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sin_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_sin(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sin(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_asin_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_asin_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_asin(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_asin(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_sinh_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sinh_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_sinh(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sinh(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_tan_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_tan_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_tan(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_tan(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_atan_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_atan_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_atan(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_atan(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_tanh_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_tanh_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_tanh(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_tanh(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_erf_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_erf_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_erf(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_erf(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_erfc_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_erfc_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_erfc(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_erfc(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_erfinv_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_erfinv_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_erfinv_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_erfinv_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_erfinv(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_erfinv(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_sqrt_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sqrt_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_sqrt(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sqrt(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_rsqrt_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_rsqrt_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_rsqrt(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_rsqrt(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ceil_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_ceil_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_ceil(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_ceil(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_floor_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_floor_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_floor(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_floor(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_round_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_round_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_round(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_round(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_trunc_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_trunc_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_trunc(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_trunc(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_frac_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_frac_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_frac_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_frac_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_frac(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_frac(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_var_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_var_out(w_result, r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_var(const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_var(r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_th_var(const at::Tensor & self, bool unbiased) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_var(r_self, unbiased);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_std_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_std_out(w_result, r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_std(const at::Tensor & self, int64_t dim, bool unbiased, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_std(r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_th_std(const at::Tensor & self, bool unbiased) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_std(r_self, unbiased);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_renorm_out(at::Tensor & result, const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_renorm_out(w_result, r_self, p, dim, maxnorm);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_renorm(const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_renorm(r_self, p, dim, maxnorm);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_renorm_(at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_renorm_(w_self, p, dim, maxnorm);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::_th_dist(const at::Tensor & self, const at::Tensor & other, at::Scalar p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_dist(r_self, r_other, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_dist(const at::Tensor & self, const at::Tensor & other, at::Scalar p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_dist(r_self, r_other, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_reciprocal_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_reciprocal_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_reciprocal(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_reciprocal(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_reciprocal_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_reciprocal_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_neg_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_neg_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_neg(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_neg(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_neg_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_neg_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_atan2_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_atan2_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_atan2_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_atan2_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_atan2(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_atan2(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_atan2(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_atan2(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_atan2_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_atan2_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_atan2_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_atan2_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_pow_out(at::Tensor & result, const at::Tensor & self, at::Scalar exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_pow_out(w_result, r_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_pow(const at::Tensor & self, at::Scalar exponent) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_pow(r_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_pow_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::_th_pow_out(w_result, r_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_pow_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::detail::infer_type(r_self).s__th_pow_out(w_result, r_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_pow(const at::Tensor & self, const at::Tensor & exponent) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::_th_pow(r_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_pow(const at::Tensor & self, const at::Tensor & exponent) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::detail::infer_type(r_self).s__th_pow(r_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_pow_out(at::Tensor & result, at::Scalar self, const at::Tensor & exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::_th_pow_out(w_result, self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_pow(at::Scalar self, const at::Tensor & exponent) const {
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::_th_pow(self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(exponent));
}

at::Tensor & XLATypeBase::_th_pow_(at::Tensor & self, at::Scalar exponent) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_pow_(w_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_pow_(at::Tensor & self, const at::Tensor & exponent) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::_th_pow_(w_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_pow_(at::Tensor & self, const at::Tensor & exponent) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::detail::infer_type(w_self).s__th_pow_(w_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_lerp_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::_th_lerp_out(w_result, r_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_lerp_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::detail::infer_type(r_self).s__th_lerp_out(w_result, r_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_lerp(const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::_th_lerp(r_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_lerp(const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::detail::infer_type(r_self).s__th_lerp(r_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_lerp_(at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::_th_lerp_(w_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_lerp_(at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::detail::infer_type(w_self).s__th_lerp_(w_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_histc_out(at::Tensor & result, const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_histc_out(w_result, r_self, bins, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_histc(const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_histc(r_self, bins, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_zero_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_zero_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_cumsum_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cumsum_out(w_result, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_cumsum(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cumsum(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_cumprod_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cumprod_out(w_result, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_cumprod(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_cumprod(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_sign_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sign_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_sign(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_sign(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_sign_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_sign_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::_th_trace(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_trace(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_fmod_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_fmod_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_fmod(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_fmod(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_fmod_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_fmod_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_fmod_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_fmod_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_fmod(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_fmod(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_fmod(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_fmod(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_fmod_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_fmod_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_fmod_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_fmod_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_fmod_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_fmod_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_remainder_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_remainder_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_remainder(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_remainder(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_remainder_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_remainder_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_remainder_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_remainder_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_remainder(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_remainder(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_remainder(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(r_self).s__th_remainder(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_remainder_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_remainder_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_remainder_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_remainder_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_remainder_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::detail::infer_type(w_self).s__th_remainder_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_clamp_out(at::Tensor & result, const at::Tensor & self, at::Scalar min, at::Scalar max) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_clamp_out(w_result, r_self, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_clamp(const at::Tensor & self, at::Scalar min, at::Scalar max) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_clamp(r_self, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_clamp_min_out(at::Tensor & result, const at::Tensor & self, at::Scalar min) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_clamp_min_out(w_result, r_self, min);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_clamp_min(const at::Tensor & self, at::Scalar min) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_clamp_min(r_self, min);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_clamp_max_out(at::Tensor & result, const at::Tensor & self, at::Scalar max) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_clamp_max_out(w_result, r_self, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_clamp_max(const at::Tensor & self, at::Scalar max) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_clamp_max(r_self, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_th_dot(const at::Tensor & self, const at::Tensor & tensor) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor = bridge::XlaToAtenTensor(tensor);
  auto&& x_result = at::_th_dot(r_self, r_tensor);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_cross_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_cross_out(w_result, r_self, r_other, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_th_cross(r_self, r_other, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_diag_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_diag_out(w_result, r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_diag(const at::Tensor & self, int64_t diagonal) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_diag(r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::_th_addmm_out(w_result, r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addmm_out(w_result, r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::_th_addmm(r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addmm(r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::_th_addmm_(w_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_addmv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::_th_addmv_out(w_result, r_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_addmv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addmv_out(w_result, r_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::_th_addmv(r_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addmv(r_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::_th_addmv_(w_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_addr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::_th_addr_out(w_result, r_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_addr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addr_out(w_result, r_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::_th_addr(r_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addr(r_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addr_(at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::_th_addr_(w_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_ger_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::_th_ger_out(w_result, r_self, r_vec2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_ger(const at::Tensor & self, const at::Tensor & vec2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::_th_ger(r_self, r_vec2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_mv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::_th_mv_out(w_result, r_self, r_vec);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_mv(const at::Tensor & self, const at::Tensor & vec) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::_th_mv(r_self, r_vec);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_mm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::_th_mm_out(w_result, r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_mm(const at::Tensor & self, const at::Tensor & mat2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::_th_mm(r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_bmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::_th_bmm_out(w_result, r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_bmm(const at::Tensor & self, const at::Tensor & mat2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::_th_bmm(r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::_th_addbmm_out(w_result, r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_addbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addbmm_out(w_result, r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::_th_addbmm(r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addbmm(r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::_th_addbmm_(w_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_baddbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::_th_baddbmm_out(w_result, r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_baddbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_baddbmm_out(w_result, r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::_th_baddbmm(r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_baddbmm(r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addcmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::_th_addcmul_out(w_result, r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_addcmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addcmul_out(w_result, r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::_th_addcmul(r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addcmul(r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::_th_addcmul_(w_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::detail::infer_type(w_self).s__th_addcmul_(w_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_addcdiv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::_th_addcdiv_out(w_result, r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::s__th_addcdiv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addcdiv_out(w_result, r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::_th_addcdiv(r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::s__th_addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::detail::infer_type(r_self).s__th_addcdiv(r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::_th_addcdiv_(w_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::s__th_addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::detail::infer_type(w_self).s__th_addcdiv_(w_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_gels_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, const at::Tensor & A) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::_th_gels_out(w_res1, w_res2, r_self, r_A);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(res1, res2);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_gels(const at::Tensor & self, const at::Tensor & A) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::_th_gels(r_self, r_A);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_trtrs_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::_th_trtrs_out(w_res1, w_res2, r_self, r_A, upper, transpose, unitriangular);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(res1, res2);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_trtrs(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::_th_trtrs(r_self, r_A, upper, transpose, unitriangular);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_symeig_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, bool eigenvectors, bool upper) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_symeig_out(w_res1, w_res2, r_self, eigenvectors, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(res1, res2);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_symeig(const at::Tensor & self, bool eigenvectors, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_symeig(r_self, eigenvectors, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_eig_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, bool eigenvectors) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_eig_out(w_res1, w_res2, r_self, eigenvectors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(res1, res2);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_eig(const at::Tensor & self, bool eigenvectors) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_eig(r_self, eigenvectors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_th_svd_out(at::Tensor & res1, at::Tensor & res2, at::Tensor & res3, const at::Tensor & self, bool some, bool compute_uv) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto w_res3 = bridge::XlaToAtenMutableTensor(res3);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_svd_out(w_res1, w_res2, w_res3, r_self, some, compute_uv);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(res1, res2, res3);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_th_svd(const at::Tensor & self, bool some, bool compute_uv) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_svd(r_self, some, compute_uv);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_th_getri_single_out(at::Tensor & output, const at::Tensor & self) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_getri_single_out(w_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_th_getri_single(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_getri_single(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_potri_out(at::Tensor & output, const at::Tensor & self, bool upper) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_potri_out(w_output, r_self, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_th_potri(const at::Tensor & self, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_potri(r_self, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_pstrf_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self, bool upper, at::Scalar tol) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_pstrf_out(w_res1, w_res2, r_self, upper, tol);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(res1, res2);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_pstrf(const at::Tensor & self, bool upper, at::Scalar tol) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_pstrf(r_self, upper, tol);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_qr_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_qr_out(w_res1, w_res2, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(res1, res2);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_qr(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_qr(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_geqrf_out(at::Tensor & res1, at::Tensor & res2, const at::Tensor & self) const {
  auto w_res1 = bridge::XlaToAtenMutableTensor(res1);
  auto w_res2 = bridge::XlaToAtenMutableTensor(res2);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_geqrf_out(w_res1, w_res2, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(res1, res2);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_geqrf(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_geqrf(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_th_orgqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto&& x_result = at::_th_orgqr_out(w_result, r_self, r_input2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_orgqr(const at::Tensor & self, const at::Tensor & input2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto&& x_result = at::_th_orgqr(r_self, r_input2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_ormqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto r_input3 = bridge::XlaToAtenTensor(input3);
  auto&& x_result = at::_th_ormqr_out(w_result, r_self, r_input2, r_input3, left, transpose);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto r_input3 = bridge::XlaToAtenTensor(input3);
  auto&& x_result = at::_th_ormqr(r_self, r_input2, r_input3, left, transpose);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_th_btrifact_out(at::Tensor & result, at::Tensor & pivots, const at::Tensor & self, bool pivot) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto w_pivots = bridge::XlaToAtenMutableTensor(pivots);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_btrifact_out(w_result, w_pivots, r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(result, pivots);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_th_btrifact(const at::Tensor & self, bool pivot) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_btrifact(r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_th_btrifact_with_info_out(at::Tensor & result, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool pivot) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto w_pivots = bridge::XlaToAtenMutableTensor(pivots);
  auto w_info = bridge::XlaToAtenMutableTensor(info);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_btrifact_with_info_out(w_result, w_pivots, w_info, r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(result, pivots, info);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_th_btrifact_with_info(const at::Tensor & self, bool pivot) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_btrifact_with_info(r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_th_btrisolve_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_LU_data = bridge::XlaToAtenTensor(LU_data);
  auto r_LU_pivots = bridge::XlaToAtenTensor(LU_pivots);
  auto&& x_result = at::_th_btrisolve_out(w_result, r_self, r_LU_data, r_LU_pivots);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_btrisolve(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_LU_data = bridge::XlaToAtenTensor(LU_data);
  auto r_LU_pivots = bridge::XlaToAtenTensor(LU_pivots);
  auto&& x_result = at::_th_btrisolve(r_self, r_LU_data, r_LU_pivots);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_random_(at::Tensor & self, int64_t from, int64_t to, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_random_(w_self, from, to, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_random_(at::Tensor & self, int64_t to, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_random_(w_self, to, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_random_(at::Tensor & self, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_random_(w_self, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_multinomial_out(at::Tensor & result, const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_multinomial_out(w_result, r_self, num_samples, replacement, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_th_multinomial(const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_multinomial(r_self, num_samples, replacement, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_uniform_(at::Tensor & self, double from, double to, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_uniform_(w_self, from, to, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_normal_out(at::Tensor & output, const at::Tensor & mean, double std, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto&& x_result = at::_th_normal_out(w_output, r_mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_th_normal(const at::Tensor & mean, double std, at::Generator * generator) const {
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto&& x_result = at::_th_normal(r_mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(mean));
}

at::Tensor & XLATypeBase::_th_normal_out(at::Tensor & output, double mean, const at::Tensor & std, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::_th_normal_out(w_output, mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_th_normal(double mean, const at::Tensor & std, at::Generator * generator) const {
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::_th_normal(mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(std));
}

at::Tensor & XLATypeBase::_th_normal_out(at::Tensor & output, const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::_th_normal_out(w_output, r_mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_th_normal(const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const {
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::_th_normal(r_mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(std));
}

at::Tensor & XLATypeBase::_th_normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_normal_(w_self, mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_cauchy_(at::Tensor & self, double median, double sigma, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_cauchy_(w_self, median, sigma, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_log_normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_log_normal_(w_self, mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_exponential_(at::Tensor & self, double lambd, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_exponential_(w_self, lambd, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_geometric_(at::Tensor & self, double p, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_th_geometric_(w_self, p, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_dirichlet_grad_out(at::Tensor & output, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_x = bridge::XlaToAtenTensor(x);
  auto r_alpha = bridge::XlaToAtenTensor(alpha);
  auto r_total = bridge::XlaToAtenTensor(total);
  auto&& x_result = at::_th_dirichlet_grad_out(w_output, r_x, r_alpha, r_total);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_th_dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const {
  auto r_x = bridge::XlaToAtenTensor(x);
  auto r_alpha = bridge::XlaToAtenTensor(alpha);
  auto r_total = bridge::XlaToAtenTensor(total);
  auto&& x_result = at::_th_dirichlet_grad(r_x, r_alpha, r_total);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(total));
}

at::Tensor XLATypeBase::_th_alias(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_th_alias(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_th_copy_ignoring_overlaps_(at::Tensor & self, const at::Tensor & src) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_src = bridge::XlaToAtenTensor(src);
  auto&& x_result = at::_th_copy_ignoring_overlaps_(w_self, r_src);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_th_cat_out(at::Tensor & self, at::TensorList tensors, int64_t dim) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::_th_cat_out(w_self, l_tensors, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::_th_cat(at::TensorList tensors, int64_t dim) const {
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::_th_cat(l_tensors, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(tensors));
}

at::Tensor & XLATypeBase::_thnn_binary_cross_entropy_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_binary_cross_entropy_forward_out(w_output, r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_binary_cross_entropy_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_binary_cross_entropy_forward(r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_binary_cross_entropy_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_binary_cross_entropy_backward_out(w_grad_input, r_grad_output, r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_binary_cross_entropy_backward(r_grad_output, r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_l1_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_l1_loss_forward_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_l1_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_l1_loss_forward(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_l1_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_l1_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_mse_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_mse_loss_forward_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_mse_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_mse_loss_forward(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_mse_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_mse_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_mse_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_multi_margin_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_multi_margin_loss_forward_out(w_output, r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_multi_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_multi_margin_loss_forward(r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_multi_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_multi_margin_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_multi_margin_loss_backward(r_grad_output, r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_multilabel_margin_loss_forward_out(at::Tensor & output, at::Tensor & is_target, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_is_target = bridge::XlaToAtenMutableTensor(is_target);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_multilabel_margin_loss_forward_out(w_output, w_is_target, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, is_target);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_multilabel_margin_loss_forward(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_multilabel_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_is_target = bridge::XlaToAtenTensor(is_target);
  auto&& x_result = at::_thnn_multilabel_margin_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction, r_is_target);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_is_target = bridge::XlaToAtenTensor(is_target);
  auto&& x_result = at::_thnn_multilabel_margin_loss_backward(r_grad_output, r_self, r_target, reduction, r_is_target);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_total_weight = bridge::XlaToAtenMutableTensor(total_weight);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_nll_loss_forward_out(w_output, w_total_weight, r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, total_weight);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_nll_loss_forward(r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_nll_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::_thnn_nll_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::_thnn_nll_loss_backward(r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_total_weight = bridge::XlaToAtenMutableTensor(total_weight);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_nll_loss2d_forward_out(w_output, w_total_weight, r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, total_weight);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_nll_loss2d_forward(r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_nll_loss2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::_thnn_nll_loss2d_backward_out(w_grad_input, r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::_thnn_nll_loss2d_backward(r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_smooth_l1_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_smooth_l1_loss_forward_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_smooth_l1_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_smooth_l1_loss_forward(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_smooth_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_smooth_l1_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_smooth_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_smooth_l1_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_soft_margin_loss_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_soft_margin_loss_forward_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_soft_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_soft_margin_loss_forward(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_soft_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_soft_margin_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_soft_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::_thnn_soft_margin_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_elu_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_elu_forward_out(w_output, r_self, alpha, scale, input_scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_elu_forward(const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_elu_forward(r_self, alpha, scale, input_scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_elu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_elu_backward_out(w_grad_input, r_grad_output, alpha, scale, input_scale, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_elu_backward(const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_elu_backward(r_grad_output, alpha, scale, input_scale, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(output));
}

at::Tensor & XLATypeBase::_thnn_elu_(at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_thnn_elu_(w_self, alpha, scale, input_scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_thnn_elu_forward_(at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_thnn_elu_forward_(w_self, alpha, scale, input_scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_thnn_glu_forward_out(at::Tensor & output, const at::Tensor & self, int64_t dim) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_glu_forward_out(w_output, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_glu_forward(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_glu_forward(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_glu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_glu_backward_out(w_grad_input, r_grad_output, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_glu_backward(r_grad_output, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_hardtanh_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_hardtanh_forward_out(w_output, r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_hardtanh_forward(const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_hardtanh_forward(r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_hardtanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_hardtanh_backward_out(w_grad_input, r_grad_output, r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_hardtanh_backward(r_grad_output, r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_hardtanh_(at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_thnn_hardtanh_(w_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_thnn_hardtanh_forward_(at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_thnn_hardtanh_forward_(w_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_thnn_leaky_relu_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar negative_slope) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_leaky_relu_forward_out(w_output, r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_leaky_relu_forward(const at::Tensor & self, at::Scalar negative_slope) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_leaky_relu_forward(r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_leaky_relu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_leaky_relu_backward_out(w_grad_input, r_grad_output, r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_leaky_relu_backward(r_grad_output, r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_leaky_relu_(at::Tensor & self, at::Scalar negative_slope) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_thnn_leaky_relu_(w_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_thnn_leaky_relu_forward_(at::Tensor & self, at::Scalar negative_slope) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::_thnn_leaky_relu_forward_(w_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_log_sigmoid_forward_out(at::Tensor & output, at::Tensor & buffer, const at::Tensor & self) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_buffer = bridge::XlaToAtenMutableTensor(buffer);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_log_sigmoid_forward_out(w_output, w_buffer, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, buffer);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_log_sigmoid_forward(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_log_sigmoid_forward(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_log_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_buffer = bridge::XlaToAtenTensor(buffer);
  auto&& x_result = at::_thnn_log_sigmoid_backward_out(w_grad_input, r_grad_output, r_self, r_buffer);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_buffer = bridge::XlaToAtenTensor(buffer);
  auto&& x_result = at::_thnn_log_sigmoid_backward(r_grad_output, r_self, r_buffer);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_rrelu_with_noise_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::_thnn_rrelu_with_noise_forward_out(w_output, r_self, r_noise, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_rrelu_with_noise_forward(const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::_thnn_rrelu_with_noise_forward(r_self, r_noise, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_rrelu_with_noise_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::_thnn_rrelu_with_noise_backward_out(w_grad_input, r_grad_output, r_self, r_noise, lower, upper, training);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_rrelu_with_noise_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::_thnn_rrelu_with_noise_backward(r_grad_output, r_self, r_noise, lower, upper, training);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_rrelu_with_noise_(at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::_thnn_rrelu_with_noise_(w_self, r_noise, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_thnn_rrelu_with_noise_forward_(at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::_thnn_rrelu_with_noise_forward_(w_self, r_noise, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_thnn_softplus_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_softplus_forward_out(w_output, r_self, beta, threshold);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_softplus_forward(const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_softplus_forward(r_self, beta, threshold);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_softplus_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_softplus_backward_out(w_grad_input, r_grad_output, r_self, beta, threshold, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_softplus_backward(r_grad_output, r_self, beta, threshold, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_softshrink_forward_out(at::Tensor & output, const at::Tensor & self, at::Scalar lambd) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_softshrink_forward_out(w_output, r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_softshrink_forward(const at::Tensor & self, at::Scalar lambd) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_softshrink_forward(r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_softshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_softshrink_backward_out(w_grad_input, r_grad_output, r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_softshrink_backward(r_grad_output, r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_adaptive_avg_pool3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_avg_pool3d_forward_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_adaptive_avg_pool3d_forward(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_avg_pool3d_forward(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_adaptive_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_avg_pool3d_backward_out(w_grad_input, r_grad_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_avg_pool3d_backward(r_grad_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_adaptive_max_pool2d_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_max_pool2d_forward_out(w_output, w_indices, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_adaptive_max_pool2d_forward(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_max_pool2d_forward(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_adaptive_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_adaptive_max_pool2d_backward_out(w_grad_input, r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_adaptive_max_pool2d_backward(r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_adaptive_max_pool3d_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_max_pool3d_forward_out(w_output, w_indices, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_adaptive_max_pool3d_forward(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_adaptive_max_pool3d_forward(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_adaptive_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_adaptive_max_pool3d_backward_out(w_grad_input, r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_adaptive_max_pool3d_backward(r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_avg_pool2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool2d_forward_out(w_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_avg_pool2d_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool2d_forward(r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_avg_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool2d_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool2d_backward(r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_avg_pool3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool3d_forward_out(w_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_avg_pool3d_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool3d_forward(r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool3d_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_avg_pool3d_backward(r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_max_pool2d_with_indices_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_max_pool2d_with_indices_forward_out(w_output, w_indices, r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_max_pool2d_with_indices_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_max_pool2d_with_indices_forward(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_max_pool2d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_pool2d_with_indices_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_pool2d_with_indices_backward(r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_max_pool3d_with_indices_forward_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_max_pool3d_with_indices_forward_out(w_output, w_indices, r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_max_pool3d_with_indices_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_max_pool3d_with_indices_forward(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_max_pool3d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_pool3d_with_indices_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_max_pool3d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_pool3d_with_indices_backward(r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_max_unpool2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool2d_forward_out(w_output, r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_max_unpool2d_forward(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool2d_forward(r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_max_unpool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool2d_backward_out(w_grad_input, r_grad_output, r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_max_unpool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool2d_backward(r_grad_output, r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_max_unpool3d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool3d_forward_out(w_output, r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_max_unpool3d_forward(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool3d_forward(r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_max_unpool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool3d_backward_out(w_grad_input, r_grad_output, r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_max_unpool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::_thnn_max_unpool3d_backward(r_grad_output, r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_linear1d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_linear1d_forward_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_upsample_linear1d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_linear1d_forward(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_linear1d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_upsample_linear1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_linear1d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_upsample_bilinear2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_bilinear2d_forward_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_upsample_bilinear2d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_bilinear2d_forward(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_bilinear2d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_bilinear2d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_upsample_bicubic2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_bicubic2d_forward_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_upsample_bicubic2d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_bicubic2d_forward(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_bicubic2d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_bicubic2d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_upsample_trilinear3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_trilinear3d_forward_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_upsample_trilinear3d_forward(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_trilinear3d_forward(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_trilinear3d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_trilinear3d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_upsample_nearest1d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_nearest1d_forward_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_upsample_nearest1d_forward(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_nearest1d_forward(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_nearest1d_backward_out(w_grad_input, r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_nearest1d_backward(r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_upsample_nearest2d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_nearest2d_forward_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_upsample_nearest2d_forward(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_nearest2d_forward(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_nearest2d_backward_out(w_grad_input, r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_nearest2d_backward(r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_upsample_nearest3d_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_nearest3d_forward_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_upsample_nearest3d_forward(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_upsample_nearest3d_forward(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_nearest3d_backward_out(w_grad_input, r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_upsample_nearest3d_backward(r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_sigmoid_forward_out(at::Tensor & output, const at::Tensor & self) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_sigmoid_forward_out(w_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_sigmoid_forward(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_sigmoid_forward(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_sigmoid_backward_out(w_grad_input, r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_sigmoid_backward(r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(output));
}

at::Tensor & XLATypeBase::_thnn_tanh_forward_out(at::Tensor & output, const at::Tensor & self) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_tanh_forward_out(w_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_tanh_forward(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_tanh_forward(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_tanh_backward_out(w_grad_input, r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_thnn_tanh_backward(r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(output));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_transpose2d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_columns = bridge::XlaToAtenMutableTensor(columns);
  auto w_ones = bridge::XlaToAtenMutableTensor(ones);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_transpose2d_forward_out(w_output, w_columns, w_ones, r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, columns, ones);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_transpose2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_transpose2d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_transpose2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::_thnn_conv_transpose2d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_columns, r_ones);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_transpose2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::_thnn_conv_transpose2d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_columns, r_ones, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_transpose3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_finput = bridge::XlaToAtenMutableTensor(finput);
  auto w_fgrad_input = bridge::XlaToAtenMutableTensor(fgrad_input);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_transpose3d_forward_out(w_output, w_finput, w_fgrad_input, r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, finput, fgrad_input);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_transpose3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_transpose3d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_transpose3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::_thnn_conv_transpose3d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_finput, r_fgrad_input);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_transpose3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::_thnn_conv_transpose3d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_finput, r_fgrad_input, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv2d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_finput = bridge::XlaToAtenMutableTensor(finput);
  auto w_fgrad_input = bridge::XlaToAtenMutableTensor(fgrad_input);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv2d_forward_out(w_output, w_finput, w_fgrad_input, r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, finput, fgrad_input);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv2d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::_thnn_conv2d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, r_finput, r_fgrad_input);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::_thnn_conv2d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, r_finput, r_fgrad_input, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_conv_depthwise2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_depthwise2d_forward_out(w_output, r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_conv_depthwise2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_depthwise2d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_depthwise2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_conv_depthwise2d_backward_out(w_grad_input, w_grad_weight, r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(grad_input, grad_weight);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_depthwise2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, std::array<bool,2> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::_thnn_conv_depthwise2d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_finput = bridge::XlaToAtenMutableTensor(finput);
  auto w_fgrad_input = bridge::XlaToAtenMutableTensor(fgrad_input);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv3d_forward_out(w_output, w_finput, w_fgrad_input, r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, finput, fgrad_input);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv3d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::_thnn_conv3d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, r_finput, r_fgrad_input);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::_thnn_conv3d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, r_finput, r_fgrad_input, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_dilated2d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_columns = bridge::XlaToAtenMutableTensor(columns);
  auto w_ones = bridge::XlaToAtenMutableTensor(ones);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_dilated2d_forward_out(w_output, w_columns, w_ones, r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, columns, ones);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_dilated2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_dilated2d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_dilated2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::_thnn_conv_dilated2d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation, r_columns, r_ones);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_dilated2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::_thnn_conv_dilated2d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation, r_columns, r_ones, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_dilated3d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_columns = bridge::XlaToAtenMutableTensor(columns);
  auto w_ones = bridge::XlaToAtenMutableTensor(ones);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_dilated3d_forward_out(w_output, w_columns, w_ones, r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, columns, ones);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_dilated3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_thnn_conv_dilated3d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::_thnn_conv_dilated3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::_thnn_conv_dilated3d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation, r_columns, r_ones);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_conv_dilated3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::_thnn_conv_dilated3d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation, r_columns, r_ones, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::_thnn_col2im_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_col2im_forward_out(w_output, r_self, output_size, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_col2im_forward(const at::Tensor & self, at::IntList output_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_col2im_forward(r_self, output_size, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_col2im_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_col2im_backward_out(w_grad_input, r_grad_output, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_col2im_backward(const at::Tensor & grad_output, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_col2im_backward(r_grad_output, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::_thnn_im2col_forward_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_im2col_forward_out(w_output, r_self, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_thnn_im2col_forward(const at::Tensor & self, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_thnn_im2col_forward(r_self, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_thnn_im2col_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList input_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_im2col_backward_out(w_grad_input, r_grad_output, input_size, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::_thnn_im2col_backward(const at::Tensor & grad_output, at::IntList input_size, at::IntList kernel_size, at::IntList dilation, at::IntList padding, at::IntList stride) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::_thnn_im2col_backward(r_grad_output, input_size, kernel_size, dilation, padding, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor XLATypeBase::_cast_Byte(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Byte(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cast_Char(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Char(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cast_Double(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Double(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cast_Float(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Float(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cast_Int(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Int(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cast_Long(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Long(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cast_Short(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Short(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cast_Half(const at::Tensor & self, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cast_Half(r_self, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_fused_dropout(const at::Tensor & self, double p, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_fused_dropout(r_self, p, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::_masked_scale(const at::Tensor & self, const at::Tensor & mask, double scale) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::_masked_scale(r_self, r_mask, scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_reshape_from_tensor(const at::Tensor & self, const at::Tensor & shape) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_shape = bridge::XlaToAtenTensor(shape);
  auto&& x_result = at::_reshape_from_tensor(r_self, r_shape);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_shape_as_tensor(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_shape_as_tensor(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::dropout(const at::Tensor & input, double p, bool train) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto&& x_result = at::dropout(r_input, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(input));
}

at::Tensor & XLATypeBase::dropout_(at::Tensor & self, double p, bool train) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::dropout_(w_self, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::feature_dropout(const at::Tensor & input, double p, bool train) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto&& x_result = at::feature_dropout(r_input, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(input));
}

at::Tensor & XLATypeBase::feature_dropout_(at::Tensor & self, double p, bool train) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::feature_dropout_(w_self, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::alpha_dropout(const at::Tensor & input, double p, bool train) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto&& x_result = at::alpha_dropout(r_input, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(input));
}

at::Tensor & XLATypeBase::alpha_dropout_(at::Tensor & self, double p, bool train) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::alpha_dropout_(w_self, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::feature_alpha_dropout(const at::Tensor & input, double p, bool train) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto&& x_result = at::feature_alpha_dropout(r_input, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(input));
}

at::Tensor & XLATypeBase::feature_alpha_dropout_(at::Tensor & self, double p, bool train) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::feature_alpha_dropout_(w_self, p, train);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::abs(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::abs(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::abs_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::abs_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::abs_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::abs_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::acos(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::acos(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::acos_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::acos_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::acos_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::acos_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::avg_pool1d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool1d(r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::adaptive_avg_pool1d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool1d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::adaptive_max_pool1d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_max_pool1d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::add(const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::add(r_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::add_(at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::add_(w_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::add_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::add_out(w_result, r_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::add(const at::Tensor & self, at::Scalar other, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::add(r_self, other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::add_(at::Tensor & self, at::Scalar other, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::add_(w_self, other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::addmv(r_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::addmv_(w_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::addmv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat = bridge::XlaToAtenTensor(mat);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::addmv_out(w_result, r_self, r_mat, r_vec, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::addr(r_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::addr_(at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::native::addr_(w_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::addr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec1 = bridge::XlaToAtenTensor(vec1);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::addr_out(w_result, r_self, r_vec1, r_vec2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::affine_grid_generator(const at::Tensor & theta, at::IntList size) const {
  auto r_theta = bridge::XlaToAtenTensor(theta);
  auto&& x_result = at::affine_grid_generator(r_theta, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(theta));
}

at::Tensor XLATypeBase::affine_grid_generator_backward(const at::Tensor & grad, at::IntList size) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto&& x_result = at::affine_grid_generator_backward(r_grad, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad));
}

at::Tensor XLATypeBase::all(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::all(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::all_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::all_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

bool XLATypeBase::allclose(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::allclose(r_self, r_other, rtol, atol, equal_nan);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor XLATypeBase::any(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::any(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::any_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::any_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::arange_out(at::Tensor & result, at::Scalar end) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::arange_out(w_result, end);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::arange_out(at::Tensor & result, at::Scalar start, at::Scalar end, at::Scalar step) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::arange_out(w_result, start, end, step);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_dim_arange(const at::Tensor & like, int64_t dim) const {
  auto r_like = bridge::XlaToAtenTensor(like);
  auto&& x_result = at::_dim_arange(r_like, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(like));
}

at::Tensor XLATypeBase::argmax(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::argmax(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::argmax(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::argmax(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_argmax(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_argmax(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::argmin(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::argmin(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::argmin(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::argmin(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_argmin(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_argmin(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::as_strided(const at::Tensor & self, at::IntList size, at::IntList stride, c10::optional<int64_t> storage_offset) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::as_strided(r_self, size, stride, storage_offset);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::as_strided_(at::Tensor & self, at::IntList size, at::IntList stride, c10::optional<int64_t> storage_offset) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::as_strided_(w_self, size, stride, storage_offset);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::asin(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::asin(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::asin_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::asin_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::asin_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::asin_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::atan(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::atan(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::atan_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::atan_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::atan_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::atan_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::baddbmm(r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::baddbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::detail::infer_type(w_self).baddbmm_(w_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::_baddbmm_mkl_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::_baddbmm_mkl_(w_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::baddbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::baddbmm_out(w_result, r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::batch_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto r_running_mean = bridge::XlaToAtenTensor(running_mean);
  auto r_running_var = bridge::XlaToAtenTensor(running_var);
  auto&& x_result = at::batch_norm(r_input, r_weight, r_bias, r_running_mean, r_running_var, training, momentum, eps, cudnn_enabled);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(running_var));
}

at::Tensor XLATypeBase::bernoulli(const at::Tensor & self, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::bernoulli(r_self, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::bernoulli_out(at::Tensor & result, const at::Tensor & self, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::bernoulli_out(w_result, r_self, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::bernoulli_(at::Tensor & self, const at::Tensor & p, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_p = bridge::XlaToAtenTensor(p);
  auto&& x_result = at::detail::infer_type(w_self).bernoulli_(w_self, r_p, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::bernoulli_(at::Tensor & self, double p, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::detail::infer_type(w_self).bernoulli_(w_self, p, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::bernoulli(const at::Tensor & self, double p, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::bernoulli(r_self, p, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::bilinear(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const at::Tensor & bias) const {
  auto r_input1 = bridge::XlaToAtenTensor(input1);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::bilinear(r_input1, r_input2, r_weight, r_bias);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::binary_cross_entropy_with_logits(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, const at::Tensor & pos_weight, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_pos_weight = bridge::XlaToAtenTensor(pos_weight);
  auto&& x_result = at::binary_cross_entropy_with_logits(r_self, r_target, r_weight, r_pos_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::binary_cross_entropy_with_logits_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, const at::Tensor & pos_weight, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_pos_weight = bridge::XlaToAtenTensor(pos_weight);
  auto&& x_result = at::binary_cross_entropy_with_logits_backward(r_grad_output, r_self, r_target, r_weight, r_pos_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::bincount(const at::Tensor & self, const at::Tensor & weights, int64_t minlength) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weights = bridge::XlaToAtenTensor(weights);
  auto&& x_result = at::bincount(r_self, r_weights, minlength);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::bmm(const at::Tensor & self, const at::Tensor & mat2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::bmm(r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::bmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::bmm_out(w_result, r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

std::vector<at::Tensor> XLATypeBase::broadcast_tensors(at::TensorList tensors) const {
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::broadcast_tensors(l_tensors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensors(x_result, bridge::XlaTensorDevice(tensors));
}

at::Tensor XLATypeBase::cat(at::TensorList tensors, int64_t dim) const {
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::cat(l_tensors, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(tensors));
}

at::Tensor & XLATypeBase::cat_out(at::Tensor & result, at::TensorList tensors, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::cat_out(w_result, l_tensors, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ceil(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ceil(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::ceil_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::ceil_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::ceil_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ceil_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::chain_matmul(at::TensorList matrices) const {
  auto l_matrices = bridge::XlaCreateTensorList(matrices);
  auto&& x_result = at::chain_matmul(l_matrices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(matrices));
}

std::vector<at::Tensor> XLATypeBase::chunk(const at::Tensor & self, int64_t chunks, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::chunk(r_self, chunks, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensors(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::clamp(const at::Tensor & self, c10::optional<at::Scalar> min, c10::optional<at::Scalar> max) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::clamp(r_self, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::clamp_(at::Tensor & self, c10::optional<at::Scalar> min, c10::optional<at::Scalar> max) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::clamp_(w_self, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::clamp_out(at::Tensor & result, const at::Tensor & self, c10::optional<at::Scalar> min, c10::optional<at::Scalar> max) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::clamp_out(w_result, r_self, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::clamp_max(const at::Tensor & self, at::Scalar max) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::clamp_max(r_self, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::clamp_max_(at::Tensor & self, at::Scalar max) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::clamp_max_(w_self, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::clamp_max_out(at::Tensor & result, const at::Tensor & self, at::Scalar max) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::clamp_max_out(w_result, r_self, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::clamp_min(const at::Tensor & self, at::Scalar min) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::clamp_min(r_self, min);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::clamp_min_(at::Tensor & self, at::Scalar min) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::clamp_min_(w_self, min);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::clamp_min_out(at::Tensor & result, const at::Tensor & self, at::Scalar min) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::clamp_min_out(w_result, r_self, min);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::constant_pad_nd(const at::Tensor & self, at::IntList pad, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::constant_pad_nd(r_self, pad, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::contiguous(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::contiguous(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::convolution(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding, int64_t groups) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::convolution(r_input, r_weight, r_bias, stride, padding, dilation, transposed, output_padding, groups);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::_convolution(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_convolution(r_input, r_weight, r_bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::_convolution_nogroup(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::_convolution_nogroup(r_input, r_weight, r_bias, stride, padding, dilation, transposed, output_padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_convolution_double_backward(const at::Tensor & ggI, const at::Tensor & ggW, const at::Tensor & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, at::IntList stride, at::IntList padding, at::IntList dilation, bool transposed, at::IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array<bool,3> output_mask) const {
  auto r_ggI = bridge::XlaToAtenTensor(ggI);
  auto r_ggW = bridge::XlaToAtenTensor(ggW);
  auto r_ggb = bridge::XlaToAtenTensor(ggb);
  auto r_gO = bridge::XlaToAtenTensor(gO);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_convolution_double_backward(r_ggI, r_ggW, r_ggb, r_gO, r_weight, r_self, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::conv1d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, int64_t groups) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv1d(r_input, r_weight, r_bias, stride, padding, dilation, groups);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::conv2d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, int64_t groups) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv2d(r_input, r_weight, r_bias, stride, padding, dilation, groups);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::conv3d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation, int64_t groups) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv3d(r_input, r_weight, r_bias, stride, padding, dilation, groups);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::conv_tbc(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv_tbc(r_self, r_weight, r_bias, pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::conv_tbc_backward(const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv_tbc_backward(r_self, r_input, r_weight, r_bias, pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::conv_transpose1d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, int64_t groups, at::IntList dilation) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv_transpose1d(r_input, r_weight, r_bias, stride, padding, output_padding, groups, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::conv_transpose2d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, int64_t groups, at::IntList dilation) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv_transpose2d(r_input, r_weight, r_bias, stride, padding, output_padding, groups, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::conv_transpose3d(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, int64_t groups, at::IntList dilation) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::conv_transpose3d(r_input, r_weight, r_bias, stride, padding, output_padding, groups, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor & XLATypeBase::s_copy_(at::Tensor & self, const at::Tensor & src, bool non_blocking) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_src = bridge::XlaToAtenTensor(src);
  auto&& x_result = at::s_copy_(w_self, r_src, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::_s_copy_from(const at::Tensor & self, const at::Tensor & dst, bool non_blocking) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_dst = bridge::XlaToAtenTensor(dst);
  auto&& x_result = at::_s_copy_from(r_self, r_dst, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

void XLATypeBase::_copy_same_type_(at::Tensor & self, const at::Tensor & src) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_src = bridge::XlaToAtenTensor(src);
  at::_copy_same_type_(w_self, r_src);
}

at::Tensor XLATypeBase::cos(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cos(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::cos_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::cos_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::cos_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cos_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::cosh(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cosh(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::cosh_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::cosh_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::cosh_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cosh_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::cosine_embedding_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) const {
  auto r_input1 = bridge::XlaToAtenTensor(input1);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::cosine_embedding_loss(r_input1, r_input2, r_target, margin, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(target));
}

at::Tensor XLATypeBase::cumsum(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumsum(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::cumsum(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumsum(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::cumsum_out(at::Tensor & result, const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumsum_out(w_result, r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::cumsum_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumsum_out(w_result, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::cumprod(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumprod(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::cumprod(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumprod(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::cumprod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumprod_out(w_result, r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::cumprod_out(at::Tensor & result, const at::Tensor & self, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cumprod_out(w_result, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntList input_lengths, at::IntList target_lengths, int64_t blank, int64_t reduction) const {
  auto r_log_probs = bridge::XlaToAtenTensor(log_probs);
  auto r_targets = bridge::XlaToAtenTensor(targets);
  auto&& x_result = at::ctc_loss(r_log_probs, r_targets, input_lengths, target_lengths, blank, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(targets));
}

at::Tensor XLATypeBase::ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, int64_t reduction) const {
  auto r_log_probs = bridge::XlaToAtenTensor(log_probs);
  auto r_targets = bridge::XlaToAtenTensor(targets);
  auto r_input_lengths = bridge::XlaToAtenTensor(input_lengths);
  auto r_target_lengths = bridge::XlaToAtenTensor(target_lengths);
  auto&& x_result = at::ctc_loss(r_log_probs, r_targets, r_input_lengths, r_target_lengths, blank, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(target_lengths));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntList input_lengths, at::IntList target_lengths, int64_t blank) const {
  auto r_log_probs = bridge::XlaToAtenTensor(log_probs);
  auto r_targets = bridge::XlaToAtenTensor(targets);
  auto&& x_result = at::_ctc_loss(r_log_probs, r_targets, input_lengths, target_lengths, blank);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(targets)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(targets)));
}

at::Tensor XLATypeBase::_ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntList input_lengths, at::IntList target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_log_probs = bridge::XlaToAtenTensor(log_probs);
  auto r_targets = bridge::XlaToAtenTensor(targets);
  auto r_neg_log_likelihood = bridge::XlaToAtenTensor(neg_log_likelihood);
  auto r_log_alpha = bridge::XlaToAtenTensor(log_alpha);
  auto&& x_result = at::_ctc_loss_backward(r_grad, r_log_probs, r_targets, input_lengths, target_lengths, r_neg_log_likelihood, r_log_alpha, blank);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(log_alpha));
}

at::Tensor XLATypeBase::det(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::det(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::diag_embed(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::diag_embed(r_self, offset, dim1, dim2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::diagflat(const at::Tensor & self, int64_t offset) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::diagflat(r_self, offset);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::diagonal(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::diagonal(r_self, offset, dim1, dim2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::div(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::div(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::div_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::div_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::div_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::div_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::div(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::div(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::div_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::div_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::dot(const at::Tensor & self, const at::Tensor & tensor) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor = bridge::XlaToAtenTensor(tensor);
  auto&& x_result = at::dot(r_self, r_tensor);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::dot_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor = bridge::XlaToAtenTensor(tensor);
  auto&& x_result = at::dot_out(w_result, r_self, r_tensor);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::einsum(std::string equation, at::TensorList tensors) const {
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::einsum(equation, l_tensors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(tensors));
}

at::Tensor XLATypeBase::embedding(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) const {
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::embedding(r_weight, r_indices, padding_idx, scale_grad_by_freq, sparse);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(indices));
}

at::Tensor XLATypeBase::embedding_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::embedding_backward(r_grad, r_indices, num_weights, padding_idx, scale_grad_by_freq, sparse);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(indices));
}

at::Tensor XLATypeBase::embedding_dense_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::embedding_dense_backward(r_grad, r_indices, num_weights, padding_idx, scale_grad_by_freq);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(indices));
}

at::Tensor & XLATypeBase::embedding_renorm_(at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::embedding_renorm_(w_self, r_indices, max_norm, norm_type);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::embedding_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::embedding_sparse_backward(r_grad, r_indices, num_weights, padding_idx, scale_grad_by_freq);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(indices));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> XLATypeBase::embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse) const {
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto r_offsets = bridge::XlaToAtenTensor(offsets);
  auto&& x_result = at::embedding_bag(r_weight, r_indices, r_offsets, scale_grad_by_freq, mode, sparse);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(offsets)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(offsets)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(offsets)), bridge::CreateXlaTensor(std::get<3>(x_result), bridge::XlaTensorDevice(offsets)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse) const {
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto r_offsets = bridge::XlaToAtenTensor(offsets);
  auto&& x_result = at::_embedding_bag(r_weight, r_indices, r_offsets, scale_grad_by_freq, mode, sparse);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(offsets)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(offsets)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(offsets)), bridge::CreateXlaTensor(std::get<3>(x_result), bridge::XlaTensorDevice(offsets)));
}

at::Tensor XLATypeBase::_embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto r_offsets = bridge::XlaToAtenTensor(offsets);
  auto r_offset2bag = bridge::XlaToAtenTensor(offset2bag);
  auto r_bag_size = bridge::XlaToAtenTensor(bag_size);
  auto r_maximum_indices = bridge::XlaToAtenTensor(maximum_indices);
  auto&& x_result = at::_embedding_bag_backward(r_grad, r_indices, r_offsets, r_offset2bag, r_bag_size, r_maximum_indices, num_weights, scale_grad_by_freq, mode, sparse);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(maximum_indices));
}

at::Tensor XLATypeBase::_embedding_bag_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto r_offsets = bridge::XlaToAtenTensor(offsets);
  auto r_offset2bag = bridge::XlaToAtenTensor(offset2bag);
  auto r_bag_size = bridge::XlaToAtenTensor(bag_size);
  auto&& x_result = at::_embedding_bag_sparse_backward(r_grad, r_indices, r_offsets, r_offset2bag, r_bag_size, num_weights, scale_grad_by_freq, mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bag_size));
}

at::Tensor XLATypeBase::_embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto r_offsets = bridge::XlaToAtenTensor(offsets);
  auto r_offset2bag = bridge::XlaToAtenTensor(offset2bag);
  auto r_bag_size = bridge::XlaToAtenTensor(bag_size);
  auto r_maximum_indices = bridge::XlaToAtenTensor(maximum_indices);
  auto&& x_result = at::_embedding_bag_dense_backward(r_grad, r_indices, r_offsets, r_offset2bag, r_bag_size, r_maximum_indices, num_weights, scale_grad_by_freq, mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(maximum_indices));
}

at::Tensor XLATypeBase::empty(at::IntList size, const at::TensorOptions & options) const {
  auto&& x_result = at::empty(size, options);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(options));
}

at::Tensor & XLATypeBase::resize_(at::Tensor & self, at::IntList size) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::detail::infer_type(w_self).resize_(w_self, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::empty_out(at::Tensor & result, at::IntList size) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::empty_out(w_result, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::empty_like(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::empty_like(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::empty_strided(at::IntList size, at::IntList stride, const at::TensorOptions & options) const {
  auto&& x_result = at::empty_strided(size, stride, options);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(options));
}

at::Tensor XLATypeBase::erf(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::erf(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::erf_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::erf_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::erf_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::erf_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::erfc(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::erfc(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::erfc_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::erfc_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::erfc_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::erfc_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::exp(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::exp(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::exp_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::exp_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::exp_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::exp_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::expm1(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::expm1(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::expm1_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::expm1_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::expm1_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::expm1_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::expand(const at::Tensor & self, at::IntList size, bool implicit) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::expand(r_self, size, implicit);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::expand_as(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::expand_as(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::eye_out(at::Tensor & result, int64_t n) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::eye_out(w_result, n);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::eye_out(at::Tensor & result, int64_t n, int64_t m) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::eye_out(w_result, n, m);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::flatten(const at::Tensor & self, int64_t start_dim, int64_t end_dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::flatten(r_self, start_dim, end_dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::fill_(at::Tensor & self, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::fill_(w_self, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::fill_(at::Tensor & self, const at::Tensor & value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_value = bridge::XlaToAtenTensor(value);
  auto&& x_result = at::fill_(w_self, r_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::floor(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::floor(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::floor_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::floor_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::floor_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::floor_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::full_out(at::Tensor & result, at::IntList size, at::Scalar fill_value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::full_out(w_result, size, fill_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::full_like(const at::Tensor & self, at::Scalar fill_value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::full_like(r_self, fill_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::grid_sampler(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_grid = bridge::XlaToAtenTensor(grid);
  auto&& x_result = at::grid_sampler(r_input, r_grid, interpolation_mode, padding_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grid));
}

at::Tensor XLATypeBase::grid_sampler_2d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_grid = bridge::XlaToAtenTensor(grid);
  auto&& x_result = at::grid_sampler_2d(r_input, r_grid, interpolation_mode, padding_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grid));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::grid_sampler_2d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_grid = bridge::XlaToAtenTensor(grid);
  auto&& x_result = at::grid_sampler_2d_backward(r_grad_output, r_input, r_grid, interpolation_mode, padding_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(grid)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(grid)));
}

at::Tensor XLATypeBase::grid_sampler_3d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_grid = bridge::XlaToAtenTensor(grid);
  auto&& x_result = at::grid_sampler_3d(r_input, r_grid, interpolation_mode, padding_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grid));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::grid_sampler_3d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_grid = bridge::XlaToAtenTensor(grid);
  auto&& x_result = at::grid_sampler_3d_backward(r_grad_output, r_input, r_grid, interpolation_mode, padding_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(grid)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(grid)));
}

at::Tensor XLATypeBase::hinge_embedding_loss(const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::hinge_embedding_loss(r_self, r_target, margin, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::ger(const at::Tensor & self, const at::Tensor & vec2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::ger(r_self, r_vec2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::ger_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec2 = bridge::XlaToAtenTensor(vec2);
  auto&& x_result = at::ger_out(w_result, r_self, r_vec2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::gesv(const at::Tensor & self, const at::Tensor & A) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::gesv(r_self, r_A);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::gesv_out(at::Tensor & solution, at::Tensor & lu, const at::Tensor & self, const at::Tensor & A) const {
  auto w_solution = bridge::XlaToAtenMutableTensor(solution);
  auto w_lu = bridge::XlaToAtenMutableTensor(lu);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::gesv_out(w_solution, w_lu, r_self, r_A);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(solution, lu);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_gesv_helper(const at::Tensor & self, const at::Tensor & A) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::_gesv_helper(r_self, r_A);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::group_norm(const at::Tensor & input, int64_t num_groups, const at::Tensor & weight, const at::Tensor & bias, double eps, bool cudnn_enabled) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::group_norm(r_input, num_groups, r_weight, r_bias, eps, cudnn_enabled);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::fft(const at::Tensor & self, int64_t signal_ndim, bool normalized) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::fft(r_self, signal_ndim, normalized);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::ifft(const at::Tensor & self, int64_t signal_ndim, bool normalized) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ifft(r_self, signal_ndim, normalized);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::rfft(const at::Tensor & self, int64_t signal_ndim, bool normalized, bool onesided) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::rfft(r_self, signal_ndim, normalized, onesided);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::irfft(const at::Tensor & self, int64_t signal_ndim, bool normalized, bool onesided, at::IntList signal_sizes) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::irfft(r_self, signal_ndim, normalized, onesided, signal_sizes);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_fft_with_size(const at::Tensor & self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, at::IntList checked_signal_sizes, bool normalized, bool onesided, at::IntList output_sizes) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_fft_with_size(r_self, signal_ndim, complex_input, complex_output, inverse, checked_signal_sizes, normalized, onesided, output_sizes);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

void XLATypeBase::_cufft_set_plan_cache_max_size(int64_t max_size) const {
  at::_cufft_set_plan_cache_max_size(max_size);
}

at::Tensor XLATypeBase::index(const at::Tensor & self, at::TensorList indices) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto l_indices = bridge::XlaCreateTensorList(indices);
  auto&& x_result = at::index(r_self, l_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::native::index_copy_(w_self, dim, r_index, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::index_put(const at::Tensor & self, at::TensorList indices, const at::Tensor & values, bool accumulate) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto l_indices = bridge::XlaCreateTensorList(indices);
  auto r_values = bridge::XlaToAtenTensor(values);
  auto&& x_result = at::index_put(r_self, l_indices, r_values, accumulate);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::index_put_(at::Tensor & self, at::TensorList indices, const at::Tensor & values, bool accumulate) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto l_indices = bridge::XlaCreateTensorList(indices);
  auto r_values = bridge::XlaToAtenTensor(values);
  auto&& x_result = at::index_put_(w_self, l_indices, r_values, accumulate);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::instance_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto r_running_mean = bridge::XlaToAtenTensor(running_mean);
  auto r_running_var = bridge::XlaToAtenTensor(running_var);
  auto&& x_result = at::instance_norm(r_input, r_weight, r_bias, r_running_mean, r_running_var, use_input_stats, momentum, eps, cudnn_enabled);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(running_var));
}

at::Tensor XLATypeBase::inverse(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::inverse(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::inverse_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::inverse_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_inverse_helper(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_inverse_helper(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::isclose(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::isclose(r_self, r_other, rtol, atol, equal_nan);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::isnan(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::isnan(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

bool XLATypeBase::is_distributed(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::is_distributed(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

bool XLATypeBase::is_floating_point(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::is_floating_point(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

bool XLATypeBase::is_complex(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::is_complex(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

bool XLATypeBase::is_nonzero(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::is_nonzero(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

bool XLATypeBase::is_same_size(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::is_same_size(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

bool XLATypeBase::is_signed(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::is_signed(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor XLATypeBase::kl_div(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::kl_div(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::kl_div_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::kl_div_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::kthvalue(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::kthvalue(r_self, k, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::kthvalue_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::kthvalue_out(w_values, w_indices, r_self, k, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

at::Tensor XLATypeBase::layer_norm(const at::Tensor & input, at::IntList normalized_shape, const at::Tensor & weight, const at::Tensor & bias, double eps, bool cudnn_enable) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::layer_norm(r_input, normalized_shape, r_weight, r_bias, eps, cudnn_enable);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::linear(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::linear(r_input, r_weight, r_bias);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

at::Tensor XLATypeBase::fbgemm_linear_int8_weight(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, at::Scalar weight_scale, at::Scalar weight_zero_point, const at::Tensor & bias) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_packed = bridge::XlaToAtenTensor(packed);
  auto r_col_offsets = bridge::XlaToAtenTensor(col_offsets);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::fbgemm_linear_int8_weight(r_input, r_weight, r_packed, r_col_offsets, weight_scale, weight_zero_point, r_bias);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(bias));
}

std::tuple<at::Tensor,at::Tensor,double,int64_t> XLATypeBase::fbgemm_linear_quantize_weight(const at::Tensor & input) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto&& x_result = at::fbgemm_linear_quantize_weight(r_input);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,double,int64_t>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(input)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(input)), std::get<2>(x_result), std::get<3>(x_result));
}

at::Tensor XLATypeBase::fbgemm_pack_quantized_matrix(const at::Tensor & input, int64_t K, int64_t N) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto&& x_result = at::fbgemm_pack_quantized_matrix(r_input, K, N);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(input));
}

at::Tensor & XLATypeBase::linspace_out(at::Tensor & result, at::Scalar start, at::Scalar end, int64_t steps) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::linspace_out(w_result, start, end, steps);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::log(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::log_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::log_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::log_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::log10(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log10(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::log10_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::log10_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::log10_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log10_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::log1p(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log1p(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::log1p_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::log1p_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::log1p_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log1p_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::log2(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log2(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::log2_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::log2_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::log2_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log2_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::logdet(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::logdet(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::logspace_out(at::Tensor & result, at::Scalar start, at::Scalar end, int64_t steps) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::logspace_out(w_result, start, end, steps);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::log_softmax(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log_softmax(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::log_softmax(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log_softmax(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_log_softmax(r_self, dim, half_to_float);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_log_softmax_backward_data(r_grad_output, r_output, dim, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::logsumexp(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::logsumexp(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::logsumexp_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::logsumexp_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::margin_ranking_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) const {
  auto r_input1 = bridge::XlaToAtenTensor(input1);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::margin_ranking_loss(r_input1, r_input2, r_target, margin, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(target));
}

at::Tensor XLATypeBase::matmul(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::matmul(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::matmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::matmul_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::matrix_rank(const at::Tensor & self, double tol, bool symmetric) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::matrix_rank(r_self, tol, symmetric);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::matrix_rank(const at::Tensor & self, bool symmetric) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::matrix_rank(r_self, symmetric);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::matrix_power(const at::Tensor & self, int64_t n) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::matrix_power(r_self, n);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::max(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::max_out(at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_max = bridge::XlaToAtenMutableTensor(max);
  auto w_max_values = bridge::XlaToAtenMutableTensor(max_values);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_out(w_max, w_max_values, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(max, max_values);
}

at::Tensor XLATypeBase::max_values(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_values(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::max_pool1d_with_indices(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool1d_with_indices(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::max_pool1d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool1d(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::max_pool2d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool2d(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::max_pool3d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool3d(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mean(const at::Tensor & self, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean(r_self, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mean(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mean(const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean(r_self, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mean(const at::Tensor & self, at::IntList dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mean(const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mean_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean_out(w_result, r_self, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::mean_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::mean_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mean_out(w_result, r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::median(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::median(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::median_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::median_out(w_values, w_indices, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::min(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::min(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_min = bridge::XlaToAtenMutableTensor(min);
  auto w_min_indices = bridge::XlaToAtenMutableTensor(min_indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::min_out(w_min, w_min_indices, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(min, min_indices);
}

at::Tensor XLATypeBase::min_values(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::min_values(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mkldnn_convolution(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::mkldnn_convolution(r_self, r_weight, r_bias, padding, stride, dilation, groups);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mkldnn_convolution_backward_input(at::IntList self_size, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool bias_defined) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::mkldnn_convolution_backward_input(self_size, r_grad_output, r_weight, padding, stride, dilation, groups, bias_defined);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(weight));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::mkldnn_convolution_backward_weights(at::IntList weight_size, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool bias_defined) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mkldnn_convolution_backward_weights(weight_size, r_grad_output, r_self, padding, stride, dilation, groups, bias_defined);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::mkldnn_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, std::array<bool,3> output_mask) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::mkldnn_convolution_backward(r_self, r_grad_output, r_weight, padding, stride, dilation, groups, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::miopen_batch_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double exponential_average_factor, double epsilon) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto r_running_mean = bridge::XlaToAtenTensor(running_mean);
  auto r_running_var = bridge::XlaToAtenTensor(running_var);
  auto&& x_result = at::miopen_batch_norm(r_input, r_weight, r_bias, r_running_mean, r_running_var, training, exponential_average_factor, epsilon);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(running_var)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(running_var)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(running_var)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::miopen_batch_norm_backward(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const at::Tensor & running_mean, const at::Tensor & running_var, const at::Tensor & save_mean, const at::Tensor & save_var, double epsilon) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_running_mean = bridge::XlaToAtenTensor(running_mean);
  auto r_running_var = bridge::XlaToAtenTensor(running_var);
  auto r_save_mean = bridge::XlaToAtenTensor(save_mean);
  auto r_save_var = bridge::XlaToAtenTensor(save_var);
  auto&& x_result = at::miopen_batch_norm_backward(r_input, r_grad_output, r_weight, r_running_mean, r_running_var, r_save_mean, r_save_var, epsilon);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(save_var)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(save_var)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(save_var)));
}

at::Tensor XLATypeBase::miopen_convolution(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::miopen_convolution(r_self, r_weight, r_bias, padding, stride, dilation, groups, benchmark, deterministic);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::miopen_convolution_backward_input(at::IntList self_size, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::miopen_convolution_backward_input(self_size, r_grad_output, r_weight, padding, stride, dilation, groups, benchmark, deterministic);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(weight));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::miopen_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::miopen_convolution_backward(r_self, r_grad_output, r_weight, padding, stride, dilation, groups, benchmark, deterministic, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::miopen_convolution_backward_bias(const at::Tensor & grad_output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::miopen_convolution_backward_bias(r_grad_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor XLATypeBase::miopen_convolution_backward_weight(at::IntList weight_size, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::miopen_convolution_backward_weight(weight_size, r_grad_output, r_self, padding, stride, dilation, groups, benchmark, deterministic);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::miopen_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, at::IntList padding, at::IntList output_padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::miopen_convolution_transpose(r_self, r_weight, r_bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::miopen_convolution_transpose_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList output_padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::miopen_convolution_transpose_backward(r_self, r_grad_output, r_weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::miopen_convolution_transpose_backward_input(const at::Tensor & grad_output, const at::Tensor & weight, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::miopen_convolution_transpose_backward_input(r_grad_output, r_weight, padding, stride, dilation, groups, benchmark, deterministic);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(weight));
}

at::Tensor XLATypeBase::miopen_convolution_transpose_backward_weight(at::IntList weight_size, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding, at::IntList stride, at::IntList dilation, int64_t groups, bool benchmark, bool deterministic) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::miopen_convolution_transpose_backward_weight(weight_size, r_grad_output, r_self, padding, stride, dilation, groups, benchmark, deterministic);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::mm(const at::Tensor & self, const at::Tensor & mat2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::mm(r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::mm_out(w_result, r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::_sparse_mm(const at::Tensor & sparse, const at::Tensor & dense) const {
  auto r_sparse = bridge::XlaToAtenTensor(sparse);
  auto r_dense = bridge::XlaToAtenTensor(dense);
  auto&& x_result = at::_sparse_mm(r_sparse, r_dense);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(dense));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::mode(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mode(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::mode_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mode_out(w_values, w_indices, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

at::Tensor XLATypeBase::mul(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::mul(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mul_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::mul_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::mul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::mul_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::mul(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mul(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mul_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::mul_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::mv(const at::Tensor & self, const at::Tensor & vec) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::mv(r_self, r_vec);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & vec) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_vec = bridge::XlaToAtenTensor(vec);
  auto&& x_result = at::mv_out(w_result, r_self, r_vec);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::mvlgamma(const at::Tensor & self, int64_t p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::mvlgamma(r_self, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mvlgamma_(at::Tensor & self, int64_t p) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::mvlgamma_(w_self, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::narrow_copy(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).narrow_copy(r_self, dim, start, length);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::narrow(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::narrow(r_self, dim, start, length);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::native_batch_norm(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto r_running_mean = bridge::XlaToAtenTensor(running_mean);
  auto r_running_var = bridge::XlaToAtenTensor(running_var);
  auto&& x_result = at::native_batch_norm(r_input, r_weight, r_bias, r_running_mean, r_running_var, training, momentum, eps);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(running_var)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(running_var)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(running_var)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::native_batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & running_mean, const at::Tensor & running_var, const at::Tensor & save_mean, const at::Tensor & save_invstd, bool train, double eps, std::array<bool,3> output_mask) const {
  auto r_grad_out = bridge::XlaToAtenTensor(grad_out);
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_running_mean = bridge::XlaToAtenTensor(running_mean);
  auto r_running_var = bridge::XlaToAtenTensor(running_var);
  auto r_save_mean = bridge::XlaToAtenTensor(save_mean);
  auto r_save_invstd = bridge::XlaToAtenTensor(save_invstd);
  auto&& x_result = at::native_batch_norm_backward(r_grad_out, r_input, r_weight, r_running_mean, r_running_var, r_save_mean, r_save_invstd, train, eps, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(save_invstd)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(save_invstd)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(save_invstd)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::batch_norm_update_stats(const at::Tensor & input, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_running_mean = bridge::XlaToAtenTensor(running_mean);
  auto r_running_var = bridge::XlaToAtenTensor(running_var);
  auto&& x_result = at::batch_norm_update_stats(r_input, r_running_mean, r_running_var, momentum);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(running_var)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(running_var)));
}

at::Tensor & XLATypeBase::ones_out(at::Tensor & result, at::IntList size) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::ones_out(w_result, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ones_like(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ones_like(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::pairwise_distance(const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim) const {
  auto r_x1 = bridge::XlaToAtenTensor(x1);
  auto r_x2 = bridge::XlaToAtenTensor(x2);
  auto&& x_result = at::pairwise_distance(r_x1, r_x2, p, eps, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(x2));
}

at::Tensor XLATypeBase::pdist(const at::Tensor & self, double p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pdist(r_self, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_pdist_forward(const at::Tensor & self, double p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_pdist_forward(r_self, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_pdist_backward(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_pdist = bridge::XlaToAtenTensor(pdist);
  auto&& x_result = at::_pdist_backward(r_grad, r_self, p, r_pdist);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::cosine_similarity(const at::Tensor & x1, const at::Tensor & x2, int64_t dim, double eps) const {
  auto r_x1 = bridge::XlaToAtenTensor(x1);
  auto r_x2 = bridge::XlaToAtenTensor(x2);
  auto&& x_result = at::cosine_similarity(r_x1, r_x2, dim, eps);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(x2));
}

at::Tensor XLATypeBase::permute(const at::Tensor & self, at::IntList dims) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::permute(r_self, dims);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::pixel_shuffle(const at::Tensor & self, int64_t upscale_factor) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pixel_shuffle(r_self, upscale_factor);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::pin_memory(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pin_memory(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::pinverse(const at::Tensor & self, double rcond) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pinverse(r_self, rcond);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::rand_out(at::Tensor & result, at::IntList size) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::rand_out(w_result, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::rand_out(at::Tensor & result, at::IntList size, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::rand_out(w_result, size, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::rand_like(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::rand_like(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::randint_out(at::Tensor & result, int64_t high, at::IntList size) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randint_out(w_result, high, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::randint_out(at::Tensor & result, int64_t high, at::IntList size, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randint_out(w_result, high, size, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::randint_out(at::Tensor & result, int64_t low, int64_t high, at::IntList size) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randint_out(w_result, low, high, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::randint_out(at::Tensor & result, int64_t low, int64_t high, at::IntList size, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randint_out(w_result, low, high, size, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::randint_like(const at::Tensor & self, int64_t high) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::randint_like(r_self, high);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::randint_like(const at::Tensor & self, int64_t low, int64_t high) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::randint_like(r_self, low, high);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::randn_out(at::Tensor & result, at::IntList size) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randn_out(w_result, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::randn_out(at::Tensor & result, at::IntList size, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randn_out(w_result, size, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::randn_like(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::randn_like(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::randperm_out(at::Tensor & result, int64_t n) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randperm_out(w_result, n);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::randperm_out(at::Tensor & result, int64_t n, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::randperm_out(w_result, n, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::range_out(at::Tensor & result, at::Scalar start, at::Scalar end, at::Scalar step) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::range_out(w_result, start, end, step);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::repeat(const at::Tensor & self, at::IntList repeats) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::repeat(r_self, repeats);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::reshape(const at::Tensor & self, at::IntList shape) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reshape(r_self, shape);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::reshape_as(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::reshape_as(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::RoiPooling2d_forward(const at::Tensor & input, const at::Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_rois = bridge::XlaToAtenTensor(rois);
  auto&& x_result = at::RoiPooling2d_forward(r_input, r_rois, pooledHeight, pooledWidth, spatialScale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(rois)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(rois)));
}

at::Tensor XLATypeBase::RoiPooling2d_backward(const at::Tensor & input, const at::Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale, const at::Tensor & gradOutput, const at::Tensor & argmaxes) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_rois = bridge::XlaToAtenTensor(rois);
  auto r_gradOutput = bridge::XlaToAtenTensor(gradOutput);
  auto r_argmaxes = bridge::XlaToAtenTensor(argmaxes);
  auto&& x_result = at::RoiPooling2d_backward(r_input, r_rois, pooledHeight, pooledWidth, spatialScale, r_gradOutput, r_argmaxes);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(argmaxes));
}

at::Tensor XLATypeBase::round(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::round(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::round_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::round_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::round_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::round_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::rrelu(const at::Tensor & self, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::rrelu(r_self, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::rrelu_(at::Tensor & self, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::rrelu_(w_self, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::relu(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::relu(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::relu_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::relu_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::prelu(const at::Tensor & self, const at::Tensor & weight) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::prelu(r_self, r_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::prelu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::prelu_backward(r_grad_output, r_self, r_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::hardshrink(const at::Tensor & self, at::Scalar lambd) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::hardshrink(r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, at::Scalar lambd) const {
  auto r_grad_out = bridge::XlaToAtenTensor(grad_out);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::hardshrink_backward(r_grad_out, r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::rsqrt(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::rsqrt(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::rsqrt_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::rsqrt_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::rsqrt_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::rsqrt_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::select(const at::Tensor & self, int64_t dim, int64_t index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::select(r_self, dim, index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::selu(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::selu(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::selu_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::selu_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::celu(const at::Tensor & self, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::celu(r_self, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::celu_(at::Tensor & self, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::celu_(w_self, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::sigmoid(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sigmoid(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sigmoid_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::sigmoid_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::sigmoid_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sigmoid_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::sin(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sin(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sin_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::sin_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::sin_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sin_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::sinh(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sinh(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sinh_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::sinh_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::sinh_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sinh_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::detach(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detach(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::detach_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::detach_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

int64_t XLATypeBase::size(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::size(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor XLATypeBase::slice(const at::Tensor & self, int64_t dim, int64_t start, int64_t end, int64_t step) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::slice(r_self, dim, start, end, step);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::slogdet(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::slogdet(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::smm(const at::Tensor & self, const at::Tensor & mat2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::smm(r_self, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::softmax(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softmax(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::softmax(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softmax(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_softmax(r_self, dim, half_to_float);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_softmax_backward_data(r_grad_output, r_output, dim, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_sparse_add_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_sparse_add_out(w_result, r_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::_sparse_dense_add_out(at::Tensor & result, const at::Tensor & self, at::SparseTensorRef other, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_dense_add_out(w_result, r_self, other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::_sparse_div_zerodim_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_sparse_div_zerodim_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::_sparse_div_scalar_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_div_scalar_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::_sparse_mul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_sparse_mul_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::_sparse_mul_zerodim_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_sparse_mul_zerodim_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::_sparse_mul_scalar_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_mul_scalar_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

std::vector<at::Tensor> XLATypeBase::split(const at::Tensor & self, int64_t split_size, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::split(r_self, split_size, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensors(x_result, bridge::XlaTensorDevice(self));
}

std::vector<at::Tensor> XLATypeBase::split_with_sizes(const at::Tensor & self, at::IntList split_sizes, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::split_with_sizes(r_self, split_sizes, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensors(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::squeeze(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::squeeze(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::squeeze(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::squeeze(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::squeeze_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::squeeze_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::squeeze_(at::Tensor & self, int64_t dim) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::squeeze_(w_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::sspaddmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::sspaddmm(r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sspaddmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::sspaddmm_out(w_result, r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::stack(at::TensorList tensors, int64_t dim) const {
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::stack(l_tensors, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(tensors));
}

at::Tensor & XLATypeBase::stack_out(at::Tensor & result, at::TensorList tensors, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::stack_out(w_result, l_tensors, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::stft(const at::Tensor & self, int64_t n_fft, c10::optional<int64_t> hop_length, c10::optional<int64_t> win_length, const at::Tensor & window, bool normalized, bool onesided) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_window = bridge::XlaToAtenTensor(window);
  auto&& x_result = at::stft(r_self, n_fft, hop_length, win_length, r_window, normalized, onesided);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

int64_t XLATypeBase::stride(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::stride(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor XLATypeBase::sum(const at::Tensor & self, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum(r_self, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::sum(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::sum(const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum(r_self, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::sum(const at::Tensor & self, at::IntList dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::sum(const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sum_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum_out(w_result, r_self, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::sum_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::sum_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sum_out(w_result, r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::sum_to_size(const at::Tensor & self, at::IntList size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::sum_to_size(r_self, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::sqrt(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sqrt(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sqrt_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::sqrt_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::sqrt_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sqrt_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::std(const at::Tensor & self, bool unbiased) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::std(r_self, unbiased);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::std(const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::std(r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::std_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::std_out(w_result, r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::prod(const at::Tensor & self, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod(r_self, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::prod(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::prod(const at::Tensor & self, int64_t dim, bool keepdim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod(r_self, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::prod(const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::prod(const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::prod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod_out(w_result, r_self, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::prod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::prod_out(at::Tensor & result, const at::Tensor & self, int64_t dim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::prod_out(w_result, r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::t(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::t(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::t_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::t_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::tan(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::tan(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::tan_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::tan_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::tan_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::tan_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::tanh(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::tanh(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::tanh_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::tanh_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::tanh_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::tanh_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::tensordot(const at::Tensor & self, const at::Tensor & other, at::IntList dims_self, at::IntList dims_other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::tensordot(r_self, r_other, dims_self, dims_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::threshold(const at::Tensor & self, at::Scalar threshold, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::threshold(r_self, threshold, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::threshold_(at::Tensor & self, at::Scalar threshold, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::threshold_(w_self, threshold, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::threshold_out(at::Tensor & result, const at::Tensor & self, at::Scalar threshold, at::Scalar value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::threshold_out(w_result, r_self, threshold, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar threshold) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::threshold_backward(r_grad_output, r_self, threshold);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::transpose(const at::Tensor & self, int64_t dim0, int64_t dim1) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::transpose(r_self, dim0, dim1);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::transpose_(at::Tensor & self, int64_t dim0, int64_t dim1) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::transpose_(w_self, dim0, dim1);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::one_hot(const at::Tensor & self, int64_t num_classes) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::one_hot(r_self, num_classes);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::flip(const at::Tensor & self, at::IntList dims) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::flip(r_self, dims);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::roll(const at::Tensor & self, at::IntList shifts, at::IntList dims) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::roll(r_self, shifts, dims);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::rot90(const at::Tensor & self, int64_t k, at::IntList dims) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::rot90(r_self, k, dims);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_trilinear(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntList expand1, at::IntList expand2, at::IntList expand3, at::IntList sumdim, int64_t unroll_dim) const {
  auto r_i1 = bridge::XlaToAtenTensor(i1);
  auto r_i2 = bridge::XlaToAtenTensor(i2);
  auto r_i3 = bridge::XlaToAtenTensor(i3);
  auto&& x_result = at::_trilinear(r_i1, r_i2, r_i3, expand1, expand2, expand3, sumdim, unroll_dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(i3));
}

at::Tensor XLATypeBase::triplet_margin_loss(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin, double p, double eps, bool swap, int64_t reduction) const {
  auto r_anchor = bridge::XlaToAtenTensor(anchor);
  auto r_positive = bridge::XlaToAtenTensor(positive);
  auto r_negative = bridge::XlaToAtenTensor(negative);
  auto&& x_result = at::triplet_margin_loss(r_anchor, r_positive, r_negative, margin, p, eps, swap, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(negative));
}

at::Tensor XLATypeBase::trunc(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::trunc(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::trunc_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::trunc_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::trunc_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::trunc_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::type_as(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::type_as(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_unique(const at::Tensor & self, bool sorted, bool return_inverse) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_unique(r_self, sorted, return_inverse);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_unique_dim(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_unique_dim(r_self, dim, sorted, return_inverse);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::_unsafe_view(const at::Tensor & self, at::IntList size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_unsafe_view(r_self, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::unsqueeze(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::unsqueeze(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::unsqueeze_(at::Tensor & self, int64_t dim) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::unsqueeze_(w_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::var(const at::Tensor & self, bool unbiased) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::var(r_self, unbiased);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::var(const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::var(r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::var_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool unbiased, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::var_out(w_result, r_self, dim, unbiased, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::view_as(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::view_as(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) const {
  auto r_condition = bridge::XlaToAtenTensor(condition);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::where(r_condition, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_s_where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) const {
  auto r_condition = bridge::XlaToAtenTensor(condition);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::_s_where(r_condition, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::norm_except_dim(const at::Tensor & v, int64_t pow, int64_t dim) const {
  auto r_v = bridge::XlaToAtenTensor(v);
  auto&& x_result = at::norm_except_dim(r_v, pow, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(v));
}

at::Tensor XLATypeBase::_weight_norm(const at::Tensor & v, const at::Tensor & g, int64_t dim) const {
  auto r_v = bridge::XlaToAtenTensor(v);
  auto r_g = bridge::XlaToAtenTensor(g);
  auto&& x_result = at::_weight_norm(r_v, r_g, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(g));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_weight_norm_cuda_interface(const at::Tensor & v, const at::Tensor & g, int64_t dim) const {
  auto r_v = bridge::XlaToAtenTensor(v);
  auto r_g = bridge::XlaToAtenTensor(g);
  auto&& x_result = at::_weight_norm_cuda_interface(r_v, r_g, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(g)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(g)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_weight_norm_cuda_interface_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) const {
  auto r_grad_w = bridge::XlaToAtenTensor(grad_w);
  auto r_saved_v = bridge::XlaToAtenTensor(saved_v);
  auto r_saved_g = bridge::XlaToAtenTensor(saved_g);
  auto r_saved_norms = bridge::XlaToAtenTensor(saved_norms);
  auto&& x_result = at::_weight_norm_cuda_interface_backward(r_grad_w, r_saved_v, r_saved_g, r_saved_norms, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(saved_norms)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(saved_norms)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_weight_norm_differentiable_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) const {
  auto r_grad_w = bridge::XlaToAtenTensor(grad_w);
  auto r_saved_v = bridge::XlaToAtenTensor(saved_v);
  auto r_saved_g = bridge::XlaToAtenTensor(saved_g);
  auto r_saved_norms = bridge::XlaToAtenTensor(saved_norms);
  auto&& x_result = at::_weight_norm_differentiable_backward(r_grad_w, r_saved_v, r_saved_g, r_saved_norms, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(saved_norms)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(saved_norms)));
}

at::Tensor & XLATypeBase::zeros_out(at::Tensor & result, at::IntList size) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto&& x_result = at::zeros_out(w_result, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::zeros_like(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::zeros_like(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_standard_gamma_grad(const at::Tensor & self, const at::Tensor & output) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::_standard_gamma_grad(r_self, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_standard_gamma(const at::Tensor & self, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_standard_gamma(r_self, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::poisson(const at::Tensor & self, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::poisson(r_self, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::native_norm(const at::Tensor & self, at::Scalar p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native_norm(r_self, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_sparse_sum(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_sum(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_sparse_sum(const at::Tensor & self, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_sum(r_self, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_sparse_sum(const at::Tensor & self, at::IntList dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_sum(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_sparse_sum(const at::Tensor & self, at::IntList dim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_sum(r_self, dim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_sparse_sum_backward(const at::Tensor & grad, const at::Tensor & self, at::IntList dim) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_sparse_sum_backward(r_grad, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::norm(const at::Tensor & self, c10::optional<at::Scalar> p, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::norm(r_self, p, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::norm(const at::Tensor & self, at::Scalar p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::norm(r_self, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::norm(const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim, at::ScalarType dtype) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::norm(r_self, p, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::norm(const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::norm(r_self, p, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::norm_out(at::Tensor & result, const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim, at::ScalarType dtype) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::norm_out(w_result, r_self, p, dim, keepdim, dtype);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor & XLATypeBase::norm_out(at::Tensor & result, const at::Tensor & self, c10::optional<at::Scalar> p, at::IntList dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::norm_out(w_result, r_self, p, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::frobenius_norm(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::frobenius_norm(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::frobenius_norm(const at::Tensor & self, at::IntList dim, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::frobenius_norm(r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::frobenius_norm_out(at::Tensor & result, const at::Tensor & self, at::IntList dim, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::frobenius_norm_out(w_result, r_self, dim, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::nuclear_norm(const at::Tensor & self, bool keepdim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::nuclear_norm(r_self, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::nuclear_norm_out(at::Tensor & result, const at::Tensor & self, bool keepdim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::nuclear_norm_out(w_result, r_self, keepdim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::native_clone(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native_clone(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::clone(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::clone(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::native_resize_as_(at::Tensor & self, const at::Tensor & the_template) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_the_template = bridge::XlaToAtenTensor(the_template);
  auto&& x_result = at::native_resize_as_(w_self, r_the_template);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::resize_as_(at::Tensor & self, const at::Tensor & the_template) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_the_template = bridge::XlaToAtenTensor(the_template);
  auto&& x_result = at::resize_as_(w_self, r_the_template);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::native_pow_out(at::Tensor & result, const at::Tensor & self, at::Scalar exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native_pow_out(w_result, r_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::native_pow(const at::Tensor & self, at::Scalar exponent) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native_pow(r_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::pow_out(at::Tensor & result, const at::Tensor & self, at::Scalar exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pow_out(w_result, r_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::pow(const at::Tensor & self, at::Scalar exponent) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pow(r_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::native_zero_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native_zero_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::zero_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::zero_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::sub_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::sub_out(w_result, r_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::sub(const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::sub(r_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sub_(at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::sub_(w_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::sub(const at::Tensor & self, at::Scalar other, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sub(r_self, other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sub_(at::Tensor & self, at::Scalar other, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::sub_(w_self, other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::rsub(const at::Tensor & self, const at::Tensor & other, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::rsub(r_self, r_other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::rsub(const at::Tensor & self, at::Scalar other, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::rsub(r_self, other, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::s_native_addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::s_native_addmm_out(w_result, r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::s_native_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::s_native_addmm(r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::s_native_addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::s_native_addmm_(w_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::_sparse_addmm(const at::Tensor & self, const at::Tensor & sparse, const at::Tensor & dense, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_sparse = bridge::XlaToAtenTensor(sparse);
  auto r_dense = bridge::XlaToAtenTensor(dense);
  auto&& x_result = at::_sparse_addmm(r_self, r_sparse, r_dense, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::addmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::addmm_out(w_result, r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::addmm(r_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::native::addmm_(w_self, r_mat1, r_mat2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::_sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, at::IntList size, const at::TensorOptions & options) const {
  auto&& x_result = at::_sparse_coo_tensor_with_dims(sparse_dim, dense_dim, size, options);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(options));
}

at::Tensor XLATypeBase::_sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, at::IntList size, const at::Tensor & indices, const at::Tensor & values, const at::TensorOptions & options) const {
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto r_values = bridge::XlaToAtenTensor(values);
  auto&& x_result = at::_sparse_coo_tensor_with_dims_and_tensors(sparse_dim, dense_dim, size, r_indices, r_values, options);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(values));
}

at::Tensor & XLATypeBase::sparse_resize_(at::Tensor & self, at::IntList size, int64_t sparse_dim, int64_t dense_dim) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::sparse_resize_(w_self, size, sparse_dim, dense_dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::sparse_resize_and_clear_(at::Tensor & self, at::IntList size, int64_t sparse_dim, int64_t dense_dim) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::sparse_resize_and_clear_(w_self, size, sparse_dim, dense_dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::sparse_mask(const at::Tensor & self, at::SparseTensorRef mask) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).sparse_mask(r_self, mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::to_dense(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).to_dense(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

int64_t XLATypeBase::sparse_dim(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).sparse_dim(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

int64_t XLATypeBase::_dimI(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self)._dimI(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

int64_t XLATypeBase::dense_dim(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).dense_dim(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

int64_t XLATypeBase::_dimV(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self)._dimV(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

int64_t XLATypeBase::_nnz(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self)._nnz(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor XLATypeBase::coalesce(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).coalesce(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

bool XLATypeBase::is_coalesced(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).is_coalesced(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor XLATypeBase::_indices(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self)._indices(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_values(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self)._values(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_coalesced_(at::Tensor & self, bool coalesced) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::detail::infer_type(w_self)._coalesced_(w_self, coalesced);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::indices(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).indices(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::values(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).values(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::hspmm_out(at::Tensor & result, const at::Tensor & mat1, const at::Tensor & mat2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::hspmm_out(w_result, r_mat1, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::hspmm(const at::Tensor & mat1, const at::Tensor & mat2) const {
  auto r_mat1 = bridge::XlaToAtenTensor(mat1);
  auto r_mat2 = bridge::XlaToAtenTensor(mat2);
  auto&& x_result = at::hspmm(r_mat1, r_mat2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(mat2));
}

at::Tensor & XLATypeBase::copy_sparse_to_sparse_(at::Tensor & self, const at::Tensor & src, bool non_blocking) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_src = bridge::XlaToAtenTensor(src);
  auto&& x_result = at::copy_sparse_to_sparse_(w_self, r_src, non_blocking);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

int64_t XLATypeBase::numel(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::numel(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

std::vector<at::Tensor> XLATypeBase::unbind(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::unbind(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensors(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::to_sparse(const at::Tensor & self, int64_t sparse_dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).to_sparse(r_self, sparse_dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::to_sparse(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::detail::infer_type(r_self).to_sparse(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::to(const at::Tensor & self, const at::TensorOptions & options, bool non_blocking, bool copy) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::to(r_self, options, non_blocking, copy);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::to(const at::Tensor & self, c10::Device device, at::ScalarType dtype, bool non_blocking, bool copy) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::to(r_self, device, dtype, non_blocking, copy);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::to(const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::to(r_self, dtype, non_blocking, copy);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::to(const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::to(r_self, r_other, non_blocking, copy);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::vector<at::Tensor> XLATypeBase::meshgrid(at::TensorList tensors) const {
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::meshgrid(l_tensors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensors(x_result, bridge::XlaTensorDevice(tensors));
}

at::Tensor XLATypeBase::cartesian_prod(at::TensorList tensors) const {
  auto l_tensors = bridge::XlaCreateTensorList(tensors);
  auto&& x_result = at::cartesian_prod(l_tensors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(tensors));
}

at::Tensor XLATypeBase::combinations(const at::Tensor & self, int64_t r, bool with_replacement) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::combinations(r_self, r, with_replacement);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Scalar XLATypeBase::item(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::item(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Scalar XLATypeBase::_local_scalar_dense(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_local_scalar_dense(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_fused_lstm_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const at::Tensor & input_bias, const at::Tensor & hidden_bias) const {
  auto r_input_gates = bridge::XlaToAtenTensor(input_gates);
  auto r_hidden_gates = bridge::XlaToAtenTensor(hidden_gates);
  auto r_cx = bridge::XlaToAtenTensor(cx);
  auto r_input_bias = bridge::XlaToAtenTensor(input_bias);
  auto r_hidden_bias = bridge::XlaToAtenTensor(hidden_bias);
  auto&& x_result = at::_thnn_fused_lstm_cell(r_input_gates, r_hidden_gates, r_cx, r_input_bias, r_hidden_bias);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hidden_bias)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hidden_bias)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(hidden_bias)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_fused_lstm_cell_backward(const at::Tensor & grad_hy, const at::Tensor & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) const {
  auto r_grad_hy = bridge::XlaToAtenTensor(grad_hy);
  auto r_grad_cy = bridge::XlaToAtenTensor(grad_cy);
  auto r_cx = bridge::XlaToAtenTensor(cx);
  auto r_cy = bridge::XlaToAtenTensor(cy);
  auto r_workspace = bridge::XlaToAtenTensor(workspace);
  auto&& x_result = at::_thnn_fused_lstm_cell_backward(r_grad_hy, r_grad_cy, r_cx, r_cy, r_workspace, has_bias);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<3>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<4>(x_result), bridge::XlaTensorDevice(workspace)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_thnn_fused_gru_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const at::Tensor & input_bias, const at::Tensor & hidden_bias) const {
  auto r_input_gates = bridge::XlaToAtenTensor(input_gates);
  auto r_hidden_gates = bridge::XlaToAtenTensor(hidden_gates);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto r_input_bias = bridge::XlaToAtenTensor(input_bias);
  auto r_hidden_bias = bridge::XlaToAtenTensor(hidden_bias);
  auto&& x_result = at::_thnn_fused_gru_cell(r_input_gates, r_hidden_gates, r_hx, r_input_bias, r_hidden_bias);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hidden_bias)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hidden_bias)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> XLATypeBase::_thnn_fused_gru_cell_backward(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias) const {
  auto r_grad_hy = bridge::XlaToAtenTensor(grad_hy);
  auto r_workspace = bridge::XlaToAtenTensor(workspace);
  auto&& x_result = at::_thnn_fused_gru_cell_backward(r_grad_hy, r_workspace, has_bias);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<3>(x_result), bridge::XlaTensorDevice(workspace)), bridge::CreateXlaTensor(std::get<4>(x_result), bridge::XlaTensorDevice(workspace)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::lstm(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto l_hx = bridge::XlaCreateTensorList(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::lstm(r_input, l_hx, l_params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(input)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(input)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(input)));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::lstm(const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const {
  auto r_data = bridge::XlaToAtenTensor(data);
  auto r_batch_sizes = bridge::XlaToAtenTensor(batch_sizes);
  auto l_hx = bridge::XlaCreateTensorList(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::lstm(r_data, r_batch_sizes, l_hx, l_params, has_biases, num_layers, dropout, train, bidirectional);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(batch_sizes)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(batch_sizes)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(batch_sizes)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::gru(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::gru(r_input, r_hx, l_params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hx)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hx)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::gru(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const {
  auto r_data = bridge::XlaToAtenTensor(data);
  auto r_batch_sizes = bridge::XlaToAtenTensor(batch_sizes);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::gru(r_data, r_batch_sizes, r_hx, l_params, has_biases, num_layers, dropout, train, bidirectional);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hx)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hx)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::rnn_tanh(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::rnn_tanh(r_input, r_hx, l_params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hx)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hx)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::rnn_tanh(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const {
  auto r_data = bridge::XlaToAtenTensor(data);
  auto r_batch_sizes = bridge::XlaToAtenTensor(batch_sizes);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::rnn_tanh(r_data, r_batch_sizes, r_hx, l_params, has_biases, num_layers, dropout, train, bidirectional);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hx)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hx)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::rnn_relu(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::rnn_relu(r_input, r_hx, l_params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hx)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hx)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::rnn_relu(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) const {
  auto r_data = bridge::XlaToAtenTensor(data);
  auto r_batch_sizes = bridge::XlaToAtenTensor(batch_sizes);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::rnn_relu(r_data, r_batch_sizes, r_hx, l_params, has_biases, num_layers, dropout, train, bidirectional);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(hx)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(hx)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto l_hx = bridge::XlaCreateTensorList(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto&& x_result = at::lstm_cell(r_input, l_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(b_hh)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(b_hh)));
}

at::Tensor XLATypeBase::gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto&& x_result = at::gru_cell(r_input, r_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(b_hh));
}

at::Tensor XLATypeBase::rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto&& x_result = at::rnn_tanh_cell(r_input, r_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(b_hh));
}

at::Tensor XLATypeBase::rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto&& x_result = at::rnn_relu_cell(r_input, r_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(b_hh));
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::quantized_lstm(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto l_hx = bridge::XlaCreateTensorList(hx);
  auto l_params = bridge::XlaCreateTensorList(params);
  auto&& x_result = at::quantized_lstm(r_input, l_hx, l_params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(input)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(input)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(input)));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::quantized_lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto l_hx = bridge::XlaCreateTensorList(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto r_packed_ih = bridge::XlaToAtenTensor(packed_ih);
  auto r_packed_hh = bridge::XlaToAtenTensor(packed_hh);
  auto r_col_offsets_ih = bridge::XlaToAtenTensor(col_offsets_ih);
  auto r_col_offsets_hh = bridge::XlaToAtenTensor(col_offsets_hh);
  auto&& x_result = at::quantized_lstm_cell(r_input, l_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh, r_packed_ih, r_packed_hh, r_col_offsets_ih, r_col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(col_offsets_hh)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(col_offsets_hh)));
}

at::Tensor XLATypeBase::quantized_gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto r_packed_ih = bridge::XlaToAtenTensor(packed_ih);
  auto r_packed_hh = bridge::XlaToAtenTensor(packed_hh);
  auto r_col_offsets_ih = bridge::XlaToAtenTensor(col_offsets_ih);
  auto r_col_offsets_hh = bridge::XlaToAtenTensor(col_offsets_hh);
  auto&& x_result = at::quantized_gru_cell(r_input, r_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh, r_packed_ih, r_packed_hh, r_col_offsets_ih, r_col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(col_offsets_hh));
}

at::Tensor XLATypeBase::quantized_rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto r_packed_ih = bridge::XlaToAtenTensor(packed_ih);
  auto r_packed_hh = bridge::XlaToAtenTensor(packed_hh);
  auto r_col_offsets_ih = bridge::XlaToAtenTensor(col_offsets_ih);
  auto r_col_offsets_hh = bridge::XlaToAtenTensor(col_offsets_hh);
  auto&& x_result = at::quantized_rnn_relu_cell(r_input, r_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh, r_packed_ih, r_packed_hh, r_col_offsets_ih, r_col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(col_offsets_hh));
}

at::Tensor XLATypeBase::quantized_rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, at::Scalar scale_ih, at::Scalar scale_hh, at::Scalar zero_point_ih, at::Scalar zero_point_hh) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_hx = bridge::XlaToAtenTensor(hx);
  auto r_w_ih = bridge::XlaToAtenTensor(w_ih);
  auto r_w_hh = bridge::XlaToAtenTensor(w_hh);
  auto r_b_ih = bridge::XlaToAtenTensor(b_ih);
  auto r_b_hh = bridge::XlaToAtenTensor(b_hh);
  auto r_packed_ih = bridge::XlaToAtenTensor(packed_ih);
  auto r_packed_hh = bridge::XlaToAtenTensor(packed_hh);
  auto r_col_offsets_ih = bridge::XlaToAtenTensor(col_offsets_ih);
  auto r_col_offsets_hh = bridge::XlaToAtenTensor(col_offsets_hh);
  auto&& x_result = at::quantized_rnn_tanh_cell(r_input, r_hx, r_w_ih, r_w_hh, r_b_ih, r_b_hh, r_packed_ih, r_packed_hh, r_col_offsets_ih, r_col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(col_offsets_hh));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_pack_padded_sequence(const at::Tensor & input, const at::Tensor & lengths, bool batch_first) const {
  auto r_input = bridge::XlaToAtenTensor(input);
  auto r_lengths = bridge::XlaToAtenTensor(lengths);
  auto&& x_result = at::_pack_padded_sequence(r_input, r_lengths, batch_first);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(lengths)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(lengths)));
}

at::Tensor XLATypeBase::_pack_padded_sequence_backward(const at::Tensor & grad, at::IntList input_size, const at::Tensor & batch_sizes, bool batch_first) const {
  auto r_grad = bridge::XlaToAtenTensor(grad);
  auto r_batch_sizes = bridge::XlaToAtenTensor(batch_sizes);
  auto&& x_result = at::_pack_padded_sequence_backward(r_grad, input_size, r_batch_sizes, batch_first);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(batch_sizes));
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::_pad_packed_sequence(const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, at::Scalar padding_value, int64_t total_length) const {
  auto r_data = bridge::XlaToAtenTensor(data);
  auto r_batch_sizes = bridge::XlaToAtenTensor(batch_sizes);
  auto&& x_result = at::_pad_packed_sequence(r_data, r_batch_sizes, batch_first, padding_value, total_length);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(batch_sizes)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(batch_sizes)));
}

void* XLATypeBase::data_ptr(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::data_ptr(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor & XLATypeBase::set_(at::Tensor & self, at::Storage source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::set_(w_self, source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::set_(at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntList size, at::IntList stride) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::set_(w_self, source, storage_offset, size, stride);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::set_(at::Tensor & self, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::native::set_(w_self, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::set_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::set_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

bool XLATypeBase::is_set_to(const at::Tensor & self, const at::Tensor & tensor) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor = bridge::XlaToAtenTensor(tensor);
  auto&& x_result = at::native::is_set_to(r_self, r_tensor);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor & XLATypeBase::masked_fill_(at::Tensor & self, const at::Tensor & mask, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::native::masked_fill_(w_self, r_mask, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto r_value = bridge::XlaToAtenTensor(value);
  auto&& x_result = at::native::masked_fill_(w_self, r_mask, r_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::native::masked_scatter_(w_self, r_mask, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::view(const at::Tensor & self, at::IntList size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::view(r_self, size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::put_(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::native::put_(w_self, r_index, r_source, accumulate);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_source = bridge::XlaToAtenTensor(source);
  auto&& x_result = at::native::index_add_(w_self, dim, r_index, r_source);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::native::index_fill_(w_self, dim, r_index, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_value = bridge::XlaToAtenTensor(value);
  auto&& x_result = at::native::index_fill_(w_self, dim, r_index, r_value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_src = bridge::XlaToAtenTensor(src);
  auto&& x_result = at::native::scatter_(w_self, dim, r_index, r_src);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::native::scatter_(w_self, dim, r_index, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto r_src = bridge::XlaToAtenTensor(src);
  auto&& x_result = at::native::scatter_add_(w_self, dim, r_index, r_src);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::lt_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::lt_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::lt_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::lt_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::gt_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::gt_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::gt_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::gt_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::le_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::le_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::le_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::le_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::ge_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::ge_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::ge_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::ge_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::eq_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::eq_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::eq_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::eq_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::ne_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::ne_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::ne_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::ne_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::__and__(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::__and__(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::__and__(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::__and__(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::__iand__(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::__iand__(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::__iand__(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::__iand__(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::__or__(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::__or__(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::__or__(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::__or__(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::__ior__(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::__ior__(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::__ior__(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::__ior__(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::__xor__(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::__xor__(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::__xor__(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::__xor__(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::__ixor__(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::__ixor__(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::__ixor__(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::__ixor__(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::__lshift__(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::__lshift__(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::__lshift__(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::__lshift__(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::__ilshift__(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::__ilshift__(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::__ilshift__(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::__ilshift__(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor XLATypeBase::__rshift__(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::__rshift__(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::__rshift__(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::__rshift__(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::__irshift__(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::__irshift__(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::__irshift__(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::__irshift__(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::lgamma_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::lgamma_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::atan2_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::atan2_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::tril_(at::Tensor & self, int64_t diagonal) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::detail::infer_type(w_self).tril_(w_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::triu_(at::Tensor & self, int64_t diagonal) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::detail::infer_type(w_self).triu_(w_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::digamma_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::digamma_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::polygamma_(at::Tensor & self, int64_t n) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::polygamma_(w_self, n);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::erfinv_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::erfinv_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::frac_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::frac_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::renorm_(at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::renorm_(w_self, p, dim, maxnorm);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::reciprocal_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::reciprocal_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::neg_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::neg_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::pow_(at::Tensor & self, at::Scalar exponent) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::pow_(w_self, exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::pow_(at::Tensor & self, const at::Tensor & exponent) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::native::pow_(w_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::lerp_(at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::native::lerp_(w_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::sign_(at::Tensor & self) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::sign_(w_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::fmod_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::fmod_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::fmod_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::fmod_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::remainder_(at::Tensor & self, at::Scalar other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::remainder_(w_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::remainder_(at::Tensor & self, const at::Tensor & other) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::native::remainder_(w_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::addbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::native::addbmm_(w_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::addbmm_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::addbmm_out(w_result, r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::Scalar beta, at::Scalar alpha) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_batch1 = bridge::XlaToAtenTensor(batch1);
  auto r_batch2 = bridge::XlaToAtenTensor(batch2);
  auto&& x_result = at::addbmm(r_self, r_batch1, r_batch2, beta, alpha);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::native::addcmul_(w_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::native::addcdiv_(w_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::random_(at::Tensor & self, int64_t from, int64_t to, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::random_(w_self, from, to, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::random_(at::Tensor & self, int64_t to, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::random_(w_self, to, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::random_(at::Tensor & self, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::random_(w_self, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::uniform_(at::Tensor & self, double from, double to, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::uniform_(w_self, from, to, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::normal_(w_self, mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::cauchy_(at::Tensor & self, double median, double sigma, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::cauchy_(w_self, median, sigma, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::log_normal_(at::Tensor & self, double mean, double std, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::log_normal_(w_self, mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::exponential_(at::Tensor & self, double lambd, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::exponential_(w_self, lambd, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::geometric_(at::Tensor & self, double p, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::native::geometric_(w_self, p, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::diag_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::diag_out(w_result, r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::diag(const at::Tensor & self, int64_t diagonal) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::diag(r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::cross_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other, int64_t dim) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::cross_out(w_result, r_self, r_other, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::cross(r_self, r_other, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::triu_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::triu_out(w_result, r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::triu(const at::Tensor & self, int64_t diagonal) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::triu(r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::tril_out(at::Tensor & result, const at::Tensor & self, int64_t diagonal) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::tril_out(w_result, r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::tril(const at::Tensor & self, int64_t diagonal) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::tril(r_self, diagonal);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::tril_indices(int64_t row, int64_t col, int64_t offset, const at::TensorOptions & options) const {
  auto&& x_result = at::tril_indices(row, col, offset, options);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(options));
}

at::Tensor XLATypeBase::triu_indices(int64_t row, int64_t col, int64_t offset, const at::TensorOptions & options) const {
  auto&& x_result = at::triu_indices(row, col, offset, options);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(options));
}

at::Tensor XLATypeBase::trace(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::trace(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::ne_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ne_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ne(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ne(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::ne_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::ne_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ne(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::ne(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::eq_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::eq_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::eq(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::eq(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::eq_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::eq_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::eq(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::eq(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::ge_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ge_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ge(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::ge(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::ge_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::ge_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ge(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::ge(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::le_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::le_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::le(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::le(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::le_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::le_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::le(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::le(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::gt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::gt_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::gt(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::gt(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::gt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::gt_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::gt(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::gt(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::lt_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::lt_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::lt(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::lt(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::lt_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::lt_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::lt(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::lt(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::take_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & index) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::take_out(w_result, r_self, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::take(const at::Tensor & self, const at::Tensor & index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::take(r_self, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::index_select_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::index_select_out(w_result, r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::index_select(r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::masked_select_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & mask) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::masked_select_out(w_result, r_self, r_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::masked_select(const at::Tensor & self, const at::Tensor & mask) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_mask = bridge::XlaToAtenTensor(mask);
  auto&& x_result = at::masked_select(r_self, r_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::nonzero_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::nonzero_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::nonzero(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::nonzero(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::gather_out(at::Tensor & result, const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::gather_out(w_result, r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::gather(const at::Tensor & self, int64_t dim, const at::Tensor & index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_index = bridge::XlaToAtenTensor(index);
  auto&& x_result = at::gather(r_self, dim, r_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::addcmul_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::addcmul_out(w_result, r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::addcmul(r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::addcdiv_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::addcdiv_out(w_result, r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, at::Scalar value) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_tensor1 = bridge::XlaToAtenTensor(tensor1);
  auto r_tensor2 = bridge::XlaToAtenTensor(tensor2);
  auto&& x_result = at::addcdiv(r_self, r_tensor1, r_tensor2, value);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::gels_out(at::Tensor & X, at::Tensor & qr, const at::Tensor & self, const at::Tensor & A) const {
  auto w_X = bridge::XlaToAtenMutableTensor(X);
  auto w_qr = bridge::XlaToAtenMutableTensor(qr);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::gels_out(w_X, w_qr, r_self, r_A);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(X, qr);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::gels(const at::Tensor & self, const at::Tensor & A) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::gels(r_self, r_A);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::trtrs_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const {
  auto w_X = bridge::XlaToAtenMutableTensor(X);
  auto w_M = bridge::XlaToAtenMutableTensor(M);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::trtrs_out(w_X, w_M, r_self, r_A, upper, transpose, unitriangular);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(X, M);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::trtrs(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::trtrs(r_self, r_A, upper, transpose, unitriangular);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::symeig_out(at::Tensor & e, at::Tensor & V, const at::Tensor & self, bool eigenvectors, bool upper) const {
  auto w_e = bridge::XlaToAtenMutableTensor(e);
  auto w_V = bridge::XlaToAtenMutableTensor(V);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::symeig_out(w_e, w_V, r_self, eigenvectors, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(e, V);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::symeig(const at::Tensor & self, bool eigenvectors, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::symeig(r_self, eigenvectors, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::eig_out(at::Tensor & e, at::Tensor & v, const at::Tensor & self, bool eigenvectors) const {
  auto w_e = bridge::XlaToAtenMutableTensor(e);
  auto w_v = bridge::XlaToAtenMutableTensor(v);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::eig_out(w_e, w_v, r_self, eigenvectors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(e, v);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::eig(const at::Tensor & self, bool eigenvectors) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::eig(r_self, eigenvectors);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some, bool compute_uv) const {
  auto w_U = bridge::XlaToAtenMutableTensor(U);
  auto w_S = bridge::XlaToAtenMutableTensor(S);
  auto w_V = bridge::XlaToAtenMutableTensor(V);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::svd_out(w_U, w_S, w_V, r_self, some, compute_uv);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(U, S, V);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::svd(const at::Tensor & self, bool some, bool compute_uv) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::svd(r_self, some, compute_uv);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::cholesky_out(at::Tensor & result, const at::Tensor & self, bool upper) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cholesky_out(w_result, r_self, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::cholesky(const at::Tensor & self, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::cholesky(r_self, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cholesky_helper(const at::Tensor & self, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::_cholesky_helper(r_self, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::cholesky_solve_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2, bool upper) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto&& x_result = at::cholesky_solve_out(w_result, r_self, r_input2, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::cholesky_solve(const at::Tensor & self, const at::Tensor & input2, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto&& x_result = at::cholesky_solve(r_self, r_input2, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::_cholesky_solve_helper(const at::Tensor & self, const at::Tensor & A, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_A = bridge::XlaToAtenTensor(A);
  auto&& x_result = at::_cholesky_solve_helper(r_self, r_A, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::potri_out(at::Tensor & result, const at::Tensor & self, bool upper) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::potri_out(w_result, r_self, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::potri(const at::Tensor & self, bool upper) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::potri(r_self, upper);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::pstrf_out(at::Tensor & u, at::Tensor & piv, const at::Tensor & self, bool upper, at::Scalar tol) const {
  auto w_u = bridge::XlaToAtenMutableTensor(u);
  auto w_piv = bridge::XlaToAtenMutableTensor(piv);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pstrf_out(w_u, w_piv, r_self, upper, tol);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(u, piv);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::pstrf(const at::Tensor & self, bool upper, at::Scalar tol) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::pstrf(r_self, upper, tol);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::qr_out(at::Tensor & Q, at::Tensor & R, const at::Tensor & self) const {
  auto w_Q = bridge::XlaToAtenMutableTensor(Q);
  auto w_R = bridge::XlaToAtenMutableTensor(R);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::qr_out(w_Q, w_R, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(Q, R);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::qr(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::qr(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::geqrf_out(at::Tensor & result0, at::Tensor & result1, const at::Tensor & self) const {
  auto w_result0 = bridge::XlaToAtenMutableTensor(result0);
  auto w_result1 = bridge::XlaToAtenMutableTensor(result1);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::geqrf_out(w_result0, w_result1, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(result0, result1);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::geqrf(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::geqrf(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::orgqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto&& x_result = at::orgqr_out(w_result, r_self, r_input2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::orgqr(const at::Tensor & self, const at::Tensor & input2) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto&& x_result = at::orgqr(r_self, r_input2);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::ormqr_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto r_input3 = bridge::XlaToAtenTensor(input3);
  auto&& x_result = at::ormqr_out(w_result, r_self, r_input2, r_input3, left, transpose);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_input2 = bridge::XlaToAtenTensor(input2);
  auto r_input3 = bridge::XlaToAtenTensor(input3);
  auto&& x_result = at::ormqr(r_self, r_input2, r_input3, left, transpose);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::btrifact_out(at::Tensor & A_LU, at::Tensor & pivots, const at::Tensor & self, bool pivot) const {
  auto w_A_LU = bridge::XlaToAtenMutableTensor(A_LU);
  auto w_pivots = bridge::XlaToAtenMutableTensor(pivots);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::btrifact_out(w_A_LU, w_pivots, r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(A_LU, pivots);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::btrifact(const at::Tensor & self, bool pivot) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::btrifact(r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::btrifact_with_info_out(at::Tensor & A_LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool pivot) const {
  auto w_A_LU = bridge::XlaToAtenMutableTensor(A_LU);
  auto w_pivots = bridge::XlaToAtenMutableTensor(pivots);
  auto w_info = bridge::XlaToAtenMutableTensor(info);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::btrifact_with_info_out(w_A_LU, w_pivots, w_info, r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(A_LU, pivots, info);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::btrifact_with_info(const at::Tensor & self, bool pivot) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::btrifact_with_info(r_self, pivot);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::btrisolve_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_LU_data = bridge::XlaToAtenTensor(LU_data);
  auto r_LU_pivots = bridge::XlaToAtenTensor(LU_pivots);
  auto&& x_result = at::btrisolve_out(w_result, r_self, r_LU_data, r_LU_pivots);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::btrisolve(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_LU_data = bridge::XlaToAtenTensor(LU_data);
  auto r_LU_pivots = bridge::XlaToAtenTensor(LU_pivots);
  auto&& x_result = at::btrisolve(r_self, r_LU_data, r_LU_pivots);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::multinomial_out(at::Tensor & result, const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::multinomial_out(w_result, r_self, num_samples, replacement, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::multinomial(const at::Tensor & self, int64_t num_samples, bool replacement, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::multinomial(r_self, num_samples, replacement, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::lgamma_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::lgamma_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::lgamma(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::lgamma(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::digamma_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::digamma_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::digamma(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::digamma(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::polygamma_out(at::Tensor & result, int64_t n, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::polygamma_out(w_result, n, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::polygamma(int64_t n, const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::polygamma(n, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::erfinv_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::erfinv_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::erfinv(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::erfinv(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::frac_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::frac_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::frac(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::frac(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::dist(const at::Tensor & self, const at::Tensor & other, at::Scalar p) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::dist(r_self, r_other, p);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::reciprocal_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reciprocal_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::reciprocal(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reciprocal(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::neg_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::neg_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::neg(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::neg(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::atan2_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::atan2_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::atan2(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::atan2(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::lerp_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::lerp_out(w_result, r_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::lerp(const at::Tensor & self, const at::Tensor & end, at::Scalar weight) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_end = bridge::XlaToAtenTensor(end);
  auto&& x_result = at::lerp(r_self, r_end, weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::histc_out(at::Tensor & result, const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::histc_out(w_result, r_self, bins, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::histc(const at::Tensor & self, int64_t bins, at::Scalar min, at::Scalar max) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::histc(r_self, bins, min, max);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::sign_out(at::Tensor & result, const at::Tensor & self) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sign_out(w_result, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::sign(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sign(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::fmod_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::fmod_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::fmod(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::fmod(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::fmod_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::fmod_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::fmod(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::fmod(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::remainder_out(at::Tensor & result, const at::Tensor & self, at::Scalar other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::remainder_out(w_result, r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::remainder(const at::Tensor & self, at::Scalar other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::remainder(r_self, other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::remainder_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::remainder_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::remainder(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::remainder(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::min_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::min_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::min(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::min(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::min(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::min(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::max_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & other) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::max_out(w_result, r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::max(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::max(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::max(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::median(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::median(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool descending) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sort_out(w_values, w_indices, r_self, dim, descending);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::sort(const at::Tensor & self, int64_t dim, bool descending) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::sort(r_self, dim, descending);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const {
  auto w_values = bridge::XlaToAtenMutableTensor(values);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::topk_out(w_values, w_indices, r_self, k, dim, largest, sorted);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(values, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::topk(r_self, k, dim, largest, sorted);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor XLATypeBase::all(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::all(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::any(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::any(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::renorm_out(at::Tensor & result, const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::renorm_out(w_result, r_self, p, dim, maxnorm);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::renorm(const at::Tensor & self, at::Scalar p, int64_t dim, at::Scalar maxnorm) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::renorm(r_self, p, dim, maxnorm);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor XLATypeBase::unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::native::unfold(r_self, dimension, size, step);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

bool XLATypeBase::equal(const at::Tensor & self, const at::Tensor & other) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_other = bridge::XlaToAtenTensor(other);
  auto&& x_result = at::equal(r_self, r_other);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return x_result;
}

at::Tensor & XLATypeBase::pow_out(at::Tensor & result, const at::Tensor & self, const at::Tensor & exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::pow_out(w_result, r_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::pow(const at::Tensor & self, const at::Tensor & exponent) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::pow(r_self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::pow_out(at::Tensor & result, at::Scalar self, const at::Tensor & exponent) const {
  auto w_result = bridge::XlaToAtenMutableTensor(result);
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::pow_out(w_result, self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return result;
}

at::Tensor XLATypeBase::pow(at::Scalar self, const at::Tensor & exponent) const {
  auto r_exponent = bridge::XlaToAtenTensor(exponent);
  auto&& x_result = at::pow(self, r_exponent);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(exponent));
}

at::Tensor & XLATypeBase::normal_out(at::Tensor & output, const at::Tensor & mean, double std, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto&& x_result = at::normal_out(w_output, r_mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::normal(const at::Tensor & mean, double std, at::Generator * generator) const {
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto&& x_result = at::normal(r_mean, std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(mean));
}

at::Tensor & XLATypeBase::normal_out(at::Tensor & output, double mean, const at::Tensor & std, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::normal_out(w_output, mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::normal(double mean, const at::Tensor & std, at::Generator * generator) const {
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::normal(mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(std));
}

at::Tensor & XLATypeBase::normal_out(at::Tensor & output, const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::normal_out(w_output, r_mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::normal(const at::Tensor & mean, const at::Tensor & std, at::Generator * generator) const {
  auto r_mean = bridge::XlaToAtenTensor(mean);
  auto r_std = bridge::XlaToAtenTensor(std);
  auto&& x_result = at::normal(r_mean, r_std, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(std));
}

at::Tensor XLATypeBase::alias(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::alias(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::_dirichlet_grad_out(at::Tensor & output, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_x = bridge::XlaToAtenTensor(x);
  auto r_alpha = bridge::XlaToAtenTensor(alpha);
  auto r_total = bridge::XlaToAtenTensor(total);
  auto&& x_result = at::_dirichlet_grad_out(w_output, r_x, r_alpha, r_total);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::_dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) const {
  auto r_x = bridge::XlaToAtenTensor(x);
  auto r_alpha = bridge::XlaToAtenTensor(alpha);
  auto r_total = bridge::XlaToAtenTensor(total);
  auto&& x_result = at::_dirichlet_grad(r_x, r_alpha, r_total);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(total));
}

at::Tensor & XLATypeBase::binary_cross_entropy_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::binary_cross_entropy_out(w_output, r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::binary_cross_entropy(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::binary_cross_entropy(r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::binary_cross_entropy_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::binary_cross_entropy_backward_out(w_grad_input, r_grad_output, r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::binary_cross_entropy_backward(r_grad_output, r_self, r_target, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mse_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::mse_loss_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::mse_loss(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::mse_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::mse_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::mse_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::l1_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::l1_loss_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::l1_loss(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::l1_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::l1_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::multi_margin_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::multi_margin_loss_out(w_output, r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::multi_margin_loss(const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::multi_margin_loss(r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::multi_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::multi_margin_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, at::Scalar p, at::Scalar margin, const at::Tensor & weight, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::multi_margin_loss_backward(r_grad_output, r_self, r_target, p, margin, r_weight, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::multilabel_margin_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::multilabel_margin_loss_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::multilabel_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::multilabel_margin_loss(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::multilabel_margin_loss_forward_out(at::Tensor & output, at::Tensor & is_target, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_is_target = bridge::XlaToAtenMutableTensor(is_target);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::multilabel_margin_loss_forward_out(w_output, w_is_target, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, is_target);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::multilabel_margin_loss_forward(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::multilabel_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_is_target = bridge::XlaToAtenTensor(is_target);
  auto&& x_result = at::multilabel_margin_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction, r_is_target);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_is_target = bridge::XlaToAtenTensor(is_target);
  auto&& x_result = at::multilabel_margin_loss_backward(r_grad_output, r_self, r_target, reduction, r_is_target);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::nll_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss_out(w_output, r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::nll_loss(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss(r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_total_weight = bridge::XlaToAtenMutableTensor(total_weight);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss_forward_out(w_output, w_total_weight, r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, total_weight);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss_forward(r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::nll_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::nll_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::nll_loss_backward(r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::nll_loss2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss2d_out(w_output, r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::nll_loss2d(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss2d(r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_total_weight = bridge::XlaToAtenMutableTensor(total_weight);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss2d_forward_out(w_output, w_total_weight, r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, total_weight);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::nll_loss2d_forward(r_self, r_target, r_weight, reduction, ignore_index);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::nll_loss2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::nll_loss2d_backward_out(w_grad_input, r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Tensor & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_total_weight = bridge::XlaToAtenTensor(total_weight);
  auto&& x_result = at::nll_loss2d_backward(r_grad_output, r_self, r_target, r_weight, reduction, ignore_index, r_total_weight);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::smooth_l1_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::smooth_l1_loss_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::smooth_l1_loss(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::smooth_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::smooth_l1_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::smooth_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::smooth_l1_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::soft_margin_loss_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::soft_margin_loss_out(w_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::soft_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::soft_margin_loss(r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::soft_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::soft_margin_loss_backward_out(w_grad_input, r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::soft_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_target = bridge::XlaToAtenTensor(target);
  auto&& x_result = at::soft_margin_loss_backward(r_grad_output, r_self, r_target, reduction);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::elu_out(at::Tensor & output, const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::elu_out(w_output, r_self, alpha, scale, input_scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::elu(const at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::elu(r_self, alpha, scale, input_scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::elu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::elu_backward_out(w_grad_input, r_grad_output, alpha, scale, input_scale, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::elu_backward(const at::Tensor & grad_output, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::elu_backward(r_grad_output, alpha, scale, input_scale, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(output));
}

at::Tensor & XLATypeBase::elu_(at::Tensor & self, at::Scalar alpha, at::Scalar scale, at::Scalar input_scale) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::elu_(w_self, alpha, scale, input_scale);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::glu_out(at::Tensor & output, const at::Tensor & self, int64_t dim) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::glu_out(w_output, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::glu(const at::Tensor & self, int64_t dim) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::glu(r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::glu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::glu_backward_out(w_grad_input, r_grad_output, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::glu_backward(r_grad_output, r_self, dim);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::hardtanh_out(at::Tensor & output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::hardtanh_out(w_output, r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::hardtanh(const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::hardtanh(r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::hardtanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::hardtanh_backward_out(w_grad_input, r_grad_output, r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::hardtanh_backward(r_grad_output, r_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::hardtanh_(at::Tensor & self, at::Scalar min_val, at::Scalar max_val) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::hardtanh_(w_self, min_val, max_val);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::leaky_relu_out(at::Tensor & output, const at::Tensor & self, at::Scalar negative_slope) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::leaky_relu_out(w_output, r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::leaky_relu(const at::Tensor & self, at::Scalar negative_slope) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::leaky_relu(r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::leaky_relu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::leaky_relu_backward_out(w_grad_input, r_grad_output, r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar negative_slope) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::leaky_relu_backward(r_grad_output, r_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::leaky_relu_(at::Tensor & self, at::Scalar negative_slope) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto&& x_result = at::leaky_relu_(w_self, negative_slope);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::log_sigmoid_out(at::Tensor & output, const at::Tensor & self) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log_sigmoid_out(w_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::log_sigmoid(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log_sigmoid(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::log_sigmoid_forward_out(at::Tensor & output, at::Tensor & buffer, const at::Tensor & self) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_buffer = bridge::XlaToAtenMutableTensor(buffer);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log_sigmoid_forward_out(w_output, w_buffer, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, buffer);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::log_sigmoid_forward(const at::Tensor & self) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::log_sigmoid_forward(r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::log_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_buffer = bridge::XlaToAtenTensor(buffer);
  auto&& x_result = at::log_sigmoid_backward_out(w_grad_input, r_grad_output, r_self, r_buffer);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_buffer = bridge::XlaToAtenTensor(buffer);
  auto&& x_result = at::log_sigmoid_backward(r_grad_output, r_self, r_buffer);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::rrelu_with_noise_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::rrelu_with_noise_out(w_output, r_self, r_noise, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::rrelu_with_noise(const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::rrelu_with_noise(r_self, r_noise, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::rrelu_with_noise_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::rrelu_with_noise_backward_out(w_grad_input, r_grad_output, r_self, r_noise, lower, upper, training);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::rrelu_with_noise_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::rrelu_with_noise_backward(r_grad_output, r_self, r_noise, lower, upper, training);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::rrelu_with_noise_(at::Tensor & self, const at::Tensor & noise, at::Scalar lower, at::Scalar upper, bool training, at::Generator * generator) const {
  auto w_self = bridge::XlaToAtenMutableTensor(self);
  auto r_noise = bridge::XlaToAtenTensor(noise);
  auto&& x_result = at::rrelu_with_noise_(w_self, r_noise, lower, upper, training, generator);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return self;
}

at::Tensor & XLATypeBase::softplus_out(at::Tensor & output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softplus_out(w_output, r_self, beta, threshold);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::softplus(const at::Tensor & self, at::Scalar beta, at::Scalar threshold) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softplus(r_self, beta, threshold);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::softplus_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::softplus_backward_out(w_grad_input, r_grad_output, r_self, beta, threshold, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar beta, at::Scalar threshold, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::softplus_backward(r_grad_output, r_self, beta, threshold, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::softshrink_out(at::Tensor & output, const at::Tensor & self, at::Scalar lambd) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softshrink_out(w_output, r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::softshrink(const at::Tensor & self, at::Scalar lambd) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softshrink(r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::softshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softshrink_backward_out(w_grad_input, r_grad_output, r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, at::Scalar lambd) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::softshrink_backward(r_grad_output, r_self, lambd);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::adaptive_avg_pool2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool2d_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::adaptive_avg_pool2d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool2d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::adaptive_avg_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool2d_backward_out(w_grad_input, r_grad_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool2d_backward(r_grad_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::adaptive_avg_pool3d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool3d_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::adaptive_avg_pool3d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool3d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::adaptive_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool3d_backward_out(w_grad_input, r_grad_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_avg_pool3d_backward(r_grad_output, r_self);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::adaptive_max_pool2d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_max_pool2d_out(w_output, w_indices, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::adaptive_max_pool2d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_max_pool2d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::adaptive_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::adaptive_max_pool2d_backward_out(w_grad_input, r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::adaptive_max_pool2d_backward(r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::adaptive_max_pool3d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_max_pool3d_out(w_output, w_indices, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::adaptive_max_pool3d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::adaptive_max_pool3d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::adaptive_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::adaptive_max_pool3d_backward_out(w_grad_input, r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::adaptive_max_pool3d_backward(r_grad_output, r_self, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::avg_pool2d_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool2d_out(w_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::avg_pool2d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool2d(r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::avg_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool2d_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool2d_backward(r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::avg_pool3d_out(at::Tensor & output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool3d_out(w_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::avg_pool3d(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool3d(r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool3d_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, bool ceil_mode, bool count_include_pad) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::avg_pool3d_backward(r_grad_output, r_self, kernel_size, stride, padding, ceil_mode, count_include_pad);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::fractional_max_pool2d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_random_samples = bridge::XlaToAtenTensor(random_samples);
  auto&& x_result = at::fractional_max_pool2d_out(w_output, w_indices, r_self, kernel_size, output_size, r_random_samples);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::fractional_max_pool2d(const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_random_samples = bridge::XlaToAtenTensor(random_samples);
  auto&& x_result = at::fractional_max_pool2d(r_self, kernel_size, output_size, r_random_samples);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::fractional_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::fractional_max_pool2d_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, output_size, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::fractional_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::fractional_max_pool2d_backward(r_grad_output, r_self, kernel_size, output_size, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::fractional_max_pool3d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_random_samples = bridge::XlaToAtenTensor(random_samples);
  auto&& x_result = at::fractional_max_pool3d_out(w_output, w_indices, r_self, kernel_size, output_size, r_random_samples);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::fractional_max_pool3d(const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & random_samples) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_random_samples = bridge::XlaToAtenTensor(random_samples);
  auto&& x_result = at::fractional_max_pool3d(r_self, kernel_size, output_size, r_random_samples);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::fractional_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::fractional_max_pool3d_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, output_size, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::fractional_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList output_size, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::fractional_max_pool3d_backward(r_grad_output, r_self, kernel_size, output_size, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::max_pool2d_with_indices_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool2d_with_indices_out(w_output, w_indices, r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::max_pool2d_with_indices(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool2d_with_indices(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::max_pool2d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_pool2d_with_indices_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_pool2d_with_indices_backward(r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::max_pool3d_with_indices_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_indices = bridge::XlaToAtenMutableTensor(indices);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool3d_with_indices_out(w_output, w_indices, r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(output, indices);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::max_pool3d_with_indices(const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::max_pool3d_with_indices(r_self, kernel_size, stride, padding, dilation, ceil_mode);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::max_pool3d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_pool3d_with_indices_backward_out(w_grad_input, r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::max_pool3d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, bool ceil_mode, const at::Tensor & indices) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_pool3d_with_indices_backward(r_grad_output, r_self, kernel_size, stride, padding, dilation, ceil_mode, r_indices);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::max_unpool2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool2d_out(w_output, r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::max_unpool2d(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool2d(r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::max_unpool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool2d_backward_out(w_grad_input, r_grad_output, r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::max_unpool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool2d_backward(r_grad_output, r_self, r_indices, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::max_unpool3d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool3d_out(w_output, r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::max_unpool3d(const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool3d(r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::max_unpool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool3d_backward_out(w_grad_input, r_grad_output, r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::max_unpool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::IntList output_size, at::IntList stride, at::IntList padding) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_indices = bridge::XlaToAtenTensor(indices);
  auto&& x_result = at::max_unpool3d_backward(r_grad_output, r_self, r_indices, output_size, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::reflection_pad1d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad1d_out(w_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::reflection_pad1d(const at::Tensor & self, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad1d(r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::reflection_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad1d_backward_out(w_grad_input, r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad1d_backward(r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::reflection_pad2d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad2d_out(w_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::reflection_pad2d(const at::Tensor & self, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad2d(r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::reflection_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad2d_backward_out(w_grad_input, r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::reflection_pad2d_backward(r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::replication_pad1d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad1d_out(w_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::replication_pad1d(const at::Tensor & self, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad1d(r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::replication_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad1d_backward_out(w_grad_input, r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad1d_backward(r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::replication_pad2d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad2d_out(w_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::replication_pad2d(const at::Tensor & self, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad2d(r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::replication_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad2d_backward_out(w_grad_input, r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad2d_backward(r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::replication_pad3d_out(at::Tensor & output, const at::Tensor & self, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad3d_out(w_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::replication_pad3d(const at::Tensor & self, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad3d(r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::replication_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad3d_backward_out(w_grad_input, r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntList padding) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::replication_pad3d_backward(r_grad_output, r_self, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_linear1d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_linear1d_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::upsample_linear1d(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_linear1d(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_linear1d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::upsample_linear1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_linear1d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::upsample_bilinear2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_bilinear2d_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::upsample_bilinear2d(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_bilinear2d(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_bilinear2d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_bilinear2d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::upsample_bicubic2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_bicubic2d_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::upsample_bicubic2d(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_bicubic2d(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_bicubic2d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_bicubic2d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::upsample_trilinear3d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_trilinear3d_out(w_output, r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::upsample_trilinear3d(const at::Tensor & self, at::IntList output_size, bool align_corners) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_trilinear3d(r_self, output_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_trilinear3d_backward_out(w_grad_input, r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size, bool align_corners) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_trilinear3d_backward(r_grad_output, output_size, input_size, align_corners);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::upsample_nearest1d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_nearest1d_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::upsample_nearest1d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_nearest1d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_nearest1d_backward_out(w_grad_input, r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_nearest1d_backward(r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::upsample_nearest2d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_nearest2d_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::upsample_nearest2d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_nearest2d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_nearest2d_backward_out(w_grad_input, r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_nearest2d_backward(r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::upsample_nearest3d_out(at::Tensor & output, const at::Tensor & self, at::IntList output_size) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_nearest3d_out(w_output, r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::upsample_nearest3d(const at::Tensor & self, at::IntList output_size) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto&& x_result = at::upsample_nearest3d(r_self, output_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_nearest3d_backward_out(w_grad_input, r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntList output_size, at::IntList input_size) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto&& x_result = at::upsample_nearest3d_backward(r_grad_output, output_size, input_size);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(grad_output));
}

at::Tensor & XLATypeBase::sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::sigmoid_backward_out(w_grad_input, r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::sigmoid_backward(r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(output));
}

at::Tensor & XLATypeBase::tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::tanh_backward_out(w_grad_input, r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return grad_input;
}

at::Tensor XLATypeBase::tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_output = bridge::XlaToAtenTensor(output);
  auto&& x_result = at::tanh_backward(r_grad_output, r_output);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(output));
}

at::Tensor & XLATypeBase::thnn_conv_transpose2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose2d_out(w_output, r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::thnn_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose2d(r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv_transpose2d_forward_out(at::Tensor & output, at::Tensor & columns, at::Tensor & ones, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_columns = bridge::XlaToAtenMutableTensor(columns);
  auto w_ones = bridge::XlaToAtenMutableTensor(ones);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose2d_forward_out(w_output, w_columns, w_ones, r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, columns, ones);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv_transpose2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose2d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv_transpose2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::thnn_conv_transpose2d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_columns, r_ones);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv_transpose2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & columns, const at::Tensor & ones, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_columns = bridge::XlaToAtenTensor(columns);
  auto r_ones = bridge::XlaToAtenTensor(ones);
  auto&& x_result = at::thnn_conv_transpose2d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_columns, r_ones, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::thnn_conv_transpose3d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose3d_out(w_output, r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::thnn_conv_transpose3d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose3d(r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv_transpose3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_finput = bridge::XlaToAtenMutableTensor(finput);
  auto w_fgrad_input = bridge::XlaToAtenMutableTensor(fgrad_input);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose3d_forward_out(w_output, w_finput, w_fgrad_input, r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, finput, fgrad_input);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv_transpose3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_transpose3d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, output_padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv_transpose3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::thnn_conv_transpose3d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_finput, r_fgrad_input);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv_transpose3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList output_padding, at::IntList dilation, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::thnn_conv_transpose3d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, output_padding, dilation, r_finput, r_fgrad_input, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::thnn_conv2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv2d_out(w_output, r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv2d(r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv2d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_finput = bridge::XlaToAtenMutableTensor(finput);
  auto w_fgrad_input = bridge::XlaToAtenMutableTensor(fgrad_input);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv2d_forward_out(w_output, w_finput, w_fgrad_input, r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, finput, fgrad_input);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv2d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::thnn_conv2d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, r_finput, r_fgrad_input);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input, std::array<bool,3> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::thnn_conv2d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, r_finput, r_fgrad_input, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::thnn_conv_depthwise2d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_depthwise2d_out(w_output, r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::thnn_conv_depthwise2d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_depthwise2d(r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

at::Tensor & XLATypeBase::thnn_conv_depthwise2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_depthwise2d_forward_out(w_output, r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::thnn_conv_depthwise2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv_depthwise2d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv_depthwise2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::thnn_conv_depthwise2d_backward_out(w_grad_input, w_grad_weight, r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &>(grad_input, grad_weight);
}

std::tuple<at::Tensor,at::Tensor> XLATypeBase::thnn_conv_depthwise2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, at::IntList dilation, std::array<bool,2> output_mask) const {
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto&& x_result = at::thnn_conv_depthwise2d_backward(r_grad_output, r_self, r_weight, kernel_size, stride, padding, dilation, output_mask);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)));
}

at::Tensor & XLATypeBase::thnn_conv3d_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv3d_out(w_output, r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return output;
}

at::Tensor XLATypeBase::thnn_conv3d(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv3d(r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return bridge::CreateXlaTensor(x_result, bridge::XlaTensorDevice(self));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv3d_forward_out(at::Tensor & output, at::Tensor & finput, at::Tensor & fgrad_input, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto w_output = bridge::XlaToAtenMutableTensor(output);
  auto w_finput = bridge::XlaToAtenMutableTensor(finput);
  auto w_fgrad_input = bridge::XlaToAtenMutableTensor(fgrad_input);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv3d_forward_out(w_output, w_finput, w_fgrad_input, r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(output, finput, fgrad_input);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, const at::Tensor & bias, at::IntList stride, at::IntList padding) const {
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_bias = bridge::XlaToAtenTensor(bias);
  auto&& x_result = at::thnn_conv3d_forward(r_self, r_weight, kernel_size, r_bias, stride, padding);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor,at::Tensor,at::Tensor>(bridge::CreateXlaTensor(std::get<0>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<1>(x_result), bridge::XlaTensorDevice(self)), bridge::CreateXlaTensor(std::get<2>(x_result), bridge::XlaTensorDevice(self)));
}

std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> XLATypeBase::thnn_conv3d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::IntList padding, const at::Tensor & finput, const at::Tensor & fgrad_input) const {
  auto w_grad_input = bridge::XlaToAtenMutableTensor(grad_input);
  auto w_grad_weight = bridge::XlaToAtenMutableTensor(grad_weight);
  auto w_grad_bias = bridge::XlaToAtenMutableTensor(grad_bias);
  auto r_grad_output = bridge::XlaToAtenTensor(grad_output);
  auto r_self = bridge::XlaToAtenTensor(self);
  auto r_weight = bridge::XlaToAtenTensor(weight);
  auto r_finput = bridge::XlaToAtenTensor(finput);
  auto r_fgrad_input = bridge::XlaToAtenTensor(fgrad_input);
  auto&& x_result = at::thnn_conv3d_backward_out(w_grad_input, w_grad_weight, w_grad_bias, r_grad_output, r_self, r_weight, kernel_size, stride, padding, r_finput, r_fgrad_input);
  static_cast<void>(x_result); // Avoid warnings in case not used
  return std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(grad_input, grad_weight, grad_bias);
}

std::tuple<at::Tensor,at::Tensor,at::Tensor> XLATypeBase::thnn_conv3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntList kernel_size, at::IntList stride, at::In