#define TORCH_ASSERT_ONLY_METHOD_OPERATORS // @generated from ../tools/autograd/templates/python_torch_functions.cpp // Python bindings for torch.* functions implemented through ATen. // // The functions are bound as static methods on a class // torch._C._VariableFunctions which is also aliased as Variable._torch // and also copied into 'torch' module. #include // Undefine the copysign macro so that at::copysign works as intended with MSVC // https://github.com/python/cpython/blob/c60394c7fc9cc09b16e9675a3eeb5844b6d8523f/PC/pyconfig.h#L196 #ifdef _MSC_VER #undef copysign #endif // _MSC_VER #include "torch/csrc/autograd/python_torch_functions.h" #include "torch/csrc/autograd/python_variable.h" #include "torch/csrc/autograd/utils/wrap_outputs.h" #include "torch/csrc/Dtype.h" #include "torch/csrc/DynamicTypes.h" #include "torch/csrc/Exceptions.h" #include "torch/csrc/utils/out_types.h" #include "torch/csrc/utils/pybind.h" #include "torch/csrc/utils/pycfunction_helpers.h" #include "torch/csrc/utils/python_arg_parser.h" #include "torch/csrc/utils/tensor_layouts.h" #include "torch/csrc/utils/tensor_new.h" #include "torch/csrc/utils/tensor_numpy.h" #include "torch/csrc/jit/frontend/tracer.h" #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" #include "torch/csrc/utils/device_lazy_init.h" #include "torch/csrc/autograd/generated/python_return_types.h" #include #ifndef AT_PER_OPERATOR_HEADERS #include #else #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include 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#include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #endif #include #include #include #include using at::Tensor; using at::Device; using at::Layout; using at::Scalar; using at::ScalarType; using at::Backend; using at::OptionalDeviceGuard; using at::DeviceGuard; using at::TensorOptions; using at::IntArrayRef; using at::Generator; using at::TensorList; using at::Dimname; using at::DimnameList; using at::ArrayRef; using torch::utils::check_out_type_matches; using namespace torch::autograd::utils; // NOTE: See [Sharded File] comment in VariableType namespace torch::autograd { // generated forward declarations start here static PyObject * THPVariable__cast_Byte(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cast_Char(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cast_Double(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cast_Float(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cast_Int(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cast_Long(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cast_Short(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cast_Half(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__make_dual(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__unpack_dual(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_align_tensors(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__assert_async(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__assert_scalar(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__functional_assert_scalar(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__functional_assert_async(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__assert_tensor_metadata(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__print(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sym_constrain_range(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sym_constrain_range_for_size(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__functional_sym_constrain_range(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__functional_sym_constrain_range_for_size(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__make_dep_token(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__use_cudnn_ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cudnn_ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__use_cudnn_rnn_flatten_weight(PyObject* self_, PyObject* args); static PyObject * THPVariable__cudnn_rnn_flatten_weight(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cudnn_rnn(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cudnn_init_dropout_state(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__debug_has_internal_overlap(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fused_dropout(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__masked_scale(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_native_dropout(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sobol_engine_draw(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sobol_engine_ff_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sobol_engine_scramble_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sobol_engine_initialize_state_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__reshape_from_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__shape_as_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_dropout(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_feature_dropout(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_feature_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_alpha_dropout(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_alpha_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_feature_alpha_dropout(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_feature_alpha_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_abs(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_abs_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_absolute(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_angle(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_view_as_real(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_view_as_complex(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sgn(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_real(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_imag(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__conj(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conj(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__conj_physical(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conj_physical(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conj_physical_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_resolve_conj(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_resolve_neg(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__neg_view(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_acos(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_acos_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arccos(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arccos_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_avg_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_adaptive_avg_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_adaptive_max_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_add(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__add_relu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__add_relu_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_addmv(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_addmv_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_addr(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_affine_grid_generator(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__is_all_true(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__is_any_true(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__test_check_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__test_functorch_fallback(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_all(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_allclose(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_any(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arange(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__dim_arange(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_argmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_argmin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_acosh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_acosh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arccosh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arccosh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_asinh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_asinh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arcsinh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arcsinh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atanh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atanh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arctanh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arctanh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_as_strided(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_as_strided_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_asin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_asin_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arcsin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arcsin_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atan(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atan_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arctan(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arctan_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atleast_1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atleast_2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atleast_3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_baddbmm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bartlett_window(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__batch_norm_impl_index(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bernoulli(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bilinear(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_binary_cross_entropy_with_logits(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bincount(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bitwise_not(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_copysign(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__lazy_clone(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logical_not(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logical_xor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logical_and(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logical_or(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_blackman_window(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bmm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_broadcast_tensors(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_broadcast_to(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_broadcast_to(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cat(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_concat(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_concatenate(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_block_diag(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ceil(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ceil_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_chain_matmul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unsafe_chunk(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_chunk(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tensor_split(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clamp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clamp_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clamp_max(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clamp_max_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clamp_min(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clamp_min_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clip(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clip_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_is_acceptable(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_complex(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_polar(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_constant_pad_nd(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__convolution_mode(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conv1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conv2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conv3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conv_tbc(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conv_transpose1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conv_transpose2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_conv_transpose3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__copy_from(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__copy_from_and_resize(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cos(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cos_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cosh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cosh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cosine_embedding_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_count_nonzero(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cov(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_corrcoef(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_affine_grid_generator(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_convolution_transpose(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__mps_convolution_transpose(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_convolution_relu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_convolution_add_relu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cudnn_grid_sampler(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cummax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cummax_helper(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cummin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cummin_helper(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cumprod(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cumsum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cumulative_trapezoid(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_diag_embed(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_diagflat(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_diagonal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_diff(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_gradient(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_div(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_divide(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_true_divide(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_dot(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_vdot(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_einsum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_embedding(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_embedding_renorm_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__embedding_bag_forward_only(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__rowwise_prune(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_row_stack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_embedding_bag(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__embedding_bag(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_empty(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_empty_permuted(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__empty_affine_quantized(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__empty_per_channel_affine_quantized(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__resize_output_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_empty_quantized(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_empty_like(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_empty_strided(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_erf(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_erf_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_erfc(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_erfc_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_exp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_exp_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_exp2(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_exp2_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_expm1(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_expm1_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_eye(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_flatten(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unflatten(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fill(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fill_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_floor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_floor_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_floor_divide(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_frac(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_frac_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_full(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_full_like(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_from_file(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_gcd(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_gcd_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lcm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lcm_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_grid_sampler(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_grid_sampler_2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__grid_sampler_2d_cpu_fallback(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_grid_sampler_3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hann_window(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hamming_window(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_kaiser_window(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hinge_embedding_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_group_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_native_group_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fft_r2c(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fft_c2r(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fft_c2c(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__validate_compressed_sparse_indices(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cufft_get_plan_cache_size(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cufft_get_plan_cache_max_size(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cufft_set_plan_cache_max_size(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cufft_clear_plan_cache(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__unsafe_index(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__unsafe_masked_index(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__unsafe_masked_index_put_accumulate(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_index_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_index_put_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_index_put(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__unsafe_index_put(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__index_put_impl_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_instance_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isclose(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isnan(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_distributed(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_floating_point(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_complex(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_conj(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__is_zerotensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_neg(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isreal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_nonzero(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_same_size(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_signed(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_inference(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_kl_div(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_kron(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_kthvalue(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_layer_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_native_layer_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rms_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nan_to_num(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nan_to_num_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mkldnn_linear_backward_weights(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cslt_compress(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cslt_sparse_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__cslt_sparse_mm_search(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_semi_structured_tile(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_semi_structured_apply(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_semi_structured_apply_dense(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_semi_structured_linear(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_semi_structured_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_semi_structured_addmm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__mixed_dtypes_linear(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fbgemm_linear_int8_weight_fp32_activation(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fbgemm_linear_int8_weight(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fbgemm_linear_quantize_weight(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fbgemm_pack_gemm_matrix_fp16(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__wrapped_linear_prepack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__wrapped_quantized_linear_prepacked(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fbgemm_linear_fp16_weight_fp32_activation(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fbgemm_linear_fp16_weight(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fbgemm_pack_quantized_matrix(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ldexp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ldexp_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_linspace(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log10(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log10_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log1p(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log1p_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log2(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log2_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logaddexp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logaddexp2(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_xlogy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_xlogy_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logspace(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_log_softmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__log_softmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__log_softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__logcumsumexp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logcumsumexp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logsumexp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_margin_ranking_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_matmul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_matrix_power(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_matrix_exp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__aminmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_aminmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__compute_linear_combination(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_max(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_amax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_max_pool1d_with_indices(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_max_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_max_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mkldnn_max_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mkldnn_max_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_max_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_max_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_max_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_max_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mean(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nanmean(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_median(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nanmedian(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_min(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_amin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__mps_convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mkldnn_convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mkldnn_rnn_layer(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_miopen_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_miopen_convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_miopen_convolution_transpose(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_miopen_depthwise_convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_miopen_convolution_relu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_miopen_convolution_add_relu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_miopen_rnn(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__int_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__convert_weight_to_int4pack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__weight_int4pack_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__convert_weight_to_int4pack_for_cpu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__weight_int4pack_mm_for_cpu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__dyn_quant_pack_4bit_weight(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__dyn_quant_matmul_4bit(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__weight_int8pack_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_sparse_matmul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mode(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_multiply(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mv(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mvlgamma(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_narrow_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_narrow(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_native_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__native_batch_norm_legit(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__native_batch_norm_legit_no_training(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm_stats(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm_elemt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm_gather_stats(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm_gather_stats_with_counts(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm_backward_reduce(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm_backward_elemt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_batch_norm_update_stats(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_is_vulkan_available(PyObject* self_, PyObject* args); static PyObject * THPVariable__nnpack_available(PyObject* self_, PyObject* args); static PyObject * THPVariable__nnpack_spatial_convolution(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ones(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ones_like(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_pairwise_distance(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cdist(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__euclidean_dist(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_pdist(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cosine_similarity(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_permute(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_movedim(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_moveaxis(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_adjoint(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_pixel_shuffle(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_pixel_unshuffle(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_channel_shuffle(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_native_channel_shuffle(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__pin_memory(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_pinverse(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_poisson_nll_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rad2deg(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rad2deg_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_deg2rad(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_deg2rad_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_scalar_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rand(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rand_like(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_randint(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_randint_like(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_randn(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_randn_like(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_randperm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ravel(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_reciprocal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_reciprocal_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_neg(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_neg_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_negative(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_negative_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_repeat_interleave(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_reshape(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__mkldnn_reshape(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_round(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_round_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rrelu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rrelu_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_relu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_relu_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_prelu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__prelu_kernel(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hardshrink(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rsqrt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rsqrt_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_select(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_selu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_selu_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_celu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_celu_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sigmoid(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sigmoid_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logit(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logit_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sin_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sinc(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sinc_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sinh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sinh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_detach(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_detach_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_slice_inverse(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_slice_scatter(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_select_scatter(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_diagonal_scatter(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_as_strided_scatter(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_smm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_softmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__softmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unsafe_split(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_split(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unsafe_split_with_sizes(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_split_with_sizes(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hsplit(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_vsplit(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_dsplit(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_squeeze(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sspaddmm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__chunk_cat(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_stack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__stack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hstack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_vstack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_dstack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_stft(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_istft(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nansum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sqrt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sqrt_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_square(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_square_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_std(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_std_mean(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_prod(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_t(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tan(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tan_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tanh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tanh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tensordot(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_threshold(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_threshold_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tile(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_transpose(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__mkldnn_transpose(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__mkldnn_transpose_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_flip(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fliplr(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_flipud(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_roll(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rot90(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_trapezoid(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_trapz(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__transform_bias_rescale_qkv(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_tensor_from_mask(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_tensor_from_mask_left_aligned(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_from_padded(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_from_padded_and_nested_example(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_view_from_buffer(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_view_from_buffer_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_view_from_jagged(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_view_from_jagged_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_values(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_values_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_offsets(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_lengths(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_ragged_idx(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_min_seqlen(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_max_seqlen(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_get_jagged_dummy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_compute_contiguous_strides_offsets(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__trilinear(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_triplet_margin_loss(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_trunc(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_trunc_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fix(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fix_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__has_compatible_shallow_copy_type(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__unique(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unique_dim(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unique_consecutive(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__unique2(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unsqueeze(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_vander(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_var(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_var_mean(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_where(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_norm_except_dim(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__weight_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__weight_norm_interface(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_zeros(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__efficientzerotensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_zeros_like(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__standard_gamma_grad(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__standard_gamma(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__dirichlet_grad(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sample_dirichlet(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_poisson(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_binomial(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_native_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_sum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_csr_sum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_csr_prod(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_log_softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_frexp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_frobenius_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nuclear_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_clone(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_positive(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_resize_as_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_resize_as_sparse_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_zero_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sub(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_subtract(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rsub(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_heaviside(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_addmm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__addmm_activation(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_grouped_mm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__validate_sparse_coo_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__validate_sparse_compressed_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__validate_sparse_csr_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__validate_sparse_csc_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__validate_sparse_bsr_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__validate_sparse_bsc_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__to_cpu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__coalesce(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hspmm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unbind(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__to_sparse_semi_structured(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantize_per_tensor_dynamic(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantize_per_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantize_per_channel(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_dequantize(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_q_scale(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_q_zero_point(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_q_per_channel_scales(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_q_per_channel_zero_points(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_q_per_channel_axis(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_int_repr(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__make_per_tensor_quantized_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__make_per_channel_quantized_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fake_quantize_per_tensor_affine(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fake_quantize_per_tensor_affine_cachemask_tensor_qparams(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fake_quantize_learnable_per_tensor_affine(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fake_quantize_per_channel_affine(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fake_quantize_learnable_per_channel_affine(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fused_moving_avg_obs_fake_quant(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fused_moving_avg_obs_fq_helper(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__choose_qparams_per_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__saturate_weight_to_fp16(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_choose_qparams_optimized(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_meshgrid(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cartesian_prod(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_combinations(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_result_type(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_can_cast(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_promote_types(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__lstm_mps(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lstm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_gru(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rnn_tanh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rnn_relu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lstm_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_gru_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rnn_tanh_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_rnn_relu_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_lstm_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_gru_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_rnn_relu_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantized_rnn_tanh_cell(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__pack_padded_sequence(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__pad_packed_sequence(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_masked_fill(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_masked_scatter(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__masked_softmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_put(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_index_add(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_index_reduce(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_index_fill(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_scatter(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_scatter_add(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_scatter_reduce(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bitwise_and(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable___and__(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bitwise_or(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable___or__(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bitwise_xor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable___xor__(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable___lshift__(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bitwise_left_shift(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable___rshift__(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bitwise_right_shift(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_addbmm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_diag(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cross(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_triu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tril(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_tril_indices(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_triu_indices(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_trace(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ne(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_not_equal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_eq(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ge(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_greater_equal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_le(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_less_equal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_gt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_greater(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_less(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_take(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_take_along_dim(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_index_select(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_masked_select(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nonzero_static(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_argwhere(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_gather(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_addcmul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_addcdiv(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_triangular_solve(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__linalg_check_errors(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_svd(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_swapaxes(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_swapdims(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cholesky(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cholesky_solve(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_cholesky_inverse(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_qr(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_geqrf(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_orgqr(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ormqr(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__lu_with_info(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lu_solve(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lu_unpack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_multinomial(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lgamma(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_digamma(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_polygamma(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_erfinv(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_i0(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_i0_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sign(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_signbit(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_dist(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_atan2(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_arctan2(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_lerp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_histc(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_histogram(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__histogramdd_bin_edges(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__histogramdd_from_bin_cts(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__histogramdd_from_bin_tensors(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_histogramdd(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fmod(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_hypot(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_igamma(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_igammac(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nextafter(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_remainder(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fmin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_fmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_maximum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_minimum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_quantile(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_nanquantile(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_sort(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_msort(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_argsort(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_topk(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_renorm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_equal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_pow(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_float_power(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_normal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__amp_foreach_non_finite_check_and_unscale_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__amp_update_scale_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_add(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_add_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sub(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sub_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_mul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_mul_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_div(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_div_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_clamp_max(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_clamp_max_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_clamp_min(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_clamp_min_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_maximum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_maximum_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_minimum(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_minimum_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_addcdiv(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_addcdiv_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_addcmul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_addcmul_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_abs(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_abs_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_acos(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_acos_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_asin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_asin_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_atan(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_atan_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_ceil(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_ceil_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_cos(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_cos_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_cosh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_cosh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_erf(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_erf_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_erfc(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_erfc_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_exp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_exp_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_expm1(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_expm1_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_floor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_floor_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_frac(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_frac_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_lerp(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_lerp_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_lgamma(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_lgamma_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log10(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log10_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log1p(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log1p_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log2(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_log2_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_max(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_neg(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_neg_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_norm(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_pow(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_pow_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_reciprocal(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_reciprocal_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_round(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_round_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_rsqrt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_rsqrt_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sigmoid(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sigmoid_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sign(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sign_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sin(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sin_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sinh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sinh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sqrt(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_sqrt_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_tan(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_tan_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_tanh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_tanh_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_trunc(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_trunc_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_zero_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foreach_copy_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_bucketize(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_searchsorted(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__convert_indices_from_coo_to_csr(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__convert_indices_from_csr_to_coo(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_mkldnn_adaptive_avg_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__adaptive_avg_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__adaptive_avg_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_column_stack(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isfinite(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isinf(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isposinf(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_isneginf(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__add_batch_dim(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__remove_batch_dim(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__linalg_det(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_det(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__linalg_slogdet(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_slogdet(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_logdet(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__linalg_eigh(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_inverse(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_inner(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_outer(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ger(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__linalg_svd(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__linalg_solve_ex(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__test_serialization_subcmul(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__test_parallel_materialize(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__test_autograd_multiple_dispatch(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__test_autograd_multiple_dispatch_view(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__test_autograd_multiple_dispatch_view_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_segment_reduce(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_tensor_from_tensor_list(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fw_primal_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__make_dual_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_view_as_real_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_view_as_complex_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__conj_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__neg_view_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_as_strided_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__sparse_broadcast_to_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_diagonal_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_expand_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_permute_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__reshape_alias_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_select_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_detach_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_slice_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_split_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_split_with_sizes_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_squeeze_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_t_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_transpose_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unsqueeze_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__values_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_values_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_crow_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_col_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_ccol_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_row_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unbind_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_view_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_unfold_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable_alias_copy(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_from_padded_tensor(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__nested_tensor_softmax_with_shape(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__safe_softmax(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__transformer_encoder_layer_fwd(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__native_multi_head_attention(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fused_sdp_choice(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_dot_product_attention_math(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_dot_product_attention_math_for_mps(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_dot_product_flash_attention(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_dot_product_flash_attention_for_cpu(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_dot_product_efficient_attention(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__scaled_dot_product_cudnn_attention(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__triton_scaled_dot_attention(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fill_mem_eff_dropout_mask_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__triton_multi_head_attention(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__foobar(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fused_adam_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fused_adamw_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fused_sgd_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__fused_adagrad_(PyObject* self_, PyObject* args, PyObject* kwargs); static PyObject * THPVariable__propagate_xla_data(PyObject* self_, PyObject* args, PyObject* kwargs); static PyMethodDef torch_functions_shard[] = { {"_cast_Byte", castPyCFunctionWithKeywords(THPVariable__cast_Byte), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cast_Char", castPyCFunctionWithKeywords(THPVariable__cast_Char), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cast_Double", castPyCFunctionWithKeywords(THPVariable__cast_Double), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cast_Float", castPyCFunctionWithKeywords(THPVariable__cast_Float), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cast_Int", castPyCFunctionWithKeywords(THPVariable__cast_Int), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cast_Long", castPyCFunctionWithKeywords(THPVariable__cast_Long), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cast_Short", castPyCFunctionWithKeywords(THPVariable__cast_Short), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cast_Half", castPyCFunctionWithKeywords(THPVariable__cast_Half), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_make_dual", castPyCFunctionWithKeywords(THPVariable__make_dual), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_unpack_dual", castPyCFunctionWithKeywords(THPVariable__unpack_dual), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"align_tensors", castPyCFunctionWithKeywords(THPVariable_align_tensors), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_assert_async", castPyCFunctionWithKeywords(THPVariable__assert_async), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_assert_scalar", castPyCFunctionWithKeywords(THPVariable__assert_scalar), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_functional_assert_scalar", castPyCFunctionWithKeywords(THPVariable__functional_assert_scalar), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_functional_assert_async", castPyCFunctionWithKeywords(THPVariable__functional_assert_async), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_assert_tensor_metadata", castPyCFunctionWithKeywords(THPVariable__assert_tensor_metadata), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_print", castPyCFunctionWithKeywords(THPVariable__print), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sym_constrain_range", castPyCFunctionWithKeywords(THPVariable_sym_constrain_range), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sym_constrain_range_for_size", castPyCFunctionWithKeywords(THPVariable_sym_constrain_range_for_size), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_functional_sym_constrain_range", castPyCFunctionWithKeywords(THPVariable__functional_sym_constrain_range), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_functional_sym_constrain_range_for_size", castPyCFunctionWithKeywords(THPVariable__functional_sym_constrain_range_for_size), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_make_dep_token", castPyCFunctionWithKeywords(THPVariable__make_dep_token), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_use_cudnn_ctc_loss", castPyCFunctionWithKeywords(THPVariable__use_cudnn_ctc_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cudnn_ctc_loss", castPyCFunctionWithKeywords(THPVariable__cudnn_ctc_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_use_cudnn_rnn_flatten_weight", THPVariable__use_cudnn_rnn_flatten_weight, METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cudnn_rnn_flatten_weight", castPyCFunctionWithKeywords(THPVariable__cudnn_rnn_flatten_weight), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cudnn_rnn", castPyCFunctionWithKeywords(THPVariable__cudnn_rnn), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cudnn_init_dropout_state", castPyCFunctionWithKeywords(THPVariable__cudnn_init_dropout_state), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_debug_has_internal_overlap", castPyCFunctionWithKeywords(THPVariable__debug_has_internal_overlap), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fused_dropout", castPyCFunctionWithKeywords(THPVariable__fused_dropout), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_masked_scale", castPyCFunctionWithKeywords(THPVariable__masked_scale), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"native_dropout", castPyCFunctionWithKeywords(THPVariable_native_dropout), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sobol_engine_draw", castPyCFunctionWithKeywords(THPVariable__sobol_engine_draw), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sobol_engine_ff_", castPyCFunctionWithKeywords(THPVariable__sobol_engine_ff_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sobol_engine_scramble_", castPyCFunctionWithKeywords(THPVariable__sobol_engine_scramble_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sobol_engine_initialize_state_", castPyCFunctionWithKeywords(THPVariable__sobol_engine_initialize_state_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_reshape_from_tensor", castPyCFunctionWithKeywords(THPVariable__reshape_from_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_shape_as_tensor", castPyCFunctionWithKeywords(THPVariable__shape_as_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"dropout", castPyCFunctionWithKeywords(THPVariable_dropout), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"dropout_", castPyCFunctionWithKeywords(THPVariable_dropout_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"feature_dropout", castPyCFunctionWithKeywords(THPVariable_feature_dropout), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"feature_dropout_", castPyCFunctionWithKeywords(THPVariable_feature_dropout_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"alpha_dropout", castPyCFunctionWithKeywords(THPVariable_alpha_dropout), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"alpha_dropout_", castPyCFunctionWithKeywords(THPVariable_alpha_dropout_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"feature_alpha_dropout", castPyCFunctionWithKeywords(THPVariable_feature_alpha_dropout), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"feature_alpha_dropout_", castPyCFunctionWithKeywords(THPVariable_feature_alpha_dropout_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"abs", castPyCFunctionWithKeywords(THPVariable_abs), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"abs_", castPyCFunctionWithKeywords(THPVariable_abs_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"absolute", castPyCFunctionWithKeywords(THPVariable_absolute), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"angle", castPyCFunctionWithKeywords(THPVariable_angle), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"view_as_real", castPyCFunctionWithKeywords(THPVariable_view_as_real), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"view_as_complex", castPyCFunctionWithKeywords(THPVariable_view_as_complex), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sgn", castPyCFunctionWithKeywords(THPVariable_sgn), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"real", castPyCFunctionWithKeywords(THPVariable_real), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"imag", castPyCFunctionWithKeywords(THPVariable_imag), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_conj", castPyCFunctionWithKeywords(THPVariable__conj), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conj", castPyCFunctionWithKeywords(THPVariable_conj), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_conj_physical", castPyCFunctionWithKeywords(THPVariable__conj_physical), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conj_physical", castPyCFunctionWithKeywords(THPVariable_conj_physical), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conj_physical_", castPyCFunctionWithKeywords(THPVariable_conj_physical_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"resolve_conj", castPyCFunctionWithKeywords(THPVariable_resolve_conj), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"resolve_neg", castPyCFunctionWithKeywords(THPVariable_resolve_neg), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_neg_view", castPyCFunctionWithKeywords(THPVariable__neg_view), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"acos", castPyCFunctionWithKeywords(THPVariable_acos), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"acos_", castPyCFunctionWithKeywords(THPVariable_acos_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arccos", castPyCFunctionWithKeywords(THPVariable_arccos), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arccos_", castPyCFunctionWithKeywords(THPVariable_arccos_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"avg_pool1d", castPyCFunctionWithKeywords(THPVariable_avg_pool1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"adaptive_avg_pool1d", castPyCFunctionWithKeywords(THPVariable_adaptive_avg_pool1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"adaptive_max_pool1d", castPyCFunctionWithKeywords(THPVariable_adaptive_max_pool1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"add", castPyCFunctionWithKeywords(THPVariable_add), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_add_relu", castPyCFunctionWithKeywords(THPVariable__add_relu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_add_relu_", castPyCFunctionWithKeywords(THPVariable__add_relu_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"addmv", castPyCFunctionWithKeywords(THPVariable_addmv), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"addmv_", castPyCFunctionWithKeywords(THPVariable_addmv_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"addr", castPyCFunctionWithKeywords(THPVariable_addr), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"affine_grid_generator", castPyCFunctionWithKeywords(THPVariable_affine_grid_generator), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_is_all_true", castPyCFunctionWithKeywords(THPVariable__is_all_true), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_is_any_true", castPyCFunctionWithKeywords(THPVariable__is_any_true), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_test_check_tensor", castPyCFunctionWithKeywords(THPVariable__test_check_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_test_functorch_fallback", castPyCFunctionWithKeywords(THPVariable__test_functorch_fallback), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"all", castPyCFunctionWithKeywords(THPVariable_all), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"allclose", castPyCFunctionWithKeywords(THPVariable_allclose), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"any", castPyCFunctionWithKeywords(THPVariable_any), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arange", castPyCFunctionWithKeywords(THPVariable_arange), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_dim_arange", castPyCFunctionWithKeywords(THPVariable__dim_arange), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"argmax", castPyCFunctionWithKeywords(THPVariable_argmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"argmin", castPyCFunctionWithKeywords(THPVariable_argmin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"acosh", castPyCFunctionWithKeywords(THPVariable_acosh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"acosh_", castPyCFunctionWithKeywords(THPVariable_acosh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arccosh", castPyCFunctionWithKeywords(THPVariable_arccosh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arccosh_", castPyCFunctionWithKeywords(THPVariable_arccosh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"asinh", castPyCFunctionWithKeywords(THPVariable_asinh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"asinh_", castPyCFunctionWithKeywords(THPVariable_asinh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arcsinh", castPyCFunctionWithKeywords(THPVariable_arcsinh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arcsinh_", castPyCFunctionWithKeywords(THPVariable_arcsinh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atanh", castPyCFunctionWithKeywords(THPVariable_atanh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atanh_", castPyCFunctionWithKeywords(THPVariable_atanh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arctanh", castPyCFunctionWithKeywords(THPVariable_arctanh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arctanh_", castPyCFunctionWithKeywords(THPVariable_arctanh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"as_strided", castPyCFunctionWithKeywords(THPVariable_as_strided), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"as_strided_", castPyCFunctionWithKeywords(THPVariable_as_strided_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"asin", castPyCFunctionWithKeywords(THPVariable_asin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"asin_", castPyCFunctionWithKeywords(THPVariable_asin_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arcsin", castPyCFunctionWithKeywords(THPVariable_arcsin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arcsin_", castPyCFunctionWithKeywords(THPVariable_arcsin_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atan", castPyCFunctionWithKeywords(THPVariable_atan), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atan_", castPyCFunctionWithKeywords(THPVariable_atan_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arctan", castPyCFunctionWithKeywords(THPVariable_arctan), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arctan_", castPyCFunctionWithKeywords(THPVariable_arctan_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atleast_1d", castPyCFunctionWithKeywords(THPVariable_atleast_1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atleast_2d", castPyCFunctionWithKeywords(THPVariable_atleast_2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atleast_3d", castPyCFunctionWithKeywords(THPVariable_atleast_3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"baddbmm", castPyCFunctionWithKeywords(THPVariable_baddbmm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bartlett_window", castPyCFunctionWithKeywords(THPVariable_bartlett_window), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm", castPyCFunctionWithKeywords(THPVariable_batch_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_batch_norm", castPyCFunctionWithKeywords(THPVariable_quantized_batch_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_batch_norm_impl_index", castPyCFunctionWithKeywords(THPVariable__batch_norm_impl_index), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bernoulli", castPyCFunctionWithKeywords(THPVariable_bernoulli), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bilinear", castPyCFunctionWithKeywords(THPVariable_bilinear), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"binary_cross_entropy_with_logits", castPyCFunctionWithKeywords(THPVariable_binary_cross_entropy_with_logits), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bincount", castPyCFunctionWithKeywords(THPVariable_bincount), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bitwise_not", castPyCFunctionWithKeywords(THPVariable_bitwise_not), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"copysign", castPyCFunctionWithKeywords(THPVariable_copysign), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_lazy_clone", castPyCFunctionWithKeywords(THPVariable__lazy_clone), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logical_not", castPyCFunctionWithKeywords(THPVariable_logical_not), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logical_xor", castPyCFunctionWithKeywords(THPVariable_logical_xor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logical_and", castPyCFunctionWithKeywords(THPVariable_logical_and), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logical_or", castPyCFunctionWithKeywords(THPVariable_logical_or), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"blackman_window", castPyCFunctionWithKeywords(THPVariable_blackman_window), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bmm", castPyCFunctionWithKeywords(THPVariable_bmm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"broadcast_tensors", castPyCFunctionWithKeywords(THPVariable_broadcast_tensors), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"broadcast_to", castPyCFunctionWithKeywords(THPVariable_broadcast_to), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_broadcast_to", castPyCFunctionWithKeywords(THPVariable__sparse_broadcast_to), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cat", castPyCFunctionWithKeywords(THPVariable_cat), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"concat", castPyCFunctionWithKeywords(THPVariable_concat), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"concatenate", castPyCFunctionWithKeywords(THPVariable_concatenate), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"block_diag", castPyCFunctionWithKeywords(THPVariable_block_diag), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ceil", castPyCFunctionWithKeywords(THPVariable_ceil), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ceil_", castPyCFunctionWithKeywords(THPVariable_ceil_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"chain_matmul", castPyCFunctionWithKeywords(THPVariable_chain_matmul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unsafe_chunk", castPyCFunctionWithKeywords(THPVariable_unsafe_chunk), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"chunk", castPyCFunctionWithKeywords(THPVariable_chunk), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tensor_split", castPyCFunctionWithKeywords(THPVariable_tensor_split), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clamp", castPyCFunctionWithKeywords(THPVariable_clamp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clamp_", castPyCFunctionWithKeywords(THPVariable_clamp_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clamp_max", castPyCFunctionWithKeywords(THPVariable_clamp_max), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clamp_max_", castPyCFunctionWithKeywords(THPVariable_clamp_max_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clamp_min", castPyCFunctionWithKeywords(THPVariable_clamp_min), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clamp_min_", castPyCFunctionWithKeywords(THPVariable_clamp_min_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clip", castPyCFunctionWithKeywords(THPVariable_clip), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clip_", castPyCFunctionWithKeywords(THPVariable_clip_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_is_acceptable", castPyCFunctionWithKeywords(THPVariable_cudnn_is_acceptable), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"complex", castPyCFunctionWithKeywords(THPVariable_complex), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"polar", castPyCFunctionWithKeywords(THPVariable_polar), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"constant_pad_nd", castPyCFunctionWithKeywords(THPVariable_constant_pad_nd), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"convolution", castPyCFunctionWithKeywords(THPVariable_convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_convolution", castPyCFunctionWithKeywords(THPVariable__convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_convolution_mode", castPyCFunctionWithKeywords(THPVariable__convolution_mode), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conv1d", castPyCFunctionWithKeywords(THPVariable_conv1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conv2d", castPyCFunctionWithKeywords(THPVariable_conv2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conv3d", castPyCFunctionWithKeywords(THPVariable_conv3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conv_tbc", castPyCFunctionWithKeywords(THPVariable_conv_tbc), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conv_transpose1d", castPyCFunctionWithKeywords(THPVariable_conv_transpose1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conv_transpose2d", castPyCFunctionWithKeywords(THPVariable_conv_transpose2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"conv_transpose3d", castPyCFunctionWithKeywords(THPVariable_conv_transpose3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_copy_from", castPyCFunctionWithKeywords(THPVariable__copy_from), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_copy_from_and_resize", castPyCFunctionWithKeywords(THPVariable__copy_from_and_resize), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cos", castPyCFunctionWithKeywords(THPVariable_cos), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cos_", castPyCFunctionWithKeywords(THPVariable_cos_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cosh", castPyCFunctionWithKeywords(THPVariable_cosh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cosh_", castPyCFunctionWithKeywords(THPVariable_cosh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cosine_embedding_loss", castPyCFunctionWithKeywords(THPVariable_cosine_embedding_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"count_nonzero", castPyCFunctionWithKeywords(THPVariable_count_nonzero), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cov", castPyCFunctionWithKeywords(THPVariable_cov), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"corrcoef", castPyCFunctionWithKeywords(THPVariable_corrcoef), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_affine_grid_generator", castPyCFunctionWithKeywords(THPVariable_cudnn_affine_grid_generator), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_batch_norm", castPyCFunctionWithKeywords(THPVariable_cudnn_batch_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_convolution", castPyCFunctionWithKeywords(THPVariable_cudnn_convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_convolution_transpose", castPyCFunctionWithKeywords(THPVariable_cudnn_convolution_transpose), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_mps_convolution_transpose", castPyCFunctionWithKeywords(THPVariable__mps_convolution_transpose), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_convolution_relu", castPyCFunctionWithKeywords(THPVariable_cudnn_convolution_relu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_convolution_add_relu", castPyCFunctionWithKeywords(THPVariable_cudnn_convolution_add_relu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cudnn_grid_sampler", castPyCFunctionWithKeywords(THPVariable_cudnn_grid_sampler), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cummax", castPyCFunctionWithKeywords(THPVariable_cummax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cummax_helper", castPyCFunctionWithKeywords(THPVariable__cummax_helper), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cummin", castPyCFunctionWithKeywords(THPVariable_cummin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cummin_helper", castPyCFunctionWithKeywords(THPVariable__cummin_helper), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cumprod", castPyCFunctionWithKeywords(THPVariable_cumprod), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cumsum", castPyCFunctionWithKeywords(THPVariable_cumsum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cumulative_trapezoid", castPyCFunctionWithKeywords(THPVariable_cumulative_trapezoid), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ctc_loss", castPyCFunctionWithKeywords(THPVariable_ctc_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_ctc_loss", castPyCFunctionWithKeywords(THPVariable__ctc_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"diag_embed", castPyCFunctionWithKeywords(THPVariable_diag_embed), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"diagflat", castPyCFunctionWithKeywords(THPVariable_diagflat), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"diagonal", castPyCFunctionWithKeywords(THPVariable_diagonal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"diff", castPyCFunctionWithKeywords(THPVariable_diff), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"gradient", castPyCFunctionWithKeywords(THPVariable_gradient), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"div", castPyCFunctionWithKeywords(THPVariable_div), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"divide", castPyCFunctionWithKeywords(THPVariable_divide), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"true_divide", castPyCFunctionWithKeywords(THPVariable_true_divide), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"dot", castPyCFunctionWithKeywords(THPVariable_dot), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"vdot", castPyCFunctionWithKeywords(THPVariable_vdot), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"einsum", castPyCFunctionWithKeywords(THPVariable_einsum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"embedding", castPyCFunctionWithKeywords(THPVariable_embedding), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"embedding_renorm_", castPyCFunctionWithKeywords(THPVariable_embedding_renorm_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_embedding_bag_forward_only", castPyCFunctionWithKeywords(THPVariable__embedding_bag_forward_only), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_rowwise_prune", castPyCFunctionWithKeywords(THPVariable__rowwise_prune), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"row_stack", castPyCFunctionWithKeywords(THPVariable_row_stack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"embedding_bag", castPyCFunctionWithKeywords(THPVariable_embedding_bag), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_embedding_bag", castPyCFunctionWithKeywords(THPVariable__embedding_bag), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"empty", castPyCFunctionWithKeywords(THPVariable_empty), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"empty_permuted", castPyCFunctionWithKeywords(THPVariable_empty_permuted), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_empty_affine_quantized", castPyCFunctionWithKeywords(THPVariable__empty_affine_quantized), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_empty_per_channel_affine_quantized", castPyCFunctionWithKeywords(THPVariable__empty_per_channel_affine_quantized), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_resize_output_", castPyCFunctionWithKeywords(THPVariable__resize_output_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"empty_quantized", castPyCFunctionWithKeywords(THPVariable_empty_quantized), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"empty_like", castPyCFunctionWithKeywords(THPVariable_empty_like), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"empty_strided", castPyCFunctionWithKeywords(THPVariable_empty_strided), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"erf", castPyCFunctionWithKeywords(THPVariable_erf), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"erf_", castPyCFunctionWithKeywords(THPVariable_erf_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"erfc", castPyCFunctionWithKeywords(THPVariable_erfc), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"erfc_", castPyCFunctionWithKeywords(THPVariable_erfc_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"exp", castPyCFunctionWithKeywords(THPVariable_exp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"exp_", castPyCFunctionWithKeywords(THPVariable_exp_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"exp2", castPyCFunctionWithKeywords(THPVariable_exp2), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"exp2_", castPyCFunctionWithKeywords(THPVariable_exp2_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"expm1", castPyCFunctionWithKeywords(THPVariable_expm1), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"expm1_", castPyCFunctionWithKeywords(THPVariable_expm1_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"eye", castPyCFunctionWithKeywords(THPVariable_eye), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"flatten", castPyCFunctionWithKeywords(THPVariable_flatten), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unflatten", castPyCFunctionWithKeywords(THPVariable_unflatten), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fill", castPyCFunctionWithKeywords(THPVariable_fill), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fill_", castPyCFunctionWithKeywords(THPVariable_fill_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"floor", castPyCFunctionWithKeywords(THPVariable_floor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"floor_", castPyCFunctionWithKeywords(THPVariable_floor_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"floor_divide", castPyCFunctionWithKeywords(THPVariable_floor_divide), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"frac", castPyCFunctionWithKeywords(THPVariable_frac), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"frac_", castPyCFunctionWithKeywords(THPVariable_frac_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"full", castPyCFunctionWithKeywords(THPVariable_full), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"full_like", castPyCFunctionWithKeywords(THPVariable_full_like), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"from_file", castPyCFunctionWithKeywords(THPVariable_from_file), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"gcd", castPyCFunctionWithKeywords(THPVariable_gcd), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"gcd_", castPyCFunctionWithKeywords(THPVariable_gcd_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lcm", castPyCFunctionWithKeywords(THPVariable_lcm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lcm_", castPyCFunctionWithKeywords(THPVariable_lcm_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"grid_sampler", castPyCFunctionWithKeywords(THPVariable_grid_sampler), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"grid_sampler_2d", castPyCFunctionWithKeywords(THPVariable_grid_sampler_2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_grid_sampler_2d_cpu_fallback", castPyCFunctionWithKeywords(THPVariable__grid_sampler_2d_cpu_fallback), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"grid_sampler_3d", castPyCFunctionWithKeywords(THPVariable_grid_sampler_3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hann_window", castPyCFunctionWithKeywords(THPVariable_hann_window), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hamming_window", castPyCFunctionWithKeywords(THPVariable_hamming_window), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"kaiser_window", castPyCFunctionWithKeywords(THPVariable_kaiser_window), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hinge_embedding_loss", castPyCFunctionWithKeywords(THPVariable_hinge_embedding_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"group_norm", castPyCFunctionWithKeywords(THPVariable_group_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"native_group_norm", castPyCFunctionWithKeywords(THPVariable_native_group_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fft_r2c", castPyCFunctionWithKeywords(THPVariable__fft_r2c), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fft_c2r", castPyCFunctionWithKeywords(THPVariable__fft_c2r), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fft_c2c", castPyCFunctionWithKeywords(THPVariable__fft_c2c), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_validate_compressed_sparse_indices", castPyCFunctionWithKeywords(THPVariable__validate_compressed_sparse_indices), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cufft_get_plan_cache_size", castPyCFunctionWithKeywords(THPVariable__cufft_get_plan_cache_size), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cufft_get_plan_cache_max_size", castPyCFunctionWithKeywords(THPVariable__cufft_get_plan_cache_max_size), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cufft_set_plan_cache_max_size", castPyCFunctionWithKeywords(THPVariable__cufft_set_plan_cache_max_size), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cufft_clear_plan_cache", castPyCFunctionWithKeywords(THPVariable__cufft_clear_plan_cache), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_unsafe_index", castPyCFunctionWithKeywords(THPVariable__unsafe_index), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_unsafe_masked_index", castPyCFunctionWithKeywords(THPVariable__unsafe_masked_index), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_unsafe_masked_index_put_accumulate", castPyCFunctionWithKeywords(THPVariable__unsafe_masked_index_put_accumulate), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"index_copy", castPyCFunctionWithKeywords(THPVariable_index_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"index_put_", castPyCFunctionWithKeywords(THPVariable_index_put_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"index_put", castPyCFunctionWithKeywords(THPVariable_index_put), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_unsafe_index_put", castPyCFunctionWithKeywords(THPVariable__unsafe_index_put), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_index_put_impl_", castPyCFunctionWithKeywords(THPVariable__index_put_impl_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"instance_norm", castPyCFunctionWithKeywords(THPVariable_instance_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isclose", castPyCFunctionWithKeywords(THPVariable_isclose), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isin", castPyCFunctionWithKeywords(THPVariable_isin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isnan", castPyCFunctionWithKeywords(THPVariable_isnan), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_distributed", castPyCFunctionWithKeywords(THPVariable_is_distributed), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_floating_point", castPyCFunctionWithKeywords(THPVariable_is_floating_point), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_complex", castPyCFunctionWithKeywords(THPVariable_is_complex), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_conj", castPyCFunctionWithKeywords(THPVariable_is_conj), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_is_zerotensor", castPyCFunctionWithKeywords(THPVariable__is_zerotensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_neg", castPyCFunctionWithKeywords(THPVariable_is_neg), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isreal", castPyCFunctionWithKeywords(THPVariable_isreal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_nonzero", castPyCFunctionWithKeywords(THPVariable_is_nonzero), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_same_size", castPyCFunctionWithKeywords(THPVariable_is_same_size), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_signed", castPyCFunctionWithKeywords(THPVariable_is_signed), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_inference", castPyCFunctionWithKeywords(THPVariable_is_inference), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"kl_div", castPyCFunctionWithKeywords(THPVariable_kl_div), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"kron", castPyCFunctionWithKeywords(THPVariable_kron), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"kthvalue", castPyCFunctionWithKeywords(THPVariable_kthvalue), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"layer_norm", castPyCFunctionWithKeywords(THPVariable_layer_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"native_layer_norm", castPyCFunctionWithKeywords(THPVariable_native_layer_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rms_norm", castPyCFunctionWithKeywords(THPVariable_rms_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nan_to_num", castPyCFunctionWithKeywords(THPVariable_nan_to_num), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nan_to_num_", castPyCFunctionWithKeywords(THPVariable_nan_to_num_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mkldnn_linear_backward_weights", castPyCFunctionWithKeywords(THPVariable_mkldnn_linear_backward_weights), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cslt_compress", castPyCFunctionWithKeywords(THPVariable__cslt_compress), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cslt_sparse_mm", castPyCFunctionWithKeywords(THPVariable__cslt_sparse_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_cslt_sparse_mm_search", castPyCFunctionWithKeywords(THPVariable__cslt_sparse_mm_search), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_semi_structured_tile", castPyCFunctionWithKeywords(THPVariable__sparse_semi_structured_tile), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_semi_structured_apply", castPyCFunctionWithKeywords(THPVariable__sparse_semi_structured_apply), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_semi_structured_apply_dense", castPyCFunctionWithKeywords(THPVariable__sparse_semi_structured_apply_dense), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_semi_structured_linear", castPyCFunctionWithKeywords(THPVariable__sparse_semi_structured_linear), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_semi_structured_mm", castPyCFunctionWithKeywords(THPVariable__sparse_semi_structured_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_semi_structured_addmm", castPyCFunctionWithKeywords(THPVariable__sparse_semi_structured_addmm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_mixed_dtypes_linear", castPyCFunctionWithKeywords(THPVariable__mixed_dtypes_linear), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fbgemm_linear_int8_weight_fp32_activation", castPyCFunctionWithKeywords(THPVariable_fbgemm_linear_int8_weight_fp32_activation), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fbgemm_linear_int8_weight", castPyCFunctionWithKeywords(THPVariable_fbgemm_linear_int8_weight), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fbgemm_linear_quantize_weight", castPyCFunctionWithKeywords(THPVariable_fbgemm_linear_quantize_weight), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fbgemm_pack_gemm_matrix_fp16", castPyCFunctionWithKeywords(THPVariable_fbgemm_pack_gemm_matrix_fp16), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_wrapped_linear_prepack", castPyCFunctionWithKeywords(THPVariable__wrapped_linear_prepack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_wrapped_quantized_linear_prepacked", castPyCFunctionWithKeywords(THPVariable__wrapped_quantized_linear_prepacked), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fbgemm_linear_fp16_weight_fp32_activation", castPyCFunctionWithKeywords(THPVariable_fbgemm_linear_fp16_weight_fp32_activation), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fbgemm_linear_fp16_weight", castPyCFunctionWithKeywords(THPVariable_fbgemm_linear_fp16_weight), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fbgemm_pack_quantized_matrix", castPyCFunctionWithKeywords(THPVariable_fbgemm_pack_quantized_matrix), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ldexp", castPyCFunctionWithKeywords(THPVariable_ldexp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ldexp_", castPyCFunctionWithKeywords(THPVariable_ldexp_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"linspace", castPyCFunctionWithKeywords(THPVariable_linspace), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log", castPyCFunctionWithKeywords(THPVariable_log), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log_", castPyCFunctionWithKeywords(THPVariable_log_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log10", castPyCFunctionWithKeywords(THPVariable_log10), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log10_", castPyCFunctionWithKeywords(THPVariable_log10_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log1p", castPyCFunctionWithKeywords(THPVariable_log1p), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log1p_", castPyCFunctionWithKeywords(THPVariable_log1p_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log2", castPyCFunctionWithKeywords(THPVariable_log2), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log2_", castPyCFunctionWithKeywords(THPVariable_log2_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logaddexp", castPyCFunctionWithKeywords(THPVariable_logaddexp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logaddexp2", castPyCFunctionWithKeywords(THPVariable_logaddexp2), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"xlogy", castPyCFunctionWithKeywords(THPVariable_xlogy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"xlogy_", castPyCFunctionWithKeywords(THPVariable_xlogy_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logspace", castPyCFunctionWithKeywords(THPVariable_logspace), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"log_softmax", castPyCFunctionWithKeywords(THPVariable_log_softmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_log_softmax", castPyCFunctionWithKeywords(THPVariable__log_softmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_log_softmax_backward_data", castPyCFunctionWithKeywords(THPVariable__log_softmax_backward_data), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_logcumsumexp", castPyCFunctionWithKeywords(THPVariable__logcumsumexp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logcumsumexp", castPyCFunctionWithKeywords(THPVariable_logcumsumexp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logsumexp", castPyCFunctionWithKeywords(THPVariable_logsumexp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"margin_ranking_loss", castPyCFunctionWithKeywords(THPVariable_margin_ranking_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"matmul", castPyCFunctionWithKeywords(THPVariable_matmul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"matrix_power", castPyCFunctionWithKeywords(THPVariable_matrix_power), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"matrix_exp", castPyCFunctionWithKeywords(THPVariable_matrix_exp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_aminmax", castPyCFunctionWithKeywords(THPVariable__aminmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"aminmax", castPyCFunctionWithKeywords(THPVariable_aminmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_compute_linear_combination", castPyCFunctionWithKeywords(THPVariable__compute_linear_combination), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"max", castPyCFunctionWithKeywords(THPVariable_max), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"amax", castPyCFunctionWithKeywords(THPVariable_amax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"max_pool1d_with_indices", castPyCFunctionWithKeywords(THPVariable_max_pool1d_with_indices), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"max_pool1d", castPyCFunctionWithKeywords(THPVariable_max_pool1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"max_pool2d", castPyCFunctionWithKeywords(THPVariable_max_pool2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mkldnn_max_pool2d", castPyCFunctionWithKeywords(THPVariable_mkldnn_max_pool2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mkldnn_max_pool3d", castPyCFunctionWithKeywords(THPVariable_mkldnn_max_pool3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_max_pool1d", castPyCFunctionWithKeywords(THPVariable_quantized_max_pool1d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_max_pool2d", castPyCFunctionWithKeywords(THPVariable_quantized_max_pool2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_max_pool3d", castPyCFunctionWithKeywords(THPVariable_quantized_max_pool3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"max_pool3d", castPyCFunctionWithKeywords(THPVariable_max_pool3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mean", castPyCFunctionWithKeywords(THPVariable_mean), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nanmean", castPyCFunctionWithKeywords(THPVariable_nanmean), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"median", castPyCFunctionWithKeywords(THPVariable_median), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nanmedian", castPyCFunctionWithKeywords(THPVariable_nanmedian), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"min", castPyCFunctionWithKeywords(THPVariable_min), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"amin", castPyCFunctionWithKeywords(THPVariable_amin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_mps_convolution", castPyCFunctionWithKeywords(THPVariable__mps_convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mkldnn_convolution", castPyCFunctionWithKeywords(THPVariable_mkldnn_convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mkldnn_rnn_layer", castPyCFunctionWithKeywords(THPVariable_mkldnn_rnn_layer), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"miopen_batch_norm", castPyCFunctionWithKeywords(THPVariable_miopen_batch_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"miopen_convolution", castPyCFunctionWithKeywords(THPVariable_miopen_convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"miopen_convolution_transpose", castPyCFunctionWithKeywords(THPVariable_miopen_convolution_transpose), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"miopen_depthwise_convolution", castPyCFunctionWithKeywords(THPVariable_miopen_depthwise_convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"miopen_convolution_relu", castPyCFunctionWithKeywords(THPVariable_miopen_convolution_relu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"miopen_convolution_add_relu", castPyCFunctionWithKeywords(THPVariable_miopen_convolution_add_relu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"miopen_rnn", castPyCFunctionWithKeywords(THPVariable_miopen_rnn), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mm", castPyCFunctionWithKeywords(THPVariable_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_int_mm", castPyCFunctionWithKeywords(THPVariable__int_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_convert_weight_to_int4pack", castPyCFunctionWithKeywords(THPVariable__convert_weight_to_int4pack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_weight_int4pack_mm", castPyCFunctionWithKeywords(THPVariable__weight_int4pack_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_convert_weight_to_int4pack_for_cpu", castPyCFunctionWithKeywords(THPVariable__convert_weight_to_int4pack_for_cpu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_weight_int4pack_mm_for_cpu", castPyCFunctionWithKeywords(THPVariable__weight_int4pack_mm_for_cpu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_dyn_quant_pack_4bit_weight", castPyCFunctionWithKeywords(THPVariable__dyn_quant_pack_4bit_weight), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_dyn_quant_matmul_4bit", castPyCFunctionWithKeywords(THPVariable__dyn_quant_matmul_4bit), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_weight_int8pack_mm", castPyCFunctionWithKeywords(THPVariable__weight_int8pack_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_sparse_matmul", castPyCFunctionWithKeywords(THPVariable__sparse_sparse_matmul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mode", castPyCFunctionWithKeywords(THPVariable_mode), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mul", castPyCFunctionWithKeywords(THPVariable_mul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"multiply", castPyCFunctionWithKeywords(THPVariable_multiply), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mv", castPyCFunctionWithKeywords(THPVariable_mv), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mvlgamma", castPyCFunctionWithKeywords(THPVariable_mvlgamma), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"narrow_copy", castPyCFunctionWithKeywords(THPVariable_narrow_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"narrow", castPyCFunctionWithKeywords(THPVariable_narrow), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"native_batch_norm", castPyCFunctionWithKeywords(THPVariable_native_batch_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_native_batch_norm_legit", castPyCFunctionWithKeywords(THPVariable__native_batch_norm_legit), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_native_batch_norm_legit_no_training", castPyCFunctionWithKeywords(THPVariable__native_batch_norm_legit_no_training), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm_stats", castPyCFunctionWithKeywords(THPVariable_batch_norm_stats), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm_elemt", castPyCFunctionWithKeywords(THPVariable_batch_norm_elemt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm_gather_stats", castPyCFunctionWithKeywords(THPVariable_batch_norm_gather_stats), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm_gather_stats_with_counts", castPyCFunctionWithKeywords(THPVariable_batch_norm_gather_stats_with_counts), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm_backward_reduce", castPyCFunctionWithKeywords(THPVariable_batch_norm_backward_reduce), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm_backward_elemt", castPyCFunctionWithKeywords(THPVariable_batch_norm_backward_elemt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"batch_norm_update_stats", castPyCFunctionWithKeywords(THPVariable_batch_norm_update_stats), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"is_vulkan_available", THPVariable_is_vulkan_available, METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nnpack_available", THPVariable__nnpack_available, METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nnpack_spatial_convolution", castPyCFunctionWithKeywords(THPVariable__nnpack_spatial_convolution), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ones", castPyCFunctionWithKeywords(THPVariable_ones), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ones_like", castPyCFunctionWithKeywords(THPVariable_ones_like), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"pairwise_distance", castPyCFunctionWithKeywords(THPVariable_pairwise_distance), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cdist", castPyCFunctionWithKeywords(THPVariable_cdist), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_euclidean_dist", castPyCFunctionWithKeywords(THPVariable__euclidean_dist), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"pdist", castPyCFunctionWithKeywords(THPVariable_pdist), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cosine_similarity", castPyCFunctionWithKeywords(THPVariable_cosine_similarity), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"permute", castPyCFunctionWithKeywords(THPVariable_permute), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"movedim", castPyCFunctionWithKeywords(THPVariable_movedim), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"moveaxis", castPyCFunctionWithKeywords(THPVariable_moveaxis), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"adjoint", castPyCFunctionWithKeywords(THPVariable_adjoint), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"pixel_shuffle", castPyCFunctionWithKeywords(THPVariable_pixel_shuffle), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"pixel_unshuffle", castPyCFunctionWithKeywords(THPVariable_pixel_unshuffle), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"channel_shuffle", castPyCFunctionWithKeywords(THPVariable_channel_shuffle), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"native_channel_shuffle", castPyCFunctionWithKeywords(THPVariable_native_channel_shuffle), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_pin_memory", castPyCFunctionWithKeywords(THPVariable__pin_memory), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"pinverse", castPyCFunctionWithKeywords(THPVariable_pinverse), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"poisson_nll_loss", castPyCFunctionWithKeywords(THPVariable_poisson_nll_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rad2deg", castPyCFunctionWithKeywords(THPVariable_rad2deg), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rad2deg_", castPyCFunctionWithKeywords(THPVariable_rad2deg_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"deg2rad", castPyCFunctionWithKeywords(THPVariable_deg2rad), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"deg2rad_", castPyCFunctionWithKeywords(THPVariable_deg2rad_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"scalar_tensor", castPyCFunctionWithKeywords(THPVariable_scalar_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rand", castPyCFunctionWithKeywords(THPVariable_rand), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rand_like", castPyCFunctionWithKeywords(THPVariable_rand_like), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"randint", castPyCFunctionWithKeywords(THPVariable_randint), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"randint_like", castPyCFunctionWithKeywords(THPVariable_randint_like), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"randn", castPyCFunctionWithKeywords(THPVariable_randn), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"randn_like", castPyCFunctionWithKeywords(THPVariable_randn_like), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"randperm", castPyCFunctionWithKeywords(THPVariable_randperm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ravel", castPyCFunctionWithKeywords(THPVariable_ravel), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"reciprocal", castPyCFunctionWithKeywords(THPVariable_reciprocal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"reciprocal_", castPyCFunctionWithKeywords(THPVariable_reciprocal_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"neg", castPyCFunctionWithKeywords(THPVariable_neg), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"neg_", castPyCFunctionWithKeywords(THPVariable_neg_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"negative", castPyCFunctionWithKeywords(THPVariable_negative), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"negative_", castPyCFunctionWithKeywords(THPVariable_negative_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"repeat_interleave", castPyCFunctionWithKeywords(THPVariable_repeat_interleave), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"reshape", castPyCFunctionWithKeywords(THPVariable_reshape), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_mkldnn_reshape", castPyCFunctionWithKeywords(THPVariable__mkldnn_reshape), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"round", castPyCFunctionWithKeywords(THPVariable_round), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"round_", castPyCFunctionWithKeywords(THPVariable_round_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rrelu", castPyCFunctionWithKeywords(THPVariable_rrelu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rrelu_", castPyCFunctionWithKeywords(THPVariable_rrelu_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"relu", castPyCFunctionWithKeywords(THPVariable_relu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"relu_", castPyCFunctionWithKeywords(THPVariable_relu_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"prelu", castPyCFunctionWithKeywords(THPVariable_prelu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_prelu_kernel", castPyCFunctionWithKeywords(THPVariable__prelu_kernel), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hardshrink", castPyCFunctionWithKeywords(THPVariable_hardshrink), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rsqrt", castPyCFunctionWithKeywords(THPVariable_rsqrt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rsqrt_", castPyCFunctionWithKeywords(THPVariable_rsqrt_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"select", castPyCFunctionWithKeywords(THPVariable_select), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"selu", castPyCFunctionWithKeywords(THPVariable_selu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"selu_", castPyCFunctionWithKeywords(THPVariable_selu_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"celu", castPyCFunctionWithKeywords(THPVariable_celu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"celu_", castPyCFunctionWithKeywords(THPVariable_celu_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sigmoid", castPyCFunctionWithKeywords(THPVariable_sigmoid), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sigmoid_", castPyCFunctionWithKeywords(THPVariable_sigmoid_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logit", castPyCFunctionWithKeywords(THPVariable_logit), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logit_", castPyCFunctionWithKeywords(THPVariable_logit_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sin", castPyCFunctionWithKeywords(THPVariable_sin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sin_", castPyCFunctionWithKeywords(THPVariable_sin_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sinc", castPyCFunctionWithKeywords(THPVariable_sinc), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sinc_", castPyCFunctionWithKeywords(THPVariable_sinc_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sinh", castPyCFunctionWithKeywords(THPVariable_sinh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sinh_", castPyCFunctionWithKeywords(THPVariable_sinh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"detach", castPyCFunctionWithKeywords(THPVariable_detach), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"detach_", castPyCFunctionWithKeywords(THPVariable_detach_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"slice_inverse", castPyCFunctionWithKeywords(THPVariable_slice_inverse), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"slice_scatter", castPyCFunctionWithKeywords(THPVariable_slice_scatter), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"select_scatter", castPyCFunctionWithKeywords(THPVariable_select_scatter), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"diagonal_scatter", castPyCFunctionWithKeywords(THPVariable_diagonal_scatter), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"as_strided_scatter", castPyCFunctionWithKeywords(THPVariable_as_strided_scatter), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"smm", castPyCFunctionWithKeywords(THPVariable_smm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"softmax", castPyCFunctionWithKeywords(THPVariable_softmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_softmax", castPyCFunctionWithKeywords(THPVariable__softmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_softmax_backward_data", castPyCFunctionWithKeywords(THPVariable__softmax_backward_data), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unsafe_split", castPyCFunctionWithKeywords(THPVariable_unsafe_split), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"split", castPyCFunctionWithKeywords(THPVariable_split), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unsafe_split_with_sizes", castPyCFunctionWithKeywords(THPVariable_unsafe_split_with_sizes), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"split_with_sizes", castPyCFunctionWithKeywords(THPVariable_split_with_sizes), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hsplit", castPyCFunctionWithKeywords(THPVariable_hsplit), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"vsplit", castPyCFunctionWithKeywords(THPVariable_vsplit), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"dsplit", castPyCFunctionWithKeywords(THPVariable_dsplit), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"squeeze", castPyCFunctionWithKeywords(THPVariable_squeeze), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sspaddmm", castPyCFunctionWithKeywords(THPVariable_sspaddmm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_chunk_cat", castPyCFunctionWithKeywords(THPVariable__chunk_cat), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"stack", castPyCFunctionWithKeywords(THPVariable_stack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_stack", castPyCFunctionWithKeywords(THPVariable__stack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hstack", castPyCFunctionWithKeywords(THPVariable_hstack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"vstack", castPyCFunctionWithKeywords(THPVariable_vstack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"dstack", castPyCFunctionWithKeywords(THPVariable_dstack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"stft", castPyCFunctionWithKeywords(THPVariable_stft), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"istft", castPyCFunctionWithKeywords(THPVariable_istft), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sum", castPyCFunctionWithKeywords(THPVariable_sum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nansum", castPyCFunctionWithKeywords(THPVariable_nansum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sqrt", castPyCFunctionWithKeywords(THPVariable_sqrt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sqrt_", castPyCFunctionWithKeywords(THPVariable_sqrt_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"square", castPyCFunctionWithKeywords(THPVariable_square), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"square_", castPyCFunctionWithKeywords(THPVariable_square_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"std", castPyCFunctionWithKeywords(THPVariable_std), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"std_mean", castPyCFunctionWithKeywords(THPVariable_std_mean), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"prod", castPyCFunctionWithKeywords(THPVariable_prod), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"t", castPyCFunctionWithKeywords(THPVariable_t), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tan", castPyCFunctionWithKeywords(THPVariable_tan), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tan_", castPyCFunctionWithKeywords(THPVariable_tan_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tanh", castPyCFunctionWithKeywords(THPVariable_tanh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tanh_", castPyCFunctionWithKeywords(THPVariable_tanh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tensordot", castPyCFunctionWithKeywords(THPVariable_tensordot), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"threshold", castPyCFunctionWithKeywords(THPVariable_threshold), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"threshold_", castPyCFunctionWithKeywords(THPVariable_threshold_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tile", castPyCFunctionWithKeywords(THPVariable_tile), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"transpose", castPyCFunctionWithKeywords(THPVariable_transpose), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_mkldnn_transpose", castPyCFunctionWithKeywords(THPVariable__mkldnn_transpose), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_mkldnn_transpose_", castPyCFunctionWithKeywords(THPVariable__mkldnn_transpose_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"flip", castPyCFunctionWithKeywords(THPVariable_flip), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fliplr", castPyCFunctionWithKeywords(THPVariable_fliplr), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"flipud", castPyCFunctionWithKeywords(THPVariable_flipud), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"roll", castPyCFunctionWithKeywords(THPVariable_roll), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rot90", castPyCFunctionWithKeywords(THPVariable_rot90), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"trapezoid", castPyCFunctionWithKeywords(THPVariable_trapezoid), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"trapz", castPyCFunctionWithKeywords(THPVariable_trapz), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_transform_bias_rescale_qkv", castPyCFunctionWithKeywords(THPVariable__transform_bias_rescale_qkv), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_tensor_from_mask", castPyCFunctionWithKeywords(THPVariable__nested_tensor_from_mask), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_tensor_from_mask_left_aligned", castPyCFunctionWithKeywords(THPVariable__nested_tensor_from_mask_left_aligned), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_from_padded", castPyCFunctionWithKeywords(THPVariable__nested_from_padded), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_from_padded_and_nested_example", castPyCFunctionWithKeywords(THPVariable__nested_from_padded_and_nested_example), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_view_from_buffer", castPyCFunctionWithKeywords(THPVariable__nested_view_from_buffer), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_view_from_buffer_copy", castPyCFunctionWithKeywords(THPVariable__nested_view_from_buffer_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_view_from_jagged", castPyCFunctionWithKeywords(THPVariable__nested_view_from_jagged), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_view_from_jagged_copy", castPyCFunctionWithKeywords(THPVariable__nested_view_from_jagged_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_values", castPyCFunctionWithKeywords(THPVariable__nested_get_values), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_values_copy", castPyCFunctionWithKeywords(THPVariable__nested_get_values_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_offsets", castPyCFunctionWithKeywords(THPVariable__nested_get_offsets), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_lengths", castPyCFunctionWithKeywords(THPVariable__nested_get_lengths), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_ragged_idx", castPyCFunctionWithKeywords(THPVariable__nested_get_ragged_idx), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_min_seqlen", castPyCFunctionWithKeywords(THPVariable__nested_get_min_seqlen), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_max_seqlen", castPyCFunctionWithKeywords(THPVariable__nested_get_max_seqlen), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_get_jagged_dummy", castPyCFunctionWithKeywords(THPVariable__nested_get_jagged_dummy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_compute_contiguous_strides_offsets", castPyCFunctionWithKeywords(THPVariable__nested_compute_contiguous_strides_offsets), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_trilinear", castPyCFunctionWithKeywords(THPVariable__trilinear), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"triplet_margin_loss", castPyCFunctionWithKeywords(THPVariable_triplet_margin_loss), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"trunc", castPyCFunctionWithKeywords(THPVariable_trunc), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"trunc_", castPyCFunctionWithKeywords(THPVariable_trunc_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fix", castPyCFunctionWithKeywords(THPVariable_fix), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fix_", castPyCFunctionWithKeywords(THPVariable_fix_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_has_compatible_shallow_copy_type", castPyCFunctionWithKeywords(THPVariable__has_compatible_shallow_copy_type), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_unique", castPyCFunctionWithKeywords(THPVariable__unique), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unique_dim", castPyCFunctionWithKeywords(THPVariable_unique_dim), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unique_consecutive", castPyCFunctionWithKeywords(THPVariable_unique_consecutive), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_unique2", castPyCFunctionWithKeywords(THPVariable__unique2), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unsqueeze", castPyCFunctionWithKeywords(THPVariable_unsqueeze), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"vander", castPyCFunctionWithKeywords(THPVariable_vander), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"var", castPyCFunctionWithKeywords(THPVariable_var), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"var_mean", castPyCFunctionWithKeywords(THPVariable_var_mean), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"where", castPyCFunctionWithKeywords(THPVariable_where), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"norm_except_dim", castPyCFunctionWithKeywords(THPVariable_norm_except_dim), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_weight_norm", castPyCFunctionWithKeywords(THPVariable__weight_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_weight_norm_interface", castPyCFunctionWithKeywords(THPVariable__weight_norm_interface), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"zeros", castPyCFunctionWithKeywords(THPVariable_zeros), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_efficientzerotensor", castPyCFunctionWithKeywords(THPVariable__efficientzerotensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"zeros_like", castPyCFunctionWithKeywords(THPVariable_zeros_like), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_standard_gamma_grad", castPyCFunctionWithKeywords(THPVariable__standard_gamma_grad), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_standard_gamma", castPyCFunctionWithKeywords(THPVariable__standard_gamma), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_dirichlet_grad", castPyCFunctionWithKeywords(THPVariable__dirichlet_grad), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sample_dirichlet", castPyCFunctionWithKeywords(THPVariable__sample_dirichlet), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"poisson", castPyCFunctionWithKeywords(THPVariable_poisson), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"binomial", castPyCFunctionWithKeywords(THPVariable_binomial), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"native_norm", castPyCFunctionWithKeywords(THPVariable_native_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_sum", castPyCFunctionWithKeywords(THPVariable__sparse_sum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_csr_sum", castPyCFunctionWithKeywords(THPVariable__sparse_csr_sum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_csr_prod", castPyCFunctionWithKeywords(THPVariable__sparse_csr_prod), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_softmax_backward_data", castPyCFunctionWithKeywords(THPVariable__sparse_softmax_backward_data), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_log_softmax_backward_data", castPyCFunctionWithKeywords(THPVariable__sparse_log_softmax_backward_data), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"norm", castPyCFunctionWithKeywords(THPVariable_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"frexp", castPyCFunctionWithKeywords(THPVariable_frexp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"frobenius_norm", castPyCFunctionWithKeywords(THPVariable_frobenius_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nuclear_norm", castPyCFunctionWithKeywords(THPVariable_nuclear_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"clone", castPyCFunctionWithKeywords(THPVariable_clone), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"positive", castPyCFunctionWithKeywords(THPVariable_positive), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"resize_as_", castPyCFunctionWithKeywords(THPVariable_resize_as_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"resize_as_sparse_", castPyCFunctionWithKeywords(THPVariable_resize_as_sparse_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"zero_", castPyCFunctionWithKeywords(THPVariable_zero_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sub", castPyCFunctionWithKeywords(THPVariable_sub), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"subtract", castPyCFunctionWithKeywords(THPVariable_subtract), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rsub", castPyCFunctionWithKeywords(THPVariable_rsub), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"heaviside", castPyCFunctionWithKeywords(THPVariable_heaviside), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"addmm", castPyCFunctionWithKeywords(THPVariable_addmm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_addmm_activation", castPyCFunctionWithKeywords(THPVariable__addmm_activation), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_mm", castPyCFunctionWithKeywords(THPVariable__scaled_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_grouped_mm", castPyCFunctionWithKeywords(THPVariable__scaled_grouped_mm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_validate_sparse_coo_tensor_args", castPyCFunctionWithKeywords(THPVariable__validate_sparse_coo_tensor_args), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_validate_sparse_compressed_tensor_args", castPyCFunctionWithKeywords(THPVariable__validate_sparse_compressed_tensor_args), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_validate_sparse_csr_tensor_args", castPyCFunctionWithKeywords(THPVariable__validate_sparse_csr_tensor_args), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_validate_sparse_csc_tensor_args", castPyCFunctionWithKeywords(THPVariable__validate_sparse_csc_tensor_args), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_validate_sparse_bsr_tensor_args", castPyCFunctionWithKeywords(THPVariable__validate_sparse_bsr_tensor_args), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_validate_sparse_bsc_tensor_args", castPyCFunctionWithKeywords(THPVariable__validate_sparse_bsc_tensor_args), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_to_cpu", castPyCFunctionWithKeywords(THPVariable__to_cpu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_coalesce", castPyCFunctionWithKeywords(THPVariable__coalesce), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hspmm", castPyCFunctionWithKeywords(THPVariable_hspmm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unbind", castPyCFunctionWithKeywords(THPVariable_unbind), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_to_sparse_semi_structured", castPyCFunctionWithKeywords(THPVariable__to_sparse_semi_structured), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantize_per_tensor_dynamic", castPyCFunctionWithKeywords(THPVariable_quantize_per_tensor_dynamic), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantize_per_tensor", castPyCFunctionWithKeywords(THPVariable_quantize_per_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantize_per_channel", castPyCFunctionWithKeywords(THPVariable_quantize_per_channel), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"dequantize", castPyCFunctionWithKeywords(THPVariable_dequantize), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"q_scale", castPyCFunctionWithKeywords(THPVariable_q_scale), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"q_zero_point", castPyCFunctionWithKeywords(THPVariable_q_zero_point), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"q_per_channel_scales", castPyCFunctionWithKeywords(THPVariable_q_per_channel_scales), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"q_per_channel_zero_points", castPyCFunctionWithKeywords(THPVariable_q_per_channel_zero_points), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"q_per_channel_axis", castPyCFunctionWithKeywords(THPVariable_q_per_channel_axis), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"int_repr", castPyCFunctionWithKeywords(THPVariable_int_repr), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_make_per_tensor_quantized_tensor", castPyCFunctionWithKeywords(THPVariable__make_per_tensor_quantized_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_make_per_channel_quantized_tensor", castPyCFunctionWithKeywords(THPVariable__make_per_channel_quantized_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fake_quantize_per_tensor_affine", castPyCFunctionWithKeywords(THPVariable_fake_quantize_per_tensor_affine), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fake_quantize_per_tensor_affine_cachemask_tensor_qparams", castPyCFunctionWithKeywords(THPVariable__fake_quantize_per_tensor_affine_cachemask_tensor_qparams), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fake_quantize_learnable_per_tensor_affine", castPyCFunctionWithKeywords(THPVariable__fake_quantize_learnable_per_tensor_affine), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fake_quantize_per_channel_affine", castPyCFunctionWithKeywords(THPVariable_fake_quantize_per_channel_affine), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fake_quantize_learnable_per_channel_affine", castPyCFunctionWithKeywords(THPVariable__fake_quantize_learnable_per_channel_affine), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fused_moving_avg_obs_fake_quant", castPyCFunctionWithKeywords(THPVariable_fused_moving_avg_obs_fake_quant), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fused_moving_avg_obs_fq_helper", castPyCFunctionWithKeywords(THPVariable__fused_moving_avg_obs_fq_helper), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_choose_qparams_per_tensor", castPyCFunctionWithKeywords(THPVariable__choose_qparams_per_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_saturate_weight_to_fp16", castPyCFunctionWithKeywords(THPVariable__saturate_weight_to_fp16), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"choose_qparams_optimized", castPyCFunctionWithKeywords(THPVariable_choose_qparams_optimized), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"meshgrid", castPyCFunctionWithKeywords(THPVariable_meshgrid), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cartesian_prod", castPyCFunctionWithKeywords(THPVariable_cartesian_prod), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"combinations", castPyCFunctionWithKeywords(THPVariable_combinations), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"result_type", castPyCFunctionWithKeywords(THPVariable_result_type), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"can_cast", castPyCFunctionWithKeywords(THPVariable_can_cast), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"promote_types", castPyCFunctionWithKeywords(THPVariable_promote_types), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_lstm_mps", castPyCFunctionWithKeywords(THPVariable__lstm_mps), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lstm", castPyCFunctionWithKeywords(THPVariable_lstm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"gru", castPyCFunctionWithKeywords(THPVariable_gru), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rnn_tanh", castPyCFunctionWithKeywords(THPVariable_rnn_tanh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rnn_relu", castPyCFunctionWithKeywords(THPVariable_rnn_relu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lstm_cell", castPyCFunctionWithKeywords(THPVariable_lstm_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"gru_cell", castPyCFunctionWithKeywords(THPVariable_gru_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rnn_tanh_cell", castPyCFunctionWithKeywords(THPVariable_rnn_tanh_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"rnn_relu_cell", castPyCFunctionWithKeywords(THPVariable_rnn_relu_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_lstm_cell", castPyCFunctionWithKeywords(THPVariable_quantized_lstm_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_gru_cell", castPyCFunctionWithKeywords(THPVariable_quantized_gru_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_rnn_relu_cell", castPyCFunctionWithKeywords(THPVariable_quantized_rnn_relu_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantized_rnn_tanh_cell", castPyCFunctionWithKeywords(THPVariable_quantized_rnn_tanh_cell), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_pack_padded_sequence", castPyCFunctionWithKeywords(THPVariable__pack_padded_sequence), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_pad_packed_sequence", castPyCFunctionWithKeywords(THPVariable__pad_packed_sequence), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"masked_fill", castPyCFunctionWithKeywords(THPVariable_masked_fill), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"masked_scatter", castPyCFunctionWithKeywords(THPVariable_masked_scatter), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_masked_softmax", castPyCFunctionWithKeywords(THPVariable__masked_softmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"put", castPyCFunctionWithKeywords(THPVariable_put), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"index_add", castPyCFunctionWithKeywords(THPVariable_index_add), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"index_reduce", castPyCFunctionWithKeywords(THPVariable_index_reduce), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"index_fill", castPyCFunctionWithKeywords(THPVariable_index_fill), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"scatter", castPyCFunctionWithKeywords(THPVariable_scatter), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"scatter_add", castPyCFunctionWithKeywords(THPVariable_scatter_add), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"scatter_reduce", castPyCFunctionWithKeywords(THPVariable_scatter_reduce), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bitwise_and", castPyCFunctionWithKeywords(THPVariable_bitwise_and), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"__and__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bitwise_or", castPyCFunctionWithKeywords(THPVariable_bitwise_or), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"__or__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bitwise_xor", castPyCFunctionWithKeywords(THPVariable_bitwise_xor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"__xor__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"__lshift__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bitwise_left_shift", castPyCFunctionWithKeywords(THPVariable_bitwise_left_shift), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"__rshift__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bitwise_right_shift", castPyCFunctionWithKeywords(THPVariable_bitwise_right_shift), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"addbmm", castPyCFunctionWithKeywords(THPVariable_addbmm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"diag", castPyCFunctionWithKeywords(THPVariable_diag), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cross", castPyCFunctionWithKeywords(THPVariable_cross), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"triu", castPyCFunctionWithKeywords(THPVariable_triu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tril", castPyCFunctionWithKeywords(THPVariable_tril), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"tril_indices", castPyCFunctionWithKeywords(THPVariable_tril_indices), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"triu_indices", castPyCFunctionWithKeywords(THPVariable_triu_indices), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"trace", castPyCFunctionWithKeywords(THPVariable_trace), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ne", castPyCFunctionWithKeywords(THPVariable_ne), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"not_equal", castPyCFunctionWithKeywords(THPVariable_not_equal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"eq", castPyCFunctionWithKeywords(THPVariable_eq), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ge", castPyCFunctionWithKeywords(THPVariable_ge), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"greater_equal", castPyCFunctionWithKeywords(THPVariable_greater_equal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"le", castPyCFunctionWithKeywords(THPVariable_le), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"less_equal", castPyCFunctionWithKeywords(THPVariable_less_equal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"gt", castPyCFunctionWithKeywords(THPVariable_gt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"greater", castPyCFunctionWithKeywords(THPVariable_greater), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lt", castPyCFunctionWithKeywords(THPVariable_lt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"less", castPyCFunctionWithKeywords(THPVariable_less), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"take", castPyCFunctionWithKeywords(THPVariable_take), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"take_along_dim", castPyCFunctionWithKeywords(THPVariable_take_along_dim), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"index_select", castPyCFunctionWithKeywords(THPVariable_index_select), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"masked_select", castPyCFunctionWithKeywords(THPVariable_masked_select), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nonzero_static", castPyCFunctionWithKeywords(THPVariable_nonzero_static), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"argwhere", castPyCFunctionWithKeywords(THPVariable_argwhere), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"gather", castPyCFunctionWithKeywords(THPVariable_gather), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"addcmul", castPyCFunctionWithKeywords(THPVariable_addcmul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"addcdiv", castPyCFunctionWithKeywords(THPVariable_addcdiv), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"triangular_solve", castPyCFunctionWithKeywords(THPVariable_triangular_solve), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_linalg_check_errors", castPyCFunctionWithKeywords(THPVariable__linalg_check_errors), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"svd", castPyCFunctionWithKeywords(THPVariable_svd), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"swapaxes", castPyCFunctionWithKeywords(THPVariable_swapaxes), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"swapdims", castPyCFunctionWithKeywords(THPVariable_swapdims), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cholesky", castPyCFunctionWithKeywords(THPVariable_cholesky), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cholesky_solve", castPyCFunctionWithKeywords(THPVariable_cholesky_solve), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"cholesky_inverse", castPyCFunctionWithKeywords(THPVariable_cholesky_inverse), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"qr", castPyCFunctionWithKeywords(THPVariable_qr), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"geqrf", castPyCFunctionWithKeywords(THPVariable_geqrf), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"orgqr", castPyCFunctionWithKeywords(THPVariable_orgqr), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ormqr", castPyCFunctionWithKeywords(THPVariable_ormqr), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_lu_with_info", castPyCFunctionWithKeywords(THPVariable__lu_with_info), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lu_solve", castPyCFunctionWithKeywords(THPVariable_lu_solve), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lu_unpack", castPyCFunctionWithKeywords(THPVariable_lu_unpack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"multinomial", castPyCFunctionWithKeywords(THPVariable_multinomial), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lgamma", castPyCFunctionWithKeywords(THPVariable_lgamma), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"digamma", castPyCFunctionWithKeywords(THPVariable_digamma), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"polygamma", castPyCFunctionWithKeywords(THPVariable_polygamma), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"erfinv", castPyCFunctionWithKeywords(THPVariable_erfinv), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"i0", castPyCFunctionWithKeywords(THPVariable_i0), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"i0_", castPyCFunctionWithKeywords(THPVariable_i0_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sign", castPyCFunctionWithKeywords(THPVariable_sign), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"signbit", castPyCFunctionWithKeywords(THPVariable_signbit), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"dist", castPyCFunctionWithKeywords(THPVariable_dist), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"atan2", castPyCFunctionWithKeywords(THPVariable_atan2), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"arctan2", castPyCFunctionWithKeywords(THPVariable_arctan2), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"lerp", castPyCFunctionWithKeywords(THPVariable_lerp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"histc", castPyCFunctionWithKeywords(THPVariable_histc), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"histogram", castPyCFunctionWithKeywords(THPVariable_histogram), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_histogramdd_bin_edges", castPyCFunctionWithKeywords(THPVariable__histogramdd_bin_edges), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_histogramdd_from_bin_cts", castPyCFunctionWithKeywords(THPVariable__histogramdd_from_bin_cts), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_histogramdd_from_bin_tensors", castPyCFunctionWithKeywords(THPVariable__histogramdd_from_bin_tensors), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"histogramdd", castPyCFunctionWithKeywords(THPVariable_histogramdd), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fmod", castPyCFunctionWithKeywords(THPVariable_fmod), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"hypot", castPyCFunctionWithKeywords(THPVariable_hypot), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"igamma", castPyCFunctionWithKeywords(THPVariable_igamma), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"igammac", castPyCFunctionWithKeywords(THPVariable_igammac), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nextafter", castPyCFunctionWithKeywords(THPVariable_nextafter), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"remainder", castPyCFunctionWithKeywords(THPVariable_remainder), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fmin", castPyCFunctionWithKeywords(THPVariable_fmin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"fmax", castPyCFunctionWithKeywords(THPVariable_fmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"maximum", castPyCFunctionWithKeywords(THPVariable_maximum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"minimum", castPyCFunctionWithKeywords(THPVariable_minimum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"quantile", castPyCFunctionWithKeywords(THPVariable_quantile), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"nanquantile", castPyCFunctionWithKeywords(THPVariable_nanquantile), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"sort", castPyCFunctionWithKeywords(THPVariable_sort), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"msort", castPyCFunctionWithKeywords(THPVariable_msort), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"argsort", castPyCFunctionWithKeywords(THPVariable_argsort), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"topk", castPyCFunctionWithKeywords(THPVariable_topk), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"renorm", castPyCFunctionWithKeywords(THPVariable_renorm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"equal", castPyCFunctionWithKeywords(THPVariable_equal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"pow", castPyCFunctionWithKeywords(THPVariable_pow), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"float_power", castPyCFunctionWithKeywords(THPVariable_float_power), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"normal", castPyCFunctionWithKeywords(THPVariable_normal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_amp_foreach_non_finite_check_and_unscale_", castPyCFunctionWithKeywords(THPVariable__amp_foreach_non_finite_check_and_unscale_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_amp_update_scale_", castPyCFunctionWithKeywords(THPVariable__amp_update_scale_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_add", castPyCFunctionWithKeywords(THPVariable__foreach_add), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_add_", castPyCFunctionWithKeywords(THPVariable__foreach_add_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sub", castPyCFunctionWithKeywords(THPVariable__foreach_sub), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sub_", castPyCFunctionWithKeywords(THPVariable__foreach_sub_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_mul", castPyCFunctionWithKeywords(THPVariable__foreach_mul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_mul_", castPyCFunctionWithKeywords(THPVariable__foreach_mul_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_div", castPyCFunctionWithKeywords(THPVariable__foreach_div), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_div_", castPyCFunctionWithKeywords(THPVariable__foreach_div_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_clamp_max", castPyCFunctionWithKeywords(THPVariable__foreach_clamp_max), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_clamp_max_", castPyCFunctionWithKeywords(THPVariable__foreach_clamp_max_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_clamp_min", castPyCFunctionWithKeywords(THPVariable__foreach_clamp_min), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_clamp_min_", castPyCFunctionWithKeywords(THPVariable__foreach_clamp_min_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_maximum", castPyCFunctionWithKeywords(THPVariable__foreach_maximum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_maximum_", castPyCFunctionWithKeywords(THPVariable__foreach_maximum_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_minimum", castPyCFunctionWithKeywords(THPVariable__foreach_minimum), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_minimum_", castPyCFunctionWithKeywords(THPVariable__foreach_minimum_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_addcdiv", castPyCFunctionWithKeywords(THPVariable__foreach_addcdiv), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_addcdiv_", castPyCFunctionWithKeywords(THPVariable__foreach_addcdiv_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_addcmul", castPyCFunctionWithKeywords(THPVariable__foreach_addcmul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_addcmul_", castPyCFunctionWithKeywords(THPVariable__foreach_addcmul_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_abs", castPyCFunctionWithKeywords(THPVariable__foreach_abs), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_abs_", castPyCFunctionWithKeywords(THPVariable__foreach_abs_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_acos", castPyCFunctionWithKeywords(THPVariable__foreach_acos), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_acos_", castPyCFunctionWithKeywords(THPVariable__foreach_acos_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_asin", castPyCFunctionWithKeywords(THPVariable__foreach_asin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_asin_", castPyCFunctionWithKeywords(THPVariable__foreach_asin_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_atan", castPyCFunctionWithKeywords(THPVariable__foreach_atan), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_atan_", castPyCFunctionWithKeywords(THPVariable__foreach_atan_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_ceil", castPyCFunctionWithKeywords(THPVariable__foreach_ceil), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_ceil_", castPyCFunctionWithKeywords(THPVariable__foreach_ceil_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_cos", castPyCFunctionWithKeywords(THPVariable__foreach_cos), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_cos_", castPyCFunctionWithKeywords(THPVariable__foreach_cos_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_cosh", castPyCFunctionWithKeywords(THPVariable__foreach_cosh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_cosh_", castPyCFunctionWithKeywords(THPVariable__foreach_cosh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_erf", castPyCFunctionWithKeywords(THPVariable__foreach_erf), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_erf_", castPyCFunctionWithKeywords(THPVariable__foreach_erf_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_erfc", castPyCFunctionWithKeywords(THPVariable__foreach_erfc), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_erfc_", castPyCFunctionWithKeywords(THPVariable__foreach_erfc_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_exp", castPyCFunctionWithKeywords(THPVariable__foreach_exp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_exp_", castPyCFunctionWithKeywords(THPVariable__foreach_exp_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_expm1", castPyCFunctionWithKeywords(THPVariable__foreach_expm1), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_expm1_", castPyCFunctionWithKeywords(THPVariable__foreach_expm1_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_floor", castPyCFunctionWithKeywords(THPVariable__foreach_floor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_floor_", castPyCFunctionWithKeywords(THPVariable__foreach_floor_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_frac", castPyCFunctionWithKeywords(THPVariable__foreach_frac), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_frac_", castPyCFunctionWithKeywords(THPVariable__foreach_frac_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_lerp", castPyCFunctionWithKeywords(THPVariable__foreach_lerp), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_lerp_", castPyCFunctionWithKeywords(THPVariable__foreach_lerp_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_lgamma", castPyCFunctionWithKeywords(THPVariable__foreach_lgamma), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_lgamma_", castPyCFunctionWithKeywords(THPVariable__foreach_lgamma_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log", castPyCFunctionWithKeywords(THPVariable__foreach_log), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log_", castPyCFunctionWithKeywords(THPVariable__foreach_log_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log10", castPyCFunctionWithKeywords(THPVariable__foreach_log10), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log10_", castPyCFunctionWithKeywords(THPVariable__foreach_log10_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log1p", castPyCFunctionWithKeywords(THPVariable__foreach_log1p), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log1p_", castPyCFunctionWithKeywords(THPVariable__foreach_log1p_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log2", castPyCFunctionWithKeywords(THPVariable__foreach_log2), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_log2_", castPyCFunctionWithKeywords(THPVariable__foreach_log2_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_max", castPyCFunctionWithKeywords(THPVariable__foreach_max), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_neg", castPyCFunctionWithKeywords(THPVariable__foreach_neg), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_neg_", castPyCFunctionWithKeywords(THPVariable__foreach_neg_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_norm", castPyCFunctionWithKeywords(THPVariable__foreach_norm), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_pow", castPyCFunctionWithKeywords(THPVariable__foreach_pow), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_pow_", castPyCFunctionWithKeywords(THPVariable__foreach_pow_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_reciprocal", castPyCFunctionWithKeywords(THPVariable__foreach_reciprocal), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_reciprocal_", castPyCFunctionWithKeywords(THPVariable__foreach_reciprocal_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_round", castPyCFunctionWithKeywords(THPVariable__foreach_round), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_round_", castPyCFunctionWithKeywords(THPVariable__foreach_round_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_rsqrt", castPyCFunctionWithKeywords(THPVariable__foreach_rsqrt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_rsqrt_", castPyCFunctionWithKeywords(THPVariable__foreach_rsqrt_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sigmoid", castPyCFunctionWithKeywords(THPVariable__foreach_sigmoid), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sigmoid_", castPyCFunctionWithKeywords(THPVariable__foreach_sigmoid_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sign", castPyCFunctionWithKeywords(THPVariable__foreach_sign), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sign_", castPyCFunctionWithKeywords(THPVariable__foreach_sign_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sin", castPyCFunctionWithKeywords(THPVariable__foreach_sin), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sin_", castPyCFunctionWithKeywords(THPVariable__foreach_sin_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sinh", castPyCFunctionWithKeywords(THPVariable__foreach_sinh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sinh_", castPyCFunctionWithKeywords(THPVariable__foreach_sinh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sqrt", castPyCFunctionWithKeywords(THPVariable__foreach_sqrt), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_sqrt_", castPyCFunctionWithKeywords(THPVariable__foreach_sqrt_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_tan", castPyCFunctionWithKeywords(THPVariable__foreach_tan), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_tan_", castPyCFunctionWithKeywords(THPVariable__foreach_tan_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_tanh", castPyCFunctionWithKeywords(THPVariable__foreach_tanh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_tanh_", castPyCFunctionWithKeywords(THPVariable__foreach_tanh_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_trunc", castPyCFunctionWithKeywords(THPVariable__foreach_trunc), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_trunc_", castPyCFunctionWithKeywords(THPVariable__foreach_trunc_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_zero_", castPyCFunctionWithKeywords(THPVariable__foreach_zero_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foreach_copy_", castPyCFunctionWithKeywords(THPVariable__foreach_copy_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"bucketize", castPyCFunctionWithKeywords(THPVariable_bucketize), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"searchsorted", castPyCFunctionWithKeywords(THPVariable_searchsorted), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_convert_indices_from_coo_to_csr", castPyCFunctionWithKeywords(THPVariable__convert_indices_from_coo_to_csr), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_convert_indices_from_csr_to_coo", castPyCFunctionWithKeywords(THPVariable__convert_indices_from_csr_to_coo), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"mkldnn_adaptive_avg_pool2d", castPyCFunctionWithKeywords(THPVariable_mkldnn_adaptive_avg_pool2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_adaptive_avg_pool2d", castPyCFunctionWithKeywords(THPVariable__adaptive_avg_pool2d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_adaptive_avg_pool3d", castPyCFunctionWithKeywords(THPVariable__adaptive_avg_pool3d), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"column_stack", castPyCFunctionWithKeywords(THPVariable_column_stack), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isfinite", castPyCFunctionWithKeywords(THPVariable_isfinite), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isinf", castPyCFunctionWithKeywords(THPVariable_isinf), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isposinf", castPyCFunctionWithKeywords(THPVariable_isposinf), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"isneginf", castPyCFunctionWithKeywords(THPVariable_isneginf), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_add_batch_dim", castPyCFunctionWithKeywords(THPVariable__add_batch_dim), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_remove_batch_dim", castPyCFunctionWithKeywords(THPVariable__remove_batch_dim), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_linalg_det", castPyCFunctionWithKeywords(THPVariable__linalg_det), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"det", castPyCFunctionWithKeywords(THPVariable_det), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_linalg_slogdet", castPyCFunctionWithKeywords(THPVariable__linalg_slogdet), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"slogdet", castPyCFunctionWithKeywords(THPVariable_slogdet), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"logdet", castPyCFunctionWithKeywords(THPVariable_logdet), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_linalg_eigh", castPyCFunctionWithKeywords(THPVariable__linalg_eigh), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"inverse", castPyCFunctionWithKeywords(THPVariable_inverse), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"inner", castPyCFunctionWithKeywords(THPVariable_inner), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"outer", castPyCFunctionWithKeywords(THPVariable_outer), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ger", castPyCFunctionWithKeywords(THPVariable_ger), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_linalg_svd", castPyCFunctionWithKeywords(THPVariable__linalg_svd), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_linalg_solve_ex", castPyCFunctionWithKeywords(THPVariable__linalg_solve_ex), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_test_serialization_subcmul", castPyCFunctionWithKeywords(THPVariable__test_serialization_subcmul), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_test_parallel_materialize", castPyCFunctionWithKeywords(THPVariable__test_parallel_materialize), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_test_autograd_multiple_dispatch", castPyCFunctionWithKeywords(THPVariable__test_autograd_multiple_dispatch), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_test_autograd_multiple_dispatch_view", castPyCFunctionWithKeywords(THPVariable__test_autograd_multiple_dispatch_view), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_test_autograd_multiple_dispatch_view_copy", castPyCFunctionWithKeywords(THPVariable__test_autograd_multiple_dispatch_view_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"segment_reduce", castPyCFunctionWithKeywords(THPVariable_segment_reduce), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_tensor_from_tensor_list", castPyCFunctionWithKeywords(THPVariable__nested_tensor_from_tensor_list), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fw_primal_copy", castPyCFunctionWithKeywords(THPVariable__fw_primal_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_make_dual_copy", castPyCFunctionWithKeywords(THPVariable__make_dual_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"view_as_real_copy", castPyCFunctionWithKeywords(THPVariable_view_as_real_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"view_as_complex_copy", castPyCFunctionWithKeywords(THPVariable_view_as_complex_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_conj_copy", castPyCFunctionWithKeywords(THPVariable__conj_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_neg_view_copy", castPyCFunctionWithKeywords(THPVariable__neg_view_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"as_strided_copy", castPyCFunctionWithKeywords(THPVariable_as_strided_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_sparse_broadcast_to_copy", castPyCFunctionWithKeywords(THPVariable__sparse_broadcast_to_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"diagonal_copy", castPyCFunctionWithKeywords(THPVariable_diagonal_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"expand_copy", castPyCFunctionWithKeywords(THPVariable_expand_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"permute_copy", castPyCFunctionWithKeywords(THPVariable_permute_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_reshape_alias_copy", castPyCFunctionWithKeywords(THPVariable__reshape_alias_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"select_copy", castPyCFunctionWithKeywords(THPVariable_select_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"detach_copy", castPyCFunctionWithKeywords(THPVariable_detach_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"slice_copy", castPyCFunctionWithKeywords(THPVariable_slice_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"split_copy", castPyCFunctionWithKeywords(THPVariable_split_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"split_with_sizes_copy", castPyCFunctionWithKeywords(THPVariable_split_with_sizes_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"squeeze_copy", castPyCFunctionWithKeywords(THPVariable_squeeze_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"t_copy", castPyCFunctionWithKeywords(THPVariable_t_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"transpose_copy", castPyCFunctionWithKeywords(THPVariable_transpose_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unsqueeze_copy", castPyCFunctionWithKeywords(THPVariable_unsqueeze_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_indices_copy", castPyCFunctionWithKeywords(THPVariable__indices_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_values_copy", castPyCFunctionWithKeywords(THPVariable__values_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"indices_copy", castPyCFunctionWithKeywords(THPVariable_indices_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"values_copy", castPyCFunctionWithKeywords(THPVariable_values_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"crow_indices_copy", castPyCFunctionWithKeywords(THPVariable_crow_indices_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"col_indices_copy", castPyCFunctionWithKeywords(THPVariable_col_indices_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"ccol_indices_copy", castPyCFunctionWithKeywords(THPVariable_ccol_indices_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"row_indices_copy", castPyCFunctionWithKeywords(THPVariable_row_indices_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unbind_copy", castPyCFunctionWithKeywords(THPVariable_unbind_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"view_copy", castPyCFunctionWithKeywords(THPVariable_view_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"unfold_copy", castPyCFunctionWithKeywords(THPVariable_unfold_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"alias_copy", castPyCFunctionWithKeywords(THPVariable_alias_copy), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_from_padded_tensor", castPyCFunctionWithKeywords(THPVariable__nested_from_padded_tensor), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_nested_tensor_softmax_with_shape", castPyCFunctionWithKeywords(THPVariable__nested_tensor_softmax_with_shape), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_safe_softmax", castPyCFunctionWithKeywords(THPVariable__safe_softmax), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_transformer_encoder_layer_fwd", castPyCFunctionWithKeywords(THPVariable__transformer_encoder_layer_fwd), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_native_multi_head_attention", castPyCFunctionWithKeywords(THPVariable__native_multi_head_attention), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fused_sdp_choice", castPyCFunctionWithKeywords(THPVariable__fused_sdp_choice), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_dot_product_attention_math", castPyCFunctionWithKeywords(THPVariable__scaled_dot_product_attention_math), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_dot_product_attention_math_for_mps", castPyCFunctionWithKeywords(THPVariable__scaled_dot_product_attention_math_for_mps), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_dot_product_flash_attention", castPyCFunctionWithKeywords(THPVariable__scaled_dot_product_flash_attention), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_dot_product_flash_attention_for_cpu", castPyCFunctionWithKeywords(THPVariable__scaled_dot_product_flash_attention_for_cpu), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_dot_product_efficient_attention", castPyCFunctionWithKeywords(THPVariable__scaled_dot_product_efficient_attention), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_scaled_dot_product_cudnn_attention", castPyCFunctionWithKeywords(THPVariable__scaled_dot_product_cudnn_attention), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_triton_scaled_dot_attention", castPyCFunctionWithKeywords(THPVariable__triton_scaled_dot_attention), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fill_mem_eff_dropout_mask_", castPyCFunctionWithKeywords(THPVariable__fill_mem_eff_dropout_mask_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_triton_multi_head_attention", castPyCFunctionWithKeywords(THPVariable__triton_multi_head_attention), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_foobar", castPyCFunctionWithKeywords(THPVariable__foobar), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fused_adam_", castPyCFunctionWithKeywords(THPVariable__fused_adam_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fused_adamw_", castPyCFunctionWithKeywords(THPVariable__fused_adamw_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fused_sgd_", castPyCFunctionWithKeywords(THPVariable__fused_sgd_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_fused_adagrad_", castPyCFunctionWithKeywords(THPVariable__fused_adagrad_), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, {"_propagate_xla_data", castPyCFunctionWithKeywords(THPVariable__propagate_xla_data), METH_VARARGS | METH_KEYWORDS | METH_STATIC, nullptr}, }; void gatherTorchFunctionsEverything(std::vector &torch_functions) { constexpr size_t num_functions = sizeof(torch_functions_shard) / sizeof(torch_functions_shard[0]); torch_functions.insert( torch_functions.end(), torch_functions_shard, torch_functions_shard + num_functions); } // generated methods start here // _cast_Byte static PyObject * THPVariable__cast_Byte(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Byte(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Byte = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Byte(self, non_blocking); }; return wrap(dispatch__cast_Byte(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cast_Char static PyObject * THPVariable__cast_Char(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Char(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Char(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Char = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Char(self, non_blocking); }; return wrap(dispatch__cast_Char(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cast_Double static PyObject * THPVariable__cast_Double(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Double(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Double = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Double(self, non_blocking); }; return wrap(dispatch__cast_Double(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cast_Float static PyObject * THPVariable__cast_Float(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Float(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Float = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Float(self, non_blocking); }; return wrap(dispatch__cast_Float(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cast_Int static PyObject * THPVariable__cast_Int(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Int(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Int = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Int(self, non_blocking); }; return wrap(dispatch__cast_Int(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cast_Long static PyObject * THPVariable__cast_Long(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Long(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Long = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Long(self, non_blocking); }; return wrap(dispatch__cast_Long(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cast_Short static PyObject * THPVariable__cast_Short(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Short(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Short(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Short = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Short(self, non_blocking); }; return wrap(dispatch__cast_Short(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cast_Half static PyObject * THPVariable__cast_Half(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cast_Half(Tensor input, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor auto dispatch__cast_Half = [](const at::Tensor & self, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cast_Half(self, non_blocking); }; return wrap(dispatch__cast_Half(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _make_dual static PyObject * THPVariable__make_dual(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_make_dual(Tensor primal, Tensor tangent, int64_t level)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a) auto dispatch__make_dual = [](const at::Tensor & primal, const at::Tensor & tangent, int64_t level) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_make_dual(primal, tangent, level); }; return wrap(dispatch__make_dual(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _unpack_dual static PyObject * THPVariable__unpack_dual(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__unpack_dual_structseq(); static PythonArgParser parser({ "_unpack_dual(Tensor dual, int64_t level)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_unpack_dual(Tensor(a) dual, int level) -> (Tensor(a) primal, Tensor tangent) auto dispatch__unpack_dual = [](const at::Tensor & dual, int64_t level) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_unpack_dual(dual, level); }; return wrap(NamedTuple, dispatch__unpack_dual(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // align_tensors static PyObject * THPVariable_align_tensors(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "align_tensors(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::align_tensors(Tensor[] tensors) -> Tensor[] auto dispatch_align_tensors = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::align_tensors(tensors); }; return wrap(dispatch_align_tensors(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _assert_async static PyObject * THPVariable__assert_async(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_assert_async(Tensor input)", "_assert_async(Tensor input, c10::string_view assert_msg)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_assert_async(Tensor self) -> () auto dispatch__assert_async = [](const at::Tensor & self) -> void { pybind11::gil_scoped_release no_gil; at::_assert_async(self); }; dispatch__assert_async(_r.tensor(0)); Py_RETURN_NONE; } case 1: { // aten::_assert_async.msg(Tensor self, str assert_msg) -> () auto dispatch__assert_async = [](const at::Tensor & self, c10::string_view assert_msg) -> void { pybind11::gil_scoped_release no_gil; at::_assert_async(self, assert_msg); }; dispatch__assert_async(_r.tensor(0), _r.stringView(1)); Py_RETURN_NONE; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _assert_scalar static PyObject * THPVariable__assert_scalar(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_assert_scalar(Scalar self, c10::string_view assert_msg)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_assert_scalar(Scalar self, str assert_msg) -> () auto dispatch__assert_scalar = [](const at::Scalar & self, c10::string_view assert_msg) -> void { pybind11::gil_scoped_release no_gil; at::_assert_scalar(self, assert_msg); }; dispatch__assert_scalar(_r.scalar(0), _r.stringView(1)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _functional_assert_scalar static PyObject * THPVariable__functional_assert_scalar(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_functional_assert_scalar(Scalar self, c10::string_view assert_msg, Tensor dep_token)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_functional_assert_scalar(Scalar self, str assert_msg, Tensor dep_token) -> Tensor auto dispatch__functional_assert_scalar = [](const at::Scalar & self, c10::string_view assert_msg, const at::Tensor & dep_token) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_functional_assert_scalar(self, assert_msg, dep_token); }; return wrap(dispatch__functional_assert_scalar(_r.scalar(0), _r.stringView(1), _r.tensor(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _functional_assert_async static PyObject * THPVariable__functional_assert_async(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_functional_assert_async(Tensor input, c10::string_view assert_msg, Tensor dep_token)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_functional_assert_async.msg(Tensor self, str assert_msg, Tensor dep_token) -> Tensor auto dispatch__functional_assert_async = [](const at::Tensor & self, c10::string_view assert_msg, const at::Tensor & dep_token) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_functional_assert_async(self, assert_msg, dep_token); }; return wrap(dispatch__functional_assert_async(_r.tensor(0), _r.stringView(1), _r.tensor(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _assert_tensor_metadata static PyObject * THPVariable__assert_tensor_metadata(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_assert_tensor_metadata(Tensor a, SymIntArrayRef? size=None, SymIntArrayRef? stride=None, ScalarType? dtype=None, *, Device? device=None, Layout? layout=None)", }, /*traceable=*/false); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_assert_tensor_metadata(Tensor a, SymInt[]? size=None, SymInt[]? stride=None, ScalarType? dtype=None, *, Device? device=None, Layout? layout=None) -> () auto dispatch__assert_tensor_metadata = [](const at::Tensor & a, at::OptionalSymIntArrayRef size, at::OptionalSymIntArrayRef stride, ::std::optional dtype, ::std::optional device, ::std::optional layout) -> void { pybind11::gil_scoped_release no_gil; at::_assert_tensor_metadata_symint(a, size, stride, dtype, device, layout); }; dispatch__assert_tensor_metadata(_r.tensor(0), _r.symintlistOptional(1), _r.symintlistOptional(2), _r.scalartypeOptional(3), _r.deviceOptional(4), _r.layoutOptional(5)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _print static PyObject * THPVariable__print(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_print(c10::string_view s)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_print(str s) -> () auto dispatch__print = [](c10::string_view s) -> void { pybind11::gil_scoped_release no_gil; at::_print(s); }; dispatch__print(_r.stringView(0)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sym_constrain_range static PyObject * THPVariable_sym_constrain_range(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sym_constrain_range(Scalar size, *, int64_t? min=None, int64_t? max=None)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::sym_constrain_range(Scalar size, *, int? min=None, int? max=None) -> () auto dispatch_sym_constrain_range = [](const at::Scalar & size, ::std::optional min, ::std::optional max) -> void { pybind11::gil_scoped_release no_gil; at::sym_constrain_range(size, min, max); }; dispatch_sym_constrain_range(_r.scalar(0), _r.toInt64Optional(1), _r.toInt64Optional(2)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sym_constrain_range_for_size static PyObject * THPVariable_sym_constrain_range_for_size(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sym_constrain_range_for_size(Scalar size, *, int64_t? min=None, int64_t? max=None)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::sym_constrain_range_for_size(Scalar size, *, int? min=None, int? max=None) -> () auto dispatch_sym_constrain_range_for_size = [](const at::Scalar & size, ::std::optional min, ::std::optional max) -> void { pybind11::gil_scoped_release no_gil; at::sym_constrain_range_for_size(size, min, max); }; dispatch_sym_constrain_range_for_size(_r.scalar(0), _r.toInt64Optional(1), _r.toInt64Optional(2)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _functional_sym_constrain_range static PyObject * THPVariable__functional_sym_constrain_range(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_functional_sym_constrain_range(Scalar size, int64_t? min, int64_t? max, Tensor dep_token)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_functional_sym_constrain_range(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor auto dispatch__functional_sym_constrain_range = [](const at::Scalar & size, ::std::optional min, ::std::optional max, const at::Tensor & dep_token) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_functional_sym_constrain_range(size, min, max, dep_token); }; return wrap(dispatch__functional_sym_constrain_range(_r.scalar(0), _r.toInt64Optional(1), _r.toInt64Optional(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _functional_sym_constrain_range_for_size static PyObject * THPVariable__functional_sym_constrain_range_for_size(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_functional_sym_constrain_range_for_size(Scalar size, int64_t? min, int64_t? max, Tensor dep_token)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_functional_sym_constrain_range_for_size(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor auto dispatch__functional_sym_constrain_range_for_size = [](const at::Scalar & size, ::std::optional min, ::std::optional max, const at::Tensor & dep_token) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_functional_sym_constrain_range_for_size(size, min, max, dep_token); }; return wrap(dispatch__functional_sym_constrain_range_for_size(_r.scalar(0), _r.toInt64Optional(1), _r.toInt64Optional(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _make_dep_token static PyObject * THPVariable__make_dep_token(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_make_dep_token(*, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_make_dep_token(*, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch__make_dep_token = [](at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::_make_dep_token(options, memory_format); }; return wrap(dispatch__make_dep_token(options, _r.memoryformatOptional(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _use_cudnn_ctc_loss static PyObject * THPVariable__use_cudnn_ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank)", "_use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int64_t blank)", }, /*traceable=*/false); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank) -> bool auto dispatch__use_cudnn_ctc_loss = [](const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank) -> bool { pybind11::gil_scoped_release no_gil; return at::_use_cudnn_ctc_loss(log_probs, targets, input_lengths, target_lengths, blank); }; return wrap(dispatch__use_cudnn_ctc_loss(_r.tensor(0), _r.tensor(1), _r.intlist(2), _r.intlist(3), _r.toInt64(4))); } case 1: { // aten::_use_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> bool auto dispatch__use_cudnn_ctc_loss = [](const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank) -> bool { pybind11::gil_scoped_release no_gil; return at::_use_cudnn_ctc_loss(log_probs, targets, input_lengths, target_lengths, blank); }; return wrap(dispatch__use_cudnn_ctc_loss(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.toInt64(4))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _cudnn_ctc_loss static PyObject * THPVariable__cudnn_ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cudnn_ctc_loss(Tensor log_probs, Tensor targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity)", "_cudnn_ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int64_t blank, bool deterministic, bool zero_infinity)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor) auto dispatch__cudnn_ctc_loss = [](const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_cudnn_ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity); }; return wrap(dispatch__cudnn_ctc_loss(_r.tensor(0), _r.tensor(1), _r.intlist(2), _r.intlist(3), _r.toInt64(4), _r.toBool(5), _r.toBool(6))); } case 1: { // aten::_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor) auto dispatch__cudnn_ctc_loss = [](const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool deterministic, bool zero_infinity) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_cudnn_ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity); }; return wrap(dispatch__cudnn_ctc_loss(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.toInt64(4), _r.toBool(5), _r.toBool(6))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _use_cudnn_rnn_flatten_weight static PyObject * THPVariable__use_cudnn_rnn_flatten_weight(PyObject* self_, PyObject* args) { HANDLE_TH_ERRORS // aten::_use_cudnn_rnn_flatten_weight() -> bool auto dispatch__use_cudnn_rnn_flatten_weight = []() -> bool { pybind11::gil_scoped_release no_gil; return at::_use_cudnn_rnn_flatten_weight(); }; return wrap(dispatch__use_cudnn_rnn_flatten_weight()); END_HANDLE_TH_ERRORS } // _cudnn_rnn_flatten_weight static PyObject * THPVariable__cudnn_rnn_flatten_weight(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, SymInt input_size, int64_t mode, SymInt hidden_size, SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor auto dispatch__cudnn_rnn_flatten_weight = [](at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cudnn_rnn_flatten_weight_symint(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional); }; return wrap(dispatch__cudnn_rnn_flatten_weight(_r.tensorlist(0), _r.toInt64(1), _r.toSymInt(2), _r.toInt64(3), _r.toSymInt(4), _r.toSymInt(5), _r.toInt64(6), _r.toBool(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cudnn_rnn static PyObject * THPVariable__cudnn_rnn(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cudnn_rnn(Tensor input, TensorList weight, int64_t weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int64_t mode, SymInt hidden_size, SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, SymIntArrayRef batch_sizes, Tensor? dropout_state)", }, /*traceable=*/true); ParsedArgs<16> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor) auto dispatch__cudnn_rnn = [](const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional & weight_buf, const at::Tensor & hx, const ::std::optional & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional & dropout_state) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_cudnn_rnn_symint(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); }; return wrap(dispatch__cudnn_rnn(_r.tensor(0), _r.tensorlist(1), _r.toInt64(2), _r.optionalTensor(3), _r.tensor(4), _r.optionalTensor(5), _r.toInt64(6), _r.toSymInt(7), _r.toSymInt(8), _r.toInt64(9), _r.toBool(10), _r.toDouble(11), _r.toBool(12), _r.toBool(13), _r.symintlist(14), _r.optionalTensor(15))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cudnn_init_dropout_state static PyObject * THPVariable__cudnn_init_dropout_state(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch__cudnn_init_dropout_state = [](double dropout, bool train, int64_t dropout_seed, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::_cudnn_init_dropout_state(dropout, train, dropout_seed, options); }; return wrap(dispatch__cudnn_init_dropout_state(_r.toDouble(0), _r.toBool(1), _r.toInt64(2), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _debug_has_internal_overlap static PyObject * THPVariable__debug_has_internal_overlap(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_debug_has_internal_overlap(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_debug_has_internal_overlap(Tensor self) -> int auto dispatch__debug_has_internal_overlap = [](const at::Tensor & self) -> int64_t { pybind11::gil_scoped_release no_gil; return at::_debug_has_internal_overlap(self); }; return wrap(dispatch__debug_has_internal_overlap(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fused_dropout static PyObject * THPVariable__fused_dropout(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fused_dropout(Tensor input, double p, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fused_dropout(Tensor self, float p, Generator? generator=None) -> (Tensor, Tensor) auto dispatch__fused_dropout = [](const at::Tensor & self, double p, ::std::optional generator) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_fused_dropout(self, p, generator); }; return wrap(dispatch__fused_dropout(_r.tensor(0), _r.toDouble(1), _r.generator(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _masked_scale static PyObject * THPVariable__masked_scale(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_masked_scale(Tensor input, Tensor mask, double scale)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_masked_scale(Tensor self, Tensor mask, float scale) -> Tensor auto dispatch__masked_scale = [](const at::Tensor & self, const at::Tensor & mask, double scale) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_masked_scale(self, mask, scale); }; return wrap(dispatch__masked_scale(_r.tensor(0), _r.tensor(1), _r.toDouble(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // native_dropout static PyObject * THPVariable_native_dropout(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "native_dropout(Tensor input, double p, bool? train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor) auto dispatch_native_dropout = [](const at::Tensor & input, double p, ::std::optional train) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::native_dropout(input, p, train); }; return wrap(dispatch_native_dropout(_r.tensor(0), _r.toDouble(1), _r.toBoolOptional(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sobol_engine_draw static PyObject * THPVariable__sobol_engine_draw(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sobol_engine_draw(Tensor quasi, int64_t n, Tensor sobolstate, int64_t dimension, int64_t num_generated, ScalarType? dtype)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sobol_engine_draw(Tensor quasi, int n, Tensor sobolstate, int dimension, int num_generated, ScalarType? dtype) -> (Tensor, Tensor) auto dispatch__sobol_engine_draw = [](const at::Tensor & quasi, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated, ::std::optional dtype) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_sobol_engine_draw(quasi, n, sobolstate, dimension, num_generated, dtype); }; return wrap(dispatch__sobol_engine_draw(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4), _r.scalartypeOptional(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sobol_engine_ff_ static PyObject * THPVariable__sobol_engine_ff_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sobol_engine_ff_(Tensor input, int64_t n, Tensor sobolstate, int64_t dimension, int64_t num_generated)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!) auto dispatch__sobol_engine_ff_ = [](at::Tensor self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sobol_engine_ff_(self, n, sobolstate, dimension, num_generated); }; return wrap(dispatch__sobol_engine_ff_(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sobol_engine_scramble_ static PyObject * THPVariable__sobol_engine_scramble_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sobol_engine_scramble_(Tensor input, Tensor ltm, int64_t dimension)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!) auto dispatch__sobol_engine_scramble_ = [](at::Tensor self, const at::Tensor & ltm, int64_t dimension) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sobol_engine_scramble_(self, ltm, dimension); }; return wrap(dispatch__sobol_engine_scramble_(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sobol_engine_initialize_state_ static PyObject * THPVariable__sobol_engine_initialize_state_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sobol_engine_initialize_state_(Tensor input, int64_t dimension)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sobol_engine_initialize_state_(Tensor(a!) self, int dimension) -> Tensor(a!) auto dispatch__sobol_engine_initialize_state_ = [](at::Tensor self, int64_t dimension) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sobol_engine_initialize_state_(self, dimension); }; return wrap(dispatch__sobol_engine_initialize_state_(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _reshape_from_tensor static PyObject * THPVariable__reshape_from_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_reshape_from_tensor(Tensor input, Tensor shape)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_reshape_from_tensor(Tensor self, Tensor shape) -> Tensor auto dispatch__reshape_from_tensor = [](const at::Tensor & self, const at::Tensor & shape) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_reshape_from_tensor(self, shape); }; return wrap(dispatch__reshape_from_tensor(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _shape_as_tensor static PyObject * THPVariable__shape_as_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_shape_as_tensor(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_shape_as_tensor(Tensor self) -> Tensor auto dispatch__shape_as_tensor = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_shape_as_tensor(self); }; return wrap(dispatch__shape_as_tensor(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // dropout static PyObject * THPVariable_dropout(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "dropout(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::dropout(Tensor input, float p, bool train) -> Tensor auto dispatch_dropout = [](const at::Tensor & input, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::dropout(input, p, train); }; return wrap(dispatch_dropout(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // dropout_ static PyObject * THPVariable_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "dropout_(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) auto dispatch_dropout_ = [](at::Tensor self, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::dropout_(self, p, train); }; return wrap(dispatch_dropout_(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // feature_dropout static PyObject * THPVariable_feature_dropout(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "feature_dropout(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::feature_dropout(Tensor input, float p, bool train) -> Tensor auto dispatch_feature_dropout = [](const at::Tensor & input, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::feature_dropout(input, p, train); }; return wrap(dispatch_feature_dropout(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // feature_dropout_ static PyObject * THPVariable_feature_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "feature_dropout_(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) auto dispatch_feature_dropout_ = [](at::Tensor self, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::feature_dropout_(self, p, train); }; return wrap(dispatch_feature_dropout_(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // alpha_dropout static PyObject * THPVariable_alpha_dropout(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "alpha_dropout(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::alpha_dropout(Tensor input, float p, bool train) -> Tensor auto dispatch_alpha_dropout = [](const at::Tensor & input, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::alpha_dropout(input, p, train); }; return wrap(dispatch_alpha_dropout(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // alpha_dropout_ static PyObject * THPVariable_alpha_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "alpha_dropout_(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) auto dispatch_alpha_dropout_ = [](at::Tensor self, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::alpha_dropout_(self, p, train); }; return wrap(dispatch_alpha_dropout_(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // feature_alpha_dropout static PyObject * THPVariable_feature_alpha_dropout(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "feature_alpha_dropout(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor auto dispatch_feature_alpha_dropout = [](const at::Tensor & input, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::feature_alpha_dropout(input, p, train); }; return wrap(dispatch_feature_alpha_dropout(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // feature_alpha_dropout_ static PyObject * THPVariable_feature_alpha_dropout_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "feature_alpha_dropout_(Tensor input, double p, bool train)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) auto dispatch_feature_alpha_dropout_ = [](at::Tensor self, double p, bool train) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::feature_alpha_dropout_(self, p, train); }; return wrap(dispatch_feature_alpha_dropout_(_r.tensor(0), _r.toDouble(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // abs static PyObject * THPVariable_abs(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "abs(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::abs(Tensor self) -> Tensor auto dispatch_abs = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.abs(); }; return wrap(dispatch_abs(_r.tensor(0))); } else { // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_abs_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::abs_out(out, self); }; return wrap(dispatch_abs_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // abs_ static PyObject * THPVariable_abs_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "abs_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::abs_(Tensor(a!) self) -> Tensor(a!) auto dispatch_abs_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.abs_(); }; return wrap(dispatch_abs_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // absolute static PyObject * THPVariable_absolute(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "absolute(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::absolute(Tensor self) -> Tensor auto dispatch_absolute = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.absolute(); }; return wrap(dispatch_absolute(_r.tensor(0))); } else { // aten::absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_absolute_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::absolute_out(out, self); }; return wrap(dispatch_absolute_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // angle static PyObject * THPVariable_angle(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "angle(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::angle(Tensor self) -> Tensor auto dispatch_angle = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.angle(); }; return wrap(dispatch_angle(_r.tensor(0))); } else { // aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_angle_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::angle_out(out, self); }; return wrap(dispatch_angle_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // view_as_real static PyObject * THPVariable_view_as_real(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "view_as_real(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::view_as_real(Tensor(a) self) -> Tensor(a) auto dispatch_view_as_real = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_as_real(self); }; return wrap(dispatch_view_as_real(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // view_as_complex static PyObject * THPVariable_view_as_complex(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "view_as_complex(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::view_as_complex(Tensor(a) self) -> Tensor(a) auto dispatch_view_as_complex = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_as_complex(self); }; return wrap(dispatch_view_as_complex(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sgn static PyObject * THPVariable_sgn(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sgn(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::sgn(Tensor self) -> Tensor auto dispatch_sgn = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sgn(); }; return wrap(dispatch_sgn(_r.tensor(0))); } else { // aten::sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sgn_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sgn_out(out, self); }; return wrap(dispatch_sgn_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // real static PyObject * THPVariable_real(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "real(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::real(Tensor(a) self) -> Tensor(a) auto dispatch_real = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::real(self); }; return wrap(dispatch_real(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // imag static PyObject * THPVariable_imag(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "imag(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::imag(Tensor(a) self) -> Tensor(a) auto dispatch_imag = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::imag(self); }; return wrap(dispatch_imag(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _conj static PyObject * THPVariable__conj(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_conj(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_conj(Tensor(a) self) -> Tensor(a) auto dispatch__conj = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self._conj(); }; return wrap(dispatch__conj(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // conj static PyObject * THPVariable_conj(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conj(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::conj(Tensor(a) self) -> Tensor(a) auto dispatch_conj = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.conj(); }; return wrap(dispatch_conj(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _conj_physical static PyObject * THPVariable__conj_physical(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_conj_physical(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_conj_physical(Tensor self) -> Tensor auto dispatch__conj_physical = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self._conj_physical(); }; return wrap(dispatch__conj_physical(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // conj_physical static PyObject * THPVariable_conj_physical(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conj_physical(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::conj_physical(Tensor self) -> Tensor auto dispatch_conj_physical = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.conj_physical(); }; return wrap(dispatch_conj_physical(_r.tensor(0))); } else { // aten::conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_conj_physical_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conj_physical_out(out, self); }; return wrap(dispatch_conj_physical_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // conj_physical_ static PyObject * THPVariable_conj_physical_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conj_physical_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::conj_physical_(Tensor(a!) self) -> Tensor(a!) auto dispatch_conj_physical_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.conj_physical_(); }; return wrap(dispatch_conj_physical_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // resolve_conj static PyObject * THPVariable_resolve_conj(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "resolve_conj(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::resolve_conj(Tensor(a) self) -> Tensor(a) auto dispatch_resolve_conj = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.resolve_conj(); }; return wrap(dispatch_resolve_conj(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // resolve_neg static PyObject * THPVariable_resolve_neg(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "resolve_neg(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::resolve_neg(Tensor(a) self) -> Tensor(a) auto dispatch_resolve_neg = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.resolve_neg(); }; return wrap(dispatch_resolve_neg(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _neg_view static PyObject * THPVariable__neg_view(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_neg_view(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_neg_view(Tensor(a) self) -> Tensor(a) auto dispatch__neg_view = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self._neg_view(); }; return wrap(dispatch__neg_view(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // acos static PyObject * THPVariable_acos(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "acos(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::acos(Tensor self) -> Tensor auto dispatch_acos = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.acos(); }; return wrap(dispatch_acos(_r.tensor(0))); } else { // aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_acos_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::acos_out(out, self); }; return wrap(dispatch_acos_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // acos_ static PyObject * THPVariable_acos_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "acos_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::acos_(Tensor(a!) self) -> Tensor(a!) auto dispatch_acos_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.acos_(); }; return wrap(dispatch_acos_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arccos static PyObject * THPVariable_arccos(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arccos(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::arccos(Tensor self) -> Tensor auto dispatch_arccos = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arccos(); }; return wrap(dispatch_arccos(_r.tensor(0))); } else { // aten::arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_arccos_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arccos_out(out, self); }; return wrap(dispatch_arccos_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arccos_ static PyObject * THPVariable_arccos_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arccos_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::arccos_(Tensor(a!) self) -> Tensor(a!) auto dispatch_arccos_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arccos_(); }; return wrap(dispatch_arccos_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // avg_pool1d static PyObject * THPVariable_avg_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "avg_pool1d(Tensor input, IntArrayRef[1] kernel_size, IntArrayRef[1] stride=None, IntArrayRef[1] padding=0, bool ceil_mode=False, bool count_include_pad=True)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, bool ceil_mode=False, bool count_include_pad=True) -> Tensor auto dispatch_avg_pool1d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::avg_pool1d(self, kernel_size, stride, padding, ceil_mode, count_include_pad); }; return wrap(dispatch_avg_pool1d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.toBool(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // adaptive_avg_pool1d static PyObject * THPVariable_adaptive_avg_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "adaptive_avg_pool1d(Tensor input, IntArrayRef[1] output_size)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor auto dispatch_adaptive_avg_pool1d = [](const at::Tensor & self, at::IntArrayRef output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::adaptive_avg_pool1d(self, output_size); }; return wrap(dispatch_adaptive_avg_pool1d(_r.tensor(0), _r.intlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // adaptive_max_pool1d static PyObject * THPVariable_adaptive_max_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "adaptive_max_pool1d(Tensor input, IntArrayRef[1] output_size)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor) auto dispatch_adaptive_max_pool1d = [](const at::Tensor & self, at::IntArrayRef output_size) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::adaptive_max_pool1d(self, output_size); }; return wrap(dispatch_adaptive_max_pool1d(_r.tensor(0), _r.intlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // add static PyObject * THPVariable_add(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "add(Tensor input, Scalar alpha, Tensor other, *, Tensor out=None)|deprecated", "add(Tensor input, Tensor other, *, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // [deprecated] aten::add(Tensor self, Scalar alpha, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_add = [](at::Tensor out, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.add(other, alpha); }; return wrap(dispatch_add(_r.tensor(3), _r.tensor(0), _r.scalar(1), _r.tensor(2))); } else { // [deprecated] aten::add(Tensor self, Scalar alpha, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_add_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::add_out(out, self, other, alpha); }; return wrap(dispatch_add_out(_r.tensor(3), _r.tensor(0), _r.scalar(1), _r.tensor(2))); } } case 1: { if (_r.isNone(3)) { // aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor auto dispatch_add = [](const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.add(other, alpha); }; return wrap(dispatch_add(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } else { // aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_add_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::add_out(out, self, other, alpha); }; return wrap(dispatch_add_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.scalar(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _add_relu static PyObject * THPVariable__add_relu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_add_relu(Tensor input, Tensor other, *, Scalar alpha=1, Tensor out=None)", "_add_relu(Tensor input, Scalar other, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor auto dispatch__add_relu = [](const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_add_relu(self, other, alpha); }; return wrap(dispatch__add_relu(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } else { // aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch__add_relu_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_add_relu_out(out, self, other, alpha); }; return wrap(dispatch__add_relu_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.scalar(2))); } } case 1: { // aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor auto dispatch__add_relu = [](const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_add_relu(self, other, alpha); }; return wrap(dispatch__add_relu(_r.tensor(0), _r.scalar(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _add_relu_ static PyObject * THPVariable__add_relu_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_add_relu_(Tensor input, Tensor other, *, Scalar alpha=1)", "_add_relu_(Tensor input, Scalar other, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) auto dispatch__add_relu_ = [](at::Tensor self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_add_relu_(self, other, alpha); }; return wrap(dispatch__add_relu_(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } case 1: { // aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) auto dispatch__add_relu_ = [](at::Tensor self, const at::Scalar & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_add_relu_(self, other, alpha); }; return wrap(dispatch__add_relu_(_r.tensor(0), _r.scalar(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // addmv static PyObject * THPVariable_addmv(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "addmv(Scalar beta, Tensor input, Scalar alpha, Tensor mat, Tensor vec, *, Tensor out=None)|deprecated", "addmv(Scalar beta, Tensor input, Tensor mat, Tensor vec, *, Tensor out=None)|deprecated", "addmv(Tensor input, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // [deprecated] aten::addmv(Scalar beta, Tensor self, Scalar alpha, Tensor mat, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmv = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & mat, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmv(mat, vec, beta, alpha); }; return wrap(dispatch_addmv(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } else { // [deprecated] aten::addmv(Scalar beta, Tensor self, Scalar alpha, Tensor mat, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmv_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & mat, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addmv_out(out, self, mat, vec, beta, alpha); }; return wrap(dispatch_addmv_out(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } } case 1: { if (_r.isNone(4)) { // [deprecated] aten::addmv(Scalar beta, Tensor self, Tensor mat, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmv = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmv(mat, vec, beta, 1); }; return wrap(dispatch_addmv(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } else { // [deprecated] aten::addmv(Scalar beta, Tensor self, Tensor mat, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmv_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addmv_out(out, self, mat, vec, beta, 1); }; return wrap(dispatch_addmv_out(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } } case 2: { if (_r.isNone(5)) { // aten::addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor auto dispatch_addmv = [](const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmv(mat, vec, beta, alpha); }; return wrap(dispatch_addmv(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } else { // aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmv_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addmv_out(out, self, mat, vec, beta, alpha); }; return wrap(dispatch_addmv_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // addmv_ static PyObject * THPVariable_addmv_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "addmv_(Scalar beta, Tensor input, Scalar alpha, Tensor mat, Tensor vec)|deprecated", "addmv_(Scalar beta, Tensor input, Tensor mat, Tensor vec)|deprecated", "addmv_(Tensor input, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // [deprecated] aten::addmv_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor mat, Tensor vec) -> Tensor(a!) auto dispatch_addmv_ = [](const at::Scalar & beta, at::Tensor self, const at::Scalar & alpha, const at::Tensor & mat, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmv_(mat, vec, beta, alpha); }; return wrap(dispatch_addmv_(_r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } case 1: { // [deprecated] aten::addmv_(Scalar beta, Tensor(a!) self, Tensor mat, Tensor vec) -> Tensor(a!) auto dispatch_addmv_ = [](const at::Scalar & beta, at::Tensor self, const at::Tensor & mat, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmv_(mat, vec, beta, 1); }; return wrap(dispatch_addmv_(_r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } case 2: { // aten::addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) auto dispatch_addmv_ = [](at::Tensor self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmv_(mat, vec, beta, alpha); }; return wrap(dispatch_addmv_(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // addr static PyObject * THPVariable_addr(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "addr(Scalar beta, Tensor input, Scalar alpha, Tensor vec1, Tensor vec2, *, Tensor out=None)|deprecated", "addr(Scalar beta, Tensor input, Tensor vec1, Tensor vec2, *, Tensor out=None)|deprecated", "addr(Tensor input, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // [deprecated] aten::addr(Scalar beta, Tensor self, Scalar alpha, Tensor vec1, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addr = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & vec1, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addr(vec1, vec2, beta, alpha); }; return wrap(dispatch_addr(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } else { // [deprecated] aten::addr(Scalar beta, Tensor self, Scalar alpha, Tensor vec1, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addr_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & vec1, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addr_out(out, self, vec1, vec2, beta, alpha); }; return wrap(dispatch_addr_out(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } } case 1: { if (_r.isNone(4)) { // [deprecated] aten::addr(Scalar beta, Tensor self, Tensor vec1, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addr = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addr(vec1, vec2, beta, 1); }; return wrap(dispatch_addr(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } else { // [deprecated] aten::addr(Scalar beta, Tensor self, Tensor vec1, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addr_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addr_out(out, self, vec1, vec2, beta, 1); }; return wrap(dispatch_addr_out(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } } case 2: { if (_r.isNone(5)) { // aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor auto dispatch_addr = [](const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addr(vec1, vec2, beta, alpha); }; return wrap(dispatch_addr(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } else { // aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_addr_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addr_out(out, self, vec1, vec2, beta, alpha); }; return wrap(dispatch_addr_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // affine_grid_generator static PyObject * THPVariable_affine_grid_generator(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "affine_grid_generator(Tensor theta, SymIntArrayRef size, bool align_corners)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::affine_grid_generator(Tensor theta, SymInt[] size, bool align_corners) -> Tensor auto dispatch_affine_grid_generator = [](const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::affine_grid_generator_symint(theta, size, align_corners); }; return wrap(dispatch_affine_grid_generator(_r.tensor(0), _r.symintlist(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _is_all_true static PyObject * THPVariable__is_all_true(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_is_all_true(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_is_all_true(Tensor self) -> Tensor auto dispatch__is_all_true = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self._is_all_true(); }; return wrap(dispatch__is_all_true(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _is_any_true static PyObject * THPVariable__is_any_true(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_is_any_true(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_is_any_true(Tensor self) -> Tensor auto dispatch__is_any_true = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self._is_any_true(); }; return wrap(dispatch__is_any_true(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _test_check_tensor static PyObject * THPVariable__test_check_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_test_check_tensor(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_test_check_tensor(Tensor self) -> Tensor auto dispatch__test_check_tensor = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_check_tensor(self); }; return wrap(dispatch__test_check_tensor(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _test_functorch_fallback static PyObject * THPVariable__test_functorch_fallback(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_test_functorch_fallback(Tensor input, Tensor other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_test_functorch_fallback(Tensor self, Tensor other) -> Tensor auto dispatch__test_functorch_fallback = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_functorch_fallback(self, other); }; return wrap(dispatch__test_functorch_fallback(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // all static PyObject * THPVariable_all(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "all(Tensor input, *, Tensor out=None)", "all(Tensor input, IntArrayRef? dim=None, bool keepdim=False, *, Tensor out=None)", "all(Tensor input, int64_t dim, bool keepdim=False, *, Tensor out=None)", "all(Tensor input, Dimname dim, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::all(Tensor self) -> Tensor auto dispatch_all = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.all(); }; return wrap(dispatch_all(_r.tensor(0))); } else { // aten::all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_all_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::all_out(out, self); }; return wrap(dispatch_all_out(_r.tensor(1), _r.tensor(0))); } } case 1: { if (_r.isNone(3)) { // aten::all.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor auto dispatch_all = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.all(dim, keepdim); }; return wrap(dispatch_all(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2))); } else { // aten::all.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_all_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::all_out(out, self, dim, keepdim); }; return wrap(dispatch_all_out(_r.tensor(3), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2))); } } case 2: { if (_r.isNone(3)) { // aten::all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor auto dispatch_all = [](const at::Tensor & self, int64_t dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.all(dim, keepdim); }; return wrap(dispatch_all(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_all_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::all_out(out, self, dim, keepdim); }; return wrap(dispatch_all_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 3: { if (_r.isNone(3)) { // aten::all.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor auto dispatch_all = [](const at::Tensor & self, at::Dimname dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.all(dim, keepdim); }; return wrap(dispatch_all(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_all_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::all_out(out, self, dim, keepdim); }; return wrap(dispatch_all_out(_r.tensor(3), _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // allclose static PyObject * THPVariable_allclose(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "allclose(Tensor input, Tensor other, double rtol=1e-05, double atol=1e-08, bool equal_nan=False)", }, /*traceable=*/false); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool auto dispatch_allclose = [](const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan) -> bool { pybind11::gil_scoped_release no_gil; return self.allclose(other, rtol, atol, equal_nan); }; return wrap(dispatch_allclose(_r.tensor(0), _r.tensor(1), _r.toDouble(2), _r.toDouble(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // any static PyObject * THPVariable_any(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "any(Tensor input, *, Tensor out=None)", "any(Tensor input, IntArrayRef? dim=None, bool keepdim=False, *, Tensor out=None)", "any(Tensor input, int64_t dim, bool keepdim=False, *, Tensor out=None)", "any(Tensor input, Dimname dim, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::any(Tensor self) -> Tensor auto dispatch_any = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.any(); }; return wrap(dispatch_any(_r.tensor(0))); } else { // aten::any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_any_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::any_out(out, self); }; return wrap(dispatch_any_out(_r.tensor(1), _r.tensor(0))); } } case 1: { if (_r.isNone(3)) { // aten::any.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor auto dispatch_any = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.any(dim, keepdim); }; return wrap(dispatch_any(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2))); } else { // aten::any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_any_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::any_out(out, self, dim, keepdim); }; return wrap(dispatch_any_out(_r.tensor(3), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2))); } } case 2: { if (_r.isNone(3)) { // aten::any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor auto dispatch_any = [](const at::Tensor & self, int64_t dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.any(dim, keepdim); }; return wrap(dispatch_any(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_any_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::any_out(out, self, dim, keepdim); }; return wrap(dispatch_any_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 3: { if (_r.isNone(3)) { // aten::any.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor auto dispatch_any = [](const at::Tensor & self, at::Dimname dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.any(dim, keepdim); }; return wrap(dispatch_any(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_any_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::any_out(out, self, dim, keepdim); }; return wrap(dispatch_any_out(_r.tensor(3), _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // arange static PyObject * THPVariable_arange(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arange(Scalar end, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "arange(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "arange(Scalar start, Scalar end, Scalar step=1, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_arange = [](const at::Scalar & end, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::arange(end, options); }; return wrap(dispatch_arange(_r.scalar(0), options)); } else { // aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(1), _r.scalartypeOptional(2), _r.isNone(2), _r.layoutOptional(3), _r.deviceWithDefault(4, torch::tensors::get_default_device()), _r.isNone(4)); auto dispatch_arange_out = [](at::Tensor out, const at::Scalar & end) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arange_out(out, end); }; return wrap(dispatch_arange_out(_r.tensor(1), _r.scalar(0)).set_requires_grad(_r.toBool(6))); } } case 1: { // aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_arange = [](const at::Scalar & start, const at::Scalar & end, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::arange(start, end, options); }; return wrap(dispatch_arange(_r.scalar(0), _r.scalar(1), options)); } case 2: { if (_r.isNone(3)) { // aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_arange = [](const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::arange(start, end, step, options); }; return wrap(dispatch_arange(_r.scalar(0), _r.scalar(1), _r.scalar(2), options)); } else { // aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(3), _r.scalartypeOptional(4), _r.isNone(4), _r.layoutOptional(5), _r.deviceWithDefault(6, torch::tensors::get_default_device()), _r.isNone(6)); auto dispatch_arange_out = [](at::Tensor out, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arange_out(out, start, end, step); }; return wrap(dispatch_arange_out(_r.tensor(3), _r.scalar(0), _r.scalar(1), _r.scalar(2)).set_requires_grad(_r.toBool(8))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _dim_arange static PyObject * THPVariable__dim_arange(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_dim_arange(Tensor like, int64_t dim)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_dim_arange(Tensor like, int dim) -> Tensor auto dispatch__dim_arange = [](const at::Tensor & like, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_dim_arange(like, dim); }; return wrap(dispatch__dim_arange(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // argmax static PyObject * THPVariable_argmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "argmax(Tensor input, int64_t? dim=None, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor auto dispatch_argmax = [](const at::Tensor & self, ::std::optional dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.argmax(dim, keepdim); }; return wrap(dispatch_argmax(_r.tensor(0), _r.toInt64Optional(1), _r.toBool(2))); } else { // aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_argmax_out = [](at::Tensor out, const at::Tensor & self, ::std::optional dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::argmax_out(out, self, dim, keepdim); }; return wrap(dispatch_argmax_out(_r.tensor(3), _r.tensor(0), _r.toInt64Optional(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // argmin static PyObject * THPVariable_argmin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "argmin(Tensor input, int64_t? dim=None, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor auto dispatch_argmin = [](const at::Tensor & self, ::std::optional dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.argmin(dim, keepdim); }; return wrap(dispatch_argmin(_r.tensor(0), _r.toInt64Optional(1), _r.toBool(2))); } else { // aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_argmin_out = [](at::Tensor out, const at::Tensor & self, ::std::optional dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::argmin_out(out, self, dim, keepdim); }; return wrap(dispatch_argmin_out(_r.tensor(3), _r.tensor(0), _r.toInt64Optional(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // acosh static PyObject * THPVariable_acosh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "acosh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::acosh(Tensor self) -> Tensor auto dispatch_acosh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.acosh(); }; return wrap(dispatch_acosh(_r.tensor(0))); } else { // aten::acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_acosh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::acosh_out(out, self); }; return wrap(dispatch_acosh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // acosh_ static PyObject * THPVariable_acosh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "acosh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::acosh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_acosh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.acosh_(); }; return wrap(dispatch_acosh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arccosh static PyObject * THPVariable_arccosh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arccosh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::arccosh(Tensor self) -> Tensor auto dispatch_arccosh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arccosh(); }; return wrap(dispatch_arccosh(_r.tensor(0))); } else { // aten::arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_arccosh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arccosh_out(out, self); }; return wrap(dispatch_arccosh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arccosh_ static PyObject * THPVariable_arccosh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arccosh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::arccosh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_arccosh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arccosh_(); }; return wrap(dispatch_arccosh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // asinh static PyObject * THPVariable_asinh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "asinh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::asinh(Tensor self) -> Tensor auto dispatch_asinh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.asinh(); }; return wrap(dispatch_asinh(_r.tensor(0))); } else { // aten::asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_asinh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::asinh_out(out, self); }; return wrap(dispatch_asinh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // asinh_ static PyObject * THPVariable_asinh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "asinh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::asinh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_asinh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.asinh_(); }; return wrap(dispatch_asinh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arcsinh static PyObject * THPVariable_arcsinh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arcsinh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::arcsinh(Tensor self) -> Tensor auto dispatch_arcsinh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arcsinh(); }; return wrap(dispatch_arcsinh(_r.tensor(0))); } else { // aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_arcsinh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arcsinh_out(out, self); }; return wrap(dispatch_arcsinh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arcsinh_ static PyObject * THPVariable_arcsinh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arcsinh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::arcsinh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_arcsinh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arcsinh_(); }; return wrap(dispatch_arcsinh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // atanh static PyObject * THPVariable_atanh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atanh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::atanh(Tensor self) -> Tensor auto dispatch_atanh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.atanh(); }; return wrap(dispatch_atanh(_r.tensor(0))); } else { // aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_atanh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::atanh_out(out, self); }; return wrap(dispatch_atanh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // atanh_ static PyObject * THPVariable_atanh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atanh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::atanh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_atanh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.atanh_(); }; return wrap(dispatch_atanh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arctanh static PyObject * THPVariable_arctanh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arctanh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::arctanh(Tensor self) -> Tensor auto dispatch_arctanh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arctanh(); }; return wrap(dispatch_arctanh(_r.tensor(0))); } else { // aten::arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_arctanh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arctanh_out(out, self); }; return wrap(dispatch_arctanh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arctanh_ static PyObject * THPVariable_arctanh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arctanh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::arctanh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_arctanh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arctanh_(); }; return wrap(dispatch_arctanh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // as_strided static PyObject * THPVariable_as_strided(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "as_strided(Tensor input, SymIntArrayRef size, SymIntArrayRef stride, SymInt? storage_offset=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a) auto dispatch_as_strided = [](const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional storage_offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.as_strided_symint(size, stride, storage_offset); }; return wrap(dispatch_as_strided(_r.tensor(0), _r.symintlist(1), _r.symintlist(2), _r.toSymIntOptional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // as_strided_ static PyObject * THPVariable_as_strided_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "as_strided_(Tensor input, SymIntArrayRef size, SymIntArrayRef stride, SymInt? storage_offset=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!) auto dispatch_as_strided_ = [](const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional storage_offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.as_strided__symint(size, stride, storage_offset); }; return wrap(dispatch_as_strided_(_r.tensor(0), _r.symintlist(1), _r.symintlist(2), _r.toSymIntOptional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // asin static PyObject * THPVariable_asin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "asin(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::asin(Tensor self) -> Tensor auto dispatch_asin = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.asin(); }; return wrap(dispatch_asin(_r.tensor(0))); } else { // aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_asin_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::asin_out(out, self); }; return wrap(dispatch_asin_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // asin_ static PyObject * THPVariable_asin_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "asin_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::asin_(Tensor(a!) self) -> Tensor(a!) auto dispatch_asin_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.asin_(); }; return wrap(dispatch_asin_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arcsin static PyObject * THPVariable_arcsin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arcsin(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::arcsin(Tensor self) -> Tensor auto dispatch_arcsin = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arcsin(); }; return wrap(dispatch_arcsin(_r.tensor(0))); } else { // aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_arcsin_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arcsin_out(out, self); }; return wrap(dispatch_arcsin_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arcsin_ static PyObject * THPVariable_arcsin_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arcsin_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::arcsin_(Tensor(a!) self) -> Tensor(a!) auto dispatch_arcsin_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arcsin_(); }; return wrap(dispatch_arcsin_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // atan static PyObject * THPVariable_atan(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atan(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::atan(Tensor self) -> Tensor auto dispatch_atan = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.atan(); }; return wrap(dispatch_atan(_r.tensor(0))); } else { // aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_atan_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::atan_out(out, self); }; return wrap(dispatch_atan_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // atan_ static PyObject * THPVariable_atan_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atan_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::atan_(Tensor(a!) self) -> Tensor(a!) auto dispatch_atan_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.atan_(); }; return wrap(dispatch_atan_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arctan static PyObject * THPVariable_arctan(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arctan(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::arctan(Tensor self) -> Tensor auto dispatch_arctan = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arctan(); }; return wrap(dispatch_arctan(_r.tensor(0))); } else { // aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_arctan_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arctan_out(out, self); }; return wrap(dispatch_arctan_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arctan_ static PyObject * THPVariable_arctan_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arctan_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::arctan_(Tensor(a!) self) -> Tensor(a!) auto dispatch_arctan_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arctan_(); }; return wrap(dispatch_arctan_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // atleast_1d static PyObject * THPVariable_atleast_1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atleast_1d(Tensor input)", "atleast_1d(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::atleast_1d(Tensor self) -> Tensor auto dispatch_atleast_1d = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::atleast_1d(self); }; return wrap(dispatch_atleast_1d(_r.tensor(0))); } case 1: { // aten::atleast_1d.Sequence(Tensor[] tensors) -> Tensor[] auto dispatch_atleast_1d = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::atleast_1d(tensors); }; return wrap(dispatch_atleast_1d(_r.tensorlist(0))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // atleast_2d static PyObject * THPVariable_atleast_2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atleast_2d(Tensor input)", "atleast_2d(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::atleast_2d(Tensor self) -> Tensor auto dispatch_atleast_2d = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::atleast_2d(self); }; return wrap(dispatch_atleast_2d(_r.tensor(0))); } case 1: { // aten::atleast_2d.Sequence(Tensor[] tensors) -> Tensor[] auto dispatch_atleast_2d = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::atleast_2d(tensors); }; return wrap(dispatch_atleast_2d(_r.tensorlist(0))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // atleast_3d static PyObject * THPVariable_atleast_3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atleast_3d(Tensor input)", "atleast_3d(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::atleast_3d(Tensor self) -> Tensor auto dispatch_atleast_3d = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::atleast_3d(self); }; return wrap(dispatch_atleast_3d(_r.tensor(0))); } case 1: { // aten::atleast_3d.Sequence(Tensor[] tensors) -> Tensor[] auto dispatch_atleast_3d = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::atleast_3d(tensors); }; return wrap(dispatch_atleast_3d(_r.tensorlist(0))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // baddbmm static PyObject * THPVariable_baddbmm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "baddbmm(Scalar beta, Tensor input, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor out=None)|deprecated", "baddbmm(Scalar beta, Tensor input, Tensor batch1, Tensor batch2, *, Tensor out=None)|deprecated", "baddbmm(Tensor input, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // [deprecated] aten::baddbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_baddbmm = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.baddbmm(batch1, batch2, beta, alpha); }; return wrap(dispatch_baddbmm(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } else { // [deprecated] aten::baddbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_baddbmm_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::baddbmm_out(out, self, batch1, batch2, beta, alpha); }; return wrap(dispatch_baddbmm_out(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } } case 1: { if (_r.isNone(4)) { // [deprecated] aten::baddbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_baddbmm = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.baddbmm(batch1, batch2, beta, 1); }; return wrap(dispatch_baddbmm(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } else { // [deprecated] aten::baddbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_baddbmm_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::baddbmm_out(out, self, batch1, batch2, beta, 1); }; return wrap(dispatch_baddbmm_out(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } } case 2: { if (_r.isNone(5)) { // aten::baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor auto dispatch_baddbmm = [](const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.baddbmm(batch1, batch2, beta, alpha); }; return wrap(dispatch_baddbmm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } else { // aten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_baddbmm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::baddbmm_out(out, self, batch1, batch2, beta, alpha); }; return wrap(dispatch_baddbmm_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bartlett_window static PyObject * THPVariable_bartlett_window(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bartlett_window(int64_t window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "bartlett_window(int64_t window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch_bartlett_window = [](int64_t window_length, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::bartlett_window(window_length, options); }; return wrap(dispatch_bartlett_window(_r.toInt64(0), options)); } case 1: { // aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_bartlett_window = [](int64_t window_length, bool periodic, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::bartlett_window(window_length, periodic, options); }; return wrap(dispatch_bartlett_window(_r.toInt64(0), _r.toBool(1), options)); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm static PyObject * THPVariable_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, double momentum, double eps, bool cudnn_enabled)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor auto dispatch_batch_norm = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const ::std::optional & running_mean, const ::std::optional & running_var, bool training, double momentum, double eps, bool cudnn_enabled) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::batch_norm(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); }; return wrap(dispatch_batch_norm(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_batch_norm static PyObject * THPVariable_quantized_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, double eps, double output_scale, int64_t output_zero_point)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor auto dispatch_quantized_batch_norm = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantized_batch_norm(input, weight, bias, mean, var, eps, output_scale, output_zero_point); }; return wrap(dispatch_quantized_batch_norm(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.tensor(3), _r.tensor(4), _r.toDouble(5), _r.toDouble(6), _r.toInt64(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _batch_norm_impl_index static PyObject * THPVariable__batch_norm_impl_index(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_batch_norm_impl_index(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, double momentum, double eps, bool cudnn_enabled)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_batch_norm_impl_index(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor, Tensor, Tensor, Tensor, int) auto dispatch__batch_norm_impl_index = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const ::std::optional & running_mean, const ::std::optional & running_var, bool training, double momentum, double eps, bool cudnn_enabled) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_batch_norm_impl_index(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); }; return wrap(dispatch__batch_norm_impl_index(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bernoulli static PyObject * THPVariable_bernoulli(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bernoulli(Tensor input, *, Generator? generator=None, Tensor out=None)", "bernoulli(Tensor input, double p, *, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::bernoulli(Tensor self, *, Generator? generator=None) -> Tensor auto dispatch_bernoulli = [](const at::Tensor & self, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bernoulli(generator); }; return wrap(dispatch_bernoulli(_r.tensor(0), _r.generator(1))); } else { // aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_bernoulli_out = [](at::Tensor out, const at::Tensor & self, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bernoulli_out(out, self, generator); }; return wrap(dispatch_bernoulli_out(_r.tensor(2), _r.tensor(0), _r.generator(1))); } } case 1: { // aten::bernoulli.p(Tensor self, float p, *, Generator? generator=None) -> Tensor auto dispatch_bernoulli = [](const at::Tensor & self, double p, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bernoulli(p, generator); }; return wrap(dispatch_bernoulli(_r.tensor(0), _r.toDouble(1), _r.generator(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // bilinear static PyObject * THPVariable_bilinear(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor auto dispatch_bilinear = [](const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const ::std::optional & bias) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bilinear(input1, input2, weight, bias); }; return wrap(dispatch_bilinear(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // binary_cross_entropy_with_logits static PyObject * THPVariable_binary_cross_entropy_with_logits(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "binary_cross_entropy_with_logits(Tensor input, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int64_t reduction=at::Reduction::Mean)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor auto dispatch_binary_cross_entropy_with_logits = [](const at::Tensor & self, const at::Tensor & target, const ::std::optional & weight, const ::std::optional & pos_weight, int64_t reduction) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::binary_cross_entropy_with_logits(self, target, weight, pos_weight, reduction); }; return wrap(dispatch_binary_cross_entropy_with_logits(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // bincount static PyObject * THPVariable_bincount(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bincount(Tensor input, Tensor? weights=None, int64_t minlength=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensor auto dispatch_bincount = [](const at::Tensor & self, const ::std::optional & weights, int64_t minlength) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bincount(weights, minlength); }; return wrap(dispatch_bincount(_r.tensor(0), _r.optionalTensor(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // bitwise_not static PyObject * THPVariable_bitwise_not(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bitwise_not(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::bitwise_not(Tensor self) -> Tensor auto dispatch_bitwise_not = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_not(); }; return wrap(dispatch_bitwise_not(_r.tensor(0))); } else { // aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_not_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_not_out(out, self); }; return wrap(dispatch_bitwise_not_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // copysign static PyObject * THPVariable_copysign(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "copysign(Tensor input, Tensor other, *, Tensor out=None)", "copysign(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::copysign.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_copysign = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.copysign(other); }; return wrap(dispatch_copysign(_r.tensor(0), _r.tensor(1))); } else { // aten::copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_copysign_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::copysign_out(out, self, other); }; return wrap(dispatch_copysign_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::copysign.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_copysign = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.copysign(other); }; return wrap(dispatch_copysign(_r.tensor(0), _r.scalar(1))); } else { // aten::copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_copysign_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::copysign_out(out, self, other); }; return wrap(dispatch_copysign_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _lazy_clone static PyObject * THPVariable__lazy_clone(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_lazy_clone(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_lazy_clone(Tensor self) -> Tensor auto dispatch__lazy_clone = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self._lazy_clone(); }; return wrap(dispatch__lazy_clone(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logical_not static PyObject * THPVariable_logical_not(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logical_not(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::logical_not(Tensor self) -> Tensor auto dispatch_logical_not = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logical_not(); }; return wrap(dispatch_logical_not(_r.tensor(0))); } else { // aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logical_not_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logical_not_out(out, self); }; return wrap(dispatch_logical_not_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logical_xor static PyObject * THPVariable_logical_xor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logical_xor(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::logical_xor(Tensor self, Tensor other) -> Tensor auto dispatch_logical_xor = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logical_xor(other); }; return wrap(dispatch_logical_xor(_r.tensor(0), _r.tensor(1))); } else { // aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logical_xor_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logical_xor_out(out, self, other); }; return wrap(dispatch_logical_xor_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logical_and static PyObject * THPVariable_logical_and(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logical_and(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::logical_and(Tensor self, Tensor other) -> Tensor auto dispatch_logical_and = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logical_and(other); }; return wrap(dispatch_logical_and(_r.tensor(0), _r.tensor(1))); } else { // aten::logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logical_and_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logical_and_out(out, self, other); }; return wrap(dispatch_logical_and_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logical_or static PyObject * THPVariable_logical_or(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logical_or(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::logical_or(Tensor self, Tensor other) -> Tensor auto dispatch_logical_or = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logical_or(other); }; return wrap(dispatch_logical_or(_r.tensor(0), _r.tensor(1))); } else { // aten::logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logical_or_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logical_or_out(out, self, other); }; return wrap(dispatch_logical_or_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // blackman_window static PyObject * THPVariable_blackman_window(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "blackman_window(int64_t window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "blackman_window(int64_t window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch_blackman_window = [](int64_t window_length, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::blackman_window(window_length, options); }; return wrap(dispatch_blackman_window(_r.toInt64(0), options)); } case 1: { // aten::blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_blackman_window = [](int64_t window_length, bool periodic, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::blackman_window(window_length, periodic, options); }; return wrap(dispatch_blackman_window(_r.toInt64(0), _r.toBool(1), options)); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // bmm static PyObject * THPVariable_bmm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bmm(Tensor input, Tensor mat2, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::bmm(Tensor self, Tensor mat2) -> Tensor auto dispatch_bmm = [](const at::Tensor & self, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bmm(mat2); }; return wrap(dispatch_bmm(_r.tensor(0), _r.tensor(1))); } else { // aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bmm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bmm_out(out, self, mat2); }; return wrap(dispatch_bmm_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // broadcast_tensors static PyObject * THPVariable_broadcast_tensors(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "broadcast_tensors(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::broadcast_tensors(Tensor[] tensors) -> Tensor[] auto dispatch_broadcast_tensors = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::broadcast_tensors(tensors); }; return wrap(dispatch_broadcast_tensors(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // broadcast_to static PyObject * THPVariable_broadcast_to(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "broadcast_to(Tensor input, SymIntArrayRef size)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a) auto dispatch_broadcast_to = [](const at::Tensor & self, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.broadcast_to_symint(size); }; return wrap(dispatch_broadcast_to(_r.tensor(0), _r.symintlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_broadcast_to static PyObject * THPVariable__sparse_broadcast_to(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_broadcast_to(Tensor input, IntArrayRef size)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a) auto dispatch__sparse_broadcast_to = [](const at::Tensor & self, at::IntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_broadcast_to(self, size); }; return wrap(dispatch__sparse_broadcast_to(_r.tensor(0), _r.intlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // cat static PyObject * THPVariable_cat(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cat(TensorList tensors, int64_t dim=0, *, Tensor out=None)", "cat(TensorList tensors, Dimname dim, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::cat(Tensor[] tensors, int dim=0) -> Tensor auto dispatch_cat = [](const at::ITensorListRef & tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cat(tensors, dim); }; return wrap(dispatch_cat(_r.tensorlist(0), _r.toInt64(1))); } else { // aten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cat_out = [](at::Tensor out, const at::ITensorListRef & tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cat_out(out, tensors, dim); }; return wrap(dispatch_cat_out(_r.tensor(2), _r.tensorlist(0), _r.toInt64(1))); } } case 1: { if (_r.isNone(2)) { // aten::cat.names(Tensor[] tensors, Dimname dim) -> Tensor auto dispatch_cat = [](at::TensorList tensors, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cat(tensors, dim); }; return wrap(dispatch_cat(_r.tensorlist(0), _r.dimname(1))); } else { // aten::cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cat_out = [](at::Tensor out, at::TensorList tensors, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cat_out(out, tensors, dim); }; return wrap(dispatch_cat_out(_r.tensor(2), _r.tensorlist(0), _r.dimname(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // concat static PyObject * THPVariable_concat(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "concat(TensorList tensors, int64_t dim=0, *, Tensor out=None)", "concat(TensorList tensors, Dimname dim, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::concat(Tensor[] tensors, int dim=0) -> Tensor auto dispatch_concat = [](at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concat(tensors, dim); }; return wrap(dispatch_concat(_r.tensorlist(0), _r.toInt64(1))); } else { // aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_concat_out = [](at::Tensor out, at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concat_out(out, tensors, dim); }; return wrap(dispatch_concat_out(_r.tensor(2), _r.tensorlist(0), _r.toInt64(1))); } } case 1: { if (_r.isNone(2)) { // aten::concat.names(Tensor[] tensors, Dimname dim) -> Tensor auto dispatch_concat = [](at::TensorList tensors, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concat(tensors, dim); }; return wrap(dispatch_concat(_r.tensorlist(0), _r.dimname(1))); } else { // aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_concat_out = [](at::Tensor out, at::TensorList tensors, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concat_out(out, tensors, dim); }; return wrap(dispatch_concat_out(_r.tensor(2), _r.tensorlist(0), _r.dimname(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // concatenate static PyObject * THPVariable_concatenate(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "concatenate(TensorList tensors, int64_t dim=0, *, Tensor out=None)", "concatenate(TensorList tensors, Dimname dim, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::concatenate(Tensor[] tensors, int dim=0) -> Tensor auto dispatch_concatenate = [](at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concatenate(tensors, dim); }; return wrap(dispatch_concatenate(_r.tensorlist(0), _r.toInt64(1))); } else { // aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_concatenate_out = [](at::Tensor out, at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concatenate_out(out, tensors, dim); }; return wrap(dispatch_concatenate_out(_r.tensor(2), _r.tensorlist(0), _r.toInt64(1))); } } case 1: { if (_r.isNone(2)) { // aten::concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor auto dispatch_concatenate = [](at::TensorList tensors, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concatenate(tensors, dim); }; return wrap(dispatch_concatenate(_r.tensorlist(0), _r.dimname(1))); } else { // aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_concatenate_out = [](at::Tensor out, at::TensorList tensors, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::concatenate_out(out, tensors, dim); }; return wrap(dispatch_concatenate_out(_r.tensor(2), _r.tensorlist(0), _r.dimname(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // block_diag static PyObject * THPVariable_block_diag(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "block_diag(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::block_diag(Tensor[] tensors) -> Tensor auto dispatch_block_diag = [](at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::block_diag(tensors); }; return wrap(dispatch_block_diag(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ceil static PyObject * THPVariable_ceil(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ceil(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::ceil(Tensor self) -> Tensor auto dispatch_ceil = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ceil(); }; return wrap(dispatch_ceil(_r.tensor(0))); } else { // aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ceil_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ceil_out(out, self); }; return wrap(dispatch_ceil_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ceil_ static PyObject * THPVariable_ceil_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ceil_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::ceil_(Tensor(a!) self) -> Tensor(a!) auto dispatch_ceil_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ceil_(); }; return wrap(dispatch_ceil_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // chain_matmul static PyObject * THPVariable_chain_matmul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "chain_matmul(TensorList matrices, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::chain_matmul(Tensor[] matrices) -> Tensor auto dispatch_chain_matmul = [](at::TensorList matrices) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::chain_matmul(matrices); }; return wrap(dispatch_chain_matmul(_r.tensorlist(0))); } else { // aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_chain_matmul_out = [](at::Tensor out, at::TensorList matrices) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::chain_matmul_out(out, matrices); }; return wrap(dispatch_chain_matmul_out(_r.tensor(1), _r.tensorlist(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unsafe_chunk static PyObject * THPVariable_unsafe_chunk(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unsafe_chunk(Tensor input, int64_t chunks, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[] auto dispatch_unsafe_chunk = [](const at::Tensor & self, int64_t chunks, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.unsafe_chunk(chunks, dim); }; return wrap(dispatch_unsafe_chunk(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // chunk static PyObject * THPVariable_chunk(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "chunk(Tensor input, int64_t chunks, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::chunk(Tensor(a -> *) self, int chunks, int dim=0) -> Tensor(a)[] auto dispatch_chunk = [](const at::Tensor & self, int64_t chunks, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.chunk(chunks, dim); }; return wrap(dispatch_chunk(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // tensor_split static PyObject * THPVariable_tensor_split(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tensor_split(Tensor input, SymIntArrayRef indices, int64_t dim=0)", "tensor_split(Tensor input, Tensor tensor_indices_or_sections, int64_t dim=0)", "tensor_split(Tensor input, SymInt sections, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[] auto dispatch_tensor_split = [](const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.tensor_split_symint(indices, dim); }; return wrap(dispatch_tensor_split(_r.tensor(0), _r.symintlist(1), _r.toInt64(2))); } case 1: { // aten::tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[] auto dispatch_tensor_split = [](const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.tensor_split(tensor_indices_or_sections, dim); }; return wrap(dispatch_tensor_split(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); } case 2: { // aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[] auto dispatch_tensor_split = [](const at::Tensor & self, c10::SymInt sections, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.tensor_split_symint(sections, dim); }; return wrap(dispatch_tensor_split(_r.tensor(0), _r.toSymInt(1), _r.toInt64(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clamp static PyObject * THPVariable_clamp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clamp(Tensor input, Tensor? min=None, Tensor? max=None, *, Tensor out=None)", "clamp(Tensor input, Scalar? min=None, Scalar? max=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::clamp.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor auto dispatch_clamp = [](const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp(min, max); }; return wrap(dispatch_clamp(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2))); } else { // aten::clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clamp_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clamp_out(out, self, min, max); }; return wrap(dispatch_clamp_out(_r.tensor(3), _r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2))); } } case 1: { if (_r.isNone(3)) { // aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor auto dispatch_clamp = [](const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp(min, max); }; return wrap(dispatch_clamp(_r.tensor(0), _r.scalarOptional(1), _r.scalarOptional(2))); } else { // aten::clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clamp_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clamp_out(out, self, min, max); }; return wrap(dispatch_clamp_out(_r.tensor(3), _r.tensor(0), _r.scalarOptional(1), _r.scalarOptional(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clamp_ static PyObject * THPVariable_clamp_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clamp_(Tensor input, Tensor? min=None, Tensor? max=None)", "clamp_(Tensor input, Scalar? min=None, Scalar? max=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::clamp_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!) auto dispatch_clamp_ = [](at::Tensor self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_(min, max); }; return wrap(dispatch_clamp_(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2))); } case 1: { // aten::clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!) auto dispatch_clamp_ = [](at::Tensor self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_(min, max); }; return wrap(dispatch_clamp_(_r.tensor(0), _r.scalarOptional(1), _r.scalarOptional(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clamp_max static PyObject * THPVariable_clamp_max(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clamp_max(Tensor input, Tensor max, *, Tensor out=None)", "clamp_max(Tensor input, Scalar max, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::clamp_max.Tensor(Tensor self, Tensor max) -> Tensor auto dispatch_clamp_max = [](const at::Tensor & self, const at::Tensor & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_max(max); }; return wrap(dispatch_clamp_max(_r.tensor(0), _r.tensor(1))); } else { // aten::clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clamp_max_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clamp_max_out(out, self, max); }; return wrap(dispatch_clamp_max_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::clamp_max(Tensor self, Scalar max) -> Tensor auto dispatch_clamp_max = [](const at::Tensor & self, const at::Scalar & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_max(max); }; return wrap(dispatch_clamp_max(_r.tensor(0), _r.scalar(1))); } else { // aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clamp_max_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clamp_max_out(out, self, max); }; return wrap(dispatch_clamp_max_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clamp_max_ static PyObject * THPVariable_clamp_max_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clamp_max_(Tensor input, Tensor max)", "clamp_max_(Tensor input, Scalar max)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::clamp_max_.Tensor(Tensor(a!) self, Tensor max) -> Tensor(a!) auto dispatch_clamp_max_ = [](at::Tensor self, const at::Tensor & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_max_(max); }; return wrap(dispatch_clamp_max_(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!) auto dispatch_clamp_max_ = [](at::Tensor self, const at::Scalar & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_max_(max); }; return wrap(dispatch_clamp_max_(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clamp_min static PyObject * THPVariable_clamp_min(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clamp_min(Tensor input, Tensor min, *, Tensor out=None)", "clamp_min(Tensor input, Scalar min, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::clamp_min.Tensor(Tensor self, Tensor min) -> Tensor auto dispatch_clamp_min = [](const at::Tensor & self, const at::Tensor & min) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_min(min); }; return wrap(dispatch_clamp_min(_r.tensor(0), _r.tensor(1))); } else { // aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clamp_min_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & min) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clamp_min_out(out, self, min); }; return wrap(dispatch_clamp_min_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::clamp_min(Tensor self, Scalar min) -> Tensor auto dispatch_clamp_min = [](const at::Tensor & self, const at::Scalar & min) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_min(min); }; return wrap(dispatch_clamp_min(_r.tensor(0), _r.scalar(1))); } else { // aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clamp_min_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & min) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clamp_min_out(out, self, min); }; return wrap(dispatch_clamp_min_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clamp_min_ static PyObject * THPVariable_clamp_min_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clamp_min_(Tensor input, Tensor min)", "clamp_min_(Tensor input, Scalar min)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!) auto dispatch_clamp_min_ = [](at::Tensor self, const at::Tensor & min) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_min_(min); }; return wrap(dispatch_clamp_min_(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!) auto dispatch_clamp_min_ = [](at::Tensor self, const at::Scalar & min) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clamp_min_(min); }; return wrap(dispatch_clamp_min_(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clip static PyObject * THPVariable_clip(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clip(Tensor input, Tensor? min=None, Tensor? max=None, *, Tensor out=None)", "clip(Tensor input, Scalar? min=None, Scalar? max=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::clip.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor auto dispatch_clip = [](const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clip(min, max); }; return wrap(dispatch_clip(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2))); } else { // aten::clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clip_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clip_out(out, self, min, max); }; return wrap(dispatch_clip_out(_r.tensor(3), _r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2))); } } case 1: { if (_r.isNone(3)) { // aten::clip(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor auto dispatch_clip = [](const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clip(min, max); }; return wrap(dispatch_clip(_r.tensor(0), _r.scalarOptional(1), _r.scalarOptional(2))); } else { // aten::clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_clip_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::clip_out(out, self, min, max); }; return wrap(dispatch_clip_out(_r.tensor(3), _r.tensor(0), _r.scalarOptional(1), _r.scalarOptional(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // clip_ static PyObject * THPVariable_clip_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clip_(Tensor input, Tensor? min=None, Tensor? max=None)", "clip_(Tensor input, Scalar? min=None, Scalar? max=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::clip_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!) auto dispatch_clip_ = [](at::Tensor self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clip_(min, max); }; return wrap(dispatch_clip_(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2))); } case 1: { // aten::clip_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!) auto dispatch_clip_ = [](at::Tensor self, const ::std::optional & min, const ::std::optional & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clip_(min, max); }; return wrap(dispatch_clip_(_r.tensor(0), _r.scalarOptional(1), _r.scalarOptional(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_is_acceptable static PyObject * THPVariable_cudnn_is_acceptable(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_is_acceptable(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cudnn_is_acceptable(Tensor self) -> bool auto dispatch_cudnn_is_acceptable = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return at::cudnn_is_acceptable(self); }; return wrap(dispatch_cudnn_is_acceptable(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // complex static PyObject * THPVariable_complex(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "complex(Tensor real, Tensor imag, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::complex(Tensor real, Tensor imag) -> Tensor auto dispatch_complex = [](const at::Tensor & real, const at::Tensor & imag) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::complex(real, imag); }; return wrap(dispatch_complex(_r.tensor(0), _r.tensor(1))); } else { // aten::complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_complex_out = [](at::Tensor out, const at::Tensor & real, const at::Tensor & imag) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::complex_out(out, real, imag); }; return wrap(dispatch_complex_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // polar static PyObject * THPVariable_polar(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "polar(Tensor abs, Tensor angle, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::polar(Tensor abs, Tensor angle) -> Tensor auto dispatch_polar = [](const at::Tensor & abs, const at::Tensor & angle) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::polar(abs, angle); }; return wrap(dispatch_polar(_r.tensor(0), _r.tensor(1))); } else { // aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_polar_out = [](at::Tensor out, const at::Tensor & abs, const at::Tensor & angle) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::polar_out(out, abs, angle); }; return wrap(dispatch_polar_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // constant_pad_nd static PyObject * THPVariable_constant_pad_nd(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "constant_pad_nd(Tensor input, SymIntArrayRef pad, Scalar value=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor auto dispatch_constant_pad_nd = [](const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::constant_pad_nd_symint(self, pad, value); }; return wrap(dispatch_constant_pad_nd(_r.tensor(0), _r.symintlist(1), _r.scalar(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // convolution static PyObject * THPVariable_convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef stride, SymIntArrayRef padding, SymIntArrayRef dilation, bool transposed, SymIntArrayRef output_padding, SymInt groups)", }, /*traceable=*/false); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor auto dispatch_convolution = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::convolution_symint(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); }; return wrap(dispatch_convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toBool(6), _r.symintlist(7), _r.toSymInt(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _convolution static PyObject * THPVariable__convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef stride, SymIntArrayRef padding, SymIntArrayRef dilation, bool transposed, IntArrayRef output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled)", "_convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef stride, SymIntArrayRef padding, SymIntArrayRef dilation, bool transposed, SymIntArrayRef output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32)", }, /*traceable=*/true); ParsedArgs<13> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor auto dispatch__convolution = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convolution_symint(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled); }; return wrap(dispatch__convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toBool(6), _r.intlist(7), _r.toSymInt(8), _r.toBool(9), _r.toBool(10), _r.toBool(11))); } case 1: { // aten::_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor auto dispatch__convolution = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convolution_symint(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32); }; return wrap(dispatch__convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toBool(6), _r.symintlist(7), _r.toSymInt(8), _r.toBool(9), _r.toBool(10), _r.toBool(11), _r.toBool(12))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _convolution_mode static PyObject * THPVariable__convolution_mode(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef stride, c10::string_view padding, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch__convolution_mode = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convolution_mode_symint(input, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch__convolution_mode(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.stringView(4), _r.symintlist(5), _r.toSymInt(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // conv1d static PyObject * THPVariable_conv1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[1] stride=1, SymIntArrayRef[1] padding=0, SymIntArrayRef[1] dilation=1, SymInt groups=1)", "conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[1] stride=1, c10::string_view padding=\"valid\", SymIntArrayRef[1] dilation=1, SymInt groups=1)", }, /*traceable=*/false); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] dilation=1, SymInt groups=1) -> Tensor auto dispatch_conv1d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv1d_symint(input, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_conv1d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); } case 1: { // aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, str padding="valid", SymInt[1] dilation=1, SymInt groups=1) -> Tensor auto dispatch_conv1d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv1d_symint(input, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_conv1d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.stringView(4), _r.symintlist(5), _r.toSymInt(6))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // conv2d static PyObject * THPVariable_conv2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[2] stride=1, SymIntArrayRef[2] padding=0, SymIntArrayRef[2] dilation=1, SymInt groups=1)", "conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[2] stride=1, c10::string_view padding=\"valid\", SymIntArrayRef[2] dilation=1, SymInt groups=1)", }, /*traceable=*/false); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, SymInt groups=1) -> Tensor auto dispatch_conv2d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv2d_symint(input, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_conv2d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); } case 1: { // aten::conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, str padding="valid", SymInt[2] dilation=1, SymInt groups=1) -> Tensor auto dispatch_conv2d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv2d_symint(input, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_conv2d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.stringView(4), _r.symintlist(5), _r.toSymInt(6))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // conv3d static PyObject * THPVariable_conv3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[3] stride=1, SymIntArrayRef[3] padding=0, SymIntArrayRef[3] dilation=1, SymInt groups=1)", "conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[3] stride=1, c10::string_view padding=\"valid\", SymIntArrayRef[3] dilation=1, SymInt groups=1)", }, /*traceable=*/false); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, SymInt groups=1) -> Tensor auto dispatch_conv3d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv3d_symint(input, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_conv3d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); } case 1: { // aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, str padding="valid", SymInt[3] dilation=1, SymInt groups=1) -> Tensor auto dispatch_conv3d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv3d_symint(input, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_conv3d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.stringView(4), _r.symintlist(5), _r.toSymInt(6))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // conv_tbc static PyObject * THPVariable_conv_tbc(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conv_tbc(Tensor input, Tensor weight, Tensor bias, int64_t pad=0)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor auto dispatch_conv_tbc = [](const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv_tbc(self, weight, bias, pad); }; return wrap(dispatch_conv_tbc(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // conv_transpose1d static PyObject * THPVariable_conv_transpose1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[1] stride=1, SymIntArrayRef[1] padding=0, SymIntArrayRef[1] output_padding=0, SymInt groups=1, SymIntArrayRef[1] dilation=1)", }, /*traceable=*/false); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] output_padding=0, SymInt groups=1, SymInt[1] dilation=1) -> Tensor auto dispatch_conv_transpose1d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv_transpose1d_symint(input, weight, bias, stride, padding, output_padding, groups, dilation); }; return wrap(dispatch_conv_transpose1d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6), _r.symintlist(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // conv_transpose2d static PyObject * THPVariable_conv_transpose2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conv_transpose2d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[2] stride=1, SymIntArrayRef[2] padding=0, SymIntArrayRef[2] output_padding=0, SymInt groups=1, SymIntArrayRef[2] dilation=1)", }, /*traceable=*/false); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt groups=1, SymInt[2] dilation=1) -> Tensor auto dispatch_conv_transpose2d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv_transpose2d_symint(input, weight, bias, stride, padding, output_padding, groups, dilation); }; return wrap(dispatch_conv_transpose2d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6), _r.symintlist(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // conv_transpose3d static PyObject * THPVariable_conv_transpose3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "conv_transpose3d(Tensor input, Tensor weight, Tensor? bias=None, SymIntArrayRef[3] stride=1, SymIntArrayRef[3] padding=0, SymIntArrayRef[3] output_padding=0, SymInt groups=1, SymIntArrayRef[3] dilation=1)", }, /*traceable=*/false); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt groups=1, SymInt[3] dilation=1) -> Tensor auto dispatch_conv_transpose3d = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::conv_transpose3d_symint(input, weight, bias, stride, padding, output_padding, groups, dilation); }; return wrap(dispatch_conv_transpose3d(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6), _r.symintlist(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _copy_from static PyObject * THPVariable__copy_from(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_copy_from(Tensor input, Tensor dst, bool non_blocking=False)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor auto dispatch__copy_from = [](const at::Tensor & self, const at::Tensor & dst, bool non_blocking) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_copy_from(self, dst, non_blocking); }; return wrap(dispatch__copy_from(_r.tensor(0), _r.tensor(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _copy_from_and_resize static PyObject * THPVariable__copy_from_and_resize(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_copy_from_and_resize(Tensor input, Tensor dst)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor auto dispatch__copy_from_and_resize = [](const at::Tensor & self, const at::Tensor & dst) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_copy_from_and_resize(self, dst); }; return wrap(dispatch__copy_from_and_resize(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cos static PyObject * THPVariable_cos(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cos(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::cos(Tensor self) -> Tensor auto dispatch_cos = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cos(); }; return wrap(dispatch_cos(_r.tensor(0))); } else { // aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cos_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cos_out(out, self); }; return wrap(dispatch_cos_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cos_ static PyObject * THPVariable_cos_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cos_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cos_(Tensor(a!) self) -> Tensor(a!) auto dispatch_cos_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cos_(); }; return wrap(dispatch_cos_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cosh static PyObject * THPVariable_cosh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cosh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::cosh(Tensor self) -> Tensor auto dispatch_cosh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cosh(); }; return wrap(dispatch_cosh(_r.tensor(0))); } else { // aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cosh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cosh_out(out, self); }; return wrap(dispatch_cosh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cosh_ static PyObject * THPVariable_cosh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cosh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cosh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_cosh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cosh_(); }; return wrap(dispatch_cosh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cosine_embedding_loss static PyObject * THPVariable_cosine_embedding_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, double margin=0.0, int64_t reduction=at::Reduction::Mean)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor auto dispatch_cosine_embedding_loss = [](const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cosine_embedding_loss(input1, input2, target, margin, reduction); }; return wrap(dispatch_cosine_embedding_loss(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toDouble(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // count_nonzero static PyObject * THPVariable_count_nonzero(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "count_nonzero(Tensor input, int64_t? dim=None)", "count_nonzero(Tensor input, IntArrayRef dim)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::count_nonzero(Tensor self, int? dim=None) -> Tensor auto dispatch_count_nonzero = [](const at::Tensor & self, ::std::optional dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.count_nonzero(dim); }; return wrap(dispatch_count_nonzero(_r.tensor(0), _r.toInt64Optional(1))); } case 1: { // aten::count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor auto dispatch_count_nonzero = [](const at::Tensor & self, at::IntArrayRef dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.count_nonzero(dim); }; return wrap(dispatch_count_nonzero(_r.tensor(0), _r.intlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cov static PyObject * THPVariable_cov(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cov(Tensor input, *, int64_t correction=1, Tensor? fweights=None, Tensor? aweights=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cov(Tensor self, *, int correction=1, Tensor? fweights=None, Tensor? aweights=None) -> Tensor auto dispatch_cov = [](const at::Tensor & self, int64_t correction, const ::std::optional & fweights, const ::std::optional & aweights) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cov(correction, fweights, aweights); }; return wrap(dispatch_cov(_r.tensor(0), _r.toInt64(1), _r.optionalTensor(2), _r.optionalTensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // corrcoef static PyObject * THPVariable_corrcoef(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "corrcoef(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::corrcoef(Tensor self) -> Tensor auto dispatch_corrcoef = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.corrcoef(); }; return wrap(dispatch_corrcoef(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_affine_grid_generator static PyObject * THPVariable_cudnn_affine_grid_generator(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_affine_grid_generator(Tensor theta, int64_t N, int64_t C, int64_t H, int64_t W)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid auto dispatch_cudnn_affine_grid_generator = [](const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cudnn_affine_grid_generator(theta, N, C, H, W); }; return wrap(dispatch_cudnn_affine_grid_generator(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toInt64(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_batch_norm static PyObject * THPVariable_cudnn_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, double exponential_average_factor, double epsilon)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor, Tensor) auto dispatch_cudnn_batch_norm = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, const ::std::optional & running_mean, const ::std::optional & running_var, bool training, double exponential_average_factor, double epsilon) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::cudnn_batch_norm(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); }; return wrap(dispatch_cudnn_batch_norm(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_convolution static PyObject * THPVariable_cudnn_convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_convolution(Tensor input, Tensor weight, SymIntArrayRef padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(9)) { // aten::cudnn_convolution(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor auto dispatch_cudnn_convolution = [](const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cudnn_convolution_symint(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); }; return wrap(dispatch_cudnn_convolution(_r.tensor(0), _r.tensor(1), _r.symintlist(2), _r.symintlist(3), _r.symintlist(4), _r.toSymInt(5), _r.toBool(6), _r.toBool(7), _r.toBool(8))); } else { // aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cudnn_convolution_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cudnn_convolution_symint_out(out, self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); }; return wrap(dispatch_cudnn_convolution_out(_r.tensor(9), _r.tensor(0), _r.tensor(1), _r.symintlist(2), _r.symintlist(3), _r.symintlist(4), _r.toSymInt(5), _r.toBool(6), _r.toBool(7), _r.toBool(8))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_convolution_transpose static PyObject * THPVariable_cudnn_convolution_transpose(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_convolution_transpose(Tensor input, Tensor weight, SymIntArrayRef padding, SymIntArrayRef output_padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cudnn_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor auto dispatch_cudnn_convolution_transpose = [](const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cudnn_convolution_transpose_symint(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); }; return wrap(dispatch_cudnn_convolution_transpose(_r.tensor(0), _r.tensor(1), _r.symintlist(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6), _r.toBool(7), _r.toBool(8), _r.toBool(9))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _mps_convolution_transpose static PyObject * THPVariable__mps_convolution_transpose(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_mps_convolution_transpose(Tensor input, Tensor weight, SymIntArrayRef padding, SymIntArrayRef output_padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_mps_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch__mps_convolution_transpose = [](const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_mps_convolution_transpose_symint(self, weight, padding, output_padding, stride, dilation, groups); }; return wrap(dispatch__mps_convolution_transpose(_r.tensor(0), _r.tensor(1), _r.symintlist(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_convolution_relu static PyObject * THPVariable_cudnn_convolution_relu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_convolution_relu(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef stride, SymIntArrayRef padding, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch_cudnn_convolution_relu = [](const at::Tensor & self, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cudnn_convolution_relu_symint(self, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_cudnn_convolution_relu(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_convolution_add_relu static PyObject * THPVariable_cudnn_convolution_add_relu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_convolution_add_relu(Tensor input, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymIntArrayRef stride, SymIntArrayRef padding, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch_cudnn_convolution_add_relu = [](const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional & alpha, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cudnn_convolution_add_relu_symint(self, weight, z, alpha, bias, stride, padding, dilation, groups); }; return wrap(dispatch_cudnn_convolution_add_relu(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalarOptional(3), _r.optionalTensor(4), _r.symintlist(5), _r.symintlist(6), _r.symintlist(7), _r.toSymInt(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cudnn_grid_sampler static PyObject * THPVariable_cudnn_grid_sampler(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cudnn_grid_sampler(Tensor input, Tensor grid)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output auto dispatch_cudnn_grid_sampler = [](const at::Tensor & self, const at::Tensor & grid) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cudnn_grid_sampler(self, grid); }; return wrap(dispatch_cudnn_grid_sampler(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // cummax static PyObject * THPVariable_cummax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_cummax_structseq(); static PyTypeObject* NamedTuple1 = generated::get_cummax_out_structseq(); static PythonArgParser parser({ "cummax(Tensor input, int64_t dim, *, TensorList[2] out=None)", "cummax(Tensor input, Dimname dim, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::cummax(Tensor self, int dim) -> (Tensor values, Tensor indices) auto dispatch_cummax = [](const at::Tensor & self, int64_t dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.cummax(dim); }; return wrap(NamedTuple, dispatch_cummax(_r.tensor(0), _r.toInt64(1))); } else { // aten::cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(2); auto dispatch_cummax_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::cummax_out(values, indices, self, dim); }; return wrap(NamedTuple1, dispatch_cummax_out(out[0], out[1], _r.tensor(0), _r.toInt64(1))); } } case 1: { if (_r.isNone(2)) { // aten::cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) auto dispatch_cummax = [](const at::Tensor & self, at::Dimname dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.cummax(dim); }; return wrap(NamedTuple, dispatch_cummax(_r.tensor(0), _r.dimname(1))); } else { // aten::cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(2); auto dispatch_cummax_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::cummax_out(values, indices, self, dim); }; return wrap(NamedTuple1, dispatch_cummax_out(out[0], out[1], _r.tensor(0), _r.dimname(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cummax_helper static PyObject * THPVariable__cummax_helper(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cummax_helper(Tensor input, Tensor values, Tensor indices, int64_t dim)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cummax_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () auto dispatch__cummax_helper = [](const at::Tensor & self, at::Tensor values, at::Tensor indices, int64_t dim) -> void { pybind11::gil_scoped_release no_gil; at::_cummax_helper(self, values, indices, dim); }; dispatch__cummax_helper(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // cummin static PyObject * THPVariable_cummin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_cummin_structseq(); static PyTypeObject* NamedTuple1 = generated::get_cummin_out_structseq(); static PythonArgParser parser({ "cummin(Tensor input, int64_t dim, *, TensorList[2] out=None)", "cummin(Tensor input, Dimname dim, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::cummin(Tensor self, int dim) -> (Tensor values, Tensor indices) auto dispatch_cummin = [](const at::Tensor & self, int64_t dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.cummin(dim); }; return wrap(NamedTuple, dispatch_cummin(_r.tensor(0), _r.toInt64(1))); } else { // aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(2); auto dispatch_cummin_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::cummin_out(values, indices, self, dim); }; return wrap(NamedTuple1, dispatch_cummin_out(out[0], out[1], _r.tensor(0), _r.toInt64(1))); } } case 1: { if (_r.isNone(2)) { // aten::cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) auto dispatch_cummin = [](const at::Tensor & self, at::Dimname dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.cummin(dim); }; return wrap(NamedTuple, dispatch_cummin(_r.tensor(0), _r.dimname(1))); } else { // aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(2); auto dispatch_cummin_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::cummin_out(values, indices, self, dim); }; return wrap(NamedTuple1, dispatch_cummin_out(out[0], out[1], _r.tensor(0), _r.dimname(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cummin_helper static PyObject * THPVariable__cummin_helper(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cummin_helper(Tensor input, Tensor values, Tensor indices, int64_t dim)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () auto dispatch__cummin_helper = [](const at::Tensor & self, at::Tensor values, at::Tensor indices, int64_t dim) -> void { pybind11::gil_scoped_release no_gil; at::_cummin_helper(self, values, indices, dim); }; dispatch__cummin_helper(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // cumprod static PyObject * THPVariable_cumprod(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cumprod(Tensor input, int64_t dim, *, ScalarType? dtype=None, Tensor out=None)", "cumprod(Tensor input, Dimname dim, *, ScalarType? dtype=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor auto dispatch_cumprod = [](const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cumprod(dim, dtype); }; return wrap(dispatch_cumprod(_r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } else { // aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_cumprod_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cumprod_out(out, self, dim, dtype); }; return wrap(dispatch_cumprod_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } } case 1: { if (_r.isNone(3)) { // aten::cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor auto dispatch_cumprod = [](const at::Tensor & self, at::Dimname dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cumprod(dim, dtype); }; return wrap(dispatch_cumprod(_r.tensor(0), _r.dimname(1), _r.scalartypeOptional(2))); } else { // aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_cumprod_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cumprod_out(out, self, dim, dtype); }; return wrap(dispatch_cumprod_out(_r.tensor(3), _r.tensor(0), _r.dimname(1), _r.scalartypeOptional(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // cumsum static PyObject * THPVariable_cumsum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cumsum(Tensor input, int64_t dim, *, ScalarType? dtype=None, Tensor out=None)", "cumsum(Tensor input, Dimname dim, *, ScalarType? dtype=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor auto dispatch_cumsum = [](const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cumsum(dim, dtype); }; return wrap(dispatch_cumsum(_r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } else { // aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_cumsum_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cumsum_out(out, self, dim, dtype); }; return wrap(dispatch_cumsum_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } } case 1: { if (_r.isNone(3)) { // aten::cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor auto dispatch_cumsum = [](const at::Tensor & self, at::Dimname dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cumsum(dim, dtype); }; return wrap(dispatch_cumsum(_r.tensor(0), _r.dimname(1), _r.scalartypeOptional(2))); } else { // aten::cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_cumsum_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cumsum_out(out, self, dim, dtype); }; return wrap(dispatch_cumsum_out(_r.tensor(3), _r.tensor(0), _r.dimname(1), _r.scalartypeOptional(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // cumulative_trapezoid static PyObject * THPVariable_cumulative_trapezoid(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cumulative_trapezoid(Tensor y, Tensor x, *, int64_t dim=-1)", "cumulative_trapezoid(Tensor y, *, Scalar dx=1, int64_t dim=-1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor auto dispatch_cumulative_trapezoid = [](const at::Tensor & y, const at::Tensor & x, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cumulative_trapezoid(y, x, dim); }; return wrap(dispatch_cumulative_trapezoid(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); } case 1: { // aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor auto dispatch_cumulative_trapezoid = [](const at::Tensor & y, const at::Scalar & dx, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cumulative_trapezoid(y, dx, dim); }; return wrap(dispatch_cumulative_trapezoid(_r.tensor(0), _r.scalar(1), _r.toInt64(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // ctc_loss static PyObject * THPVariable_ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ctc_loss(Tensor log_probs, Tensor targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=False)", "ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor auto dispatch_ctc_loss = [](const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); }; return wrap(dispatch_ctc_loss(_r.tensor(0), _r.tensor(1), _r.intlist(2), _r.intlist(3), _r.toInt64(4), _r.toInt64(5), _r.toBool(6))); } case 1: { // aten::ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor auto dispatch_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, bool zero_infinity) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); }; return wrap(dispatch_ctc_loss(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.toInt64(4), _r.toInt64(5), _r.toBool(6))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _ctc_loss static PyObject * THPVariable__ctc_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_ctc_loss(Tensor log_probs, Tensor targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=False)", "_ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int64_t blank=0, bool zero_infinity=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) auto dispatch__ctc_loss = [](const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); }; return wrap(dispatch__ctc_loss(_r.tensor(0), _r.tensor(1), _r.intlist(2), _r.intlist(3), _r.toInt64(4), _r.toBool(5))); } case 1: { // aten::_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) auto dispatch__ctc_loss = [](const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); }; return wrap(dispatch__ctc_loss(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.toInt64(4), _r.toBool(5))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // diag_embed static PyObject * THPVariable_diag_embed(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "diag_embed(Tensor input, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor auto dispatch_diag_embed = [](const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.diag_embed(offset, dim1, dim2); }; return wrap(dispatch_diag_embed(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toInt64(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // diagflat static PyObject * THPVariable_diagflat(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "diagflat(Tensor input, int64_t offset=0)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::diagflat(Tensor self, int offset=0) -> Tensor auto dispatch_diagflat = [](const at::Tensor & self, int64_t offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.diagflat(offset); }; return wrap(dispatch_diagflat(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // diagonal static PyObject * THPVariable_diagonal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "diagonal(Tensor input, *, Dimname outdim, Dimname dim1, Dimname dim2, int64_t offset=0)", "diagonal(Tensor input, int64_t offset=0, int64_t dim1=0, int64_t dim2=1)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a) auto dispatch_diagonal = [](const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.diagonal(outdim, dim1, dim2, offset); }; return wrap(dispatch_diagonal(_r.tensor(0), _r.dimname(1), _r.dimname(2), _r.dimname(3), _r.toInt64(4))); } case 1: { // aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a) auto dispatch_diagonal = [](const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.diagonal(offset, dim1, dim2); }; return wrap(dispatch_diagonal(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toInt64(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // diff static PyObject * THPVariable_diff(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "diff(Tensor input, int64_t n=1, int64_t dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(5)) { // aten::diff(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None) -> Tensor auto dispatch_diff = [](const at::Tensor & self, int64_t n, int64_t dim, const ::std::optional & prepend, const ::std::optional & append) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.diff(n, dim, prepend, append); }; return wrap(dispatch_diff(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.optionalTensor(3), _r.optionalTensor(4))); } else { // aten::diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_diff_out = [](at::Tensor out, const at::Tensor & self, int64_t n, int64_t dim, const ::std::optional & prepend, const ::std::optional & append) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::diff_out(out, self, n, dim, prepend, append); }; return wrap(dispatch_diff_out(_r.tensor(5), _r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.optionalTensor(3), _r.optionalTensor(4))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // gradient static PyObject * THPVariable_gradient(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "gradient(Tensor input, *, IntArrayRef dim, int64_t edge_order=1)", "gradient(Tensor input, *, Scalar spacing, IntArrayRef dim, int64_t edge_order=1)", "gradient(Tensor input, *, Scalar? spacing=None, int64_t? dim=None, int64_t edge_order=1)", "gradient(Tensor input, *, ScalarList spacing, int64_t? dim=None, int64_t edge_order=1)", "gradient(Tensor input, *, ScalarList spacing, IntArrayRef dim, int64_t edge_order=1)", "gradient(Tensor input, *, TensorList spacing, int64_t? dim=None, int64_t edge_order=1)", "gradient(Tensor input, *, TensorList spacing, IntArrayRef dim, int64_t edge_order=1)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::gradient.array(Tensor self, *, int[] dim, int edge_order=1) -> Tensor[] auto dispatch_gradient = [](const at::Tensor & self, at::IntArrayRef dim, int64_t edge_order) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::gradient(self, dim, edge_order); }; return wrap(dispatch_gradient(_r.tensor(0), _r.intlist(1), _r.toInt64(2))); } case 1: { // aten::gradient.scalararray(Tensor self, *, Scalar spacing, int[] dim, int edge_order=1) -> Tensor[] auto dispatch_gradient = [](const at::Tensor & self, const at::Scalar & spacing, at::IntArrayRef dim, int64_t edge_order) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::gradient(self, spacing, dim, edge_order); }; return wrap(dispatch_gradient(_r.tensor(0), _r.scalar(1), _r.intlist(2), _r.toInt64(3))); } case 2: { // aten::gradient.scalarint(Tensor self, *, Scalar? spacing=None, int? dim=None, int edge_order=1) -> Tensor[] auto dispatch_gradient = [](const at::Tensor & self, const ::std::optional & spacing, ::std::optional dim, int64_t edge_order) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::gradient(self, spacing, dim, edge_order); }; return wrap(dispatch_gradient(_r.tensor(0), _r.scalarOptional(1), _r.toInt64Optional(2), _r.toInt64(3))); } case 3: { // aten::gradient.scalarrayint(Tensor self, *, Scalar[] spacing, int? dim=None, int edge_order=1) -> Tensor[] auto dispatch_gradient = [](const at::Tensor & self, at::ArrayRef spacing, ::std::optional dim, int64_t edge_order) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::gradient(self, spacing, dim, edge_order); }; return wrap(dispatch_gradient(_r.tensor(0), _r.scalarlist(1), _r.toInt64Optional(2), _r.toInt64(3))); } case 4: { // aten::gradient.scalarrayarray(Tensor self, *, Scalar[] spacing, int[] dim, int edge_order=1) -> Tensor[] auto dispatch_gradient = [](const at::Tensor & self, at::ArrayRef spacing, at::IntArrayRef dim, int64_t edge_order) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::gradient(self, spacing, dim, edge_order); }; return wrap(dispatch_gradient(_r.tensor(0), _r.scalarlist(1), _r.intlist(2), _r.toInt64(3))); } case 5: { // aten::gradient.tensorarrayint(Tensor self, *, Tensor[] spacing, int? dim=None, int edge_order=1) -> Tensor[] auto dispatch_gradient = [](const at::Tensor & self, at::TensorList spacing, ::std::optional dim, int64_t edge_order) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::gradient(self, spacing, dim, edge_order); }; return wrap(dispatch_gradient(_r.tensor(0), _r.tensorlist(1), _r.toInt64Optional(2), _r.toInt64(3))); } case 6: { // aten::gradient.tensorarray(Tensor self, *, Tensor[] spacing, int[] dim, int edge_order=1) -> Tensor[] auto dispatch_gradient = [](const at::Tensor & self, at::TensorList spacing, at::IntArrayRef dim, int64_t edge_order) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::gradient(self, spacing, dim, edge_order); }; return wrap(dispatch_gradient(_r.tensor(0), _r.tensorlist(1), _r.intlist(2), _r.toInt64(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // div static PyObject * THPVariable_div(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "div(Tensor input, Tensor other, *, Tensor out=None)", "div(Tensor input, Tensor other, *, c10::string_view? rounding_mode, Tensor out=None)", "div(Tensor input, Scalar other, *, c10::string_view? rounding_mode)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::div.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_div = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.div(other); }; return wrap(dispatch_div(_r.tensor(0), _r.tensor(1))); } else { // aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_div_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::div_out(out, self, other); }; return wrap(dispatch_div_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(3)) { // aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor auto dispatch_div = [](const at::Tensor & self, const at::Tensor & other, ::std::optional rounding_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.div(other, rounding_mode); }; return wrap(dispatch_div(_r.tensor(0), _r.tensor(1), _r.stringViewOptional(2))); } else { // aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) auto dispatch_div_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, ::std::optional rounding_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::div_out(out, self, other, rounding_mode); }; return wrap(dispatch_div_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.stringViewOptional(2))); } } case 2: { // aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor auto dispatch_div = [](const at::Tensor & self, const at::Scalar & other, ::std::optional rounding_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.div(other, rounding_mode); }; return wrap(dispatch_div(_r.tensor(0), _r.scalar(1), _r.stringViewOptional(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // divide static PyObject * THPVariable_divide(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "divide(Tensor input, Tensor other, *, Tensor out=None)", "divide(Tensor input, Tensor other, *, c10::string_view? rounding_mode, Tensor out=None)", "divide(Tensor input, Scalar other)", "divide(Tensor input, Scalar other, *, c10::string_view? rounding_mode)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::divide.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_divide = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.divide(other); }; return wrap(dispatch_divide(_r.tensor(0), _r.tensor(1))); } else { // aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_divide_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::divide_out(out, self, other); }; return wrap(dispatch_divide_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(3)) { // aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor auto dispatch_divide = [](const at::Tensor & self, const at::Tensor & other, ::std::optional rounding_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.divide(other, rounding_mode); }; return wrap(dispatch_divide(_r.tensor(0), _r.tensor(1), _r.stringViewOptional(2))); } else { // aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) auto dispatch_divide_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, ::std::optional rounding_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::divide_out(out, self, other, rounding_mode); }; return wrap(dispatch_divide_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.stringViewOptional(2))); } } case 2: { // aten::divide.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_divide = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.divide(other); }; return wrap(dispatch_divide(_r.tensor(0), _r.scalar(1))); } case 3: { // aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor auto dispatch_divide = [](const at::Tensor & self, const at::Scalar & other, ::std::optional rounding_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.divide(other, rounding_mode); }; return wrap(dispatch_divide(_r.tensor(0), _r.scalar(1), _r.stringViewOptional(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // true_divide static PyObject * THPVariable_true_divide(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "true_divide(Tensor input, Tensor other, *, Tensor out=None)", "true_divide(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::true_divide.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_true_divide = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.true_divide(other); }; return wrap(dispatch_true_divide(_r.tensor(0), _r.tensor(1))); } else { // aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_true_divide_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::true_divide_out(out, self, other); }; return wrap(dispatch_true_divide_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::true_divide.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_true_divide = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.true_divide(other); }; return wrap(dispatch_true_divide(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // dot static PyObject * THPVariable_dot(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "dot(Tensor input, Tensor tensor, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::dot(Tensor self, Tensor tensor) -> Tensor auto dispatch_dot = [](const at::Tensor & self, const at::Tensor & tensor) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.dot(tensor); }; return wrap(dispatch_dot(_r.tensor(0), _r.tensor(1))); } else { // aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_dot_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & tensor) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::dot_out(out, self, tensor); }; return wrap(dispatch_dot_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // vdot static PyObject * THPVariable_vdot(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "vdot(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::vdot(Tensor self, Tensor other) -> Tensor auto dispatch_vdot = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.vdot(other); }; return wrap(dispatch_vdot(_r.tensor(0), _r.tensor(1))); } else { // aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_vdot_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::vdot_out(out, self, other); }; return wrap(dispatch_vdot_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // einsum static PyObject * THPVariable_einsum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "einsum(c10::string_view equation, TensorList tensors, *, IntArrayRef? path=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor auto dispatch_einsum = [](c10::string_view equation, at::TensorList tensors, at::OptionalIntArrayRef path) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::einsum(equation, tensors, path); }; return wrap(dispatch_einsum(_r.stringView(0), _r.tensorlist(1), _r.intlistOptional(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // embedding static PyObject * THPVariable_embedding(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor auto dispatch_embedding = [](const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::embedding_symint(weight, indices, padding_idx, scale_grad_by_freq, sparse); }; return wrap(dispatch_embedding(_r.tensor(0), _r.tensor(1), _r.toSymInt(2), _r.toBool(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // embedding_renorm_ static PyObject * THPVariable_embedding_renorm_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "embedding_renorm_(Tensor input, Tensor indices, double max_norm, double norm_type)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!) auto dispatch_embedding_renorm_ = [](at::Tensor self, const at::Tensor & indices, double max_norm, double norm_type) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::embedding_renorm_(self, indices, max_norm, norm_type); }; return wrap(dispatch_embedding_renorm_(_r.tensor(0), _r.tensor(1), _r.toDouble(2), _r.toDouble(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _embedding_bag_forward_only static PyObject * THPVariable__embedding_bag_forward_only(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int64_t mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int64_t padding_idx=-1)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) auto dispatch__embedding_bag_forward_only = [](const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_embedding_bag_forward_only(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); }; return wrap(dispatch__embedding_bag_forward_only(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3), _r.toInt64(4), _r.toBool(5), _r.optionalTensor(6), _r.toBool(7), _r.toInt64(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _rowwise_prune static PyObject * THPVariable__rowwise_prune(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype) -> (Tensor, Tensor) auto dispatch__rowwise_prune = [](const at::Tensor & weight, const at::Tensor & mask, at::ScalarType compressed_indices_dtype) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_rowwise_prune(weight, mask, compressed_indices_dtype); }; return wrap(dispatch__rowwise_prune(_r.tensor(0), _r.tensor(1), _r.scalartype(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // row_stack static PyObject * THPVariable_row_stack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "row_stack(TensorList tensors, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::row_stack(Tensor[] tensors) -> Tensor auto dispatch_row_stack = [](at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::row_stack(tensors); }; return wrap(dispatch_row_stack(_r.tensorlist(0))); } else { // aten::row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_row_stack_out = [](at::Tensor out, at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::row_stack_out(out, tensors); }; return wrap(dispatch_row_stack_out(_r.tensor(1), _r.tensorlist(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // embedding_bag static PyObject * THPVariable_embedding_bag(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int64_t? padding_idx)", "embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int64_t mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor) auto dispatch_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 ::std::optional & per_sample_weights, bool include_last_offset, ::std::optional padding_idx) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::embedding_bag(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); }; return wrap(dispatch_embedding_bag(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3), _r.toInt64(4), _r.toBool(5), _r.optionalTensor(6), _r.toBool(7), _r.toInt64Optional(8))); } case 1: { // aten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor) auto dispatch_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 ::std::optional & per_sample_weights, bool include_last_offset) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::embedding_bag(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset); }; return wrap(dispatch_embedding_bag(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3), _r.toInt64(4), _r.toBool(5), _r.optionalTensor(6), _r.toBool(7))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _embedding_bag static PyObject * THPVariable__embedding_bag(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int64_t mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int64_t padding_idx=-1)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) auto dispatch__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 ::std::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_embedding_bag(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); }; return wrap(dispatch__embedding_bag(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3), _r.toInt64(4), _r.toBool(5), _r.optionalTensor(6), _r.toBool(7), _r.toInt64(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // empty static PyObject * THPVariable_empty(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "empty(IntArrayRef size, *, DimnameList? names, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "empty(SymIntArrayRef size, *, MemoryFormat? memory_format=None, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto __names = _r.toDimnameListOptional(1); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_empty = [](at::IntArrayRef size, ::std::optional names, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::empty(size, names, options, memory_format); }; return wrap(dispatch_empty(_r.intlist(0), names, options, _r.memoryformatOptional(2))); } case 1: { if (_r.isNone(2)) { // aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_empty = [](c10::SymIntArrayRef size, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::empty_symint(size, options, memory_format); }; return wrap(dispatch_empty(_r.symintlist(0), options, _r.memoryformatOptional(1))); } else { // aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(2), _r.scalartypeOptional(3), _r.isNone(3), _r.layoutOptional(4), _r.deviceWithDefault(5, torch::tensors::get_default_device()), _r.isNone(5)); auto dispatch_empty_out = [](at::Tensor out, c10::SymIntArrayRef size, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::empty_symint_out(out, size, memory_format); }; return wrap(dispatch_empty_out(_r.tensor(2), _r.symintlist(0), _r.memoryformatOptional(1)).set_requires_grad(_r.toBool(7))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // empty_permuted static PyObject * THPVariable_empty_permuted(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "empty_permuted(SymIntArrayRef size, IntArrayRef physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_empty_permuted = [](c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::empty_permuted_symint(size, physical_layout, options); }; return wrap(dispatch_empty_permuted(_r.symintlist(0), _r.intlist(1), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _empty_affine_quantized static PyObject * THPVariable__empty_affine_quantized(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_empty_affine_quantized(SymIntArrayRef size, *, double scale=1, int64_t zero_point=0, MemoryFormat? memory_format=c10::MemoryFormat::Contiguous, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch__empty_affine_quantized = [](c10::SymIntArrayRef size, at::TensorOptions options, double scale, int64_t zero_point, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::_empty_affine_quantized_symint(size, options, scale, zero_point, memory_format); }; return wrap(dispatch__empty_affine_quantized(_r.symintlist(0), options, _r.toDouble(1), _r.toInt64(2), _r.memoryformat(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _empty_per_channel_affine_quantized static PyObject * THPVariable__empty_per_channel_affine_quantized(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_empty_per_channel_affine_quantized(SymIntArrayRef size, *, Tensor scales, Tensor zero_points, int64_t axis, MemoryFormat? memory_format=c10::MemoryFormat::Contiguous, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(5)) .device(_r.deviceWithDefault(7, torch::tensors::get_default_device())) .layout(_r.layoutOptional(6)) .requires_grad(_r.toBool(9)) .pinned_memory(_r.toBool(8)); torch::utils::maybe_initialize_device(options); auto dispatch__empty_per_channel_affine_quantized = [](c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::_empty_per_channel_affine_quantized_symint(size, scales, zero_points, axis, options, memory_format); }; return wrap(dispatch__empty_per_channel_affine_quantized(_r.symintlist(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), options, _r.memoryformat(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _resize_output_ static PyObject * THPVariable__resize_output_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_resize_output_(Tensor input, SymIntArrayRef size, Device device)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_resize_output_(Tensor(a!) self, SymInt[] size, Device device) -> Tensor(a!) auto dispatch__resize_output_ = [](const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_resize_output__symint(self, size, device); }; return wrap(dispatch__resize_output_(_r.tensor(0), _r.symintlist(1), _r.device(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // empty_quantized static PyObject * THPVariable_empty_quantized(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "empty_quantized(IntArrayRef size, Tensor qtensor, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::empty_quantized(int[] size, Tensor qtensor, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_empty_quantized = [](at::IntArrayRef size, const at::Tensor & qtensor, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::empty_quantized(size, qtensor, options, memory_format); }; return wrap(dispatch_empty_quantized(_r.intlist(0), _r.tensor(1), options, _r.memoryformatOptional(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // empty_like static PyObject * THPVariable_empty_like(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "empty_like(Tensor input, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceOptional(4)) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_empty_like = [](const at::Tensor & self, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::empty_like(self, options, memory_format); }; return wrap(dispatch_empty_like(self, options, _r.memoryformatOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // empty_strided static PyObject * THPVariable_empty_strided(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "empty_strided(SymIntArrayRef size, SymIntArrayRef stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_empty_strided = [](c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::empty_strided_symint(size, stride, options); }; return wrap(dispatch_empty_strided(_r.symintlist(0), _r.symintlist(1), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // erf static PyObject * THPVariable_erf(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "erf(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::erf(Tensor self) -> Tensor auto dispatch_erf = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.erf(); }; return wrap(dispatch_erf(_r.tensor(0))); } else { // aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_erf_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::erf_out(out, self); }; return wrap(dispatch_erf_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // erf_ static PyObject * THPVariable_erf_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "erf_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::erf_(Tensor(a!) self) -> Tensor(a!) auto dispatch_erf_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.erf_(); }; return wrap(dispatch_erf_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // erfc static PyObject * THPVariable_erfc(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "erfc(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::erfc(Tensor self) -> Tensor auto dispatch_erfc = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.erfc(); }; return wrap(dispatch_erfc(_r.tensor(0))); } else { // aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_erfc_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::erfc_out(out, self); }; return wrap(dispatch_erfc_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // erfc_ static PyObject * THPVariable_erfc_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "erfc_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::erfc_(Tensor(a!) self) -> Tensor(a!) auto dispatch_erfc_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.erfc_(); }; return wrap(dispatch_erfc_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // exp static PyObject * THPVariable_exp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "exp(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::exp(Tensor self) -> Tensor auto dispatch_exp = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.exp(); }; return wrap(dispatch_exp(_r.tensor(0))); } else { // aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_exp_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::exp_out(out, self); }; return wrap(dispatch_exp_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // exp_ static PyObject * THPVariable_exp_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "exp_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::exp_(Tensor(a!) self) -> Tensor(a!) auto dispatch_exp_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.exp_(); }; return wrap(dispatch_exp_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // exp2 static PyObject * THPVariable_exp2(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "exp2(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::exp2(Tensor self) -> Tensor auto dispatch_exp2 = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.exp2(); }; return wrap(dispatch_exp2(_r.tensor(0))); } else { // aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_exp2_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::exp2_out(out, self); }; return wrap(dispatch_exp2_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // exp2_ static PyObject * THPVariable_exp2_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "exp2_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::exp2_(Tensor(a!) self) -> Tensor(a!) auto dispatch_exp2_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.exp2_(); }; return wrap(dispatch_exp2_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // expm1 static PyObject * THPVariable_expm1(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "expm1(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::expm1(Tensor self) -> Tensor auto dispatch_expm1 = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.expm1(); }; return wrap(dispatch_expm1(_r.tensor(0))); } else { // aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_expm1_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::expm1_out(out, self); }; return wrap(dispatch_expm1_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // expm1_ static PyObject * THPVariable_expm1_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "expm1_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::expm1_(Tensor(a!) self) -> Tensor(a!) auto dispatch_expm1_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.expm1_(); }; return wrap(dispatch_expm1_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // eye static PyObject * THPVariable_eye(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "eye(SymInt n, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "eye(SymInt n, SymInt m, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_eye = [](c10::SymInt n, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::eye_symint(n, options); }; return wrap(dispatch_eye(_r.toSymInt(0), options)); } else { // aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(1), _r.scalartypeOptional(2), _r.isNone(2), _r.layoutOptional(3), _r.deviceWithDefault(4, torch::tensors::get_default_device()), _r.isNone(4)); auto dispatch_eye_out = [](at::Tensor out, c10::SymInt n) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::eye_symint_out(out, n); }; return wrap(dispatch_eye_out(_r.tensor(1), _r.toSymInt(0)).set_requires_grad(_r.toBool(6))); } } case 1: { if (_r.isNone(2)) { // aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_eye = [](c10::SymInt n, c10::SymInt m, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::eye_symint(n, m, options); }; return wrap(dispatch_eye(_r.toSymInt(0), _r.toSymInt(1), options)); } else { // aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(2), _r.scalartypeOptional(3), _r.isNone(3), _r.layoutOptional(4), _r.deviceWithDefault(5, torch::tensors::get_default_device()), _r.isNone(5)); auto dispatch_eye_out = [](at::Tensor out, c10::SymInt n, c10::SymInt m) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::eye_symint_out(out, n, m); }; return wrap(dispatch_eye_out(_r.tensor(2), _r.toSymInt(0), _r.toSymInt(1)).set_requires_grad(_r.toBool(7))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // flatten static PyObject * THPVariable_flatten(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "flatten(Tensor input, int64_t start_dim, int64_t end_dim, Dimname out_dim)", "flatten(Tensor input, int64_t start_dim=0, int64_t end_dim=-1)", "flatten(Tensor input, Dimname start_dim, Dimname end_dim, Dimname out_dim)", "flatten(Tensor input, DimnameList dims, Dimname out_dim)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a) auto dispatch_flatten = [](const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.flatten(start_dim, end_dim, out_dim); }; return wrap(dispatch_flatten(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.dimname(3))); } case 1: { // aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a) auto dispatch_flatten = [](const at::Tensor & self, int64_t start_dim, int64_t end_dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.flatten(start_dim, end_dim); }; return wrap(dispatch_flatten(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); } case 2: { // aten::flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a) auto dispatch_flatten = [](const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.flatten(start_dim, end_dim, out_dim); }; return wrap(dispatch_flatten(_r.tensor(0), _r.dimname(1), _r.dimname(2), _r.dimname(3))); } case 3: { // aten::flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a) auto dispatch_flatten = [](const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.flatten(dims, out_dim); }; return wrap(dispatch_flatten(_r.tensor(0), _r.dimnamelist(1), _r.dimname(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // unflatten static PyObject * THPVariable_unflatten(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unflatten(Tensor input, Dimname dim, SymIntArrayRef sizes, DimnameList names)", "unflatten(Tensor input, int64_t dim, SymIntArrayRef sizes)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a) auto dispatch_unflatten = [](const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.unflatten_symint(dim, sizes, names); }; return wrap(dispatch_unflatten(_r.tensor(0), _r.dimname(1), _r.symintlist(2), _r.dimnamelist(3))); } case 1: { // aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a) auto dispatch_unflatten = [](const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.unflatten_symint(dim, sizes); }; return wrap(dispatch_unflatten(_r.tensor(0), _r.toInt64(1), _r.symintlist(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // fill static PyObject * THPVariable_fill(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fill(Tensor input, Tensor value)", "fill(Tensor input, Scalar value)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::fill.Tensor(Tensor self, Tensor value) -> Tensor auto dispatch_fill = [](const at::Tensor & self, const at::Tensor & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fill(self, value); }; return wrap(dispatch_fill(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::fill.Scalar(Tensor self, Scalar value) -> Tensor auto dispatch_fill = [](const at::Tensor & self, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fill(self, value); }; return wrap(dispatch_fill(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // fill_ static PyObject * THPVariable_fill_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fill_(Tensor input, Tensor value)", "fill_(Tensor input, Scalar value)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::fill_.Tensor(Tensor(a!) self, Tensor value) -> Tensor(a!) auto dispatch_fill_ = [](at::Tensor self, const at::Tensor & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fill_(value); }; return wrap(dispatch_fill_(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!) auto dispatch_fill_ = [](at::Tensor self, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fill_(value); }; return wrap(dispatch_fill_(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // floor static PyObject * THPVariable_floor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "floor(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::floor(Tensor self) -> Tensor auto dispatch_floor = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.floor(); }; return wrap(dispatch_floor(_r.tensor(0))); } else { // aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_floor_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::floor_out(out, self); }; return wrap(dispatch_floor_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // floor_ static PyObject * THPVariable_floor_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "floor_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::floor_(Tensor(a!) self) -> Tensor(a!) auto dispatch_floor_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.floor_(); }; return wrap(dispatch_floor_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // floor_divide static PyObject * THPVariable_floor_divide(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "floor_divide(Tensor input, Tensor other, *, Tensor out=None)", "floor_divide(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::floor_divide(Tensor self, Tensor other) -> Tensor auto dispatch_floor_divide = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.floor_divide(other); }; return wrap(dispatch_floor_divide(_r.tensor(0), _r.tensor(1))); } else { // aten::floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_floor_divide_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::floor_divide_out(out, self, other); }; return wrap(dispatch_floor_divide_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::floor_divide.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_floor_divide = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.floor_divide(other); }; return wrap(dispatch_floor_divide(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // frac static PyObject * THPVariable_frac(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "frac(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::frac(Tensor self) -> Tensor auto dispatch_frac = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.frac(); }; return wrap(dispatch_frac(_r.tensor(0))); } else { // aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_frac_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::frac_out(out, self); }; return wrap(dispatch_frac_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // frac_ static PyObject * THPVariable_frac_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "frac_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::frac_(Tensor(a!) self) -> Tensor(a!) auto dispatch_frac_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.frac_(); }; return wrap(dispatch_frac_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // full static PyObject * THPVariable_full(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "full(IntArrayRef size, Scalar fill_value, *, DimnameList? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "full(SymIntArrayRef size, Scalar fill_value, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto __names = _r.toDimnameListOptional(2); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_full = [](at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional names, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::full(size, fill_value, names, options); }; return wrap(dispatch_full(_r.intlist(0), _r.scalar(1), names, options)); } case 1: { if (_r.isNone(2)) { // aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_full = [](c10::SymIntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::full_symint(size, fill_value, options); }; return wrap(dispatch_full(_r.symintlist(0), _r.scalar(1), options)); } else { // aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(2), _r.scalartypeOptional(3), _r.isNone(3), _r.layoutOptional(4), _r.deviceWithDefault(5, torch::tensors::get_default_device()), _r.isNone(5)); auto dispatch_full_out = [](at::Tensor out, c10::SymIntArrayRef size, const at::Scalar & fill_value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::full_symint_out(out, size, fill_value); }; return wrap(dispatch_full_out(_r.tensor(2), _r.symintlist(0), _r.scalar(1)).set_requires_grad(_r.toBool(7))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // full_like static PyObject * THPVariable_full_like(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "full_like(Tensor input, Scalar fill_value, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceOptional(5)) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_full_like = [](const at::Tensor & self, const at::Scalar & fill_value, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::full_like(self, fill_value, options, memory_format); }; return wrap(dispatch_full_like(self, _r.scalar(1), options, _r.memoryformatOptional(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // from_file static PyObject * THPVariable_from_file(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "from_file(c10::string_view filename, bool? shared=None, int64_t? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_from_file = [](c10::string_view filename, ::std::optional shared, ::std::optional size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::from_file(filename, shared, size, options); }; return wrap(dispatch_from_file(_r.stringView(0), _r.toBoolOptional(1), _r.toInt64(2), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // gcd static PyObject * THPVariable_gcd(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "gcd(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::gcd(Tensor self, Tensor other) -> Tensor auto dispatch_gcd = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.gcd(other); }; return wrap(dispatch_gcd(_r.tensor(0), _r.tensor(1))); } else { // aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_gcd_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::gcd_out(out, self, other); }; return wrap(dispatch_gcd_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // gcd_ static PyObject * THPVariable_gcd_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "gcd_(Tensor input, Tensor other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!) auto dispatch_gcd_ = [](at::Tensor self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.gcd_(other); }; return wrap(dispatch_gcd_(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // lcm static PyObject * THPVariable_lcm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lcm(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::lcm(Tensor self, Tensor other) -> Tensor auto dispatch_lcm = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lcm(other); }; return wrap(dispatch_lcm(_r.tensor(0), _r.tensor(1))); } else { // aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_lcm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::lcm_out(out, self, other); }; return wrap(dispatch_lcm_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // lcm_ static PyObject * THPVariable_lcm_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lcm_(Tensor input, Tensor other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!) auto dispatch_lcm_ = [](at::Tensor self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lcm_(other); }; return wrap(dispatch_lcm_(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // grid_sampler static PyObject * THPVariable_grid_sampler(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "grid_sampler(Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor auto dispatch_grid_sampler = [](const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::grid_sampler(input, grid, interpolation_mode, padding_mode, align_corners); }; return wrap(dispatch_grid_sampler(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toInt64(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // grid_sampler_2d static PyObject * THPVariable_grid_sampler_2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "grid_sampler_2d(Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor auto dispatch_grid_sampler_2d = [](const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::grid_sampler_2d(input, grid, interpolation_mode, padding_mode, align_corners); }; return wrap(dispatch_grid_sampler_2d(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toInt64(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _grid_sampler_2d_cpu_fallback static PyObject * THPVariable__grid_sampler_2d_cpu_fallback(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor auto dispatch__grid_sampler_2d_cpu_fallback = [](const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_grid_sampler_2d_cpu_fallback(input, grid, interpolation_mode, padding_mode, align_corners); }; return wrap(dispatch__grid_sampler_2d_cpu_fallback(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toInt64(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // grid_sampler_3d static PyObject * THPVariable_grid_sampler_3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "grid_sampler_3d(Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor auto dispatch_grid_sampler_3d = [](const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::grid_sampler_3d(input, grid, interpolation_mode, padding_mode, align_corners); }; return wrap(dispatch_grid_sampler_3d(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toInt64(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // hann_window static PyObject * THPVariable_hann_window(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hann_window(int64_t window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "hann_window(int64_t window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch_hann_window = [](int64_t window_length, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::hann_window(window_length, options); }; return wrap(dispatch_hann_window(_r.toInt64(0), options)); } case 1: { // aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_hann_window = [](int64_t window_length, bool periodic, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::hann_window(window_length, periodic, options); }; return wrap(dispatch_hann_window(_r.toInt64(0), _r.toBool(1), options)); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // hamming_window static PyObject * THPVariable_hamming_window(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hamming_window(int64_t window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "hamming_window(int64_t window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "hamming_window(int64_t window_length, bool periodic, double alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "hamming_window(int64_t window_length, bool periodic, double alpha, double beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch_hamming_window = [](int64_t window_length, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::hamming_window(window_length, options); }; return wrap(dispatch_hamming_window(_r.toInt64(0), options)); } case 1: { // aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_hamming_window = [](int64_t window_length, bool periodic, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::hamming_window(window_length, periodic, options); }; return wrap(dispatch_hamming_window(_r.toInt64(0), _r.toBool(1), options)); } case 2: { // aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_hamming_window = [](int64_t window_length, bool periodic, double alpha, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::hamming_window(window_length, periodic, alpha, options); }; return wrap(dispatch_hamming_window(_r.toInt64(0), _r.toBool(1), _r.toDouble(2), options)); } case 3: { // aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_hamming_window = [](int64_t window_length, bool periodic, double alpha, double beta, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::hamming_window(window_length, periodic, alpha, beta, options); }; return wrap(dispatch_hamming_window(_r.toInt64(0), _r.toBool(1), _r.toDouble(2), _r.toDouble(3), options)); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // kaiser_window static PyObject * THPVariable_kaiser_window(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "kaiser_window(int64_t window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "kaiser_window(int64_t window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "kaiser_window(int64_t window_length, bool periodic, double beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch_kaiser_window = [](int64_t window_length, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::kaiser_window(window_length, options); }; return wrap(dispatch_kaiser_window(_r.toInt64(0), options)); } case 1: { // aten::kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_kaiser_window = [](int64_t window_length, bool periodic, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::kaiser_window(window_length, periodic, options); }; return wrap(dispatch_kaiser_window(_r.toInt64(0), _r.toBool(1), options)); } case 2: { // aten::kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_kaiser_window = [](int64_t window_length, bool periodic, double beta, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::kaiser_window(window_length, periodic, beta, options); }; return wrap(dispatch_kaiser_window(_r.toInt64(0), _r.toBool(1), _r.toDouble(2), options)); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // hinge_embedding_loss static PyObject * THPVariable_hinge_embedding_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hinge_embedding_loss(Tensor input, Tensor target, double margin=1.0, int64_t reduction=at::Reduction::Mean)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor auto dispatch_hinge_embedding_loss = [](const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::hinge_embedding_loss(self, target, margin, reduction); }; return wrap(dispatch_hinge_embedding_loss(_r.tensor(0), _r.tensor(1), _r.toDouble(2), _r.toInt64(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // group_norm static PyObject * THPVariable_group_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "group_norm(Tensor input, int64_t num_groups, Tensor? weight=None, Tensor? bias=None, double eps=1e-05, bool cudnn_enabled=True)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor auto dispatch_group_norm = [](const at::Tensor & input, int64_t num_groups, const ::std::optional & weight, const ::std::optional & bias, double eps, bool cudnn_enabled) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::group_norm(input, num_groups, weight, bias, eps, cudnn_enabled); }; return wrap(dispatch_group_norm(_r.tensor(0), _r.toInt64(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.toDouble(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // native_group_norm static PyObject * THPVariable_native_group_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int64_t group, double eps)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor) auto dispatch_native_group_norm = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::native_group_norm_symint(input, weight, bias, N, C, HxW, group, eps); }; return wrap(dispatch_native_group_norm(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.toSymInt(3), _r.toSymInt(4), _r.toSymInt(5), _r.toInt64(6), _r.toDouble(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fft_r2c static PyObject * THPVariable__fft_r2c(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fft_r2c(Tensor input, IntArrayRef dim, int64_t normalization, bool onesided, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor auto dispatch__fft_r2c = [](const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fft_r2c(self, dim, normalization, onesided); }; return wrap(dispatch__fft_r2c(_r.tensor(0), _r.intlist(1), _r.toInt64(2), _r.toBool(3))); } else { // aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__fft_r2c_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fft_r2c_out(out, self, dim, normalization, onesided); }; return wrap(dispatch__fft_r2c_out(_r.tensor(4), _r.tensor(0), _r.intlist(1), _r.toInt64(2), _r.toBool(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fft_c2r static PyObject * THPVariable__fft_c2r(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fft_c2r(Tensor input, IntArrayRef dim, int64_t normalization, SymInt last_dim_size, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_fft_c2r(Tensor self, int[] dim, int normalization, SymInt last_dim_size) -> Tensor auto dispatch__fft_c2r = [](const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fft_c2r_symint(self, dim, normalization, last_dim_size); }; return wrap(dispatch__fft_c2r(_r.tensor(0), _r.intlist(1), _r.toInt64(2), _r.toSymInt(3))); } else { // aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__fft_c2r_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fft_c2r_symint_out(out, self, dim, normalization, last_dim_size); }; return wrap(dispatch__fft_c2r_out(_r.tensor(4), _r.tensor(0), _r.intlist(1), _r.toInt64(2), _r.toSymInt(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fft_c2c static PyObject * THPVariable__fft_c2c(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fft_c2c(Tensor input, SymIntArrayRef dim, int64_t normalization, bool forward, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor auto dispatch__fft_c2c = [](const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fft_c2c_symint(self, dim, normalization, forward); }; return wrap(dispatch__fft_c2c(_r.tensor(0), _r.symintlist(1), _r.toInt64(2), _r.toBool(3))); } else { // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__fft_c2c_out = [](at::Tensor out, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fft_c2c_symint_out(out, self, dim, normalization, forward); }; return wrap(dispatch__fft_c2c_out(_r.tensor(4), _r.tensor(0), _r.symintlist(1), _r.toInt64(2), _r.toBool(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _validate_compressed_sparse_indices static PyObject * THPVariable__validate_compressed_sparse_indices(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int64_t cdim, int64_t dim, int64_t nnz)", }, /*traceable=*/false); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> () auto dispatch__validate_compressed_sparse_indices = [](bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) -> void { pybind11::gil_scoped_release no_gil; at::_validate_compressed_sparse_indices(is_crow, compressed_idx, plain_idx, cdim, dim, nnz); }; dispatch__validate_compressed_sparse_indices(_r.toBool(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4), _r.toInt64(5)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cufft_get_plan_cache_size static PyObject * THPVariable__cufft_get_plan_cache_size(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cufft_get_plan_cache_size(DeviceIndex device_index)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cufft_get_plan_cache_size(DeviceIndex device_index) -> int auto dispatch__cufft_get_plan_cache_size = [](at::DeviceIndex device_index) -> int64_t { pybind11::gil_scoped_release no_gil; return at::_cufft_get_plan_cache_size(device_index); }; return wrap(dispatch__cufft_get_plan_cache_size(_r.toInt64(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cufft_get_plan_cache_max_size static PyObject * THPVariable__cufft_get_plan_cache_max_size(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cufft_get_plan_cache_max_size(DeviceIndex device_index)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cufft_get_plan_cache_max_size(DeviceIndex device_index) -> int auto dispatch__cufft_get_plan_cache_max_size = [](at::DeviceIndex device_index) -> int64_t { pybind11::gil_scoped_release no_gil; return at::_cufft_get_plan_cache_max_size(device_index); }; return wrap(dispatch__cufft_get_plan_cache_max_size(_r.toInt64(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cufft_set_plan_cache_max_size static PyObject * THPVariable__cufft_set_plan_cache_max_size(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cufft_set_plan_cache_max_size(DeviceIndex device_index, int64_t max_size)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cufft_set_plan_cache_max_size(DeviceIndex device_index, int max_size) -> () auto dispatch__cufft_set_plan_cache_max_size = [](at::DeviceIndex device_index, int64_t max_size) -> void { pybind11::gil_scoped_release no_gil; at::_cufft_set_plan_cache_max_size(device_index, max_size); }; dispatch__cufft_set_plan_cache_max_size(_r.toInt64(0), _r.toInt64(1)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cufft_clear_plan_cache static PyObject * THPVariable__cufft_clear_plan_cache(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cufft_clear_plan_cache(DeviceIndex device_index)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cufft_clear_plan_cache(DeviceIndex device_index) -> () auto dispatch__cufft_clear_plan_cache = [](at::DeviceIndex device_index) -> void { pybind11::gil_scoped_release no_gil; at::_cufft_clear_plan_cache(device_index); }; dispatch__cufft_clear_plan_cache(_r.toInt64(0)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _unsafe_index static PyObject * THPVariable__unsafe_index(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_unsafe_index(Tensor input, c10::List<::std::optional> indices)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_unsafe_index.Tensor(Tensor self, Tensor?[] indices) -> Tensor auto dispatch__unsafe_index = [](const at::Tensor & self, const c10::List<::std::optional> & indices) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_unsafe_index(self, indices); }; return wrap(dispatch__unsafe_index(_r.tensor(0), _r.list_of_optional_tensors(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _unsafe_masked_index static PyObject * THPVariable__unsafe_masked_index(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_unsafe_masked_index(Tensor input, Tensor mask, c10::List<::std::optional> indices, Scalar fill)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_unsafe_masked_index(Tensor self, Tensor mask, Tensor?[] indices, Scalar fill) -> Tensor auto dispatch__unsafe_masked_index = [](const at::Tensor & self, const at::Tensor & mask, const c10::List<::std::optional> & indices, const at::Scalar & fill) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_unsafe_masked_index(self, mask, indices, fill); }; return wrap(dispatch__unsafe_masked_index(_r.tensor(0), _r.tensor(1), _r.list_of_optional_tensors(2), _r.scalar(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _unsafe_masked_index_put_accumulate static PyObject * THPVariable__unsafe_masked_index_put_accumulate(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_unsafe_masked_index_put_accumulate(Tensor input, Tensor mask, c10::List<::std::optional> indices, Tensor values)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_unsafe_masked_index_put_accumulate(Tensor self, Tensor mask, Tensor?[] indices, Tensor values) -> Tensor auto dispatch__unsafe_masked_index_put_accumulate = [](const at::Tensor & self, const at::Tensor & mask, const c10::List<::std::optional> & indices, const at::Tensor & values) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_unsafe_masked_index_put_accumulate(self, mask, indices, values); }; return wrap(dispatch__unsafe_masked_index_put_accumulate(_r.tensor(0), _r.tensor(1), _r.list_of_optional_tensors(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // index_copy static PyObject * THPVariable_index_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "index_copy(Tensor input, int64_t dim, Tensor index, Tensor source, *, Tensor out=None)", "index_copy(Tensor input, Dimname dim, Tensor index, Tensor source)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor auto dispatch_index_copy = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_copy(dim, index, source); }; return wrap(dispatch_index_copy(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3))); } else { // aten::index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_index_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::index_copy_out(out, self, dim, index, source); }; return wrap(dispatch_index_copy_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3))); } } case 1: { // aten::index_copy.dimname(Tensor self, Dimname dim, Tensor index, Tensor source) -> Tensor auto dispatch_index_copy = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_copy(dim, index, source); }; return wrap(dispatch_index_copy(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.tensor(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // index_put_ static PyObject * THPVariable_index_put_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "index_put_(Tensor input, c10::List<::std::optional> indices, Tensor values, bool accumulate=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!) auto dispatch_index_put_ = [](at::Tensor self, const c10::List<::std::optional> & indices, const at::Tensor & values, bool accumulate) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_put_(indices, values, accumulate); }; return wrap(dispatch_index_put_(_r.tensor(0), _r.list_of_optional_tensors(1), _r.tensor(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // index_put static PyObject * THPVariable_index_put(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "index_put(Tensor input, c10::List<::std::optional> indices, Tensor values, bool accumulate=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor auto dispatch_index_put = [](const at::Tensor & self, const c10::List<::std::optional> & indices, const at::Tensor & values, bool accumulate) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_put(indices, values, accumulate); }; return wrap(dispatch_index_put(_r.tensor(0), _r.list_of_optional_tensors(1), _r.tensor(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _unsafe_index_put static PyObject * THPVariable__unsafe_index_put(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_unsafe_index_put(Tensor input, c10::List<::std::optional> indices, Tensor values, bool accumulate=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_unsafe_index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor auto dispatch__unsafe_index_put = [](const at::Tensor & self, const c10::List<::std::optional> & indices, const at::Tensor & values, bool accumulate) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_unsafe_index_put(self, indices, values, accumulate); }; return wrap(dispatch__unsafe_index_put(_r.tensor(0), _r.list_of_optional_tensors(1), _r.tensor(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _index_put_impl_ static PyObject * THPVariable__index_put_impl_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_index_put_impl_(Tensor input, c10::List<::std::optional> indices, Tensor values, bool accumulate=False, bool unsafe=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_index_put_impl_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor(a!) auto dispatch__index_put_impl_ = [](at::Tensor self, const c10::List<::std::optional> & indices, const at::Tensor & values, bool accumulate, bool unsafe) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_index_put_impl_(self, indices, values, accumulate, unsafe); }; return wrap(dispatch__index_put_impl_(_r.tensor(0), _r.list_of_optional_tensors(1), _r.tensor(2), _r.toBool(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // instance_norm static PyObject * THPVariable_instance_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensor auto dispatch_instance_norm = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const ::std::optional & running_mean, const ::std::optional & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::instance_norm(input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled); }; return wrap(dispatch_instance_norm(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // isclose static PyObject * THPVariable_isclose(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isclose(Tensor input, Tensor other, double rtol=1e-05, double atol=1e-08, bool equal_nan=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor auto dispatch_isclose = [](const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.isclose(other, rtol, atol, equal_nan); }; return wrap(dispatch_isclose(_r.tensor(0), _r.tensor(1), _r.toDouble(2), _r.toDouble(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // isin static PyObject * THPVariable_isin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isin(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor out=None)", "isin(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor out=None)", "isin(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::isin.Tensor_Tensor(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor auto dispatch_isin = [](const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isin(elements, test_elements, assume_unique, invert); }; return wrap(dispatch_isin(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } else { // aten::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_isin_out = [](at::Tensor out, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isin_out(out, elements, test_elements, assume_unique, invert); }; return wrap(dispatch_isin_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } } case 1: { if (_r.isNone(4)) { // aten::isin.Scalar_Tensor(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor auto dispatch_isin = [](const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isin(element, test_elements, assume_unique, invert); }; return wrap(dispatch_isin(_r.scalar(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } else { // aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_isin_out = [](at::Tensor out, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isin_out(out, element, test_elements, assume_unique, invert); }; return wrap(dispatch_isin_out(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } } case 2: { if (_r.isNone(4)) { // aten::isin.Tensor_Scalar(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False) -> Tensor auto dispatch_isin = [](const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isin(elements, test_element, assume_unique, invert); }; return wrap(dispatch_isin(_r.tensor(0), _r.scalar(1), _r.toBool(2), _r.toBool(3))); } else { // aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_isin_out = [](at::Tensor out, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isin_out(out, elements, test_element, assume_unique, invert); }; return wrap(dispatch_isin_out(_r.tensor(4), _r.tensor(0), _r.scalar(1), _r.toBool(2), _r.toBool(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // isnan static PyObject * THPVariable_isnan(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isnan(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::isnan(Tensor self) -> Tensor auto dispatch_isnan = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.isnan(); }; return wrap(dispatch_isnan(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_distributed static PyObject * THPVariable_is_distributed(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_distributed(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_distributed(Tensor self) -> bool auto dispatch_is_distributed = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_distributed(); }; return wrap(dispatch_is_distributed(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_floating_point static PyObject * THPVariable_is_floating_point(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_floating_point(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_floating_point(Tensor self) -> bool auto dispatch_is_floating_point = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_floating_point(); }; return wrap(dispatch_is_floating_point(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_complex static PyObject * THPVariable_is_complex(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_complex(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_complex(Tensor self) -> bool auto dispatch_is_complex = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_complex(); }; return wrap(dispatch_is_complex(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_conj static PyObject * THPVariable_is_conj(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_conj(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_conj(Tensor self) -> bool auto dispatch_is_conj = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_conj(); }; return wrap(dispatch_is_conj(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _is_zerotensor static PyObject * THPVariable__is_zerotensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_is_zerotensor(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_is_zerotensor(Tensor self) -> bool auto dispatch__is_zerotensor = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self._is_zerotensor(); }; return wrap(dispatch__is_zerotensor(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_neg static PyObject * THPVariable_is_neg(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_neg(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_neg(Tensor self) -> bool auto dispatch_is_neg = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_neg(); }; return wrap(dispatch_is_neg(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // isreal static PyObject * THPVariable_isreal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isreal(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::isreal(Tensor self) -> Tensor auto dispatch_isreal = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.isreal(); }; return wrap(dispatch_isreal(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_nonzero static PyObject * THPVariable_is_nonzero(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_nonzero(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_nonzero(Tensor self) -> bool auto dispatch_is_nonzero = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_nonzero(); }; return wrap(dispatch_is_nonzero(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_same_size static PyObject * THPVariable_is_same_size(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_same_size(Tensor input, Tensor other)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_same_size(Tensor self, Tensor other) -> bool auto dispatch_is_same_size = [](const at::Tensor & self, const at::Tensor & other) -> bool { pybind11::gil_scoped_release no_gil; return self.is_same_size(other); }; return wrap(dispatch_is_same_size(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_signed static PyObject * THPVariable_is_signed(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_signed(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_signed(Tensor self) -> bool auto dispatch_is_signed = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_signed(); }; return wrap(dispatch_is_signed(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_inference static PyObject * THPVariable_is_inference(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "is_inference(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::is_inference(Tensor self) -> bool auto dispatch_is_inference = [](const at::Tensor & self) -> bool { pybind11::gil_scoped_release no_gil; return self.is_inference(); }; return wrap(dispatch_is_inference(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // kl_div static PyObject * THPVariable_kl_div(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "kl_div(Tensor input, Tensor target, int64_t reduction=at::Reduction::Mean, *, bool log_target=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor auto dispatch_kl_div = [](const at::Tensor & self, const at::Tensor & target, int64_t reduction, bool log_target) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::kl_div(self, target, reduction, log_target); }; return wrap(dispatch_kl_div(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // kron static PyObject * THPVariable_kron(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "kron(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::kron(Tensor self, Tensor other) -> Tensor auto dispatch_kron = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.kron(other); }; return wrap(dispatch_kron(_r.tensor(0), _r.tensor(1))); } else { // aten::kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_kron_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::kron_out(out, self, other); }; return wrap(dispatch_kron_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // kthvalue static PyObject * THPVariable_kthvalue(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_kthvalue_structseq(); static PyTypeObject* NamedTuple1 = generated::get_kthvalue_out_structseq(); static PythonArgParser parser({ "kthvalue(Tensor input, int64_t k, int64_t dim=-1, bool keepdim=False, *, TensorList[2] out=None)", "kthvalue(Tensor input, int64_t k, Dimname dim, bool keepdim=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_kthvalue = [](const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.kthvalue(k, dim, keepdim); }; return wrap(NamedTuple, dispatch_kthvalue(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toBool(3))); } else { // aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(4); auto dispatch_kthvalue_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::kthvalue_out(values, indices, self, k, dim, keepdim); }; return wrap(NamedTuple1, dispatch_kthvalue_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toBool(3))); } } case 1: { if (_r.isNone(4)) { // aten::kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_kthvalue = [](const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.kthvalue(k, dim, keepdim); }; return wrap(NamedTuple, dispatch_kthvalue(_r.tensor(0), _r.toInt64(1), _r.dimname(2), _r.toBool(3))); } else { // aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(4); auto dispatch_kthvalue_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::kthvalue_out(values, indices, self, k, dim, keepdim); }; return wrap(NamedTuple1, dispatch_kthvalue_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.dimname(2), _r.toBool(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // layer_norm static PyObject * THPVariable_layer_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "layer_norm(Tensor input, SymIntArrayRef normalized_shape, Tensor? weight=None, Tensor? bias=None, double eps=1e-05, bool cudnn_enable=True)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor auto dispatch_layer_norm = [](const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps, bool cudnn_enable) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::layer_norm_symint(input, normalized_shape, weight, bias, eps, cudnn_enable); }; return wrap(dispatch_layer_norm(_r.tensor(0), _r.symintlist(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.toDouble(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // native_layer_norm static PyObject * THPVariable_native_layer_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "native_layer_norm(Tensor input, SymIntArrayRef normalized_shape, Tensor? weight, Tensor? bias, double eps)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor) auto dispatch_native_layer_norm = [](const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional & weight, const ::std::optional & bias, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::native_layer_norm_symint(input, normalized_shape, weight, bias, eps); }; return wrap(dispatch_native_layer_norm(_r.tensor(0), _r.symintlist(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.toDouble(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rms_norm static PyObject * THPVariable_rms_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rms_norm(Tensor input, SymIntArrayRef normalized_shape, Tensor? weight=None, double? eps=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rms_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, float? eps=None) -> Tensor auto dispatch_rms_norm = [](const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional & weight, ::std::optional eps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rms_norm_symint(input, normalized_shape, weight, eps); }; return wrap(dispatch_rms_norm(_r.tensor(0), _r.symintlist(1), _r.optionalTensor(2), _r.toDoubleOptional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // nan_to_num static PyObject * THPVariable_nan_to_num(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nan_to_num(Tensor input, double? nan=None, double? posinf=None, double? neginf=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor auto dispatch_nan_to_num = [](const at::Tensor & self, ::std::optional nan, ::std::optional posinf, ::std::optional neginf) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nan_to_num(nan, posinf, neginf); }; return wrap(dispatch_nan_to_num(_r.tensor(0), _r.toDoubleOptional(1), _r.toDoubleOptional(2), _r.toDoubleOptional(3))); } else { // aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_nan_to_num_out = [](at::Tensor out, const at::Tensor & self, ::std::optional nan, ::std::optional posinf, ::std::optional neginf) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nan_to_num_out(out, self, nan, posinf, neginf); }; return wrap(dispatch_nan_to_num_out(_r.tensor(4), _r.tensor(0), _r.toDoubleOptional(1), _r.toDoubleOptional(2), _r.toDoubleOptional(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // nan_to_num_ static PyObject * THPVariable_nan_to_num_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nan_to_num_(Tensor input, double? nan=None, double? posinf=None, double? neginf=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!) auto dispatch_nan_to_num_ = [](at::Tensor self, ::std::optional nan, ::std::optional posinf, ::std::optional neginf) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nan_to_num_(nan, posinf, neginf); }; return wrap(dispatch_nan_to_num_(_r.tensor(0), _r.toDoubleOptional(1), _r.toDoubleOptional(2), _r.toDoubleOptional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mkldnn_linear_backward_weights static PyObject * THPVariable_mkldnn_linear_backward_weights(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined) -> (Tensor, Tensor) auto dispatch_mkldnn_linear_backward_weights = [](const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::mkldnn_linear_backward_weights(grad_output, input, weight, bias_defined); }; return wrap(dispatch_mkldnn_linear_backward_weights(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cslt_compress static PyObject * THPVariable__cslt_compress(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cslt_compress(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cslt_compress(Tensor input) -> Tensor auto dispatch__cslt_compress = [](const at::Tensor & input) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cslt_compress(input); }; return wrap(dispatch__cslt_compress(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cslt_sparse_mm static PyObject * THPVariable__cslt_sparse_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cslt_sparse_mm(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False, int64_t alg_id=0, int64_t split_k=1, bool split_k_one_kernel=True)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cslt_sparse_mm(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False, int alg_id=0, int split_k=1, bool split_k_one_kernel=True) -> Tensor auto dispatch__cslt_sparse_mm = [](const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional & bias, const ::std::optional & alpha, ::std::optional out_dtype, bool transpose_result, int64_t alg_id, int64_t split_k, bool split_k_one_kernel) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_cslt_sparse_mm(compressed_A, dense_B, bias, alpha, out_dtype, transpose_result, alg_id, split_k, split_k_one_kernel); }; return wrap(dispatch__cslt_sparse_mm(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.scalartypeOptional(4), _r.toBool(5), _r.toInt64(6), _r.toInt64(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _cslt_sparse_mm_search static PyObject * THPVariable__cslt_sparse_mm_search(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_cslt_sparse_mm_search(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False)", }, /*traceable=*/false); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_cslt_sparse_mm_search(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False) -> int auto dispatch__cslt_sparse_mm_search = [](const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional & bias, const ::std::optional & alpha, ::std::optional out_dtype, bool transpose_result) -> int64_t { pybind11::gil_scoped_release no_gil; return at::_cslt_sparse_mm_search(compressed_A, dense_B, bias, alpha, out_dtype, transpose_result); }; return wrap(dispatch__cslt_sparse_mm_search(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.scalartypeOptional(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_semi_structured_tile static PyObject * THPVariable__sparse_semi_structured_tile(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_semi_structured_tile(Tensor input, c10::string_view algorithm=\"\", bool use_cutlass=True)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_semi_structured_tile(Tensor input, str algorithm="", bool use_cutlass=True) -> (Tensor, Tensor, Tensor, Tensor, Tensor) auto dispatch__sparse_semi_structured_tile = [](const at::Tensor & input, c10::string_view algorithm, bool use_cutlass) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_sparse_semi_structured_tile(input, algorithm, use_cutlass); }; return wrap(dispatch__sparse_semi_structured_tile(_r.tensor(0), _r.stringView(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_semi_structured_apply static PyObject * THPVariable__sparse_semi_structured_apply(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_semi_structured_apply(Tensor input, Tensor thread_masks)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_semi_structured_apply(Tensor input, Tensor thread_masks) -> (Tensor, Tensor) auto dispatch__sparse_semi_structured_apply = [](const at::Tensor & input, const at::Tensor & thread_masks) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_sparse_semi_structured_apply(input, thread_masks); }; return wrap(dispatch__sparse_semi_structured_apply(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_semi_structured_apply_dense static PyObject * THPVariable__sparse_semi_structured_apply_dense(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_semi_structured_apply_dense(Tensor input, Tensor thread_masks)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_semi_structured_apply_dense(Tensor input, Tensor thread_masks) -> Tensor auto dispatch__sparse_semi_structured_apply_dense = [](const at::Tensor & input, const at::Tensor & thread_masks) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_semi_structured_apply_dense(input, thread_masks); }; return wrap(dispatch__sparse_semi_structured_apply_dense(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_semi_structured_linear static PyObject * THPVariable__sparse_semi_structured_linear(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_semi_structured_linear(Tensor input, Tensor weight, Tensor meta, *, Tensor? bias=None, c10::string_view? activation=None, ScalarType? out_dtype=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_semi_structured_linear(Tensor input, Tensor weight, Tensor meta, *, Tensor? bias=None, str? activation=None, ScalarType? out_dtype=None) -> Tensor auto dispatch__sparse_semi_structured_linear = [](const at::Tensor & input, const at::Tensor & weight, const at::Tensor & meta, const ::std::optional & bias, ::std::optional activation, ::std::optional out_dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_semi_structured_linear(input, weight, meta, bias, activation, out_dtype); }; return wrap(dispatch__sparse_semi_structured_linear(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.stringViewOptional(4), _r.scalartypeOptional(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_semi_structured_mm static PyObject * THPVariable__sparse_semi_structured_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_semi_structured_mm(Tensor mat1, Tensor mat1_meta, Tensor mat2, *, ScalarType? out_dtype=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_semi_structured_mm(Tensor mat1, Tensor mat1_meta, Tensor mat2, *, ScalarType? out_dtype=None) -> Tensor auto dispatch__sparse_semi_structured_mm = [](const at::Tensor & mat1, const at::Tensor & mat1_meta, const at::Tensor & mat2, ::std::optional out_dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_semi_structured_mm(mat1, mat1_meta, mat2, out_dtype); }; return wrap(dispatch__sparse_semi_structured_mm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalartypeOptional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_semi_structured_addmm static PyObject * THPVariable__sparse_semi_structured_addmm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_semi_structured_addmm(Tensor input, Tensor mat1, Tensor mat1_meta, Tensor mat2, *, Scalar alpha=1, Scalar beta=1, ScalarType? out_dtype=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_semi_structured_addmm(Tensor input, Tensor mat1, Tensor mat1_meta, Tensor mat2, *, Scalar alpha=1, Scalar beta=1, ScalarType? out_dtype=None) -> Tensor auto dispatch__sparse_semi_structured_addmm = [](const at::Tensor & input, const at::Tensor & mat1, const at::Tensor & mat1_meta, const at::Tensor & mat2, const at::Scalar & alpha, const at::Scalar & beta, ::std::optional out_dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_semi_structured_addmm(input, mat1, mat1_meta, mat2, alpha, beta, out_dtype); }; return wrap(dispatch__sparse_semi_structured_addmm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.scalar(4), _r.scalar(5), _r.scalartypeOptional(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _mixed_dtypes_linear static PyObject * THPVariable__mixed_dtypes_linear(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_mixed_dtypes_linear(Tensor input, Tensor weight, Tensor scale, *, Tensor? bias=None, c10::string_view? activation=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_mixed_dtypes_linear(Tensor input, Tensor weight, Tensor scale, *, Tensor? bias=None, str? activation=None) -> Tensor auto dispatch__mixed_dtypes_linear = [](const at::Tensor & input, const at::Tensor & weight, const at::Tensor & scale, const ::std::optional & bias, ::std::optional activation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_mixed_dtypes_linear(input, weight, scale, bias, activation); }; return wrap(dispatch__mixed_dtypes_linear(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.stringViewOptional(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fbgemm_linear_int8_weight_fp32_activation static PyObject * THPVariable_fbgemm_linear_int8_weight_fp32_activation(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor auto dispatch_fbgemm_linear_int8_weight_fp32_activation = [](const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fbgemm_linear_int8_weight_fp32_activation(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); }; return wrap(dispatch_fbgemm_linear_int8_weight_fp32_activation(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.scalar(4), _r.scalar(5), _r.tensor(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fbgemm_linear_int8_weight static PyObject * THPVariable_fbgemm_linear_int8_weight(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor auto dispatch_fbgemm_linear_int8_weight = [](const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fbgemm_linear_int8_weight(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); }; return wrap(dispatch_fbgemm_linear_int8_weight(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.scalar(4), _r.scalar(5), _r.tensor(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fbgemm_linear_quantize_weight static PyObject * THPVariable_fbgemm_linear_quantize_weight(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fbgemm_linear_quantize_weight(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fbgemm_linear_quantize_weight(Tensor input) -> (Tensor, Tensor, float, int) auto dispatch_fbgemm_linear_quantize_weight = [](const at::Tensor & input) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::fbgemm_linear_quantize_weight(input); }; return wrap(dispatch_fbgemm_linear_quantize_weight(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fbgemm_pack_gemm_matrix_fp16 static PyObject * THPVariable_fbgemm_pack_gemm_matrix_fp16(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fbgemm_pack_gemm_matrix_fp16(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fbgemm_pack_gemm_matrix_fp16(Tensor input) -> Tensor auto dispatch_fbgemm_pack_gemm_matrix_fp16 = [](const at::Tensor & input) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fbgemm_pack_gemm_matrix_fp16(input); }; return wrap(dispatch_fbgemm_pack_gemm_matrix_fp16(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _wrapped_linear_prepack static PyObject * THPVariable__wrapped_linear_prepack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_wrapped_linear_prepack(Tensor weight, Tensor weight_scale, Tensor weight_zero_point, Tensor bias)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_wrapped_linear_prepack(Tensor weight, Tensor weight_scale, Tensor weight_zero_point, Tensor bias) -> Tensor auto dispatch__wrapped_linear_prepack = [](const at::Tensor & weight, const at::Tensor & weight_scale, const at::Tensor & weight_zero_point, const at::Tensor & bias) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_wrapped_linear_prepack(weight, weight_scale, weight_zero_point, bias); }; return wrap(dispatch__wrapped_linear_prepack(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _wrapped_quantized_linear_prepacked static PyObject * THPVariable__wrapped_quantized_linear_prepacked(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_wrapped_quantized_linear_prepacked(Tensor input, Tensor input_scale, Tensor input_zero_point, Tensor packed_weight, Tensor output_scale, Tensor output_zero_point, int64_t out_channel)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_wrapped_quantized_linear_prepacked(Tensor input, Tensor input_scale, Tensor input_zero_point, Tensor packed_weight, Tensor output_scale, Tensor output_zero_point, int out_channel) -> Tensor auto dispatch__wrapped_quantized_linear_prepacked = [](const at::Tensor & input, const at::Tensor & input_scale, const at::Tensor & input_zero_point, const at::Tensor & packed_weight, const at::Tensor & output_scale, const at::Tensor & output_zero_point, int64_t out_channel) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_wrapped_quantized_linear_prepacked(input, input_scale, input_zero_point, packed_weight, output_scale, output_zero_point, out_channel); }; return wrap(dispatch__wrapped_quantized_linear_prepacked(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.toInt64(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fbgemm_linear_fp16_weight_fp32_activation static PyObject * THPVariable_fbgemm_linear_fp16_weight_fp32_activation(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor auto dispatch_fbgemm_linear_fp16_weight_fp32_activation = [](const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fbgemm_linear_fp16_weight_fp32_activation(input, packed_weight, bias); }; return wrap(dispatch_fbgemm_linear_fp16_weight_fp32_activation(_r.tensor(0), _r.tensor(1), _r.tensor(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fbgemm_linear_fp16_weight static PyObject * THPVariable_fbgemm_linear_fp16_weight(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor auto dispatch_fbgemm_linear_fp16_weight = [](const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fbgemm_linear_fp16_weight(input, packed_weight, bias); }; return wrap(dispatch_fbgemm_linear_fp16_weight(_r.tensor(0), _r.tensor(1), _r.tensor(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // fbgemm_pack_quantized_matrix static PyObject * THPVariable_fbgemm_pack_quantized_matrix(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fbgemm_pack_quantized_matrix(Tensor input)", "fbgemm_pack_quantized_matrix(Tensor input, int64_t K, int64_t N)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::fbgemm_pack_quantized_matrix(Tensor input) -> Tensor auto dispatch_fbgemm_pack_quantized_matrix = [](const at::Tensor & input) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fbgemm_pack_quantized_matrix(input); }; return wrap(dispatch_fbgemm_pack_quantized_matrix(_r.tensor(0))); } case 1: { // aten::fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor auto dispatch_fbgemm_pack_quantized_matrix = [](const at::Tensor & input, int64_t K, int64_t N) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fbgemm_pack_quantized_matrix(input, K, N); }; return wrap(dispatch_fbgemm_pack_quantized_matrix(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ldexp static PyObject * THPVariable_ldexp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ldexp(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::ldexp.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_ldexp = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ldexp(other); }; return wrap(dispatch_ldexp(_r.tensor(0), _r.tensor(1))); } else { // aten::ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ldexp_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ldexp_out(out, self, other); }; return wrap(dispatch_ldexp_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ldexp_ static PyObject * THPVariable_ldexp_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ldexp_(Tensor input, Tensor other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!) auto dispatch_ldexp_ = [](at::Tensor self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ldexp_(other); }; return wrap(dispatch_ldexp_(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // linspace static PyObject * THPVariable_linspace(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "linspace(Tensor start, Tensor end, int64_t steps, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "linspace(Scalar start, Tensor end, int64_t steps, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "linspace(Tensor start, Scalar end, int64_t steps, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "linspace(Scalar start, Scalar end, int64_t steps, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::linspace.Tensor_Tensor(Tensor start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_linspace = [](const at::Tensor & start, const at::Tensor & end, int64_t steps, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::linspace(start, end, steps, options); }; return wrap(dispatch_linspace(_r.tensor(0), _r.tensor(1), _r.toInt64(2), options)); } else { // aten::linspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(3), _r.scalartypeOptional(4), _r.isNone(4), _r.layoutOptional(5), _r.deviceWithDefault(6, torch::tensors::get_default_device()), _r.isNone(6)); auto dispatch_linspace_out = [](at::Tensor out, const at::Tensor & start, const at::Tensor & end, int64_t steps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::linspace_out(out, start, end, steps); }; return wrap(dispatch_linspace_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.toInt64(2)).set_requires_grad(_r.toBool(8))); } } case 1: { if (_r.isNone(3)) { // aten::linspace.Scalar_Tensor(Scalar start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_linspace = [](const at::Scalar & start, const at::Tensor & end, int64_t steps, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::linspace(start, end, steps, options); }; return wrap(dispatch_linspace(_r.scalar(0), _r.tensor(1), _r.toInt64(2), options)); } else { // aten::linspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(3), _r.scalartypeOptional(4), _r.isNone(4), _r.layoutOptional(5), _r.deviceWithDefault(6, torch::tensors::get_default_device()), _r.isNone(6)); auto dispatch_linspace_out = [](at::Tensor out, const at::Scalar & start, const at::Tensor & end, int64_t steps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::linspace_out(out, start, end, steps); }; return wrap(dispatch_linspace_out(_r.tensor(3), _r.scalar(0), _r.tensor(1), _r.toInt64(2)).set_requires_grad(_r.toBool(8))); } } case 2: { if (_r.isNone(3)) { // aten::linspace.Tensor_Scalar(Tensor start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_linspace = [](const at::Tensor & start, const at::Scalar & end, int64_t steps, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::linspace(start, end, steps, options); }; return wrap(dispatch_linspace(_r.tensor(0), _r.scalar(1), _r.toInt64(2), options)); } else { // aten::linspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(3), _r.scalartypeOptional(4), _r.isNone(4), _r.layoutOptional(5), _r.deviceWithDefault(6, torch::tensors::get_default_device()), _r.isNone(6)); auto dispatch_linspace_out = [](at::Tensor out, const at::Tensor & start, const at::Scalar & end, int64_t steps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::linspace_out(out, start, end, steps); }; return wrap(dispatch_linspace_out(_r.tensor(3), _r.tensor(0), _r.scalar(1), _r.toInt64(2)).set_requires_grad(_r.toBool(8))); } } case 3: { if (_r.isNone(3)) { // aten::linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_linspace = [](const at::Scalar & start, const at::Scalar & end, int64_t steps, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::linspace(start, end, steps, options); }; return wrap(dispatch_linspace(_r.scalar(0), _r.scalar(1), _r.toInt64(2), options)); } else { // aten::linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(3), _r.scalartypeOptional(4), _r.isNone(4), _r.layoutOptional(5), _r.deviceWithDefault(6, torch::tensors::get_default_device()), _r.isNone(6)); auto dispatch_linspace_out = [](at::Tensor out, const at::Scalar & start, const at::Scalar & end, int64_t steps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::linspace_out(out, start, end, steps); }; return wrap(dispatch_linspace_out(_r.tensor(3), _r.scalar(0), _r.scalar(1), _r.toInt64(2)).set_requires_grad(_r.toBool(8))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log static PyObject * THPVariable_log(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::log(Tensor self) -> Tensor auto dispatch_log = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log(); }; return wrap(dispatch_log(_r.tensor(0))); } else { // aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_log_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::log_out(out, self); }; return wrap(dispatch_log_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log_ static PyObject * THPVariable_log_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::log_(Tensor(a!) self) -> Tensor(a!) auto dispatch_log_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log_(); }; return wrap(dispatch_log_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log10 static PyObject * THPVariable_log10(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log10(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::log10(Tensor self) -> Tensor auto dispatch_log10 = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log10(); }; return wrap(dispatch_log10(_r.tensor(0))); } else { // aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_log10_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::log10_out(out, self); }; return wrap(dispatch_log10_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log10_ static PyObject * THPVariable_log10_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log10_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::log10_(Tensor(a!) self) -> Tensor(a!) auto dispatch_log10_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log10_(); }; return wrap(dispatch_log10_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log1p static PyObject * THPVariable_log1p(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log1p(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::log1p(Tensor self) -> Tensor auto dispatch_log1p = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log1p(); }; return wrap(dispatch_log1p(_r.tensor(0))); } else { // aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_log1p_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::log1p_out(out, self); }; return wrap(dispatch_log1p_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log1p_ static PyObject * THPVariable_log1p_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log1p_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::log1p_(Tensor(a!) self) -> Tensor(a!) auto dispatch_log1p_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log1p_(); }; return wrap(dispatch_log1p_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log2 static PyObject * THPVariable_log2(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log2(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::log2(Tensor self) -> Tensor auto dispatch_log2 = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log2(); }; return wrap(dispatch_log2(_r.tensor(0))); } else { // aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_log2_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::log2_out(out, self); }; return wrap(dispatch_log2_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // log2_ static PyObject * THPVariable_log2_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log2_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::log2_(Tensor(a!) self) -> Tensor(a!) auto dispatch_log2_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log2_(); }; return wrap(dispatch_log2_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logaddexp static PyObject * THPVariable_logaddexp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logaddexp(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::logaddexp(Tensor self, Tensor other) -> Tensor auto dispatch_logaddexp = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logaddexp(other); }; return wrap(dispatch_logaddexp(_r.tensor(0), _r.tensor(1))); } else { // aten::logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logaddexp_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logaddexp_out(out, self, other); }; return wrap(dispatch_logaddexp_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logaddexp2 static PyObject * THPVariable_logaddexp2(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logaddexp2(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::logaddexp2(Tensor self, Tensor other) -> Tensor auto dispatch_logaddexp2 = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logaddexp2(other); }; return wrap(dispatch_logaddexp2(_r.tensor(0), _r.tensor(1))); } else { // aten::logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logaddexp2_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logaddexp2_out(out, self, other); }; return wrap(dispatch_logaddexp2_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // xlogy static PyObject * THPVariable_xlogy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "xlogy(Tensor input, Tensor other, *, Tensor out=None)", "xlogy(Scalar self, Tensor other, *, Tensor out=None)", "xlogy(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_xlogy = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.xlogy(other); }; return wrap(dispatch_xlogy(_r.tensor(0), _r.tensor(1))); } else { // aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_xlogy_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::xlogy_out(out, self, other); }; return wrap(dispatch_xlogy_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor auto dispatch_xlogy = [](const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::xlogy(self, other); }; return wrap(dispatch_xlogy(_r.scalar(0), _r.tensor(1))); } else { // aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_xlogy_out = [](at::Tensor out, const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::xlogy_out(out, self, other); }; return wrap(dispatch_xlogy_out(_r.tensor(2), _r.scalar(0), _r.tensor(1))); } } case 2: { if (_r.isNone(2)) { // aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor auto dispatch_xlogy = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.xlogy(other); }; return wrap(dispatch_xlogy(_r.tensor(0), _r.scalar(1))); } else { // aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_xlogy_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::xlogy_out(out, self, other); }; return wrap(dispatch_xlogy_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // xlogy_ static PyObject * THPVariable_xlogy_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "xlogy_(Tensor input, Tensor other)", "xlogy_(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) auto dispatch_xlogy_ = [](at::Tensor self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.xlogy_(other); }; return wrap(dispatch_xlogy_(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!) auto dispatch_xlogy_ = [](at::Tensor self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.xlogy_(other); }; return wrap(dispatch_xlogy_(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // logspace static PyObject * THPVariable_logspace(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logspace(Tensor start, Tensor end, int64_t steps, double base=10.0, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "logspace(Scalar start, Tensor end, int64_t steps, double base=10.0, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "logspace(Tensor start, Scalar end, int64_t steps, double base=10.0, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "logspace(Scalar start, Scalar end, int64_t steps, double base=10.0, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::logspace.Tensor_Tensor(Tensor start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(5)) .device(_r.deviceWithDefault(7, torch::tensors::get_default_device())) .layout(_r.layoutOptional(6)) .requires_grad(_r.toBool(9)) .pinned_memory(_r.toBool(8)); torch::utils::maybe_initialize_device(options); auto dispatch_logspace = [](const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::logspace(start, end, steps, base, options); }; return wrap(dispatch_logspace(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toDouble(3), options)); } else { // aten::logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(4), _r.scalartypeOptional(5), _r.isNone(5), _r.layoutOptional(6), _r.deviceWithDefault(7, torch::tensors::get_default_device()), _r.isNone(7)); auto dispatch_logspace_out = [](at::Tensor out, const at::Tensor & start, const at::Tensor & end, int64_t steps, double base) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logspace_out(out, start, end, steps, base); }; return wrap(dispatch_logspace_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toDouble(3)).set_requires_grad(_r.toBool(9))); } } case 1: { if (_r.isNone(4)) { // aten::logspace.Scalar_Tensor(Scalar start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(5)) .device(_r.deviceWithDefault(7, torch::tensors::get_default_device())) .layout(_r.layoutOptional(6)) .requires_grad(_r.toBool(9)) .pinned_memory(_r.toBool(8)); torch::utils::maybe_initialize_device(options); auto dispatch_logspace = [](const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::logspace(start, end, steps, base, options); }; return wrap(dispatch_logspace(_r.scalar(0), _r.tensor(1), _r.toInt64(2), _r.toDouble(3), options)); } else { // aten::logspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(4), _r.scalartypeOptional(5), _r.isNone(5), _r.layoutOptional(6), _r.deviceWithDefault(7, torch::tensors::get_default_device()), _r.isNone(7)); auto dispatch_logspace_out = [](at::Tensor out, const at::Scalar & start, const at::Tensor & end, int64_t steps, double base) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logspace_out(out, start, end, steps, base); }; return wrap(dispatch_logspace_out(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.toInt64(2), _r.toDouble(3)).set_requires_grad(_r.toBool(9))); } } case 2: { if (_r.isNone(4)) { // aten::logspace.Tensor_Scalar(Tensor start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(5)) .device(_r.deviceWithDefault(7, torch::tensors::get_default_device())) .layout(_r.layoutOptional(6)) .requires_grad(_r.toBool(9)) .pinned_memory(_r.toBool(8)); torch::utils::maybe_initialize_device(options); auto dispatch_logspace = [](const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::logspace(start, end, steps, base, options); }; return wrap(dispatch_logspace(_r.tensor(0), _r.scalar(1), _r.toInt64(2), _r.toDouble(3), options)); } else { // aten::logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(4), _r.scalartypeOptional(5), _r.isNone(5), _r.layoutOptional(6), _r.deviceWithDefault(7, torch::tensors::get_default_device()), _r.isNone(7)); auto dispatch_logspace_out = [](at::Tensor out, const at::Tensor & start, const at::Scalar & end, int64_t steps, double base) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logspace_out(out, start, end, steps, base); }; return wrap(dispatch_logspace_out(_r.tensor(4), _r.tensor(0), _r.scalar(1), _r.toInt64(2), _r.toDouble(3)).set_requires_grad(_r.toBool(9))); } } case 3: { if (_r.isNone(4)) { // aten::logspace(Scalar start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(5)) .device(_r.deviceWithDefault(7, torch::tensors::get_default_device())) .layout(_r.layoutOptional(6)) .requires_grad(_r.toBool(9)) .pinned_memory(_r.toBool(8)); torch::utils::maybe_initialize_device(options); auto dispatch_logspace = [](const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::logspace(start, end, steps, base, options); }; return wrap(dispatch_logspace(_r.scalar(0), _r.scalar(1), _r.toInt64(2), _r.toDouble(3), options)); } else { // aten::logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(4), _r.scalartypeOptional(5), _r.isNone(5), _r.layoutOptional(6), _r.deviceWithDefault(7, torch::tensors::get_default_device()), _r.isNone(7)); auto dispatch_logspace_out = [](at::Tensor out, const at::Scalar & start, const at::Scalar & end, int64_t steps, double base) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logspace_out(out, start, end, steps, base); }; return wrap(dispatch_logspace_out(_r.tensor(4), _r.scalar(0), _r.scalar(1), _r.toInt64(2), _r.toDouble(3)).set_requires_grad(_r.toBool(9))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // log_softmax static PyObject * THPVariable_log_softmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "log_softmax(Tensor input, int64_t dim, ScalarType? dtype=None, *, Tensor out=None)", "log_softmax(Tensor input, Dimname dim, *, ScalarType? dtype=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor auto dispatch_log_softmax = [](const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log_softmax(dim, dtype); }; return wrap(dispatch_log_softmax(_r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } else { // aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_log_softmax_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::log_softmax_out(out, self, dim, dtype); }; return wrap(dispatch_log_softmax_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } } case 1: { // aten::log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor auto dispatch_log_softmax = [](const at::Tensor & self, at::Dimname dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.log_softmax(dim, dtype); }; return wrap(dispatch_log_softmax(_r.tensor(0), _r.dimname(1), _r.scalartypeOptional(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _log_softmax static PyObject * THPVariable__log_softmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_log_softmax(Tensor input, int64_t dim, bool half_to_float, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor auto dispatch__log_softmax = [](const at::Tensor & self, int64_t dim, bool half_to_float) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_log_softmax(self, dim, half_to_float); }; return wrap(dispatch__log_softmax(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__log_softmax_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, bool half_to_float) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_log_softmax_out(out, self, dim, half_to_float); }; return wrap(dispatch__log_softmax_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _log_softmax_backward_data static PyObject * THPVariable__log_softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_log_softmax_backward_data(Tensor grad_output, Tensor output, int64_t dim, ScalarType input_dtype, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor auto dispatch__log_softmax_backward_data = [](const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_log_softmax_backward_data(grad_output, output, dim, input_dtype); }; return wrap(dispatch__log_softmax_backward_data(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.scalartype(3))); } else { // aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__log_softmax_backward_data_out = [](at::Tensor out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_log_softmax_backward_data_out(out, grad_output, output, dim, input_dtype); }; return wrap(dispatch__log_softmax_backward_data_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.scalartype(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _logcumsumexp static PyObject * THPVariable__logcumsumexp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_logcumsumexp(Tensor input, int64_t dim, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::_logcumsumexp(Tensor self, int dim) -> Tensor auto dispatch__logcumsumexp = [](const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_logcumsumexp(self, dim); }; return wrap(dispatch__logcumsumexp(_r.tensor(0), _r.toInt64(1))); } else { // aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__logcumsumexp_out = [](at::Tensor out, const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_logcumsumexp_out(out, self, dim); }; return wrap(dispatch__logcumsumexp_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // logcumsumexp static PyObject * THPVariable_logcumsumexp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logcumsumexp(Tensor input, int64_t dim, *, Tensor out=None)", "logcumsumexp(Tensor input, Dimname dim, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::logcumsumexp(Tensor self, int dim) -> Tensor auto dispatch_logcumsumexp = [](const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logcumsumexp(dim); }; return wrap(dispatch_logcumsumexp(_r.tensor(0), _r.toInt64(1))); } else { // aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logcumsumexp_out = [](at::Tensor out, const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logcumsumexp_out(out, self, dim); }; return wrap(dispatch_logcumsumexp_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } } case 1: { if (_r.isNone(2)) { // aten::logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor auto dispatch_logcumsumexp = [](const at::Tensor & self, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logcumsumexp(dim); }; return wrap(dispatch_logcumsumexp(_r.tensor(0), _r.dimname(1))); } else { // aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logcumsumexp_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logcumsumexp_out(out, self, dim); }; return wrap(dispatch_logcumsumexp_out(_r.tensor(2), _r.tensor(0), _r.dimname(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // logsumexp static PyObject * THPVariable_logsumexp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logsumexp(Tensor input, IntArrayRef[1] dim, bool keepdim=False, *, Tensor out=None)", "logsumexp(Tensor input, DimnameList[1] dim, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor auto dispatch_logsumexp = [](const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logsumexp(dim, keepdim); }; return wrap(dispatch_logsumexp(_r.tensor(0), _r.intlist(1), _r.toBool(2))); } else { // aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logsumexp_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logsumexp_out(out, self, dim, keepdim); }; return wrap(dispatch_logsumexp_out(_r.tensor(3), _r.tensor(0), _r.intlist(1), _r.toBool(2))); } } case 1: { if (_r.isNone(3)) { // aten::logsumexp.names(Tensor self, Dimname[1] dim, bool keepdim=False) -> Tensor auto dispatch_logsumexp = [](const at::Tensor & self, at::DimnameList dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logsumexp(dim, keepdim); }; return wrap(dispatch_logsumexp(_r.tensor(0), _r.dimnamelist(1), _r.toBool(2))); } else { // aten::logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logsumexp_out = [](at::Tensor out, const at::Tensor & self, at::DimnameList dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logsumexp_out(out, self, dim, keepdim); }; return wrap(dispatch_logsumexp_out(_r.tensor(3), _r.tensor(0), _r.dimnamelist(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // margin_ranking_loss static PyObject * THPVariable_margin_ranking_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, double margin=0.0, int64_t reduction=at::Reduction::Mean)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor auto dispatch_margin_ranking_loss = [](const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::margin_ranking_loss(input1, input2, target, margin, reduction); }; return wrap(dispatch_margin_ranking_loss(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toDouble(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // matmul static PyObject * THPVariable_matmul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "matmul(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::matmul(Tensor self, Tensor other) -> Tensor auto dispatch_matmul = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.matmul(other); }; return wrap(dispatch_matmul(_r.tensor(0), _r.tensor(1))); } else { // aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_matmul_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::matmul_out(out, self, other); }; return wrap(dispatch_matmul_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // matrix_power static PyObject * THPVariable_matrix_power(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "matrix_power(Tensor input, int64_t n, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::matrix_power(Tensor self, int n) -> Tensor auto dispatch_matrix_power = [](const at::Tensor & self, int64_t n) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.matrix_power(n); }; return wrap(dispatch_matrix_power(_r.tensor(0), _r.toInt64(1))); } else { // aten::matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_matrix_power_out = [](at::Tensor out, const at::Tensor & self, int64_t n) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::matrix_power_out(out, self, n); }; return wrap(dispatch_matrix_power_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // matrix_exp static PyObject * THPVariable_matrix_exp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "matrix_exp(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::matrix_exp(Tensor self) -> Tensor auto dispatch_matrix_exp = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.matrix_exp(); }; return wrap(dispatch_matrix_exp(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _aminmax static PyObject * THPVariable__aminmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_aminmax(Tensor input)", "_aminmax(Tensor input, int64_t dim, bool keepdim=False)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_aminmax(Tensor self) -> (Tensor, Tensor) auto dispatch__aminmax = [](const at::Tensor & self) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_aminmax(self); }; return wrap(dispatch__aminmax(_r.tensor(0))); } case 1: { // aten::_aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor) auto dispatch__aminmax = [](const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_aminmax(self, dim, keepdim); }; return wrap(dispatch__aminmax(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // aminmax static PyObject * THPVariable_aminmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_aminmax_structseq(); static PyTypeObject* NamedTuple1 = generated::get_aminmax_out_structseq(); static PythonArgParser parser({ "aminmax(Tensor input, *, int64_t? dim=None, bool keepdim=False, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::aminmax(Tensor self, *, int? dim=None, bool keepdim=False) -> (Tensor min, Tensor max) auto dispatch_aminmax = [](const at::Tensor & self, ::std::optional dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.aminmax(dim, keepdim); }; return wrap(NamedTuple, dispatch_aminmax(_r.tensor(0), _r.toInt64Optional(1), _r.toBool(2))); } else { // aten::aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max) auto out = _r.tensorlist_n<2>(3); auto dispatch_aminmax_out = [](at::Tensor & min, at::Tensor & max, const at::Tensor & self, ::std::optional dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::aminmax_out(min, max, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_aminmax_out(out[0], out[1], _r.tensor(0), _r.toInt64Optional(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _compute_linear_combination static PyObject * THPVariable__compute_linear_combination(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_compute_linear_combination(Tensor input, Tensor coefficients, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::_compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor auto dispatch__compute_linear_combination = [](const at::Tensor & input, const at::Tensor & coefficients) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_compute_linear_combination(input, coefficients); }; return wrap(dispatch__compute_linear_combination(_r.tensor(0), _r.tensor(1))); } else { // aten::_compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__compute_linear_combination_out = [](at::Tensor out, const at::Tensor & input, const at::Tensor & coefficients) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_compute_linear_combination_out(out, input, coefficients); }; return wrap(dispatch__compute_linear_combination_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // max static PyObject * THPVariable_max(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_max_structseq(); static PyTypeObject* NamedTuple1 = generated::get_max_out_structseq(); static PythonArgParser parser({ "max(Tensor input, *, Tensor out=None)", "max(Tensor input, Tensor other, *, Tensor out=None)", "max(Tensor input, int64_t dim, bool keepdim=False, *, TensorList[2] out=None)", "max(Tensor input, Dimname dim, bool keepdim=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::max(Tensor self) -> Tensor auto dispatch_max = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.max(); }; return wrap(dispatch_max(_r.tensor(0))); } else { // aten::max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_max_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::max_out(out, self); }; return wrap(dispatch_max_out(_r.tensor(1), _r.tensor(0))); } } case 1: { if (_r.isNone(2)) { // aten::max.other(Tensor self, Tensor other) -> Tensor auto dispatch_max = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.max(other); }; return wrap(dispatch_max(_r.tensor(0), _r.tensor(1))); } else { // aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_max_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::max_out(out, self, other); }; return wrap(dispatch_max_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 2: { if (_r.isNone(3)) { // aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_max = [](const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.max(dim, keepdim); }; return wrap(NamedTuple, dispatch_max(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_max_out = [](at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::max_out(max, max_values, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_max_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 3: { if (_r.isNone(3)) { // aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_max = [](const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.max(dim, keepdim); }; return wrap(NamedTuple, dispatch_max(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_max_out = [](at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::max_out(max, max_values, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_max_out(out[0], out[1], _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // amax static PyObject * THPVariable_amax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "amax(Tensor input, IntArrayRef[1] dim=None, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor auto dispatch_amax = [](const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.amax(dim, keepdim); }; return wrap(dispatch_amax(_r.tensor(0), _r.intlist(1), _r.toBool(2))); } else { // aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_amax_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::amax_out(out, self, dim, keepdim); }; return wrap(dispatch_amax_out(_r.tensor(3), _r.tensor(0), _r.intlist(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // max_pool1d_with_indices static PyObject * THPVariable_max_pool1d_with_indices(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "max_pool1d_with_indices(Tensor input, IntArrayRef[1] kernel_size, IntArrayRef[1] stride=None, IntArrayRef[1] padding=0, IntArrayRef[1] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::max_pool1d_with_indices(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) auto dispatch_max_pool1d_with_indices = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::max_pool1d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_max_pool1d_with_indices(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // max_pool1d static PyObject * THPVariable_max_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "max_pool1d(Tensor input, IntArrayRef[1] kernel_size, IntArrayRef[1] stride=None, IntArrayRef[1] padding=0, IntArrayRef[1] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_max_pool1d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::max_pool1d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_max_pool1d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // max_pool2d static PyObject * THPVariable_max_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "max_pool2d(Tensor input, IntArrayRef[2] kernel_size, IntArrayRef[2] stride=None, IntArrayRef[2] padding=0, IntArrayRef[2] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_max_pool2d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_max_pool2d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mkldnn_max_pool2d static PyObject * THPVariable_mkldnn_max_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mkldnn_max_pool2d(Tensor input, IntArrayRef[2] kernel_size, IntArrayRef[2] stride=None, IntArrayRef[2] padding=0, IntArrayRef[2] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_mkldnn_max_pool2d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mkldnn_max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_mkldnn_max_pool2d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mkldnn_max_pool3d static PyObject * THPVariable_mkldnn_max_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mkldnn_max_pool3d(Tensor input, IntArrayRef[3] kernel_size, IntArrayRef[3] stride=None, IntArrayRef[3] padding=0, IntArrayRef[3] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::mkldnn_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_mkldnn_max_pool3d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mkldnn_max_pool3d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_mkldnn_max_pool3d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_max_pool1d static PyObject * THPVariable_quantized_max_pool1d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_max_pool1d(Tensor input, IntArrayRef[1] kernel_size, IntArrayRef[1] stride=None, IntArrayRef[1] padding=0, IntArrayRef[1] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_quantized_max_pool1d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantized_max_pool1d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_quantized_max_pool1d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_max_pool2d static PyObject * THPVariable_quantized_max_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_max_pool2d(Tensor input, IntArrayRef[2] kernel_size, IntArrayRef[2] stride=None, IntArrayRef[2] padding=0, IntArrayRef[2] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_quantized_max_pool2d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantized_max_pool2d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_quantized_max_pool2d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_max_pool3d static PyObject * THPVariable_quantized_max_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_max_pool3d(Tensor input, IntArrayRef[3] kernel_size, IntArrayRef[3] stride=None, IntArrayRef[3] padding=0, IntArrayRef[3] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_quantized_max_pool3d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantized_max_pool3d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_quantized_max_pool3d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // max_pool3d static PyObject * THPVariable_max_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "max_pool3d(Tensor input, IntArrayRef[3] kernel_size, IntArrayRef[3] stride=None, IntArrayRef[3] padding=0, IntArrayRef[3] dilation=1, bool ceil_mode=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor auto dispatch_max_pool3d = [](const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::max_pool3d(self, kernel_size, stride, padding, dilation, ceil_mode); }; return wrap(dispatch_max_pool3d(_r.tensor(0), _r.intlist(1), _r.intlist(2), _r.intlist(3), _r.intlist(4), _r.toBool(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // mean static PyObject * THPVariable_mean(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mean(Tensor input, *, ScalarType? dtype=None, Tensor out=None)", "mean(Tensor input, IntArrayRef[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", "mean(Tensor input, DimnameList[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor auto dispatch_mean = [](const at::Tensor & self, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.mean(dtype); }; return wrap(dispatch_mean(_r.tensor(0), _r.scalartypeOptional(1))); } else { // aten::mean.dtype_out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_mean_out = [](at::Tensor out, const at::Tensor & self, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mean_out(out, self, dtype); }; return wrap(dispatch_mean_out(_r.tensor(2), _r.tensor(0), _r.scalartypeOptional(1))); } } case 1: { if (_r.isNone(4)) { // aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_mean = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.mean(dim, keepdim, dtype); }; return wrap(dispatch_mean(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_mean_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mean_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_mean_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } } case 2: { if (_r.isNone(4)) { // aten::mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_mean = [](const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.mean(dim, keepdim, dtype); }; return wrap(dispatch_mean(_r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_mean_out = [](at::Tensor out, const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mean_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_mean_out(_r.tensor(4), _r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.scalartypeOptional(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // nanmean static PyObject * THPVariable_nanmean(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nanmean(Tensor input, IntArrayRef[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_nanmean = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nanmean(dim, keepdim, dtype); }; return wrap(dispatch_nanmean(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_nanmean_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nanmean_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_nanmean_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // median static PyObject * THPVariable_median(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_median_structseq(); static PyTypeObject* NamedTuple1 = generated::get_median_out_structseq(); static PythonArgParser parser({ "median(Tensor input)", "median(Tensor input, int64_t dim, bool keepdim=False, *, TensorList[2] out=None)", "median(Tensor input, Dimname dim, bool keepdim=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::median(Tensor self) -> Tensor auto dispatch_median = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.median(); }; return wrap(dispatch_median(_r.tensor(0))); } case 1: { if (_r.isNone(3)) { // aten::median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_median = [](const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.median(dim, keepdim); }; return wrap(NamedTuple, dispatch_median(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_median_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::median_out(values, indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_median_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 2: { if (_r.isNone(3)) { // aten::median.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_median = [](const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.median(dim, keepdim); }; return wrap(NamedTuple, dispatch_median(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_median_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::median_out(values, indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_median_out(out[0], out[1], _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // nanmedian static PyObject * THPVariable_nanmedian(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_nanmedian_structseq(); static PyTypeObject* NamedTuple1 = generated::get_nanmedian_out_structseq(); static PythonArgParser parser({ "nanmedian(Tensor input)", "nanmedian(Tensor input, int64_t dim, bool keepdim=False, *, TensorList[2] out=None)", "nanmedian(Tensor input, Dimname dim, bool keepdim=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::nanmedian(Tensor self) -> Tensor auto dispatch_nanmedian = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nanmedian(); }; return wrap(dispatch_nanmedian(_r.tensor(0))); } case 1: { if (_r.isNone(3)) { // aten::nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_nanmedian = [](const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.nanmedian(dim, keepdim); }; return wrap(NamedTuple, dispatch_nanmedian(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_nanmedian_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::nanmedian_out(values, indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_nanmedian_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 2: { if (_r.isNone(3)) { // aten::nanmedian.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_nanmedian = [](const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.nanmedian(dim, keepdim); }; return wrap(NamedTuple, dispatch_nanmedian(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_nanmedian_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::nanmedian_out(values, indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_nanmedian_out(out[0], out[1], _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // min static PyObject * THPVariable_min(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_min_structseq(); static PyTypeObject* NamedTuple1 = generated::get_min_out_structseq(); static PythonArgParser parser({ "min(Tensor input, *, Tensor out=None)", "min(Tensor input, Tensor other, *, Tensor out=None)", "min(Tensor input, int64_t dim, bool keepdim=False, *, TensorList[2] out=None)", "min(Tensor input, Dimname dim, bool keepdim=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::min(Tensor self) -> Tensor auto dispatch_min = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.min(); }; return wrap(dispatch_min(_r.tensor(0))); } else { // aten::min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_min_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::min_out(out, self); }; return wrap(dispatch_min_out(_r.tensor(1), _r.tensor(0))); } } case 1: { if (_r.isNone(2)) { // aten::min.other(Tensor self, Tensor other) -> Tensor auto dispatch_min = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.min(other); }; return wrap(dispatch_min(_r.tensor(0), _r.tensor(1))); } else { // aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_min_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::min_out(out, self, other); }; return wrap(dispatch_min_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 2: { if (_r.isNone(3)) { // aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_min = [](const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.min(dim, keepdim); }; return wrap(NamedTuple, dispatch_min(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_min_out = [](at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::min_out(min, min_indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_min_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 3: { if (_r.isNone(3)) { // aten::min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_min = [](const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.min(dim, keepdim); }; return wrap(NamedTuple, dispatch_min(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_min_out = [](at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::min_out(min, min_indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_min_out(out[0], out[1], _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // amin static PyObject * THPVariable_amin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "amin(Tensor input, IntArrayRef[1] dim=None, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor auto dispatch_amin = [](const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.amin(dim, keepdim); }; return wrap(dispatch_amin(_r.tensor(0), _r.intlist(1), _r.toBool(2))); } else { // aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_amin_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::amin_out(out, self, dim, keepdim); }; return wrap(dispatch_amin_out(_r.tensor(3), _r.tensor(0), _r.intlist(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _mps_convolution static PyObject * THPVariable__mps_convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_mps_convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch__mps_convolution = [](const at::Tensor & self, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_mps_convolution_symint(self, weight, bias, padding, stride, dilation, groups); }; return wrap(dispatch__mps_convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mkldnn_convolution static PyObject * THPVariable_mkldnn_convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mkldnn_convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch_mkldnn_convolution = [](const at::Tensor & self, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mkldnn_convolution_symint(self, weight, bias, padding, stride, dilation, groups); }; return wrap(dispatch_mkldnn_convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mkldnn_rnn_layer static PyObject * THPVariable_mkldnn_rnn_layer(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mkldnn_rnn_layer(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train)", }, /*traceable=*/true); ParsedArgs<16> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::mkldnn_rnn_layer(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) -> (Tensor, Tensor, Tensor, Tensor) auto dispatch_mkldnn_rnn_layer = [](const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::mkldnn_rnn_layer(input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train); }; return wrap(dispatch_mkldnn_rnn_layer(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.toBool(7), _r.intlist(8), _r.toInt64(9), _r.toInt64(10), _r.toInt64(11), _r.toBool(12), _r.toBool(13), _r.toBool(14), _r.toBool(15))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // miopen_batch_norm static PyObject * THPVariable_miopen_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, double exponential_average_factor, double epsilon)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor) auto dispatch_miopen_batch_norm = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, const ::std::optional & running_mean, const ::std::optional & running_var, bool training, double exponential_average_factor, double epsilon) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::miopen_batch_norm(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); }; return wrap(dispatch_miopen_batch_norm(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // miopen_convolution static PyObject * THPVariable_miopen_convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "miopen_convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups, bool benchmark, bool deterministic)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor auto dispatch_miopen_convolution = [](const at::Tensor & self, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::miopen_convolution_symint(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); }; return wrap(dispatch_miopen_convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6), _r.toBool(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // miopen_convolution_transpose static PyObject * THPVariable_miopen_convolution_transpose(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "miopen_convolution_transpose(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef padding, SymIntArrayRef output_padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups, bool benchmark, bool deterministic)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor auto dispatch_miopen_convolution_transpose = [](const at::Tensor & self, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::miopen_convolution_transpose_symint(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic); }; return wrap(dispatch_miopen_convolution_transpose(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.symintlist(6), _r.toSymInt(7), _r.toBool(8), _r.toBool(9))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // miopen_depthwise_convolution static PyObject * THPVariable_miopen_depthwise_convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "miopen_depthwise_convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef padding, SymIntArrayRef stride, SymIntArrayRef dilation, SymInt groups, bool benchmark, bool deterministic)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor auto dispatch_miopen_depthwise_convolution = [](const at::Tensor & self, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::miopen_depthwise_convolution_symint(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); }; return wrap(dispatch_miopen_depthwise_convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6), _r.toBool(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // miopen_convolution_relu static PyObject * THPVariable_miopen_convolution_relu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "miopen_convolution_relu(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef stride, SymIntArrayRef padding, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch_miopen_convolution_relu = [](const at::Tensor & self, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::miopen_convolution_relu_symint(self, weight, bias, stride, padding, dilation, groups); }; return wrap(dispatch_miopen_convolution_relu(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4), _r.symintlist(5), _r.toSymInt(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // miopen_convolution_add_relu static PyObject * THPVariable_miopen_convolution_add_relu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "miopen_convolution_add_relu(Tensor input, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymIntArrayRef stride, SymIntArrayRef padding, SymIntArrayRef dilation, SymInt groups)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor auto dispatch_miopen_convolution_add_relu = [](const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional & alpha, const ::std::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::miopen_convolution_add_relu_symint(self, weight, z, alpha, bias, stride, padding, dilation, groups); }; return wrap(dispatch_miopen_convolution_add_relu(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalarOptional(3), _r.optionalTensor(4), _r.symintlist(5), _r.symintlist(6), _r.symintlist(7), _r.toSymInt(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // miopen_rnn static PyObject * THPVariable_miopen_rnn(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "miopen_rnn(Tensor input, TensorList weight, int64_t weight_stride0, Tensor hx, Tensor? cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, Tensor? dropout_state)", }, /*traceable=*/true); ParsedArgs<14> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor) auto dispatch_miopen_rnn = [](const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const ::std::optional & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional & dropout_state) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::miopen_rnn(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); }; return wrap(dispatch_miopen_rnn(_r.tensor(0), _r.tensorlist(1), _r.toInt64(2), _r.tensor(3), _r.optionalTensor(4), _r.toInt64(5), _r.toInt64(6), _r.toInt64(7), _r.toBool(8), _r.toDouble(9), _r.toBool(10), _r.toBool(11), _r.intlist(12), _r.optionalTensor(13))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mm static PyObject * THPVariable_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mm(Tensor input, Tensor mat2, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::mm(Tensor self, Tensor mat2) -> Tensor auto dispatch_mm = [](const at::Tensor & self, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.mm(mat2); }; return wrap(dispatch_mm(_r.tensor(0), _r.tensor(1))); } else { // aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_mm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mm_out(out, self, mat2); }; return wrap(dispatch_mm_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _int_mm static PyObject * THPVariable__int_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_int_mm(Tensor input, Tensor mat2, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::_int_mm(Tensor self, Tensor mat2) -> Tensor auto dispatch__int_mm = [](const at::Tensor & self, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_int_mm(self, mat2); }; return wrap(dispatch__int_mm(_r.tensor(0), _r.tensor(1))); } else { // aten::_int_mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__int_mm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_int_mm_out(out, self, mat2); }; return wrap(dispatch__int_mm_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _convert_weight_to_int4pack static PyObject * THPVariable__convert_weight_to_int4pack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_convert_weight_to_int4pack(Tensor input, int64_t innerKTiles)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_convert_weight_to_int4pack(Tensor self, int innerKTiles) -> Tensor auto dispatch__convert_weight_to_int4pack = [](const at::Tensor & self, int64_t innerKTiles) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convert_weight_to_int4pack(self, innerKTiles); }; return wrap(dispatch__convert_weight_to_int4pack(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _weight_int4pack_mm static PyObject * THPVariable__weight_int4pack_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_weight_int4pack_mm(Tensor input, Tensor mat2, int64_t qGroupSize, Tensor qScaleAndZeros)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_weight_int4pack_mm(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor auto dispatch__weight_int4pack_mm = [](const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_weight_int4pack_mm(self, mat2, qGroupSize, qScaleAndZeros); }; return wrap(dispatch__weight_int4pack_mm(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _convert_weight_to_int4pack_for_cpu static PyObject * THPVariable__convert_weight_to_int4pack_for_cpu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_convert_weight_to_int4pack_for_cpu(Tensor input, int64_t innerKTiles)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_convert_weight_to_int4pack_for_cpu(Tensor self, int innerKTiles) -> Tensor auto dispatch__convert_weight_to_int4pack_for_cpu = [](const at::Tensor & self, int64_t innerKTiles) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convert_weight_to_int4pack_for_cpu(self, innerKTiles); }; return wrap(dispatch__convert_weight_to_int4pack_for_cpu(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _weight_int4pack_mm_for_cpu static PyObject * THPVariable__weight_int4pack_mm_for_cpu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_weight_int4pack_mm_for_cpu(Tensor input, Tensor mat2, int64_t qGroupSize, Tensor qScaleAndZeros)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_weight_int4pack_mm_for_cpu(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor auto dispatch__weight_int4pack_mm_for_cpu = [](const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_weight_int4pack_mm_for_cpu(self, mat2, qGroupSize, qScaleAndZeros); }; return wrap(dispatch__weight_int4pack_mm_for_cpu(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _dyn_quant_pack_4bit_weight static PyObject * THPVariable__dyn_quant_pack_4bit_weight(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_dyn_quant_pack_4bit_weight(Tensor weights, Tensor scales_zeros, Tensor? bias, int64_t block_size, int64_t in_features, int64_t out_features)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_dyn_quant_pack_4bit_weight(Tensor weights, Tensor scales_zeros, Tensor? bias, int block_size, int in_features, int out_features) -> Tensor auto dispatch__dyn_quant_pack_4bit_weight = [](const at::Tensor & weights, const at::Tensor & scales_zeros, const ::std::optional & bias, int64_t block_size, int64_t in_features, int64_t out_features) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_dyn_quant_pack_4bit_weight(weights, scales_zeros, bias, block_size, in_features, out_features); }; return wrap(dispatch__dyn_quant_pack_4bit_weight(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.toInt64(3), _r.toInt64(4), _r.toInt64(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _dyn_quant_matmul_4bit static PyObject * THPVariable__dyn_quant_matmul_4bit(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_dyn_quant_matmul_4bit(Tensor inp, Tensor packed_weights, int64_t block_size, int64_t in_features, int64_t out_features)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_dyn_quant_matmul_4bit(Tensor inp, Tensor packed_weights, int block_size, int in_features, int out_features) -> Tensor auto dispatch__dyn_quant_matmul_4bit = [](const at::Tensor & inp, const at::Tensor & packed_weights, int64_t block_size, int64_t in_features, int64_t out_features) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_dyn_quant_matmul_4bit(inp, packed_weights, block_size, in_features, out_features); }; return wrap(dispatch__dyn_quant_matmul_4bit(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toInt64(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _weight_int8pack_mm static PyObject * THPVariable__weight_int8pack_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_weight_int8pack_mm(Tensor input, Tensor mat2, Tensor scales)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_weight_int8pack_mm(Tensor self, Tensor mat2, Tensor scales) -> Tensor auto dispatch__weight_int8pack_mm = [](const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scales) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_weight_int8pack_mm(self, mat2, scales); }; return wrap(dispatch__weight_int8pack_mm(_r.tensor(0), _r.tensor(1), _r.tensor(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_sparse_matmul static PyObject * THPVariable__sparse_sparse_matmul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_sparse_matmul(Tensor input, Tensor other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_sparse_matmul(Tensor self, Tensor other) -> Tensor auto dispatch__sparse_sparse_matmul = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_sparse_matmul(self, other); }; return wrap(dispatch__sparse_sparse_matmul(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // mode static PyObject * THPVariable_mode(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_mode_structseq(); static PyTypeObject* NamedTuple1 = generated::get_mode_out_structseq(); static PythonArgParser parser({ "mode(Tensor input, int64_t dim=-1, bool keepdim=False, *, TensorList[2] out=None)", "mode(Tensor input, Dimname dim, bool keepdim=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_mode = [](const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.mode(dim, keepdim); }; return wrap(NamedTuple, dispatch_mode(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_mode_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::mode_out(values, indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_mode_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 1: { if (_r.isNone(3)) { // aten::mode.dimname(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) auto dispatch_mode = [](const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.mode(dim, keepdim); }; return wrap(NamedTuple, dispatch_mode(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_mode_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::mode_out(values, indices, self, dim, keepdim); }; return wrap(NamedTuple1, dispatch_mode_out(out[0], out[1], _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mul static PyObject * THPVariable_mul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mul(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::mul.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_mul = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.mul(other); }; return wrap(dispatch_mul(_r.tensor(0), _r.tensor(1))); } else { // aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_mul_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mul_out(out, self, other); }; return wrap(dispatch_mul_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // multiply static PyObject * THPVariable_multiply(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "multiply(Tensor input, Tensor other, *, Tensor out=None)", "multiply(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::multiply.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_multiply = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.multiply(other); }; return wrap(dispatch_multiply(_r.tensor(0), _r.tensor(1))); } else { // aten::multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_multiply_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::multiply_out(out, self, other); }; return wrap(dispatch_multiply_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::multiply.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_multiply = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.multiply(other); }; return wrap(dispatch_multiply(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mv static PyObject * THPVariable_mv(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mv(Tensor input, Tensor vec, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::mv(Tensor self, Tensor vec) -> Tensor auto dispatch_mv = [](const at::Tensor & self, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.mv(vec); }; return wrap(dispatch_mv(_r.tensor(0), _r.tensor(1))); } else { // aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_mv_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & vec) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mv_out(out, self, vec); }; return wrap(dispatch_mv_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mvlgamma static PyObject * THPVariable_mvlgamma(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mvlgamma(Tensor input, int64_t p, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::mvlgamma(Tensor self, int p) -> Tensor auto dispatch_mvlgamma = [](const at::Tensor & self, int64_t p) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.mvlgamma(p); }; return wrap(dispatch_mvlgamma(_r.tensor(0), _r.toInt64(1))); } else { // aten::mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_mvlgamma_out = [](at::Tensor out, const at::Tensor & self, int64_t p) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mvlgamma_out(out, self, p); }; return wrap(dispatch_mvlgamma_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // narrow_copy static PyObject * THPVariable_narrow_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "narrow_copy(Tensor input, int64_t dim, SymInt start, SymInt length, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor auto dispatch_narrow_copy = [](const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.narrow_copy_symint(dim, start, length); }; return wrap(dispatch_narrow_copy(_r.tensor(0), _r.toInt64(1), _r.toSymInt(2), _r.toSymInt(3))); } else { // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_narrow_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::narrow_copy_symint_out(out, self, dim, start, length); }; return wrap(dispatch_narrow_copy_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.toSymInt(2), _r.toSymInt(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // narrow static PyObject * THPVariable_narrow(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "narrow(Tensor input, int64_t dim, Tensor start, SymInt length)", "narrow(Tensor input, int64_t dim, SymInt start, SymInt length)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a) auto dispatch_narrow = [](const at::Tensor & self, int64_t dim, const at::Tensor & start, c10::SymInt length) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.narrow_symint(dim, start, length); }; return wrap(dispatch_narrow(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.toSymInt(3))); } case 1: { // aten::narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a) auto dispatch_narrow = [](const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.narrow_symint(dim, start, length); }; return wrap(dispatch_narrow(_r.tensor(0), _r.toInt64(1), _r.toSymInt(2), _r.toSymInt(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // native_batch_norm static PyObject * THPVariable_native_batch_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, double momentum, double eps, *, TensorList[3] out=None)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(8)) { // aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) auto dispatch_native_batch_norm = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const ::std::optional & running_mean, const ::std::optional & running_var, bool training, double momentum, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::native_batch_norm(input, weight, bias, running_mean, running_var, training, momentum, eps); }; return wrap(dispatch_native_batch_norm(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7))); } else { // aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) auto out = _r.tensorlist_n<3>(8); auto dispatch_native_batch_norm_out = [](at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const ::std::optional & running_mean, const ::std::optional & running_var, bool training, double momentum, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::native_batch_norm_out(out, save_mean, save_invstd, input, weight, bias, running_mean, running_var, training, momentum, eps); }; return wrap(dispatch_native_batch_norm_out(out[0], out[1], out[2], _r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _native_batch_norm_legit static PyObject * THPVariable__native_batch_norm_legit(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, double momentum, double eps, *, TensorList[3] out=None)", "_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, bool training, double momentum, double eps, *, TensorList[3] out=None)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(8)) { // aten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) auto dispatch__native_batch_norm_legit = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, at::Tensor running_mean, at::Tensor running_var, bool training, double momentum, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_native_batch_norm_legit(input, weight, bias, running_mean, running_var, training, momentum, eps); }; return wrap(dispatch__native_batch_norm_legit(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.tensor(3), _r.tensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7))); } else { // aten::_native_batch_norm_legit.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd) -> (Tensor(d!), Tensor(e!), Tensor(f!)) auto out = _r.tensorlist_n<3>(8); auto dispatch__native_batch_norm_legit_out = [](at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, at::Tensor running_mean, at::Tensor running_var, bool training, double momentum, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_native_batch_norm_legit_out(out, save_mean, save_invstd, input, weight, bias, running_mean, running_var, training, momentum, eps); }; return wrap(dispatch__native_batch_norm_legit_out(out[0], out[1], out[2], _r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.tensor(3), _r.tensor(4), _r.toBool(5), _r.toDouble(6), _r.toDouble(7))); } } case 1: { if (_r.isNone(6)) { // aten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) auto dispatch__native_batch_norm_legit = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, bool training, double momentum, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_native_batch_norm_legit(input, weight, bias, training, momentum, eps); }; return wrap(dispatch__native_batch_norm_legit(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.toBool(3), _r.toDouble(4), _r.toDouble(5))); } else { // aten::_native_batch_norm_legit.no_stats_out(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) auto out = _r.tensorlist_n<3>(6); auto dispatch__native_batch_norm_legit_out = [](at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, bool training, double momentum, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_native_batch_norm_legit_out(out, save_mean, save_invstd, input, weight, bias, training, momentum, eps); }; return wrap(dispatch__native_batch_norm_legit_out(out[0], out[1], out[2], _r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.toBool(3), _r.toDouble(4), _r.toDouble(5))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _native_batch_norm_legit_no_training static PyObject * THPVariable__native_batch_norm_legit_no_training(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, double momentum, double eps)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor) auto dispatch__native_batch_norm_legit_no_training = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_native_batch_norm_legit_no_training(input, weight, bias, running_mean, running_var, momentum, eps); }; return wrap(dispatch__native_batch_norm_legit_no_training(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.tensor(3), _r.tensor(4), _r.toDouble(5), _r.toDouble(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm_stats static PyObject * THPVariable_batch_norm_stats(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm_stats(Tensor input, double eps)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor) auto dispatch_batch_norm_stats = [](const at::Tensor & input, double eps) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::batch_norm_stats(input, eps); }; return wrap(dispatch_batch_norm_stats(_r.tensor(0), _r.toDouble(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm_elemt static PyObject * THPVariable_batch_norm_elemt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, double eps, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(6)) { // aten::batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensor auto dispatch_batch_norm_elemt = [](const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::batch_norm_elemt(input, weight, bias, mean, invstd, eps); }; return wrap(dispatch_batch_norm_elemt(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.tensor(3), _r.tensor(4), _r.toDouble(5))); } else { // aten::batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_batch_norm_elemt_out = [](at::Tensor out, const at::Tensor & input, const ::std::optional & weight, const ::std::optional & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::batch_norm_elemt_out(out, input, weight, bias, mean, invstd, eps); }; return wrap(dispatch_batch_norm_elemt_out(_r.tensor(6), _r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.tensor(3), _r.tensor(4), _r.toDouble(5))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm_gather_stats static PyObject * THPVariable_batch_norm_gather_stats(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, double momentum, double eps, int64_t count)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) auto dispatch_batch_norm_gather_stats = [](const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional & running_mean, const ::std::optional & running_var, double momentum, double eps, int64_t count) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::batch_norm_gather_stats(input, mean, invstd, running_mean, running_var, momentum, eps, count); }; return wrap(dispatch_batch_norm_gather_stats(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toDouble(5), _r.toDouble(6), _r.toInt64(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm_gather_stats_with_counts static PyObject * THPVariable_batch_norm_gather_stats_with_counts(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, double momentum, double eps, Tensor counts)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor) auto dispatch_batch_norm_gather_stats_with_counts = [](const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional & running_mean, const ::std::optional & running_var, double momentum, double eps, const at::Tensor & counts) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::batch_norm_gather_stats_with_counts(input, mean, invstd, running_mean, running_var, momentum, eps, counts); }; return wrap(dispatch_batch_norm_gather_stats_with_counts(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toDouble(5), _r.toDouble(6), _r.tensor(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm_backward_reduce static PyObject * THPVariable_batch_norm_backward_reduce(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor) auto dispatch_batch_norm_backward_reduce = [](const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional & weight, bool input_g, bool weight_g, bool bias_g) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::batch_norm_backward_reduce(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g); }; return wrap(dispatch_batch_norm_backward_reduce(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.toBool(5), _r.toBool(6), _r.toBool(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm_backward_elemt static PyObject * THPVariable_batch_norm_backward_elemt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count) -> Tensor auto dispatch_batch_norm_backward_elemt = [](const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::batch_norm_backward_elemt(grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count); }; return wrap(dispatch_batch_norm_backward_elemt(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.tensor(5), _r.tensor(6), _r.tensor(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // batch_norm_update_stats static PyObject * THPVariable_batch_norm_update_stats(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, double momentum)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor) auto dispatch_batch_norm_update_stats = [](const at::Tensor & input, const ::std::optional & running_mean, const ::std::optional & running_var, double momentum) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::batch_norm_update_stats(input, running_mean, running_var, momentum); }; return wrap(dispatch_batch_norm_update_stats(_r.tensor(0), _r.optionalTensor(1), _r.optionalTensor(2), _r.toDouble(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // is_vulkan_available static PyObject * THPVariable_is_vulkan_available(PyObject* self_, PyObject* args) { HANDLE_TH_ERRORS // aten::is_vulkan_available() -> bool auto dispatch_is_vulkan_available = []() -> bool { pybind11::gil_scoped_release no_gil; return at::is_vulkan_available(); }; return wrap(dispatch_is_vulkan_available()); END_HANDLE_TH_ERRORS } // _nnpack_available static PyObject * THPVariable__nnpack_available(PyObject* self_, PyObject* args) { HANDLE_TH_ERRORS // aten::_nnpack_available() -> bool auto dispatch__nnpack_available = []() -> bool { pybind11::gil_scoped_release no_gil; return at::_nnpack_available(); }; return wrap(dispatch__nnpack_available()); END_HANDLE_TH_ERRORS } // _nnpack_spatial_convolution static PyObject * THPVariable__nnpack_spatial_convolution(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymIntArrayRef[2] padding, SymIntArrayRef[2] stride=1)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1) -> Tensor auto dispatch__nnpack_spatial_convolution = [](const at::Tensor & input, const at::Tensor & weight, const ::std::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nnpack_spatial_convolution_symint(input, weight, bias, padding, stride); }; return wrap(dispatch__nnpack_spatial_convolution(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.symintlist(3), _r.symintlist(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // ones static PyObject * THPVariable_ones(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ones(IntArrayRef size, *, DimnameList? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "ones(SymIntArrayRef size, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto __names = _r.toDimnameListOptional(1); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_ones = [](at::IntArrayRef size, ::std::optional names, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::ones(size, names, options); }; return wrap(dispatch_ones(_r.intlist(0), names, options)); } case 1: { if (_r.isNone(1)) { // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_ones = [](c10::SymIntArrayRef size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::ones_symint(size, options); }; return wrap(dispatch_ones(_r.symintlist(0), options)); } else { // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(1), _r.scalartypeOptional(2), _r.isNone(2), _r.layoutOptional(3), _r.deviceWithDefault(4, torch::tensors::get_default_device()), _r.isNone(4)); auto dispatch_ones_out = [](at::Tensor out, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ones_symint_out(out, size); }; return wrap(dispatch_ones_out(_r.tensor(1), _r.symintlist(0)).set_requires_grad(_r.toBool(6))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ones_like static PyObject * THPVariable_ones_like(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ones_like(Tensor input, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceOptional(4)) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_ones_like = [](const at::Tensor & self, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::ones_like(self, options, memory_format); }; return wrap(dispatch_ones_like(self, options, _r.memoryformatOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // pairwise_distance static PyObject * THPVariable_pairwise_distance(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "pairwise_distance(Tensor x1, Tensor x2, double p=2, double eps=1e-06, bool keepdim=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor auto dispatch_pairwise_distance = [](const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pairwise_distance(x1, x2, p, eps, keepdim); }; return wrap(dispatch_pairwise_distance(_r.tensor(0), _r.tensor(1), _r.toDouble(2), _r.toDouble(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cdist static PyObject * THPVariable_cdist(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cdist(Tensor x1, Tensor x2, double p=2, int64_t? compute_mode=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cdist(Tensor x1, Tensor x2, float p=2, int? compute_mode=None) -> Tensor auto dispatch_cdist = [](const at::Tensor & x1, const at::Tensor & x2, double p, ::std::optional compute_mode) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cdist(x1, x2, p, compute_mode); }; return wrap(dispatch_cdist(_r.tensor(0), _r.tensor(1), _r.toDouble(2), _r.toInt64Optional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _euclidean_dist static PyObject * THPVariable__euclidean_dist(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_euclidean_dist(Tensor x1, Tensor x2)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_euclidean_dist(Tensor x1, Tensor x2) -> Tensor auto dispatch__euclidean_dist = [](const at::Tensor & x1, const at::Tensor & x2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_euclidean_dist(x1, x2); }; return wrap(dispatch__euclidean_dist(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // pdist static PyObject * THPVariable_pdist(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "pdist(Tensor input, double p=2)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::pdist(Tensor self, float p=2) -> Tensor auto dispatch_pdist = [](const at::Tensor & self, double p) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pdist(self, p); }; return wrap(dispatch_pdist(_r.tensor(0), _r.toDouble(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cosine_similarity static PyObject * THPVariable_cosine_similarity(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cosine_similarity(Tensor x1, Tensor x2, int64_t dim=1, double eps=1e-08)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor auto dispatch_cosine_similarity = [](const at::Tensor & x1, const at::Tensor & x2, int64_t dim, double eps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cosine_similarity(x1, x2, dim, eps); }; return wrap(dispatch_cosine_similarity(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toDouble(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // permute static PyObject * THPVariable_permute(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "permute(Tensor input, IntArrayRef dims)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::permute(Tensor(a) self, int[] dims) -> Tensor(a) auto dispatch_permute = [](const at::Tensor & self, at::IntArrayRef dims) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.permute(dims); }; return wrap(dispatch_permute(_r.tensor(0), _r.intlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // movedim static PyObject * THPVariable_movedim(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "movedim(Tensor input, int64_t source, int64_t destination)", "movedim(Tensor input, IntArrayRef source, IntArrayRef destination)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a) auto dispatch_movedim = [](const at::Tensor & self, int64_t source, int64_t destination) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.movedim(source, destination); }; return wrap(dispatch_movedim(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); } case 1: { // aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) auto dispatch_movedim = [](const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.movedim(source, destination); }; return wrap(dispatch_movedim(_r.tensor(0), _r.intlist(1), _r.intlist(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // moveaxis static PyObject * THPVariable_moveaxis(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "moveaxis(Tensor input, int64_t source, int64_t destination)", "moveaxis(Tensor input, IntArrayRef source, IntArrayRef destination)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a) auto dispatch_moveaxis = [](const at::Tensor & self, int64_t source, int64_t destination) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.moveaxis(source, destination); }; return wrap(dispatch_moveaxis(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); } case 1: { // aten::moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) auto dispatch_moveaxis = [](const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.moveaxis(source, destination); }; return wrap(dispatch_moveaxis(_r.tensor(0), _r.intlist(1), _r.intlist(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // adjoint static PyObject * THPVariable_adjoint(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "adjoint(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::adjoint(Tensor(a) self) -> Tensor(a) auto dispatch_adjoint = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.adjoint(); }; return wrap(dispatch_adjoint(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // pixel_shuffle static PyObject * THPVariable_pixel_shuffle(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "pixel_shuffle(Tensor input, int64_t upscale_factor)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor auto dispatch_pixel_shuffle = [](const at::Tensor & self, int64_t upscale_factor) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pixel_shuffle(self, upscale_factor); }; return wrap(dispatch_pixel_shuffle(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // pixel_unshuffle static PyObject * THPVariable_pixel_unshuffle(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "pixel_unshuffle(Tensor input, int64_t downscale_factor)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor auto dispatch_pixel_unshuffle = [](const at::Tensor & self, int64_t downscale_factor) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pixel_unshuffle(self, downscale_factor); }; return wrap(dispatch_pixel_unshuffle(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // channel_shuffle static PyObject * THPVariable_channel_shuffle(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "channel_shuffle(Tensor input, SymInt groups)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::channel_shuffle(Tensor self, SymInt groups) -> Tensor auto dispatch_channel_shuffle = [](const at::Tensor & self, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::channel_shuffle_symint(self, groups); }; return wrap(dispatch_channel_shuffle(_r.tensor(0), _r.toSymInt(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // native_channel_shuffle static PyObject * THPVariable_native_channel_shuffle(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "native_channel_shuffle(Tensor input, SymInt groups)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::native_channel_shuffle(Tensor self, SymInt groups) -> Tensor auto dispatch_native_channel_shuffle = [](const at::Tensor & self, c10::SymInt groups) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::native_channel_shuffle_symint(self, groups); }; return wrap(dispatch_native_channel_shuffle(_r.tensor(0), _r.toSymInt(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _pin_memory static PyObject * THPVariable__pin_memory(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_pin_memory(Tensor input, Device? device=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_pin_memory(Tensor self, Device? device=None) -> Tensor auto dispatch__pin_memory = [](const at::Tensor & self, ::std::optional device) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_pin_memory(self, device); }; return wrap(dispatch__pin_memory(_r.tensor(0), _r.deviceOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // pinverse static PyObject * THPVariable_pinverse(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "pinverse(Tensor input, double rcond=1e-15)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor auto dispatch_pinverse = [](const at::Tensor & self, double rcond) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.pinverse(rcond); }; return wrap(dispatch_pinverse(_r.tensor(0), _r.toDouble(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // poisson_nll_loss static PyObject * THPVariable_poisson_nll_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, double eps, int64_t reduction)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, float eps, int reduction) -> Tensor auto dispatch_poisson_nll_loss = [](const at::Tensor & input, const at::Tensor & target, bool log_input, bool full, double eps, int64_t reduction) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::poisson_nll_loss(input, target, log_input, full, eps, reduction); }; return wrap(dispatch_poisson_nll_loss(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3), _r.toDouble(4), _r.toInt64(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rad2deg static PyObject * THPVariable_rad2deg(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rad2deg(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::rad2deg(Tensor self) -> Tensor auto dispatch_rad2deg = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.rad2deg(); }; return wrap(dispatch_rad2deg(_r.tensor(0))); } else { // aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_rad2deg_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rad2deg_out(out, self); }; return wrap(dispatch_rad2deg_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rad2deg_ static PyObject * THPVariable_rad2deg_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rad2deg_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rad2deg_(Tensor(a!) self) -> Tensor(a!) auto dispatch_rad2deg_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.rad2deg_(); }; return wrap(dispatch_rad2deg_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // deg2rad static PyObject * THPVariable_deg2rad(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "deg2rad(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::deg2rad(Tensor self) -> Tensor auto dispatch_deg2rad = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.deg2rad(); }; return wrap(dispatch_deg2rad(_r.tensor(0))); } else { // aten::deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_deg2rad_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::deg2rad_out(out, self); }; return wrap(dispatch_deg2rad_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // deg2rad_ static PyObject * THPVariable_deg2rad_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "deg2rad_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::deg2rad_(Tensor(a!) self) -> Tensor(a!) auto dispatch_deg2rad_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.deg2rad_(); }; return wrap(dispatch_deg2rad_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // scalar_tensor static PyObject * THPVariable_scalar_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch_scalar_tensor = [](const at::Scalar & s, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::scalar_tensor(s, options); }; return wrap(dispatch_scalar_tensor(_r.scalar(0), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // rand static PyObject * THPVariable_rand(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rand(SymIntArrayRef size, *, Generator? generator, DimnameList? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "rand(SymIntArrayRef size, *, Generator? generator, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "rand(SymIntArrayRef size, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "rand(SymIntArrayRef size, *, DimnameList? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto __names = _r.toDimnameListOptional(2); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_rand = [](c10::SymIntArrayRef size, ::std::optional generator, ::std::optional names, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::rand_symint(size, generator, names, options); }; return wrap(dispatch_rand(_r.symintlist(0), _r.generator(1), names, options)); } case 1: { if (_r.isNone(2)) { // aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_rand = [](c10::SymIntArrayRef size, ::std::optional generator, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::rand_symint(size, generator, options); }; return wrap(dispatch_rand(_r.symintlist(0), _r.generator(1), options)); } else { // aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(2), _r.scalartypeOptional(3), _r.isNone(3), _r.layoutOptional(4), _r.deviceWithDefault(5, torch::tensors::get_default_device()), _r.isNone(5)); auto dispatch_rand_out = [](at::Tensor out, c10::SymIntArrayRef size, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rand_symint_out(out, size, generator); }; return wrap(dispatch_rand_out(_r.tensor(2), _r.symintlist(0), _r.generator(1)).set_requires_grad(_r.toBool(7))); } } case 2: { if (_r.isNone(1)) { // aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_rand = [](c10::SymIntArrayRef size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::rand_symint(size, options); }; return wrap(dispatch_rand(_r.symintlist(0), options)); } else { // aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(1), _r.scalartypeOptional(2), _r.isNone(2), _r.layoutOptional(3), _r.deviceWithDefault(4, torch::tensors::get_default_device()), _r.isNone(4)); auto dispatch_rand_out = [](at::Tensor out, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rand_symint_out(out, size); }; return wrap(dispatch_rand_out(_r.tensor(1), _r.symintlist(0)).set_requires_grad(_r.toBool(6))); } } case 3: { // aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto __names = _r.toDimnameListOptional(1); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_rand = [](c10::SymIntArrayRef size, ::std::optional names, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::rand_symint(size, names, options); }; return wrap(dispatch_rand(_r.symintlist(0), names, options)); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rand_like static PyObject * THPVariable_rand_like(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rand_like(Tensor input, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceOptional(4)) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_rand_like = [](const at::Tensor & self, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::rand_like(self, options, memory_format); }; return wrap(dispatch_rand_like(self, options, _r.memoryformatOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // randint static PyObject * THPVariable_randint(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "randint(SymInt high, SymIntArrayRef size, *, Generator? generator, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randint(SymInt high, SymIntArrayRef size, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randint(SymInt low, SymInt high, SymIntArrayRef size, *, Generator? generator, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randint(SymInt low, SymInt high, SymIntArrayRef size, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(4, at::kLong)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_randint = [](c10::SymInt high, c10::SymIntArrayRef size, ::std::optional generator, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randint_symint(high, size, generator, options); }; return wrap(dispatch_randint(_r.toSymInt(0), _r.symintlist(1), _r.generator(2), options)); } else { // aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(3), _r.scalartypeWithDefault(4, at::kLong), _r.isNone(4), _r.layoutOptional(5), _r.deviceWithDefault(6, torch::tensors::get_default_device()), _r.isNone(6)); auto dispatch_randint_out = [](at::Tensor out, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randint_symint_out(out, high, size, generator); }; return wrap(dispatch_randint_out(_r.tensor(3), _r.toSymInt(0), _r.symintlist(1), _r.generator(2)).set_requires_grad(_r.toBool(8))); } } case 1: { if (_r.isNone(2)) { // aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(3, at::kLong)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_randint = [](c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randint_symint(high, size, options); }; return wrap(dispatch_randint(_r.toSymInt(0), _r.symintlist(1), options)); } else { // aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(2), _r.scalartypeWithDefault(3, at::kLong), _r.isNone(3), _r.layoutOptional(4), _r.deviceWithDefault(5, torch::tensors::get_default_device()), _r.isNone(5)); auto dispatch_randint_out = [](at::Tensor out, c10::SymInt high, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randint_symint_out(out, high, size); }; return wrap(dispatch_randint_out(_r.tensor(2), _r.toSymInt(0), _r.symintlist(1)).set_requires_grad(_r.toBool(7))); } } case 2: { if (_r.isNone(4)) { // aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(5, at::kLong)) .device(_r.deviceWithDefault(7, torch::tensors::get_default_device())) .layout(_r.layoutOptional(6)) .requires_grad(_r.toBool(9)) .pinned_memory(_r.toBool(8)); torch::utils::maybe_initialize_device(options); auto dispatch_randint = [](c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional generator, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randint_symint(low, high, size, generator, options); }; return wrap(dispatch_randint(_r.toSymInt(0), _r.toSymInt(1), _r.symintlist(2), _r.generator(3), options)); } else { // aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(4), _r.scalartypeWithDefault(5, at::kLong), _r.isNone(5), _r.layoutOptional(6), _r.deviceWithDefault(7, torch::tensors::get_default_device()), _r.isNone(7)); auto dispatch_randint_out = [](at::Tensor out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randint_symint_out(out, low, high, size, generator); }; return wrap(dispatch_randint_out(_r.tensor(4), _r.toSymInt(0), _r.toSymInt(1), _r.symintlist(2), _r.generator(3)).set_requires_grad(_r.toBool(9))); } } case 3: { if (_r.isNone(3)) { // aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(4, at::kLong)) .device(_r.deviceWithDefault(6, torch::tensors::get_default_device())) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_randint = [](c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randint_symint(low, high, size, options); }; return wrap(dispatch_randint(_r.toSymInt(0), _r.toSymInt(1), _r.symintlist(2), options)); } else { // aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(3), _r.scalartypeWithDefault(4, at::kLong), _r.isNone(4), _r.layoutOptional(5), _r.deviceWithDefault(6, torch::tensors::get_default_device()), _r.isNone(6)); auto dispatch_randint_out = [](at::Tensor out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randint_symint_out(out, low, high, size); }; return wrap(dispatch_randint_out(_r.tensor(3), _r.toSymInt(0), _r.toSymInt(1), _r.symintlist(2)).set_requires_grad(_r.toBool(8))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // randint_like static PyObject * THPVariable_randint_like(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "randint_like(Tensor input, SymInt high, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randint_like(Tensor input, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceOptional(5)) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_randint_like = [](const at::Tensor & self, c10::SymInt high, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randint_like_symint(self, high, options, memory_format); }; return wrap(dispatch_randint_like(self, _r.toSymInt(1), options, _r.memoryformatOptional(2))); } case 1: { // aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(4)) .device(_r.deviceOptional(6)) .layout(_r.layoutOptional(5)) .requires_grad(_r.toBool(8)) .pinned_memory(_r.toBool(7)); torch::utils::maybe_initialize_device(options); auto dispatch_randint_like = [](const at::Tensor & self, c10::SymInt low, c10::SymInt high, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randint_like_symint(self, low, high, options, memory_format); }; return wrap(dispatch_randint_like(self, _r.toSymInt(1), _r.toSymInt(2), options, _r.memoryformatOptional(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // randn static PyObject * THPVariable_randn(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "randn(SymIntArrayRef size, *, Generator? generator, DimnameList? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randn(SymIntArrayRef size, *, Generator? generator, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randn(SymIntArrayRef size, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randn(SymIntArrayRef size, *, DimnameList? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto __names = _r.toDimnameListOptional(2); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_randn = [](c10::SymIntArrayRef size, ::std::optional generator, ::std::optional names, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randn_symint(size, generator, names, options); }; return wrap(dispatch_randn(_r.symintlist(0), _r.generator(1), names, options)); } case 1: { if (_r.isNone(2)) { // aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(3)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_randn = [](c10::SymIntArrayRef size, ::std::optional generator, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randn_symint(size, generator, options); }; return wrap(dispatch_randn(_r.symintlist(0), _r.generator(1), options)); } else { // aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(2), _r.scalartypeOptional(3), _r.isNone(3), _r.layoutOptional(4), _r.deviceWithDefault(5, torch::tensors::get_default_device()), _r.isNone(5)); auto dispatch_randn_out = [](at::Tensor out, c10::SymIntArrayRef size, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randn_symint_out(out, size, generator); }; return wrap(dispatch_randn_out(_r.tensor(2), _r.symintlist(0), _r.generator(1)).set_requires_grad(_r.toBool(7))); } } case 2: { if (_r.isNone(1)) { // aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_randn = [](c10::SymIntArrayRef size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randn_symint(size, options); }; return wrap(dispatch_randn(_r.symintlist(0), options)); } else { // aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(1), _r.scalartypeOptional(2), _r.isNone(2), _r.layoutOptional(3), _r.deviceWithDefault(4, torch::tensors::get_default_device()), _r.isNone(4)); auto dispatch_randn_out = [](at::Tensor out, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randn_symint_out(out, size); }; return wrap(dispatch_randn_out(_r.tensor(1), _r.symintlist(0)).set_requires_grad(_r.toBool(6))); } } case 3: { // aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto __names = _r.toDimnameListOptional(1); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_randn = [](c10::SymIntArrayRef size, ::std::optional names, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randn_symint(size, names, options); }; return wrap(dispatch_randn(_r.symintlist(0), names, options)); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // randn_like static PyObject * THPVariable_randn_like(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "randn_like(Tensor input, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceOptional(4)) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_randn_like = [](const at::Tensor & self, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randn_like(self, options, memory_format); }; return wrap(dispatch_randn_like(self, options, _r.memoryformatOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // randperm static PyObject * THPVariable_randperm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "randperm(SymInt n, *, Generator? generator, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "randperm(SymInt n, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(3, at::kLong)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_randperm = [](c10::SymInt n, ::std::optional generator, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randperm_symint(n, generator, options); }; return wrap(dispatch_randperm(_r.toSymInt(0), _r.generator(1), options)); } else { // aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(2), _r.scalartypeWithDefault(3, at::kLong), _r.isNone(3), _r.layoutOptional(4), _r.deviceWithDefault(5, torch::tensors::get_default_device()), _r.isNone(5)); auto dispatch_randperm_out = [](at::Tensor out, c10::SymInt n, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randperm_symint_out(out, n, generator); }; return wrap(dispatch_randperm_out(_r.tensor(2), _r.toSymInt(0), _r.generator(1)).set_requires_grad(_r.toBool(7))); } } case 1: { if (_r.isNone(1)) { // aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(2, at::kLong)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_randperm = [](c10::SymInt n, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::randperm_symint(n, options); }; return wrap(dispatch_randperm(_r.toSymInt(0), options)); } else { // aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(1), _r.scalartypeWithDefault(2, at::kLong), _r.isNone(2), _r.layoutOptional(3), _r.deviceWithDefault(4, torch::tensors::get_default_device()), _r.isNone(4)); auto dispatch_randperm_out = [](at::Tensor out, c10::SymInt n) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::randperm_symint_out(out, n); }; return wrap(dispatch_randperm_out(_r.tensor(1), _r.toSymInt(0)).set_requires_grad(_r.toBool(6))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ravel static PyObject * THPVariable_ravel(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ravel(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::ravel(Tensor(a) self) -> Tensor(a) auto dispatch_ravel = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ravel(); }; return wrap(dispatch_ravel(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // reciprocal static PyObject * THPVariable_reciprocal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "reciprocal(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::reciprocal(Tensor self) -> Tensor auto dispatch_reciprocal = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.reciprocal(); }; return wrap(dispatch_reciprocal(_r.tensor(0))); } else { // aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_reciprocal_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::reciprocal_out(out, self); }; return wrap(dispatch_reciprocal_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // reciprocal_ static PyObject * THPVariable_reciprocal_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "reciprocal_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::reciprocal_(Tensor(a!) self) -> Tensor(a!) auto dispatch_reciprocal_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.reciprocal_(); }; return wrap(dispatch_reciprocal_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // neg static PyObject * THPVariable_neg(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "neg(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::neg(Tensor self) -> Tensor auto dispatch_neg = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.neg(); }; return wrap(dispatch_neg(_r.tensor(0))); } else { // aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_neg_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::neg_out(out, self); }; return wrap(dispatch_neg_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // neg_ static PyObject * THPVariable_neg_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "neg_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::neg_(Tensor(a!) self) -> Tensor(a!) auto dispatch_neg_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.neg_(); }; return wrap(dispatch_neg_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // negative static PyObject * THPVariable_negative(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "negative(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::negative(Tensor self) -> Tensor auto dispatch_negative = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.negative(); }; return wrap(dispatch_negative(_r.tensor(0))); } else { // aten::negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_negative_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::negative_out(out, self); }; return wrap(dispatch_negative_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // negative_ static PyObject * THPVariable_negative_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "negative_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::negative_(Tensor(a!) self) -> Tensor(a!) auto dispatch_negative_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.negative_(); }; return wrap(dispatch_negative_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // repeat_interleave static PyObject * THPVariable_repeat_interleave(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "repeat_interleave(Tensor input, Tensor repeats, int64_t? dim=None, *, SymInt? output_size=None)", "repeat_interleave(Tensor repeats, *, SymInt? output_size=None)", "repeat_interleave(Tensor input, SymInt repeats, int64_t? dim=None, *, SymInt? output_size=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor auto dispatch_repeat_interleave = [](const at::Tensor & self, const at::Tensor & repeats, ::std::optional dim, ::std::optional output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.repeat_interleave_symint(repeats, dim, output_size); }; return wrap(dispatch_repeat_interleave(_r.tensor(0), _r.tensor(1), _r.toInt64Optional(2), _r.toSymIntOptional(3))); } case 1: { // aten::repeat_interleave.Tensor(Tensor repeats, *, SymInt? output_size=None) -> Tensor auto dispatch_repeat_interleave = [](const at::Tensor & repeats, ::std::optional output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::repeat_interleave_symint(repeats, output_size); }; return wrap(dispatch_repeat_interleave(_r.tensor(0), _r.toSymIntOptional(1))); } case 2: { // aten::repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor auto dispatch_repeat_interleave = [](const at::Tensor & self, c10::SymInt repeats, ::std::optional dim, ::std::optional output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.repeat_interleave_symint(repeats, dim, output_size); }; return wrap(dispatch_repeat_interleave(_r.tensor(0), _r.toSymInt(1), _r.toInt64Optional(2), _r.toSymIntOptional(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // reshape static PyObject * THPVariable_reshape(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "reshape(Tensor input, SymIntArrayRef shape)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a) auto dispatch_reshape = [](const at::Tensor & self, c10::SymIntArrayRef shape) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.reshape_symint(shape); }; return wrap(dispatch_reshape(_r.tensor(0), _r.symintlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _mkldnn_reshape static PyObject * THPVariable__mkldnn_reshape(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_mkldnn_reshape(Tensor input, IntArrayRef shape)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_mkldnn_reshape(Tensor self, int[] shape) -> Tensor auto dispatch__mkldnn_reshape = [](const at::Tensor & self, at::IntArrayRef shape) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_mkldnn_reshape(self, shape); }; return wrap(dispatch__mkldnn_reshape(_r.tensor(0), _r.intlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // round static PyObject * THPVariable_round(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "round(Tensor input, *, Tensor out=None)", "round(Tensor input, *, int64_t decimals, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::round(Tensor self) -> Tensor auto dispatch_round = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.round(); }; return wrap(dispatch_round(_r.tensor(0))); } else { // aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_round_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::round_out(out, self); }; return wrap(dispatch_round_out(_r.tensor(1), _r.tensor(0))); } } case 1: { if (_r.isNone(2)) { // aten::round.decimals(Tensor self, *, int decimals) -> Tensor auto dispatch_round = [](const at::Tensor & self, int64_t decimals) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.round(decimals); }; return wrap(dispatch_round(_r.tensor(0), _r.toInt64(1))); } else { // aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!) auto dispatch_round_out = [](at::Tensor out, const at::Tensor & self, int64_t decimals) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::round_out(out, self, decimals); }; return wrap(dispatch_round_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // round_ static PyObject * THPVariable_round_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "round_(Tensor input)", "round_(Tensor input, *, int64_t decimals)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::round_(Tensor(a!) self) -> Tensor(a!) auto dispatch_round_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.round_(); }; return wrap(dispatch_round_(_r.tensor(0))); } case 1: { // aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!) auto dispatch_round_ = [](at::Tensor self, int64_t decimals) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.round_(decimals); }; return wrap(dispatch_round_(_r.tensor(0), _r.toInt64(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rrelu static PyObject * THPVariable_rrelu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rrelu(Tensor input, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor auto dispatch_rrelu = [](const at::Tensor & self, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rrelu(self, lower, upper, training, generator); }; return wrap(dispatch_rrelu(_r.tensor(0), _r.scalar(1), _r.scalar(2), _r.toBool(3), _r.generator(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rrelu_ static PyObject * THPVariable_rrelu_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rrelu_(Tensor input, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rrelu_(Tensor(a!) self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!) auto dispatch_rrelu_ = [](at::Tensor self, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rrelu_(self, lower, upper, training, generator); }; return wrap(dispatch_rrelu_(_r.tensor(0), _r.scalar(1), _r.scalar(2), _r.toBool(3), _r.generator(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // relu static PyObject * THPVariable_relu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "relu(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::relu(Tensor self) -> Tensor auto dispatch_relu = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.relu(); }; return wrap(dispatch_relu(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // relu_ static PyObject * THPVariable_relu_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "relu_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::relu_(Tensor(a!) self) -> Tensor(a!) auto dispatch_relu_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.relu_(); }; return wrap(dispatch_relu_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // prelu static PyObject * THPVariable_prelu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "prelu(Tensor input, Tensor weight)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::prelu(Tensor self, Tensor weight) -> Tensor auto dispatch_prelu = [](const at::Tensor & self, const at::Tensor & weight) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.prelu(weight); }; return wrap(dispatch_prelu(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _prelu_kernel static PyObject * THPVariable__prelu_kernel(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_prelu_kernel(Tensor input, Tensor weight)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_prelu_kernel(Tensor self, Tensor weight) -> Tensor auto dispatch__prelu_kernel = [](const at::Tensor & self, const at::Tensor & weight) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_prelu_kernel(self, weight); }; return wrap(dispatch__prelu_kernel(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // hardshrink static PyObject * THPVariable_hardshrink(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hardshrink(Tensor input, Scalar lambd=0.5, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor auto dispatch_hardshrink = [](const at::Tensor & self, const at::Scalar & lambd) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.hardshrink(lambd); }; return wrap(dispatch_hardshrink(_r.tensor(0), _r.scalar(1))); } else { // aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_hardshrink_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & lambd) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::hardshrink_out(out, self, lambd); }; return wrap(dispatch_hardshrink_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rsqrt static PyObject * THPVariable_rsqrt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rsqrt(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::rsqrt(Tensor self) -> Tensor auto dispatch_rsqrt = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.rsqrt(); }; return wrap(dispatch_rsqrt(_r.tensor(0))); } else { // aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_rsqrt_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rsqrt_out(out, self); }; return wrap(dispatch_rsqrt_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rsqrt_ static PyObject * THPVariable_rsqrt_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rsqrt_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rsqrt_(Tensor(a!) self) -> Tensor(a!) auto dispatch_rsqrt_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.rsqrt_(); }; return wrap(dispatch_rsqrt_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // select static PyObject * THPVariable_select(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "select(Tensor input, Dimname dim, int64_t index)", "select(Tensor input, int64_t dim, SymInt index)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) auto dispatch_select = [](const at::Tensor & self, at::Dimname dim, int64_t index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.select(dim, index); }; return wrap(dispatch_select(_r.tensor(0), _r.dimname(1), _r.toInt64(2))); } case 1: { // aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a) auto dispatch_select = [](const at::Tensor & self, int64_t dim, c10::SymInt index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.select_symint(dim, index); }; return wrap(dispatch_select(_r.tensor(0), _r.toInt64(1), _r.toSymInt(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // selu static PyObject * THPVariable_selu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "selu(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::selu(Tensor self) -> Tensor auto dispatch_selu = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::selu(self); }; return wrap(dispatch_selu(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // selu_ static PyObject * THPVariable_selu_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "selu_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::selu_(Tensor(a!) self) -> Tensor(a!) auto dispatch_selu_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::selu_(self); }; return wrap(dispatch_selu_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // celu static PyObject * THPVariable_celu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "celu(Tensor input, Scalar alpha=1.0)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::celu(Tensor self, Scalar alpha=1.0) -> Tensor auto dispatch_celu = [](const at::Tensor & self, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::celu(self, alpha); }; return wrap(dispatch_celu(_r.tensor(0), _r.scalar(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // celu_ static PyObject * THPVariable_celu_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "celu_(Tensor input, Scalar alpha=1.0)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!) auto dispatch_celu_ = [](at::Tensor self, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::celu_(self, alpha); }; return wrap(dispatch_celu_(_r.tensor(0), _r.scalar(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sigmoid static PyObject * THPVariable_sigmoid(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sigmoid(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::sigmoid(Tensor self) -> Tensor auto dispatch_sigmoid = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sigmoid(); }; return wrap(dispatch_sigmoid(_r.tensor(0))); } else { // aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sigmoid_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sigmoid_out(out, self); }; return wrap(dispatch_sigmoid_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sigmoid_ static PyObject * THPVariable_sigmoid_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sigmoid_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::sigmoid_(Tensor(a!) self) -> Tensor(a!) auto dispatch_sigmoid_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sigmoid_(); }; return wrap(dispatch_sigmoid_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logit static PyObject * THPVariable_logit(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logit(Tensor input, double? eps=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::logit(Tensor self, float? eps=None) -> Tensor auto dispatch_logit = [](const at::Tensor & self, ::std::optional eps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logit(eps); }; return wrap(dispatch_logit(_r.tensor(0), _r.toDoubleOptional(1))); } else { // aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_logit_out = [](at::Tensor out, const at::Tensor & self, ::std::optional eps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::logit_out(out, self, eps); }; return wrap(dispatch_logit_out(_r.tensor(2), _r.tensor(0), _r.toDoubleOptional(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logit_ static PyObject * THPVariable_logit_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logit_(Tensor input, double? eps=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!) auto dispatch_logit_ = [](at::Tensor self, ::std::optional eps) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logit_(eps); }; return wrap(dispatch_logit_(_r.tensor(0), _r.toDoubleOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sin static PyObject * THPVariable_sin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sin(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::sin(Tensor self) -> Tensor auto dispatch_sin = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sin(); }; return wrap(dispatch_sin(_r.tensor(0))); } else { // aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sin_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sin_out(out, self); }; return wrap(dispatch_sin_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sin_ static PyObject * THPVariable_sin_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sin_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::sin_(Tensor(a!) self) -> Tensor(a!) auto dispatch_sin_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sin_(); }; return wrap(dispatch_sin_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sinc static PyObject * THPVariable_sinc(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sinc(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::sinc(Tensor self) -> Tensor auto dispatch_sinc = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sinc(); }; return wrap(dispatch_sinc(_r.tensor(0))); } else { // aten::sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sinc_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sinc_out(out, self); }; return wrap(dispatch_sinc_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sinc_ static PyObject * THPVariable_sinc_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sinc_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::sinc_(Tensor(a!) self) -> Tensor(a!) auto dispatch_sinc_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sinc_(); }; return wrap(dispatch_sinc_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sinh static PyObject * THPVariable_sinh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sinh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::sinh(Tensor self) -> Tensor auto dispatch_sinh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sinh(); }; return wrap(dispatch_sinh(_r.tensor(0))); } else { // aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sinh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sinh_out(out, self); }; return wrap(dispatch_sinh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sinh_ static PyObject * THPVariable_sinh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sinh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::sinh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_sinh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sinh_(); }; return wrap(dispatch_sinh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // detach static PyObject * THPVariable_detach(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "detach(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::detach(Tensor(a) self) -> Tensor(a) auto dispatch_detach = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.detach(); }; return wrap(dispatch_detach(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // detach_ static PyObject * THPVariable_detach_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "detach_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::detach_(Tensor(a!) self) -> Tensor(a!) auto dispatch_detach_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.detach_(); }; return wrap(dispatch_detach_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // slice_inverse static PyObject * THPVariable_slice_inverse(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "slice_inverse(Tensor input, Tensor src, int64_t dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) auto dispatch_slice_inverse = [](const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional start, ::std::optional end, c10::SymInt step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.slice_inverse_symint(src, dim, start, end, step); }; return wrap(dispatch_slice_inverse(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toSymIntOptional(3), _r.toSymIntOptional(4), _r.toSymInt(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // slice_scatter static PyObject * THPVariable_slice_scatter(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "slice_scatter(Tensor input, Tensor src, int64_t dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(6)) { // aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor auto dispatch_slice_scatter = [](const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional start, ::std::optional end, c10::SymInt step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.slice_scatter_symint(src, dim, start, end, step); }; return wrap(dispatch_slice_scatter(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toSymIntOptional(3), _r.toSymIntOptional(4), _r.toSymInt(5))); } else { // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_slice_scatter_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional start, ::std::optional end, c10::SymInt step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::slice_scatter_symint_out(out, self, src, dim, start, end, step); }; return wrap(dispatch_slice_scatter_out(_r.tensor(6), _r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toSymIntOptional(3), _r.toSymIntOptional(4), _r.toSymInt(5))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // select_scatter static PyObject * THPVariable_select_scatter(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "select_scatter(Tensor input, Tensor src, int64_t dim, SymInt index)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor auto dispatch_select_scatter = [](const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.select_scatter_symint(src, dim, index); }; return wrap(dispatch_select_scatter(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toSymInt(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // diagonal_scatter static PyObject * THPVariable_diagonal_scatter(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "diagonal_scatter(Tensor input, Tensor src, int64_t offset=0, int64_t dim1=0, int64_t dim2=1)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::diagonal_scatter(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1) -> Tensor auto dispatch_diagonal_scatter = [](const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.diagonal_scatter(src, offset, dim1, dim2); }; return wrap(dispatch_diagonal_scatter(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.toInt64(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // as_strided_scatter static PyObject * THPVariable_as_strided_scatter(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "as_strided_scatter(Tensor input, Tensor src, SymIntArrayRef size, SymIntArrayRef stride, SymInt? storage_offset=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor auto dispatch_as_strided_scatter = [](const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional storage_offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.as_strided_scatter_symint(src, size, stride, storage_offset); }; return wrap(dispatch_as_strided_scatter(_r.tensor(0), _r.tensor(1), _r.symintlist(2), _r.symintlist(3), _r.toSymIntOptional(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // smm static PyObject * THPVariable_smm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "smm(Tensor input, Tensor mat2)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::smm(Tensor self, Tensor mat2) -> Tensor auto dispatch_smm = [](const at::Tensor & self, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.smm(mat2); }; return wrap(dispatch_smm(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // softmax static PyObject * THPVariable_softmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "softmax(Tensor input, int64_t dim, ScalarType? dtype=None, *, Tensor out=None)", "softmax(Tensor input, Dimname dim, *, ScalarType? dtype=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor auto dispatch_softmax = [](const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.softmax(dim, dtype); }; return wrap(dispatch_softmax(_r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } else { // aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_softmax_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::softmax_out(out, self, dim, dtype); }; return wrap(dispatch_softmax_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); } } case 1: { // aten::softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor auto dispatch_softmax = [](const at::Tensor & self, at::Dimname dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.softmax(dim, dtype); }; return wrap(dispatch_softmax(_r.tensor(0), _r.dimname(1), _r.scalartypeOptional(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _softmax static PyObject * THPVariable__softmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_softmax(Tensor input, int64_t dim, bool half_to_float, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor auto dispatch__softmax = [](const at::Tensor & self, int64_t dim, bool half_to_float) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_softmax(self, dim, half_to_float); }; return wrap(dispatch__softmax(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__softmax_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, bool half_to_float) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_softmax_out(out, self, dim, half_to_float); }; return wrap(dispatch__softmax_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _softmax_backward_data static PyObject * THPVariable__softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_softmax_backward_data(Tensor grad_output, Tensor output, int64_t dim, ScalarType input_dtype, *, Tensor grad_input=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor auto dispatch__softmax_backward_data = [](const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_softmax_backward_data(grad_output, output, dim, input_dtype); }; return wrap(dispatch__softmax_backward_data(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.scalartype(3))); } else { // aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!) auto dispatch__softmax_backward_data_out = [](at::Tensor grad_input, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_softmax_backward_data_out(grad_input, grad_output, output, dim, input_dtype); }; return wrap(dispatch__softmax_backward_data_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.scalartype(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unsafe_split static PyObject * THPVariable_unsafe_split(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unsafe_split(Tensor input, SymInt split_size, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] auto dispatch_unsafe_split = [](const at::Tensor & self, c10::SymInt split_size, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.unsafe_split_symint(split_size, dim); }; return wrap(dispatch_unsafe_split(_r.tensor(0), _r.toSymInt(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // split static PyObject * THPVariable_split(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "split(Tensor input, SymInt split_size, int64_t dim=0)", "split(Tensor input, SymIntArrayRef split_size, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[] auto dispatch_split = [](const at::Tensor & self, c10::SymInt split_size, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.split_symint(split_size, dim); }; return wrap(dispatch_split(_r.tensor(0), _r.toSymInt(1), _r.toInt64(2))); } case 1: { // aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[] auto dispatch_split = [](const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.split_symint(split_size, dim); }; return wrap(dispatch_split(_r.tensor(0), _r.symintlist(1), _r.toInt64(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unsafe_split_with_sizes static PyObject * THPVariable_unsafe_split_with_sizes(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unsafe_split_with_sizes(Tensor input, SymIntArrayRef split_sizes, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] auto dispatch_unsafe_split_with_sizes = [](const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.unsafe_split_with_sizes_symint(split_sizes, dim); }; return wrap(dispatch_unsafe_split_with_sizes(_r.tensor(0), _r.symintlist(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // split_with_sizes static PyObject * THPVariable_split_with_sizes(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "split_with_sizes(Tensor input, SymIntArrayRef split_sizes, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[] auto dispatch_split_with_sizes = [](const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.split_with_sizes_symint(split_sizes, dim); }; return wrap(dispatch_split_with_sizes(_r.tensor(0), _r.symintlist(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // hsplit static PyObject * THPVariable_hsplit(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hsplit(Tensor input, int64_t sections)", "hsplit(Tensor input, IntArrayRef indices)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] auto dispatch_hsplit = [](const at::Tensor & self, int64_t sections) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.hsplit(sections); }; return wrap(dispatch_hsplit(_r.tensor(0), _r.toInt64(1))); } case 1: { // aten::hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] auto dispatch_hsplit = [](const at::Tensor & self, at::IntArrayRef indices) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.hsplit(indices); }; return wrap(dispatch_hsplit(_r.tensor(0), _r.intlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // vsplit static PyObject * THPVariable_vsplit(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "vsplit(Tensor input, int64_t sections)", "vsplit(Tensor input, IntArrayRef indices)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] auto dispatch_vsplit = [](const at::Tensor & self, int64_t sections) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.vsplit(sections); }; return wrap(dispatch_vsplit(_r.tensor(0), _r.toInt64(1))); } case 1: { // aten::vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] auto dispatch_vsplit = [](const at::Tensor & self, at::IntArrayRef indices) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.vsplit(indices); }; return wrap(dispatch_vsplit(_r.tensor(0), _r.intlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // dsplit static PyObject * THPVariable_dsplit(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "dsplit(Tensor input, int64_t sections)", "dsplit(Tensor input, IntArrayRef indices)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] auto dispatch_dsplit = [](const at::Tensor & self, int64_t sections) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.dsplit(sections); }; return wrap(dispatch_dsplit(_r.tensor(0), _r.toInt64(1))); } case 1: { // aten::dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] auto dispatch_dsplit = [](const at::Tensor & self, at::IntArrayRef indices) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.dsplit(indices); }; return wrap(dispatch_dsplit(_r.tensor(0), _r.intlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // squeeze static PyObject * THPVariable_squeeze(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "squeeze(Tensor input)", "squeeze(Tensor input, int64_t dim)", "squeeze(Tensor input, IntArrayRef dim)", "squeeze(Tensor input, Dimname dim)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::squeeze(Tensor(a) self) -> Tensor(a) auto dispatch_squeeze = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.squeeze(); }; return wrap(dispatch_squeeze(_r.tensor(0))); } case 1: { // aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a) auto dispatch_squeeze = [](const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.squeeze(dim); }; return wrap(dispatch_squeeze(_r.tensor(0), _r.toInt64(1))); } case 2: { // aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a) auto dispatch_squeeze = [](const at::Tensor & self, at::IntArrayRef dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.squeeze(dim); }; return wrap(dispatch_squeeze(_r.tensor(0), _r.intlist(1))); } case 3: { // aten::squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a) auto dispatch_squeeze = [](const at::Tensor & self, at::Dimname dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.squeeze(dim); }; return wrap(dispatch_squeeze(_r.tensor(0), _r.dimname(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // sspaddmm static PyObject * THPVariable_sspaddmm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sspaddmm(Scalar beta, Tensor input, Scalar alpha, Tensor mat1, Tensor mat2)|deprecated", "sspaddmm(Scalar beta, Tensor input, Tensor mat1, Tensor mat2)|deprecated", "sspaddmm(Tensor input, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // [deprecated] aten::sspaddmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2) -> Tensor auto dispatch_sspaddmm = [](const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sspaddmm(mat1, mat2, beta, alpha); }; return wrap(dispatch_sspaddmm(_r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } case 1: { // [deprecated] aten::sspaddmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2) -> Tensor auto dispatch_sspaddmm = [](const at::Scalar & beta, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sspaddmm(mat1, mat2, beta, 1); }; return wrap(dispatch_sspaddmm(_r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } case 2: { if (_r.isNone(5)) { // aten::sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor auto dispatch_sspaddmm = [](const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sspaddmm(mat1, mat2, beta, alpha); }; return wrap(dispatch_sspaddmm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } else { // aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_sspaddmm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sspaddmm_out(out, self, mat1, mat2, beta, alpha); }; return wrap(dispatch_sspaddmm_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _chunk_cat static PyObject * THPVariable__chunk_cat(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_chunk_cat(TensorList tensors, int64_t dim, int64_t num_chunks, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::_chunk_cat(Tensor[] tensors, int dim, int num_chunks) -> Tensor auto dispatch__chunk_cat = [](at::TensorList tensors, int64_t dim, int64_t num_chunks) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_chunk_cat(tensors, dim, num_chunks); }; return wrap(dispatch__chunk_cat(_r.tensorlist(0), _r.toInt64(1), _r.toInt64(2))); } else { // aten::_chunk_cat.out(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__chunk_cat_out = [](at::Tensor out, at::TensorList tensors, int64_t dim, int64_t num_chunks) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_chunk_cat_out(out, tensors, dim, num_chunks); }; return wrap(dispatch__chunk_cat_out(_r.tensor(3), _r.tensorlist(0), _r.toInt64(1), _r.toInt64(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // stack static PyObject * THPVariable_stack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "stack(TensorList tensors, int64_t dim=0, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::stack(Tensor[] tensors, int dim=0) -> Tensor auto dispatch_stack = [](at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::stack(tensors, dim); }; return wrap(dispatch_stack(_r.tensorlist(0), _r.toInt64(1))); } else { // aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_stack_out = [](at::Tensor out, at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::stack_out(out, tensors, dim); }; return wrap(dispatch_stack_out(_r.tensor(2), _r.tensorlist(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _stack static PyObject * THPVariable__stack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_stack(TensorList tensors, int64_t dim=0, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::_stack(Tensor[] tensors, int dim=0) -> Tensor auto dispatch__stack = [](at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_stack(tensors, dim); }; return wrap(dispatch__stack(_r.tensorlist(0), _r.toInt64(1))); } else { // aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__stack_out = [](at::Tensor out, at::TensorList tensors, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_stack_out(out, tensors, dim); }; return wrap(dispatch__stack_out(_r.tensor(2), _r.tensorlist(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // hstack static PyObject * THPVariable_hstack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hstack(TensorList tensors, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::hstack(Tensor[] tensors) -> Tensor auto dispatch_hstack = [](at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::hstack(tensors); }; return wrap(dispatch_hstack(_r.tensorlist(0))); } else { // aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_hstack_out = [](at::Tensor out, at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::hstack_out(out, tensors); }; return wrap(dispatch_hstack_out(_r.tensor(1), _r.tensorlist(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // vstack static PyObject * THPVariable_vstack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "vstack(TensorList tensors, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::vstack(Tensor[] tensors) -> Tensor auto dispatch_vstack = [](at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::vstack(tensors); }; return wrap(dispatch_vstack(_r.tensorlist(0))); } else { // aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_vstack_out = [](at::Tensor out, at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::vstack_out(out, tensors); }; return wrap(dispatch_vstack_out(_r.tensor(1), _r.tensorlist(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // dstack static PyObject * THPVariable_dstack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "dstack(TensorList tensors, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::dstack(Tensor[] tensors) -> Tensor auto dispatch_dstack = [](at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::dstack(tensors); }; return wrap(dispatch_dstack(_r.tensorlist(0))); } else { // aten::dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_dstack_out = [](at::Tensor out, at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::dstack_out(out, tensors); }; return wrap(dispatch_dstack_out(_r.tensor(1), _r.tensorlist(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // stft static PyObject * THPVariable_stft(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "stft(Tensor input, int64_t n_fft, int64_t? hop_length=None, int64_t? win_length=None, Tensor? window=None, bool center=True, c10::string_view pad_mode=\"reflect\", bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None)", "stft(Tensor input, int64_t n_fft, int64_t? hop_length=None, int64_t? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None)", }, /*traceable=*/true); ParsedArgs<11> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None) -> Tensor auto dispatch_stft = [](const at::Tensor & self, int64_t n_fft, ::std::optional hop_length, ::std::optional win_length, const ::std::optional & window, bool center, c10::string_view pad_mode, bool normalized, ::std::optional onesided, ::std::optional return_complex, ::std::optional align_to_window) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.stft(n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex, align_to_window); }; return wrap(dispatch_stft(_r.tensor(0), _r.toInt64(1), _r.toInt64Optional(2), _r.toInt64Optional(3), _r.optionalTensor(4), _r.toBool(5), _r.stringView(6), _r.toBool(7), _r.toBoolOptional(8), _r.toBoolOptional(9), _r.toBoolOptional(10))); } case 1: { // aten::stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None) -> Tensor auto dispatch_stft = [](const at::Tensor & self, int64_t n_fft, ::std::optional hop_length, ::std::optional win_length, const ::std::optional & window, bool normalized, ::std::optional onesided, ::std::optional return_complex, ::std::optional align_to_window) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.stft(n_fft, hop_length, win_length, window, normalized, onesided, return_complex, align_to_window); }; return wrap(dispatch_stft(_r.tensor(0), _r.toInt64(1), _r.toInt64Optional(2), _r.toInt64Optional(3), _r.optionalTensor(4), _r.toBool(5), _r.toBoolOptional(6), _r.toBoolOptional(7), _r.toBoolOptional(8))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // istft static PyObject * THPVariable_istft(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "istft(Tensor input, int64_t n_fft, int64_t? hop_length=None, int64_t? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int64_t? length=None, bool return_complex=False)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::istft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int? length=None, bool return_complex=False) -> Tensor auto dispatch_istft = [](const at::Tensor & self, int64_t n_fft, ::std::optional hop_length, ::std::optional win_length, const ::std::optional & window, bool center, bool normalized, ::std::optional onesided, ::std::optional length, bool return_complex) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.istft(n_fft, hop_length, win_length, window, center, normalized, onesided, length, return_complex); }; return wrap(dispatch_istft(_r.tensor(0), _r.toInt64(1), _r.toInt64Optional(2), _r.toInt64Optional(3), _r.optionalTensor(4), _r.toBool(5), _r.toBool(6), _r.toBoolOptional(7), _r.toInt64Optional(8), _r.toBool(9))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // sum static PyObject * THPVariable_sum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sum(Tensor input, *, ScalarType? dtype=None)", "sum(Tensor input, IntArrayRef[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", "sum(Tensor input, DimnameList[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor auto dispatch_sum = [](const at::Tensor & self, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sum(dtype); }; return wrap(dispatch_sum(_r.tensor(0), _r.scalartypeOptional(1))); } case 1: { if (_r.isNone(4)) { // aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_sum = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sum(dim, keepdim, dtype); }; return wrap(dispatch_sum(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_sum_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sum_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_sum_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } } case 2: { if (_r.isNone(4)) { // aten::sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_sum = [](const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sum(dim, keepdim, dtype); }; return wrap(dispatch_sum(_r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_sum_out = [](at::Tensor out, const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sum_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_sum_out(_r.tensor(4), _r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.scalartypeOptional(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // nansum static PyObject * THPVariable_nansum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nansum(Tensor input, IntArrayRef[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::nansum(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_nansum = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nansum(dim, keepdim, dtype); }; return wrap(dispatch_nansum(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_nansum_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nansum_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_nansum_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.scalartypeOptional(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sqrt static PyObject * THPVariable_sqrt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sqrt(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::sqrt(Tensor self) -> Tensor auto dispatch_sqrt = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sqrt(); }; return wrap(dispatch_sqrt(_r.tensor(0))); } else { // aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sqrt_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sqrt_out(out, self); }; return wrap(dispatch_sqrt_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sqrt_ static PyObject * THPVariable_sqrt_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sqrt_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::sqrt_(Tensor(a!) self) -> Tensor(a!) auto dispatch_sqrt_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sqrt_(); }; return wrap(dispatch_sqrt_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // square static PyObject * THPVariable_square(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "square(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::square(Tensor self) -> Tensor auto dispatch_square = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.square(); }; return wrap(dispatch_square(_r.tensor(0))); } else { // aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_square_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::square_out(out, self); }; return wrap(dispatch_square_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // square_ static PyObject * THPVariable_square_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "square_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::square_(Tensor(a!) self) -> Tensor(a!) auto dispatch_square_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.square_(); }; return wrap(dispatch_square_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // std static PyObject * THPVariable_std(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "std(Tensor input, IntArrayRef[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor out=None)", "std(Tensor input, IntArrayRef[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor out=None)", "std(Tensor input, bool unbiased=True)", "std(Tensor input, DimnameList[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor out=None)", "std(Tensor input, DimnameList[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor auto dispatch_std = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.std(dim, unbiased, keepdim); }; return wrap(dispatch_std(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.toBool(3))); } else { // aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_std_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::std_out(out, self, dim, unbiased, keepdim); }; return wrap(dispatch_std_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.toBool(3))); } } case 1: { if (_r.isNone(4)) { // aten::std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor auto dispatch_std = [](const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.std(dim, correction, keepdim); }; return wrap(dispatch_std(_r.tensor(0), _r.intlistOptional(1), _r.scalarOptional(2), _r.toBool(3))); } else { // aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_std_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::std_out(out, self, dim, correction, keepdim); }; return wrap(dispatch_std_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.scalarOptional(2), _r.toBool(3))); } } case 2: { // aten::std(Tensor self, bool unbiased=True) -> Tensor auto dispatch_std = [](const at::Tensor & self, bool unbiased) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.std(unbiased); }; return wrap(dispatch_std(_r.tensor(0), _r.toBool(1))); } case 3: { if (_r.isNone(4)) { // aten::std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor auto dispatch_std = [](const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.std(dim, unbiased, keepdim); }; return wrap(dispatch_std(_r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.toBool(3))); } else { // aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_std_out = [](at::Tensor out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::std_out(out, self, dim, unbiased, keepdim); }; return wrap(dispatch_std_out(_r.tensor(4), _r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.toBool(3))); } } case 4: { if (_r.isNone(4)) { // aten::std.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor auto dispatch_std = [](const at::Tensor & self, at::DimnameList dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.std(dim, correction, keepdim); }; return wrap(dispatch_std(_r.tensor(0), _r.dimnamelist(1), _r.scalarOptional(2), _r.toBool(3))); } else { // aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_std_out = [](at::Tensor out, const at::Tensor & self, at::DimnameList dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::std_out(out, self, dim, correction, keepdim); }; return wrap(dispatch_std_out(_r.tensor(4), _r.tensor(0), _r.dimnamelist(1), _r.scalarOptional(2), _r.toBool(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // std_mean static PyObject * THPVariable_std_mean(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "std_mean(Tensor input, IntArrayRef[1]? dim, bool unbiased=True, bool keepdim=False)", "std_mean(Tensor input, IntArrayRef[1]? dim=None, *, Scalar? correction=None, bool keepdim=False)", "std_mean(Tensor input, bool unbiased=True)", "std_mean(Tensor input, DimnameList[1] dim, bool unbiased=True, bool keepdim=False)", "std_mean(Tensor input, DimnameList[1] dim, *, Scalar? correction=None, bool keepdim=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_std_mean = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::std_mean(self, dim, unbiased, keepdim); }; return wrap(dispatch_std_mean(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.toBool(3))); } case 1: { // aten::std_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_std_mean = [](const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional & correction, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::std_mean(self, dim, correction, keepdim); }; return wrap(dispatch_std_mean(_r.tensor(0), _r.intlistOptional(1), _r.scalarOptional(2), _r.toBool(3))); } case 2: { // aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) auto dispatch_std_mean = [](const at::Tensor & self, bool unbiased) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::std_mean(self, unbiased); }; return wrap(dispatch_std_mean(_r.tensor(0), _r.toBool(1))); } case 3: { // aten::std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_std_mean = [](const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::std_mean(self, dim, unbiased, keepdim); }; return wrap(dispatch_std_mean(_r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.toBool(3))); } case 4: { // aten::std_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_std_mean = [](const at::Tensor & self, at::DimnameList dim, const ::std::optional & correction, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::std_mean(self, dim, correction, keepdim); }; return wrap(dispatch_std_mean(_r.tensor(0), _r.dimnamelist(1), _r.scalarOptional(2), _r.toBool(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // prod static PyObject * THPVariable_prod(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "prod(Tensor input, *, ScalarType? dtype=None)", "prod(Tensor input, int64_t dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", "prod(Tensor input, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensor auto dispatch_prod = [](const at::Tensor & self, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.prod(dtype); }; return wrap(dispatch_prod(_r.tensor(0), _r.scalartypeOptional(1))); } case 1: { if (_r.isNone(4)) { // aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_prod = [](const at::Tensor & self, int64_t dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.prod(dim, keepdim, dtype); }; return wrap(dispatch_prod(_r.tensor(0), _r.toInt64(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_prod_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::prod_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_prod_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.toBool(2), _r.scalartypeOptional(3))); } } case 2: { if (_r.isNone(4)) { // aten::prod.dim_Dimname(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch_prod = [](const at::Tensor & self, at::Dimname dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.prod(dim, keepdim, dtype); }; return wrap(dispatch_prod(_r.tensor(0), _r.dimname(1), _r.toBool(2), _r.scalartypeOptional(3))); } else { // aten::prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_prod_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::prod_out(out, self, dim, keepdim, dtype); }; return wrap(dispatch_prod_out(_r.tensor(4), _r.tensor(0), _r.dimname(1), _r.toBool(2), _r.scalartypeOptional(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // t static PyObject * THPVariable_t(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "t(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::t(Tensor(a) self) -> Tensor(a) auto dispatch_t = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.t(); }; return wrap(dispatch_t(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tan static PyObject * THPVariable_tan(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tan(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::tan(Tensor self) -> Tensor auto dispatch_tan = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.tan(); }; return wrap(dispatch_tan(_r.tensor(0))); } else { // aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_tan_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::tan_out(out, self); }; return wrap(dispatch_tan_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tan_ static PyObject * THPVariable_tan_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tan_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::tan_(Tensor(a!) self) -> Tensor(a!) auto dispatch_tan_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.tan_(); }; return wrap(dispatch_tan_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tanh static PyObject * THPVariable_tanh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tanh(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::tanh(Tensor self) -> Tensor auto dispatch_tanh = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.tanh(); }; return wrap(dispatch_tanh(_r.tensor(0))); } else { // aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_tanh_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::tanh_out(out, self); }; return wrap(dispatch_tanh_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tanh_ static PyObject * THPVariable_tanh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tanh_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::tanh_(Tensor(a!) self) -> Tensor(a!) auto dispatch_tanh_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.tanh_(); }; return wrap(dispatch_tanh_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tensordot static PyObject * THPVariable_tensordot(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tensordot(Tensor input, Tensor other, IntArrayRef dims_self, IntArrayRef dims_other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor auto dispatch_tensordot = [](const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::tensordot(self, other, dims_self, dims_other); }; return wrap(dispatch_tensordot(_r.tensor(0), _r.tensor(1), _r.intlist(2), _r.intlist(3))); } else { // aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_tensordot_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::tensordot_out(out, self, other, dims_self, dims_other); }; return wrap(dispatch_tensordot_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.intlist(2), _r.intlist(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // threshold static PyObject * THPVariable_threshold(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "threshold(Tensor input, Scalar threshold, Scalar value, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor auto dispatch_threshold = [](const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::threshold(self, threshold, value); }; return wrap(dispatch_threshold(_r.tensor(0), _r.scalar(1), _r.scalar(2))); } else { // aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_threshold_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::threshold_out(out, self, threshold, value); }; return wrap(dispatch_threshold_out(_r.tensor(3), _r.tensor(0), _r.scalar(1), _r.scalar(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // threshold_ static PyObject * THPVariable_threshold_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "threshold_(Tensor input, Scalar threshold, Scalar value)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) auto dispatch_threshold_ = [](at::Tensor self, const at::Scalar & threshold, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::threshold_(self, threshold, value); }; return wrap(dispatch_threshold_(_r.tensor(0), _r.scalar(1), _r.scalar(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tile static PyObject * THPVariable_tile(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tile(Tensor input, SymIntArrayRef dims)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::tile(Tensor self, SymInt[] dims) -> Tensor auto dispatch_tile = [](const at::Tensor & self, c10::SymIntArrayRef dims) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.tile_symint(dims); }; return wrap(dispatch_tile(_r.tensor(0), _r.symintlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // transpose static PyObject * THPVariable_transpose(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "transpose(Tensor input, int64_t dim0, int64_t dim1)", "transpose(Tensor input, Dimname dim0, Dimname dim1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) auto dispatch_transpose = [](const at::Tensor & self, int64_t dim0, int64_t dim1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.transpose(dim0, dim1); }; return wrap(dispatch_transpose(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); } case 1: { // aten::transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a) auto dispatch_transpose = [](const at::Tensor & self, at::Dimname dim0, at::Dimname dim1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.transpose(dim0, dim1); }; return wrap(dispatch_transpose(_r.tensor(0), _r.dimname(1), _r.dimname(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _mkldnn_transpose static PyObject * THPVariable__mkldnn_transpose(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_mkldnn_transpose(Tensor input, int64_t dim0, int64_t dim1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor auto dispatch__mkldnn_transpose = [](const at::Tensor & self, int64_t dim0, int64_t dim1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_mkldnn_transpose(self, dim0, dim1); }; return wrap(dispatch__mkldnn_transpose(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _mkldnn_transpose_ static PyObject * THPVariable__mkldnn_transpose_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_mkldnn_transpose_(Tensor input, int64_t dim0, int64_t dim1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) auto dispatch__mkldnn_transpose_ = [](at::Tensor self, int64_t dim0, int64_t dim1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_mkldnn_transpose_(self, dim0, dim1); }; return wrap(dispatch__mkldnn_transpose_(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // flip static PyObject * THPVariable_flip(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "flip(Tensor input, IntArrayRef dims)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::flip(Tensor self, int[] dims) -> Tensor auto dispatch_flip = [](const at::Tensor & self, at::IntArrayRef dims) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.flip(dims); }; return wrap(dispatch_flip(_r.tensor(0), _r.intlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fliplr static PyObject * THPVariable_fliplr(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fliplr(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fliplr(Tensor self) -> Tensor auto dispatch_fliplr = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fliplr(); }; return wrap(dispatch_fliplr(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // flipud static PyObject * THPVariable_flipud(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "flipud(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::flipud(Tensor self) -> Tensor auto dispatch_flipud = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.flipud(); }; return wrap(dispatch_flipud(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // roll static PyObject * THPVariable_roll(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "roll(Tensor input, SymIntArrayRef[1] shifts, IntArrayRef[1] dims=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::roll(Tensor self, SymInt[1] shifts, int[1] dims=[]) -> Tensor auto dispatch_roll = [](const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.roll_symint(shifts, dims); }; return wrap(dispatch_roll(_r.tensor(0), _r.symintlist(1), _r.intlist(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rot90 static PyObject * THPVariable_rot90(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rot90(Tensor input, int64_t k=1, IntArrayRef dims={0,1})", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor auto dispatch_rot90 = [](const at::Tensor & self, int64_t k, at::IntArrayRef dims) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.rot90(k, dims); }; return wrap(dispatch_rot90(_r.tensor(0), _r.toInt64(1), _r.intlist(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // trapezoid static PyObject * THPVariable_trapezoid(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "trapezoid(Tensor y, Tensor x, *, int64_t dim=-1)", "trapezoid(Tensor y, *, Scalar dx=1, int64_t dim=-1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor auto dispatch_trapezoid = [](const at::Tensor & y, const at::Tensor & x, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::trapezoid(y, x, dim); }; return wrap(dispatch_trapezoid(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); } case 1: { // aten::trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor auto dispatch_trapezoid = [](const at::Tensor & y, const at::Scalar & dx, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::trapezoid(y, dx, dim); }; return wrap(dispatch_trapezoid(_r.tensor(0), _r.scalar(1), _r.toInt64(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // trapz static PyObject * THPVariable_trapz(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "trapz(Tensor y, *, double dx=1, int64_t dim=-1)", "trapz(Tensor y, Tensor x, *, int64_t dim=-1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor auto dispatch_trapz = [](const at::Tensor & y, double dx, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::trapz(y, dx, dim); }; return wrap(dispatch_trapz(_r.tensor(0), _r.toDouble(1), _r.toInt64(2))); } case 1: { // aten::trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor auto dispatch_trapz = [](const at::Tensor & y, const at::Tensor & x, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::trapz(y, x, dim); }; return wrap(dispatch_trapz(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _transform_bias_rescale_qkv static PyObject * THPVariable__transform_bias_rescale_qkv(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_transform_bias_rescale_qkv(Tensor qkv, Tensor qkv_bias, int64_t num_heads)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_transform_bias_rescale_qkv(Tensor qkv, Tensor qkv_bias, int num_heads) -> (Tensor, Tensor, Tensor) auto dispatch__transform_bias_rescale_qkv = [](const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_transform_bias_rescale_qkv(qkv, qkv_bias, num_heads); }; return wrap(dispatch__transform_bias_rescale_qkv(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_tensor_from_mask static PyObject * THPVariable__nested_tensor_from_mask(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor auto dispatch__nested_tensor_from_mask = [](const at::Tensor & t, const at::Tensor & mask, bool mask_check) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_tensor_from_mask(t, mask, mask_check); }; return wrap(dispatch__nested_tensor_from_mask(_r.tensor(0), _r.tensor(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_tensor_from_mask_left_aligned static PyObject * THPVariable__nested_tensor_from_mask_left_aligned(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_tensor_from_mask_left_aligned(Tensor t, Tensor mask)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_tensor_from_mask_left_aligned(Tensor t, Tensor mask) -> bool auto dispatch__nested_tensor_from_mask_left_aligned = [](const at::Tensor & t, const at::Tensor & mask) -> bool { pybind11::gil_scoped_release no_gil; return at::_nested_tensor_from_mask_left_aligned(t, mask); }; return wrap(dispatch__nested_tensor_from_mask_left_aligned(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_from_padded static PyObject * THPVariable__nested_from_padded(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor auto dispatch__nested_from_padded = [](const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_from_padded(padded, cpu_nested_shape_example, fuse_transform_0213); }; return wrap(dispatch__nested_from_padded(_r.tensor(0), _r.tensor(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_from_padded_and_nested_example static PyObject * THPVariable__nested_from_padded_and_nested_example(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor auto dispatch__nested_from_padded_and_nested_example = [](const at::Tensor & padded, const at::Tensor & nt_example) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_from_padded_and_nested_example(padded, nt_example); }; return wrap(dispatch__nested_from_padded_and_nested_example(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_view_from_buffer static PyObject * THPVariable__nested_view_from_buffer(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_view_from_buffer(Tensor input, Tensor nested_size, Tensor nested_strides, Tensor offsets)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor(a) auto dispatch__nested_view_from_buffer = [](const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_view_from_buffer(self, nested_size, nested_strides, offsets); }; return wrap(dispatch__nested_view_from_buffer(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_view_from_buffer_copy static PyObject * THPVariable__nested_view_from_buffer_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_view_from_buffer_copy(Tensor input, Tensor nested_size, Tensor nested_strides, Tensor offsets, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_nested_view_from_buffer_copy(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor auto dispatch__nested_view_from_buffer_copy = [](const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_view_from_buffer_copy(self, nested_size, nested_strides, offsets); }; return wrap(dispatch__nested_view_from_buffer_copy(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } else { // aten::_nested_view_from_buffer_copy.out(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__nested_view_from_buffer_copy_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_view_from_buffer_copy_out(out, self, nested_size, nested_strides, offsets); }; return wrap(dispatch__nested_view_from_buffer_copy_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_view_from_jagged static PyObject * THPVariable__nested_view_from_jagged(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_view_from_jagged(Tensor input, Tensor offsets, Tensor dummy, Tensor? lengths=None, int64_t ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_view_from_jagged(Tensor(a) self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None) -> Tensor(a) auto dispatch__nested_view_from_jagged = [](const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional & lengths, int64_t ragged_idx, const ::std::optional & min_seqlen, const ::std::optional & max_seqlen) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_view_from_jagged(self, offsets, dummy, lengths, ragged_idx, min_seqlen, max_seqlen); }; return wrap(dispatch__nested_view_from_jagged(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toInt64(4), _r.optionalTensor(5), _r.optionalTensor(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_view_from_jagged_copy static PyObject * THPVariable__nested_view_from_jagged_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_view_from_jagged_copy(Tensor input, Tensor offsets, Tensor dummy, Tensor? lengths=None, int64_t ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(7)) { // aten::_nested_view_from_jagged_copy(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None) -> Tensor auto dispatch__nested_view_from_jagged_copy = [](const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional & lengths, int64_t ragged_idx, const ::std::optional & min_seqlen, const ::std::optional & max_seqlen) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_view_from_jagged_copy(self, offsets, dummy, lengths, ragged_idx, min_seqlen, max_seqlen); }; return wrap(dispatch__nested_view_from_jagged_copy(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toInt64(4), _r.optionalTensor(5), _r.optionalTensor(6))); } else { // aten::_nested_view_from_jagged_copy.out(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__nested_view_from_jagged_copy_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional & lengths, int64_t ragged_idx, const ::std::optional & min_seqlen, const ::std::optional & max_seqlen) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_view_from_jagged_copy_out(out, self, offsets, dummy, lengths, ragged_idx, min_seqlen, max_seqlen); }; return wrap(dispatch__nested_view_from_jagged_copy_out(_r.tensor(7), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toInt64(4), _r.optionalTensor(5), _r.optionalTensor(6))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_values static PyObject * THPVariable__nested_get_values(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_values(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_get_values(Tensor(a) self) -> Tensor(a) auto dispatch__nested_get_values = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_values(self); }; return wrap(dispatch__nested_get_values(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_values_copy static PyObject * THPVariable__nested_get_values_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_values_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_nested_get_values_copy(Tensor self) -> Tensor auto dispatch__nested_get_values_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_values_copy(self); }; return wrap(dispatch__nested_get_values_copy(_r.tensor(0))); } else { // aten::_nested_get_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__nested_get_values_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_values_copy_out(out, self); }; return wrap(dispatch__nested_get_values_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_offsets static PyObject * THPVariable__nested_get_offsets(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_offsets(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_get_offsets(Tensor self) -> Tensor auto dispatch__nested_get_offsets = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_offsets(self); }; return wrap(dispatch__nested_get_offsets(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_lengths static PyObject * THPVariable__nested_get_lengths(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_lengths(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_get_lengths(Tensor self) -> Tensor auto dispatch__nested_get_lengths = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_lengths(self); }; return wrap(dispatch__nested_get_lengths(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_ragged_idx static PyObject * THPVariable__nested_get_ragged_idx(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_ragged_idx(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_get_ragged_idx(Tensor self) -> int auto dispatch__nested_get_ragged_idx = [](const at::Tensor & self) -> int64_t { pybind11::gil_scoped_release no_gil; return at::_nested_get_ragged_idx(self); }; return wrap(dispatch__nested_get_ragged_idx(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_min_seqlen static PyObject * THPVariable__nested_get_min_seqlen(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_min_seqlen(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_get_min_seqlen(Tensor self) -> Tensor auto dispatch__nested_get_min_seqlen = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_min_seqlen(self); }; return wrap(dispatch__nested_get_min_seqlen(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_max_seqlen static PyObject * THPVariable__nested_get_max_seqlen(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_max_seqlen(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_get_max_seqlen(Tensor self) -> Tensor auto dispatch__nested_get_max_seqlen = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_max_seqlen(self); }; return wrap(dispatch__nested_get_max_seqlen(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_get_jagged_dummy static PyObject * THPVariable__nested_get_jagged_dummy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_get_jagged_dummy(Tensor any)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_get_jagged_dummy(Tensor any) -> Tensor auto dispatch__nested_get_jagged_dummy = [](const at::Tensor & any) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_get_jagged_dummy(any); }; return wrap(dispatch__nested_get_jagged_dummy(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_compute_contiguous_strides_offsets static PyObject * THPVariable__nested_compute_contiguous_strides_offsets(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_compute_contiguous_strides_offsets(Tensor nested_size)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_compute_contiguous_strides_offsets(Tensor nested_size) -> (Tensor, Tensor) auto dispatch__nested_compute_contiguous_strides_offsets = [](const at::Tensor & nested_size) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_nested_compute_contiguous_strides_offsets(nested_size); }; return wrap(dispatch__nested_compute_contiguous_strides_offsets(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _trilinear static PyObject * THPVariable__trilinear(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_trilinear(Tensor i1, Tensor i2, Tensor i3, IntArrayRef expand1, IntArrayRef expand2, IntArrayRef expand3, IntArrayRef sumdim, int64_t unroll_dim=1)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor auto dispatch__trilinear = [](const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_trilinear(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); }; return wrap(dispatch__trilinear(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.intlist(3), _r.intlist(4), _r.intlist(5), _r.intlist(6), _r.toInt64(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // triplet_margin_loss static PyObject * THPVariable_triplet_margin_loss(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=False, int64_t reduction=at::Reduction::Mean)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, float margin=1.0, float p=2, float eps=1e-06, bool swap=False, int reduction=Mean) -> Tensor auto dispatch_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) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::triplet_margin_loss(anchor, positive, negative, margin, p, eps, swap, reduction); }; return wrap(dispatch_triplet_margin_loss(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toDouble(3), _r.toDouble(4), _r.toDouble(5), _r.toBool(6), _r.toInt64(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // trunc static PyObject * THPVariable_trunc(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "trunc(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::trunc(Tensor self) -> Tensor auto dispatch_trunc = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.trunc(); }; return wrap(dispatch_trunc(_r.tensor(0))); } else { // aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_trunc_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::trunc_out(out, self); }; return wrap(dispatch_trunc_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // trunc_ static PyObject * THPVariable_trunc_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "trunc_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::trunc_(Tensor(a!) self) -> Tensor(a!) auto dispatch_trunc_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.trunc_(); }; return wrap(dispatch_trunc_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fix static PyObject * THPVariable_fix(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fix(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::fix(Tensor self) -> Tensor auto dispatch_fix = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fix(); }; return wrap(dispatch_fix(_r.tensor(0))); } else { // aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_fix_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fix_out(out, self); }; return wrap(dispatch_fix_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fix_ static PyObject * THPVariable_fix_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fix_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fix_(Tensor(a!) self) -> Tensor(a!) auto dispatch_fix_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fix_(); }; return wrap(dispatch_fix_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _has_compatible_shallow_copy_type static PyObject * THPVariable__has_compatible_shallow_copy_type(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_has_compatible_shallow_copy_type(Tensor input, Tensor from)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool auto dispatch__has_compatible_shallow_copy_type = [](const at::Tensor & self, const at::Tensor & from) -> bool { pybind11::gil_scoped_release no_gil; return at::_has_compatible_shallow_copy_type(self, from); }; return wrap(dispatch__has_compatible_shallow_copy_type(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _unique static PyObject * THPVariable__unique(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_unique(Tensor input, bool sorted=True, bool return_inverse=False)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_unique(Tensor self, bool sorted=True, bool return_inverse=False) -> (Tensor, Tensor) auto dispatch__unique = [](const at::Tensor & self, bool sorted, bool return_inverse) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_unique(self, sorted, return_inverse); }; return wrap(dispatch__unique(_r.tensor(0), _r.toBool(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unique_dim static PyObject * THPVariable_unique_dim(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unique_dim(Tensor input, int64_t dim, bool sorted=True, bool return_inverse=False, bool return_counts=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) auto dispatch_unique_dim = [](const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::unique_dim(self, dim, sorted, return_inverse, return_counts); }; return wrap(dispatch_unique_dim(_r.tensor(0), _r.toInt64(1), _r.toBool(2), _r.toBool(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unique_consecutive static PyObject * THPVariable_unique_consecutive(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unique_consecutive(Tensor input, bool return_inverse=False, bool return_counts=False, int64_t? dim=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor) auto dispatch_unique_consecutive = [](const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::unique_consecutive(self, return_inverse, return_counts, dim); }; return wrap(dispatch_unique_consecutive(_r.tensor(0), _r.toBool(1), _r.toBool(2), _r.toInt64Optional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _unique2 static PyObject * THPVariable__unique2(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_unique2(Tensor input, bool sorted=True, bool return_inverse=False, bool return_counts=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) auto dispatch__unique2 = [](const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_unique2(self, sorted, return_inverse, return_counts); }; return wrap(dispatch__unique2(_r.tensor(0), _r.toBool(1), _r.toBool(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unsqueeze static PyObject * THPVariable_unsqueeze(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unsqueeze(Tensor input, int64_t dim)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a) auto dispatch_unsqueeze = [](const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.unsqueeze(dim); }; return wrap(dispatch_unsqueeze(_r.tensor(0), _r.toInt64(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // vander static PyObject * THPVariable_vander(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "vander(Tensor x, int64_t? N=None, bool increasing=False)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor auto dispatch_vander = [](const at::Tensor & x, ::std::optional N, bool increasing) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::vander(x, N, increasing); }; return wrap(dispatch_vander(_r.tensor(0), _r.toInt64Optional(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // var static PyObject * THPVariable_var(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "var(Tensor input, IntArrayRef[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor out=None)", "var(Tensor input, IntArrayRef[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor out=None)", "var(Tensor input, bool unbiased=True)", "var(Tensor input, DimnameList[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor out=None)", "var(Tensor input, DimnameList[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor auto dispatch_var = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.var(dim, unbiased, keepdim); }; return wrap(dispatch_var(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.toBool(3))); } else { // aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_var_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::var_out(out, self, dim, unbiased, keepdim); }; return wrap(dispatch_var_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.toBool(3))); } } case 1: { if (_r.isNone(4)) { // aten::var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor auto dispatch_var = [](const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.var(dim, correction, keepdim); }; return wrap(dispatch_var(_r.tensor(0), _r.intlistOptional(1), _r.scalarOptional(2), _r.toBool(3))); } else { // aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_var_out = [](at::Tensor out, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::var_out(out, self, dim, correction, keepdim); }; return wrap(dispatch_var_out(_r.tensor(4), _r.tensor(0), _r.intlistOptional(1), _r.scalarOptional(2), _r.toBool(3))); } } case 2: { // aten::var(Tensor self, bool unbiased=True) -> Tensor auto dispatch_var = [](const at::Tensor & self, bool unbiased) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.var(unbiased); }; return wrap(dispatch_var(_r.tensor(0), _r.toBool(1))); } case 3: { if (_r.isNone(4)) { // aten::var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor auto dispatch_var = [](const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.var(dim, unbiased, keepdim); }; return wrap(dispatch_var(_r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.toBool(3))); } else { // aten::var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_var_out = [](at::Tensor out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::var_out(out, self, dim, unbiased, keepdim); }; return wrap(dispatch_var_out(_r.tensor(4), _r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.toBool(3))); } } case 4: { if (_r.isNone(4)) { // aten::var.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor auto dispatch_var = [](const at::Tensor & self, at::DimnameList dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.var(dim, correction, keepdim); }; return wrap(dispatch_var(_r.tensor(0), _r.dimnamelist(1), _r.scalarOptional(2), _r.toBool(3))); } else { // aten::var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_var_out = [](at::Tensor out, const at::Tensor & self, at::DimnameList dim, const ::std::optional & correction, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::var_out(out, self, dim, correction, keepdim); }; return wrap(dispatch_var_out(_r.tensor(4), _r.tensor(0), _r.dimnamelist(1), _r.scalarOptional(2), _r.toBool(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // var_mean static PyObject * THPVariable_var_mean(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "var_mean(Tensor input, IntArrayRef[1]? dim, bool unbiased=True, bool keepdim=False)", "var_mean(Tensor input, IntArrayRef[1]? dim=None, *, Scalar? correction=None, bool keepdim=False)", "var_mean(Tensor input, bool unbiased=True)", "var_mean(Tensor input, DimnameList[1] dim, bool unbiased=True, bool keepdim=False)", "var_mean(Tensor input, DimnameList[1] dim, *, Scalar? correction=None, bool keepdim=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_var_mean = [](const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::var_mean(self, dim, unbiased, keepdim); }; return wrap(dispatch_var_mean(_r.tensor(0), _r.intlistOptional(1), _r.toBool(2), _r.toBool(3))); } case 1: { // aten::var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_var_mean = [](const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional & correction, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::var_mean(self, dim, correction, keepdim); }; return wrap(dispatch_var_mean(_r.tensor(0), _r.intlistOptional(1), _r.scalarOptional(2), _r.toBool(3))); } case 2: { // aten::var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) auto dispatch_var_mean = [](const at::Tensor & self, bool unbiased) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::var_mean(self, unbiased); }; return wrap(dispatch_var_mean(_r.tensor(0), _r.toBool(1))); } case 3: { // aten::var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_var_mean = [](const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::var_mean(self, dim, unbiased, keepdim); }; return wrap(dispatch_var_mean(_r.tensor(0), _r.dimnamelist(1), _r.toBool(2), _r.toBool(3))); } case 4: { // aten::var_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) auto dispatch_var_mean = [](const at::Tensor & self, at::DimnameList dim, const ::std::optional & correction, bool keepdim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::var_mean(self, dim, correction, keepdim); }; return wrap(dispatch_var_mean(_r.tensor(0), _r.dimnamelist(1), _r.scalarOptional(2), _r.toBool(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // where static PyObject * THPVariable_where(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "where(Tensor condition)", "where(Tensor condition, Tensor input, Tensor other, *, Tensor out=None)", "where(Tensor condition, Scalar self, Tensor other)", "where(Tensor condition, Tensor input, Scalar other)", "where(Tensor condition, Scalar self, Scalar other)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::where(Tensor condition) -> Tensor[] auto dispatch_where = [](const at::Tensor & condition) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::where(condition); }; return wrap(dispatch_where(_r.tensor(0))); } case 1: { if (_r.isNone(3)) { // aten::where.self(Tensor condition, Tensor self, Tensor other) -> Tensor auto dispatch_where = [](const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.where(condition, other); }; return wrap(dispatch_where(_r.tensor(0), _r.tensor(1), _r.tensor(2))); } else { // aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_where_out = [](at::Tensor out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::where_out(out, condition, self, other); }; return wrap(dispatch_where_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.tensor(2))); } } case 2: { // aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor auto dispatch_where = [](const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::where(condition, self, other); }; return wrap(dispatch_where(_r.tensor(0), _r.scalar(1), _r.tensor(2))); } case 3: { // aten::where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor auto dispatch_where = [](const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.where(condition, other); }; return wrap(dispatch_where(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } case 4: { // aten::where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor auto dispatch_where = [](const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::where(condition, self, other); }; return wrap(dispatch_where(_r.tensor(0), _r.scalar(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // norm_except_dim static PyObject * THPVariable_norm_except_dim(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "norm_except_dim(Tensor v, int64_t pow=2, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor auto dispatch_norm_except_dim = [](const at::Tensor & v, int64_t pow, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::norm_except_dim(v, pow, dim); }; return wrap(dispatch_norm_except_dim(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _weight_norm static PyObject * THPVariable__weight_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_weight_norm(Tensor v, Tensor g, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor auto dispatch__weight_norm = [](const at::Tensor & v, const at::Tensor & g, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_weight_norm(v, g, dim); }; return wrap(dispatch__weight_norm(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _weight_norm_interface static PyObject * THPVariable__weight_norm_interface(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_weight_norm_interface(Tensor v, Tensor g, int64_t dim=0)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor) auto dispatch__weight_norm_interface = [](const at::Tensor & v, const at::Tensor & g, int64_t dim) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_weight_norm_interface(v, g, dim); }; return wrap(dispatch__weight_norm_interface(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // zeros static PyObject * THPVariable_zeros(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "zeros(IntArrayRef size, *, DimnameList? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", "zeros(SymIntArrayRef size, *, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto __names = _r.toDimnameListOptional(1); ::std::optional names = __names ? ::std::make_optional(DimnameList(__names.value())) : ::std::nullopt; const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_zeros = [](at::IntArrayRef size, ::std::optional names, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::zeros(size, names, options); }; return wrap(dispatch_zeros(_r.intlist(0), names, options)); } case 1: { if (_r.isNone(1)) { // aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceWithDefault(4, torch::tensors::get_default_device())) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_zeros = [](c10::SymIntArrayRef size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::zeros_symint(size, options); }; return wrap(dispatch_zeros(_r.symintlist(0), options)); } else { // aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(1), _r.scalartypeOptional(2), _r.isNone(2), _r.layoutOptional(3), _r.deviceWithDefault(4, torch::tensors::get_default_device()), _r.isNone(4)); auto dispatch_zeros_out = [](at::Tensor out, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::zeros_symint_out(out, size); }; return wrap(dispatch_zeros_out(_r.tensor(1), _r.symintlist(0)).set_requires_grad(_r.toBool(6))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _efficientzerotensor static PyObject * THPVariable__efficientzerotensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_efficientzerotensor(SymIntArrayRef size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(1)) .device(_r.deviceWithDefault(3, torch::tensors::get_default_device())) .layout(_r.layoutOptional(2)) .requires_grad(_r.toBool(5)) .pinned_memory(_r.toBool(4)); torch::utils::maybe_initialize_device(options); auto dispatch__efficientzerotensor = [](c10::SymIntArrayRef size, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::_efficientzerotensor_symint(size, options); }; return wrap(dispatch__efficientzerotensor(_r.symintlist(0), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // zeros_like static PyObject * THPVariable_zeros_like(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "zeros_like(Tensor input, *, MemoryFormat? memory_format=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor auto self = _r.tensor(0); const auto options = TensorOptions() .dtype(_r.scalartypeOptional(2)) .device(_r.deviceOptional(4)) .layout(_r.layoutOptional(3)) .requires_grad(_r.toBool(6)) .pinned_memory(_r.toBool(5)); torch::utils::maybe_initialize_device(options); auto dispatch_zeros_like = [](const at::Tensor & self, at::TensorOptions options, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::zeros_like(self, options, memory_format); }; return wrap(dispatch_zeros_like(self, options, _r.memoryformatOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _standard_gamma_grad static PyObject * THPVariable__standard_gamma_grad(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_standard_gamma_grad(Tensor input, Tensor output)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_standard_gamma_grad(Tensor self, Tensor output) -> Tensor auto dispatch__standard_gamma_grad = [](const at::Tensor & self, const at::Tensor & output) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_standard_gamma_grad(self, output); }; return wrap(dispatch__standard_gamma_grad(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _standard_gamma static PyObject * THPVariable__standard_gamma(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_standard_gamma(Tensor input, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_standard_gamma(Tensor self, Generator? generator=None) -> Tensor auto dispatch__standard_gamma = [](const at::Tensor & self, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_standard_gamma(self, generator); }; return wrap(dispatch__standard_gamma(_r.tensor(0), _r.generator(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _dirichlet_grad static PyObject * THPVariable__dirichlet_grad(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_dirichlet_grad(Tensor x, Tensor alpha, Tensor total)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor auto dispatch__dirichlet_grad = [](const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_dirichlet_grad(x, alpha, total); }; return wrap(dispatch__dirichlet_grad(_r.tensor(0), _r.tensor(1), _r.tensor(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sample_dirichlet static PyObject * THPVariable__sample_dirichlet(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sample_dirichlet(Tensor input, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sample_dirichlet(Tensor self, Generator? generator=None) -> Tensor auto dispatch__sample_dirichlet = [](const at::Tensor & self, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sample_dirichlet(self, generator); }; return wrap(dispatch__sample_dirichlet(_r.tensor(0), _r.generator(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // poisson static PyObject * THPVariable_poisson(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "poisson(Tensor input, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::poisson(Tensor self, Generator? generator=None) -> Tensor auto dispatch_poisson = [](const at::Tensor & self, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::poisson(self, generator); }; return wrap(dispatch_poisson(_r.tensor(0), _r.generator(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // binomial static PyObject * THPVariable_binomial(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "binomial(Tensor count, Tensor prob, Generator? generator=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::binomial(Tensor count, Tensor prob, Generator? generator=None) -> Tensor auto dispatch_binomial = [](const at::Tensor & count, const at::Tensor & prob, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::binomial(count, prob, generator); }; return wrap(dispatch_binomial(_r.tensor(0), _r.tensor(1), _r.generator(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // native_norm static PyObject * THPVariable_native_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "native_norm(Tensor input, Scalar p=2)", "native_norm(Tensor input, Scalar? p, IntArrayRef[1] dim, bool keepdim, ScalarType? dtype)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::native_norm(Tensor self, Scalar p=2) -> Tensor auto dispatch_native_norm = [](const at::Tensor & self, const at::Scalar & p) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::native_norm(self, p); }; return wrap(dispatch_native_norm(_r.tensor(0), _r.scalar(1))); } case 1: { // aten::native_norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype) -> Tensor auto dispatch_native_norm = [](const at::Tensor & self, const ::std::optional & p, at::IntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::native_norm(self, p, dim, keepdim, dtype); }; return wrap(dispatch_native_norm(_r.tensor(0), _r.scalarOptional(1), _r.intlist(2), _r.toBool(3), _r.scalartypeOptional(4))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _sparse_sum static PyObject * THPVariable__sparse_sum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_sum(Tensor input)", "_sparse_sum(Tensor input, *, ScalarType dtype)", "_sparse_sum(Tensor input, IntArrayRef[1] dim)", "_sparse_sum(Tensor input, IntArrayRef[1] dim, *, ScalarType dtype)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_sparse_sum(Tensor self) -> Tensor auto dispatch__sparse_sum = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_sum(self); }; return wrap(dispatch__sparse_sum(_r.tensor(0))); } case 1: { // aten::_sparse_sum.dtype(Tensor self, *, ScalarType dtype) -> Tensor auto dispatch__sparse_sum = [](const at::Tensor & self, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_sum(self, dtype); }; return wrap(dispatch__sparse_sum(_r.tensor(0), _r.scalartype(1))); } case 2: { // aten::_sparse_sum.dim(Tensor self, int[1] dim) -> Tensor auto dispatch__sparse_sum = [](const at::Tensor & self, at::IntArrayRef dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_sum(self, dim); }; return wrap(dispatch__sparse_sum(_r.tensor(0), _r.intlist(1))); } case 3: { // aten::_sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor auto dispatch__sparse_sum = [](const at::Tensor & self, at::IntArrayRef dim, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_sum(self, dim, dtype); }; return wrap(dispatch__sparse_sum(_r.tensor(0), _r.intlist(1), _r.scalartype(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_csr_sum static PyObject * THPVariable__sparse_csr_sum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_csr_sum(Tensor input, IntArrayRef[1] dim, bool keepdim=False, *, ScalarType? dtype=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_csr_sum.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch__sparse_csr_sum = [](const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_csr_sum(self, dim, keepdim, dtype); }; return wrap(dispatch__sparse_csr_sum(_r.tensor(0), _r.intlist(1), _r.toBool(2), _r.scalartypeOptional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_csr_prod static PyObject * THPVariable__sparse_csr_prod(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_csr_prod(Tensor input, IntArrayRef[1] dim, bool keepdim=False, *, ScalarType? dtype=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor auto dispatch__sparse_csr_prod = [](const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_csr_prod(self, dim, keepdim, dtype); }; return wrap(dispatch__sparse_csr_prod(_r.tensor(0), _r.intlist(1), _r.toBool(2), _r.scalartypeOptional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_softmax_backward_data static PyObject * THPVariable__sparse_softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int64_t dim, Tensor input)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor auto dispatch__sparse_softmax_backward_data = [](const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_softmax_backward_data(grad_output, output, dim, self); }; return wrap(dispatch__sparse_softmax_backward_data(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_log_softmax_backward_data static PyObject * THPVariable__sparse_log_softmax_backward_data(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int64_t dim, Tensor input)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor auto dispatch__sparse_log_softmax_backward_data = [](const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_log_softmax_backward_data(grad_output, output, dim, self); }; return wrap(dispatch__sparse_log_softmax_backward_data(_r.tensor(0), _r.tensor(1), _r.toInt64(2), _r.tensor(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // norm static PyObject * THPVariable_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "norm(Tensor input, Scalar p=2)", "norm(Tensor input, Scalar? p, *, ScalarType dtype)", "norm(Tensor input, Scalar? p, IntArrayRef[1] dim, bool keepdim, *, ScalarType dtype, Tensor out=None)", "norm(Tensor input, Scalar? p, IntArrayRef[1] dim, bool keepdim=False, *, Tensor out=None)", "norm(Tensor input, Scalar? p, DimnameList[1] dim, bool keepdim, *, ScalarType dtype, Tensor out=None)", "norm(Tensor input, Scalar? p, DimnameList[1] dim, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::norm.Scalar(Tensor self, Scalar p=2) -> Tensor auto dispatch_norm = [](const at::Tensor & self, const at::Scalar & p) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.norm(p); }; return wrap(dispatch_norm(_r.tensor(0), _r.scalar(1))); } case 1: { // aten::norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor auto dispatch_norm = [](const at::Tensor & self, const ::std::optional & p, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.norm(p, dtype); }; return wrap(dispatch_norm(_r.tensor(0), _r.scalarOptional(1), _r.scalartype(2))); } case 2: { if (_r.isNone(5)) { // aten::norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor auto dispatch_norm = [](const at::Tensor & self, const ::std::optional & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.norm(p, dim, keepdim, dtype); }; return wrap(dispatch_norm(_r.tensor(0), _r.scalarOptional(1), _r.intlist(2), _r.toBool(3), _r.scalartype(4))); } else { // aten::norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) auto dispatch_norm_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::norm_out(out, self, p, dim, keepdim, dtype); }; return wrap(dispatch_norm_out(_r.tensor(5), _r.tensor(0), _r.scalarOptional(1), _r.intlist(2), _r.toBool(3), _r.scalartype(4))); } } case 3: { if (_r.isNone(4)) { // aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor auto dispatch_norm = [](const at::Tensor & self, const ::std::optional & p, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.norm(p, dim, keepdim); }; return wrap(dispatch_norm(_r.tensor(0), _r.scalarOptional(1), _r.intlist(2), _r.toBool(3))); } else { // aten::norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_norm_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & p, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::norm_out(out, self, p, dim, keepdim); }; return wrap(dispatch_norm_out(_r.tensor(4), _r.tensor(0), _r.scalarOptional(1), _r.intlist(2), _r.toBool(3))); } } case 4: { if (_r.isNone(5)) { // aten::norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor auto dispatch_norm = [](const at::Tensor & self, const ::std::optional & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.norm(p, dim, keepdim, dtype); }; return wrap(dispatch_norm(_r.tensor(0), _r.scalarOptional(1), _r.dimnamelist(2), _r.toBool(3), _r.scalartype(4))); } else { // aten::norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) auto dispatch_norm_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::norm_out(out, self, p, dim, keepdim, dtype); }; return wrap(dispatch_norm_out(_r.tensor(5), _r.tensor(0), _r.scalarOptional(1), _r.dimnamelist(2), _r.toBool(3), _r.scalartype(4))); } } case 5: { if (_r.isNone(4)) { // aten::norm.names_ScalarOpt_dim(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False) -> Tensor auto dispatch_norm = [](const at::Tensor & self, const ::std::optional & p, at::DimnameList dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.norm(p, dim, keepdim); }; return wrap(dispatch_norm(_r.tensor(0), _r.scalarOptional(1), _r.dimnamelist(2), _r.toBool(3))); } else { // aten::norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_norm_out = [](at::Tensor out, const at::Tensor & self, const ::std::optional & p, at::DimnameList dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::norm_out(out, self, p, dim, keepdim); }; return wrap(dispatch_norm_out(_r.tensor(4), _r.tensor(0), _r.scalarOptional(1), _r.dimnamelist(2), _r.toBool(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // frexp static PyObject * THPVariable_frexp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_frexp_structseq(); static PyTypeObject* NamedTuple1 = generated::get_frexp_out_structseq(); static PythonArgParser parser({ "frexp(Tensor input, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent) auto dispatch_frexp = [](const at::Tensor & self) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.frexp(); }; return wrap(NamedTuple, dispatch_frexp(_r.tensor(0))); } else { // aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent) auto out = _r.tensorlist_n<2>(1); auto dispatch_frexp_out = [](at::Tensor & mantissa, at::Tensor & exponent, const at::Tensor & self) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::frexp_out(mantissa, exponent, self); }; return wrap(NamedTuple1, dispatch_frexp_out(out[0], out[1], _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // frobenius_norm static PyObject * THPVariable_frobenius_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "frobenius_norm(Tensor input, IntArrayRef[1] dim, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor auto dispatch_frobenius_norm = [](const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::frobenius_norm(self, dim, keepdim); }; return wrap(dispatch_frobenius_norm(_r.tensor(0), _r.intlist(1), _r.toBool(2))); } else { // aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_frobenius_norm_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::frobenius_norm_out(out, self, dim, keepdim); }; return wrap(dispatch_frobenius_norm_out(_r.tensor(3), _r.tensor(0), _r.intlist(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // nuclear_norm static PyObject * THPVariable_nuclear_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nuclear_norm(Tensor input, IntArrayRef[2] dim, bool keepdim=False, *, Tensor out=None)", "nuclear_norm(Tensor input, bool keepdim=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::nuclear_norm.dim(Tensor self, int[2] dim, bool keepdim=False) -> Tensor auto dispatch_nuclear_norm = [](const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nuclear_norm(self, dim, keepdim); }; return wrap(dispatch_nuclear_norm(_r.tensor(0), _r.intlist(1), _r.toBool(2))); } else { // aten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_nuclear_norm_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nuclear_norm_out(out, self, dim, keepdim); }; return wrap(dispatch_nuclear_norm_out(_r.tensor(3), _r.tensor(0), _r.intlist(1), _r.toBool(2))); } } case 1: { if (_r.isNone(2)) { // aten::nuclear_norm(Tensor self, bool keepdim=False) -> Tensor auto dispatch_nuclear_norm = [](const at::Tensor & self, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nuclear_norm(self, keepdim); }; return wrap(dispatch_nuclear_norm(_r.tensor(0), _r.toBool(1))); } else { // aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_nuclear_norm_out = [](at::Tensor out, const at::Tensor & self, bool keepdim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nuclear_norm_out(out, self, keepdim); }; return wrap(dispatch_nuclear_norm_out(_r.tensor(2), _r.tensor(0), _r.toBool(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // clone static PyObject * THPVariable_clone(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "clone(Tensor input, *, MemoryFormat? memory_format=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor auto dispatch_clone = [](const at::Tensor & self, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.clone(memory_format); }; return wrap(dispatch_clone(_r.tensor(0), _r.memoryformatOptional(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // positive static PyObject * THPVariable_positive(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "positive(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::positive(Tensor(a) self) -> Tensor(a) auto dispatch_positive = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.positive(); }; return wrap(dispatch_positive(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // resize_as_ static PyObject * THPVariable_resize_as_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "resize_as_(Tensor input, Tensor the_template, *, MemoryFormat? memory_format=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!) auto dispatch_resize_as_ = [](const at::Tensor & self, const at::Tensor & the_template, ::std::optional memory_format) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.resize_as_(the_template, memory_format); }; return wrap(dispatch_resize_as_(_r.tensor(0), _r.tensor(1), _r.memoryformatOptional(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // resize_as_sparse_ static PyObject * THPVariable_resize_as_sparse_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "resize_as_sparse_(Tensor input, Tensor the_template)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!) auto dispatch_resize_as_sparse_ = [](const at::Tensor & self, const at::Tensor & the_template) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.resize_as_sparse_(the_template); }; return wrap(dispatch_resize_as_sparse_(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // zero_ static PyObject * THPVariable_zero_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "zero_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::zero_(Tensor(a!) self) -> Tensor(a!) auto dispatch_zero_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.zero_(); }; return wrap(dispatch_zero_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // sub static PyObject * THPVariable_sub(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sub(Tensor input, Scalar alpha, Tensor other, *, Tensor out=None)|deprecated", "sub(Tensor input, Tensor other, *, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // [deprecated] aten::sub(Tensor self, Scalar alpha, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sub = [](at::Tensor out, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sub(other, alpha); }; return wrap(dispatch_sub(_r.tensor(3), _r.tensor(0), _r.scalar(1), _r.tensor(2))); } else { // [deprecated] aten::sub(Tensor self, Scalar alpha, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sub_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sub_out(out, self, other, alpha); }; return wrap(dispatch_sub_out(_r.tensor(3), _r.tensor(0), _r.scalar(1), _r.tensor(2))); } } case 1: { if (_r.isNone(3)) { // aten::sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor auto dispatch_sub = [](const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sub(other, alpha); }; return wrap(dispatch_sub(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } else { // aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_sub_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sub_out(out, self, other, alpha); }; return wrap(dispatch_sub_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.scalar(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // subtract static PyObject * THPVariable_subtract(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "subtract(Tensor input, Tensor other, *, Scalar alpha=1, Tensor out=None)", "subtract(Tensor input, Scalar other, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor auto dispatch_subtract = [](const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.subtract(other, alpha); }; return wrap(dispatch_subtract(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } else { // aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_subtract_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::subtract_out(out, self, other, alpha); }; return wrap(dispatch_subtract_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.scalar(2))); } } case 1: { // aten::subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor auto dispatch_subtract = [](const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.subtract(other, alpha); }; return wrap(dispatch_subtract(_r.tensor(0), _r.scalar(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // rsub static PyObject * THPVariable_rsub(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rsub(Tensor input, Tensor other, *, Scalar alpha=1)", "rsub(Tensor input, Scalar other, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor auto dispatch_rsub = [](const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rsub(self, other, alpha); }; return wrap(dispatch_rsub(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } case 1: { // aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor auto dispatch_rsub = [](const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rsub(self, other, alpha); }; return wrap(dispatch_rsub(_r.tensor(0), _r.scalar(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // heaviside static PyObject * THPVariable_heaviside(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "heaviside(Tensor input, Tensor values, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::heaviside(Tensor self, Tensor values) -> Tensor auto dispatch_heaviside = [](const at::Tensor & self, const at::Tensor & values) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.heaviside(values); }; return wrap(dispatch_heaviside(_r.tensor(0), _r.tensor(1))); } else { // aten::heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_heaviside_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & values) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::heaviside_out(out, self, values); }; return wrap(dispatch_heaviside_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // addmm static PyObject * THPVariable_addmm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "addmm(Scalar beta, Tensor input, Scalar alpha, Tensor mat1, Tensor mat2, *, Tensor out=None)|deprecated", "addmm(Scalar beta, Tensor input, Tensor mat1, Tensor mat2, *, Tensor out=None)|deprecated", "addmm(Tensor input, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // [deprecated] aten::addmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmm = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmm(mat1, mat2, beta, alpha); }; return wrap(dispatch_addmm(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } else { // [deprecated] aten::addmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmm_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addmm_out(out, self, mat1, mat2, beta, alpha); }; return wrap(dispatch_addmm_out(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } } case 1: { if (_r.isNone(4)) { // [deprecated] aten::addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmm = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmm(mat1, mat2, beta, 1); }; return wrap(dispatch_addmm(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } else { // [deprecated] aten::addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmm_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addmm_out(out, self, mat1, mat2, beta, 1); }; return wrap(dispatch_addmm_out(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } } case 2: { if (_r.isNone(5)) { // aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor auto dispatch_addmm = [](const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addmm(mat1, mat2, beta, alpha); }; return wrap(dispatch_addmm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } else { // aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_addmm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addmm_out(out, self, mat1, mat2, beta, alpha); }; return wrap(dispatch_addmm_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _addmm_activation static PyObject * THPVariable__addmm_activation(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_addmm_activation(Tensor input, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(6)) { // aten::_addmm_activation(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False) -> Tensor auto dispatch__addmm_activation = [](const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self._addmm_activation(mat1, mat2, beta, alpha, use_gelu); }; return wrap(dispatch__addmm_activation(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4), _r.toBool(5))); } else { // aten::_addmm_activation.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor(a!) out) -> Tensor(a!) auto dispatch__addmm_activation_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_addmm_activation_out(out, self, mat1, mat2, beta, alpha, use_gelu); }; return wrap(dispatch__addmm_activation_out(_r.tensor(6), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4), _r.toBool(5))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_mm static PyObject * THPVariable__scaled_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_scaled_mm(Tensor input, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(8)) { // aten::_scaled_mm(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False) -> Tensor auto dispatch__scaled_mm = [](const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional & bias, const ::std::optional & scale_result, ::std::optional out_dtype, bool use_fast_accum) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_scaled_mm(self, mat2, scale_a, scale_b, bias, scale_result, out_dtype, use_fast_accum); }; return wrap(dispatch__scaled_mm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.optionalTensor(5), _r.scalartypeOptional(6), _r.toBool(7))); } else { // aten::_scaled_mm.out(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__scaled_mm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional & bias, const ::std::optional & scale_result, ::std::optional out_dtype, bool use_fast_accum) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_scaled_mm_out(out, self, mat2, scale_a, scale_b, bias, scale_result, out_dtype, use_fast_accum); }; return wrap(dispatch__scaled_mm_out(_r.tensor(8), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.optionalTensor(5), _r.scalartypeOptional(6), _r.toBool(7))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_grouped_mm static PyObject * THPVariable__scaled_grouped_mm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_scaled_grouped_mm(Tensor input, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? offs=None, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_scaled_grouped_mm(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? offs=None, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False) -> Tensor auto dispatch__scaled_grouped_mm = [](const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional & offs, const ::std::optional & bias, const ::std::optional & scale_result, ::std::optional out_dtype, bool use_fast_accum) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_scaled_grouped_mm(self, mat2, scale_a, scale_b, offs, bias, scale_result, out_dtype, use_fast_accum); }; return wrap(dispatch__scaled_grouped_mm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.optionalTensor(5), _r.optionalTensor(6), _r.scalartypeOptional(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _validate_sparse_coo_tensor_args static PyObject * THPVariable__validate_sparse_coo_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_validate_sparse_coo_tensor_args(Tensor indices, Tensor values, IntArrayRef size, bool? is_coalesced=None)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size, bool? is_coalesced=None) -> () auto dispatch__validate_sparse_coo_tensor_args = [](const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional is_coalesced) -> void { pybind11::gil_scoped_release no_gil; at::_validate_sparse_coo_tensor_args(indices, values, size, is_coalesced); }; dispatch__validate_sparse_coo_tensor_args(_r.tensor(0), _r.tensor(1), _r.intlist(2), _r.toBoolOptional(3)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _validate_sparse_compressed_tensor_args static PyObject * THPVariable__validate_sparse_compressed_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, IntArrayRef size, Layout layout)", }, /*traceable=*/false); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, Layout layout) -> () auto dispatch__validate_sparse_compressed_tensor_args = [](const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::Layout layout) -> void { pybind11::gil_scoped_release no_gil; at::_validate_sparse_compressed_tensor_args(compressed_indices, plain_indices, values, size, layout); }; dispatch__validate_sparse_compressed_tensor_args(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.intlist(3), _r.layout(4)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _validate_sparse_csr_tensor_args static PyObject * THPVariable__validate_sparse_csr_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, IntArrayRef size)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> () auto dispatch__validate_sparse_csr_tensor_args = [](const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) -> void { pybind11::gil_scoped_release no_gil; at::_validate_sparse_csr_tensor_args(crow_indices, col_indices, values, size); }; dispatch__validate_sparse_csr_tensor_args(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.intlist(3)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _validate_sparse_csc_tensor_args static PyObject * THPVariable__validate_sparse_csc_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, IntArrayRef size)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> () auto dispatch__validate_sparse_csc_tensor_args = [](const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) -> void { pybind11::gil_scoped_release no_gil; at::_validate_sparse_csc_tensor_args(ccol_indices, row_indices, values, size); }; dispatch__validate_sparse_csc_tensor_args(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.intlist(3)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _validate_sparse_bsr_tensor_args static PyObject * THPVariable__validate_sparse_bsr_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_validate_sparse_bsr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, IntArrayRef size)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_validate_sparse_bsr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> () auto dispatch__validate_sparse_bsr_tensor_args = [](const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) -> void { pybind11::gil_scoped_release no_gil; at::_validate_sparse_bsr_tensor_args(crow_indices, col_indices, values, size); }; dispatch__validate_sparse_bsr_tensor_args(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.intlist(3)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _validate_sparse_bsc_tensor_args static PyObject * THPVariable__validate_sparse_bsc_tensor_args(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, IntArrayRef size)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> () auto dispatch__validate_sparse_bsc_tensor_args = [](const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) -> void { pybind11::gil_scoped_release no_gil; at::_validate_sparse_bsc_tensor_args(ccol_indices, row_indices, values, size); }; dispatch__validate_sparse_bsc_tensor_args(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.intlist(3)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _to_cpu static PyObject * THPVariable__to_cpu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_to_cpu(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_to_cpu(Tensor[] tensors) -> Tensor[] auto dispatch__to_cpu = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_to_cpu(tensors); }; return wrap(dispatch__to_cpu(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _coalesce static PyObject * THPVariable__coalesce(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_coalesce(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_coalesce(Tensor self) -> Tensor auto dispatch__coalesce = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_coalesce(self); }; return wrap(dispatch__coalesce(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // hspmm static PyObject * THPVariable_hspmm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hspmm(Tensor mat1, Tensor mat2, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::hspmm(Tensor mat1, Tensor mat2) -> Tensor auto dispatch_hspmm = [](const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::hspmm(mat1, mat2); }; return wrap(dispatch_hspmm(_r.tensor(0), _r.tensor(1))); } else { // aten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_hspmm_out = [](at::Tensor out, const at::Tensor & mat1, const at::Tensor & mat2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::hspmm_out(out, mat1, mat2); }; return wrap(dispatch_hspmm_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // unbind static PyObject * THPVariable_unbind(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unbind(Tensor input, int64_t dim=0)", "unbind(Tensor input, Dimname dim)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[] auto dispatch_unbind = [](const at::Tensor & self, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.unbind(dim); }; return wrap(dispatch_unbind(_r.tensor(0), _r.toInt64(1))); } case 1: { // aten::unbind.Dimname(Tensor(a -> *) self, Dimname dim) -> Tensor(a)[] auto dispatch_unbind = [](const at::Tensor & self, at::Dimname dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return self.unbind(dim); }; return wrap(dispatch_unbind(_r.tensor(0), _r.dimname(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _to_sparse_semi_structured static PyObject * THPVariable__to_sparse_semi_structured(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_to_sparse_semi_structured(Tensor dense)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_to_sparse_semi_structured(Tensor dense) -> (Tensor, Tensor) auto dispatch__to_sparse_semi_structured = [](const at::Tensor & dense) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_to_sparse_semi_structured(dense); }; return wrap(dispatch__to_sparse_semi_structured(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantize_per_tensor_dynamic static PyObject * THPVariable_quantize_per_tensor_dynamic(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantize_per_tensor_dynamic(Tensor input, ScalarType dtype, bool reduce_range)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantize_per_tensor_dynamic(Tensor self, ScalarType dtype, bool reduce_range) -> Tensor auto dispatch_quantize_per_tensor_dynamic = [](const at::Tensor & self, at::ScalarType dtype, bool reduce_range) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantize_per_tensor_dynamic(self, dtype, reduce_range); }; return wrap(dispatch_quantize_per_tensor_dynamic(_r.tensor(0), _r.scalartype(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // quantize_per_tensor static PyObject * THPVariable_quantize_per_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantize_per_tensor(Tensor input, Tensor scale, Tensor zero_point, ScalarType dtype)", "quantize_per_tensor(Tensor input, double scale, int64_t zero_point, ScalarType dtype)", "quantize_per_tensor(TensorList tensors, Tensor scales, Tensor zero_points, ScalarType dtype)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor auto dispatch_quantize_per_tensor = [](const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantize_per_tensor(self, scale, zero_point, dtype); }; return wrap(dispatch_quantize_per_tensor(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalartype(3))); } case 1: { // aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor auto dispatch_quantize_per_tensor = [](const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantize_per_tensor(self, scale, zero_point, dtype); }; return wrap(dispatch_quantize_per_tensor(_r.tensor(0), _r.toDouble(1), _r.toInt64(2), _r.scalartype(3))); } case 2: { // aten::quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[] auto dispatch_quantize_per_tensor = [](at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::quantize_per_tensor(tensors, scales, zero_points, dtype); }; return wrap(dispatch_quantize_per_tensor(_r.tensorlist(0), _r.tensor(1), _r.tensor(2), _r.scalartype(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantize_per_channel static PyObject * THPVariable_quantize_per_channel(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantize_per_channel(Tensor input, Tensor scales, Tensor zero_points, int64_t axis, ScalarType dtype)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor auto dispatch_quantize_per_channel = [](const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantize_per_channel(self, scales, zero_points, axis, dtype); }; return wrap(dispatch_quantize_per_channel(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.scalartype(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // dequantize static PyObject * THPVariable_dequantize(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "dequantize(Tensor input)", "dequantize(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::dequantize.self(Tensor self) -> Tensor auto dispatch_dequantize = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.dequantize(); }; return wrap(dispatch_dequantize(_r.tensor(0))); } case 1: { // aten::dequantize.tensors(Tensor[] tensors) -> Tensor[] auto dispatch_dequantize = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::dequantize(tensors); }; return wrap(dispatch_dequantize(_r.tensorlist(0))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // q_scale static PyObject * THPVariable_q_scale(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "q_scale(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::q_scale(Tensor self) -> float auto dispatch_q_scale = [](const at::Tensor & self) -> double { pybind11::gil_scoped_release no_gil; return self.q_scale(); }; return wrap(dispatch_q_scale(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // q_zero_point static PyObject * THPVariable_q_zero_point(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "q_zero_point(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::q_zero_point(Tensor self) -> int auto dispatch_q_zero_point = [](const at::Tensor & self) -> int64_t { pybind11::gil_scoped_release no_gil; return self.q_zero_point(); }; return wrap(dispatch_q_zero_point(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // q_per_channel_scales static PyObject * THPVariable_q_per_channel_scales(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "q_per_channel_scales(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::q_per_channel_scales(Tensor self) -> Tensor auto dispatch_q_per_channel_scales = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.q_per_channel_scales(); }; return wrap(dispatch_q_per_channel_scales(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // q_per_channel_zero_points static PyObject * THPVariable_q_per_channel_zero_points(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "q_per_channel_zero_points(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::q_per_channel_zero_points(Tensor self) -> Tensor auto dispatch_q_per_channel_zero_points = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.q_per_channel_zero_points(); }; return wrap(dispatch_q_per_channel_zero_points(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // q_per_channel_axis static PyObject * THPVariable_q_per_channel_axis(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "q_per_channel_axis(Tensor input)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::q_per_channel_axis(Tensor self) -> int auto dispatch_q_per_channel_axis = [](const at::Tensor & self) -> int64_t { pybind11::gil_scoped_release no_gil; return self.q_per_channel_axis(); }; return wrap(dispatch_q_per_channel_axis(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // int_repr static PyObject * THPVariable_int_repr(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "int_repr(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::int_repr(Tensor self) -> Tensor auto dispatch_int_repr = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.int_repr(); }; return wrap(dispatch_int_repr(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _make_per_tensor_quantized_tensor static PyObject * THPVariable__make_per_tensor_quantized_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_make_per_tensor_quantized_tensor(Tensor input, double scale, int64_t zero_point)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor auto dispatch__make_per_tensor_quantized_tensor = [](const at::Tensor & self, double scale, int64_t zero_point) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_make_per_tensor_quantized_tensor(self, scale, zero_point); }; return wrap(dispatch__make_per_tensor_quantized_tensor(_r.tensor(0), _r.toDouble(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _make_per_channel_quantized_tensor static PyObject * THPVariable__make_per_channel_quantized_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_make_per_channel_quantized_tensor(Tensor input, Tensor scale, Tensor zero_point, int64_t axis)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_make_per_channel_quantized_tensor(Tensor self, Tensor scale, Tensor zero_point, int axis) -> Tensor auto dispatch__make_per_channel_quantized_tensor = [](const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_make_per_channel_quantized_tensor(self, scale, zero_point, axis); }; return wrap(dispatch__make_per_channel_quantized_tensor(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // fake_quantize_per_tensor_affine static PyObject * THPVariable_fake_quantize_per_tensor_affine(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fake_quantize_per_tensor_affine(Tensor input, Tensor scale, Tensor zero_point, int64_t quant_min, int64_t quant_max)", "fake_quantize_per_tensor_affine(Tensor input, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor auto dispatch_fake_quantize_per_tensor_affine = [](const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fake_quantize_per_tensor_affine(self, scale, zero_point, quant_min, quant_max); }; return wrap(dispatch_fake_quantize_per_tensor_affine(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4))); } case 1: { // aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor auto dispatch_fake_quantize_per_tensor_affine = [](const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fake_quantize_per_tensor_affine(self, scale, zero_point, quant_min, quant_max); }; return wrap(dispatch_fake_quantize_per_tensor_affine(_r.tensor(0), _r.toDouble(1), _r.toInt64(2), _r.toInt64(3), _r.toInt64(4))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fake_quantize_per_tensor_affine_cachemask_tensor_qparams static PyObject * THPVariable__fake_quantize_per_tensor_affine_cachemask_tensor_qparams(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__fake_quantize_per_tensor_affine_cachemask_tensor_qparams_structseq(); static PythonArgParser parser({ "_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(Tensor input, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int64_t quant_min, int64_t quant_max)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max) -> (Tensor output, Tensor mask) auto dispatch__fake_quantize_per_tensor_affine_cachemask_tensor_qparams = [](const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(self, scale, zero_point, fake_quant_enabled, quant_min, quant_max); }; return wrap(NamedTuple, dispatch__fake_quantize_per_tensor_affine_cachemask_tensor_qparams(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.toInt64(4), _r.toInt64(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fake_quantize_learnable_per_tensor_affine static PyObject * THPVariable__fake_quantize_learnable_per_tensor_affine(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fake_quantize_learnable_per_tensor_affine(Tensor input, Tensor scale, Tensor zero_point, int64_t quant_min, int64_t quant_max, double grad_factor=1.0)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fake_quantize_learnable_per_tensor_affine(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor auto dispatch__fake_quantize_learnable_per_tensor_affine = [](const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fake_quantize_learnable_per_tensor_affine(self, scale, zero_point, quant_min, quant_max, grad_factor); }; return wrap(dispatch__fake_quantize_learnable_per_tensor_affine(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4), _r.toDouble(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fake_quantize_per_channel_affine static PyObject * THPVariable_fake_quantize_per_channel_affine(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fake_quantize_per_channel_affine(Tensor input, Tensor scale, Tensor zero_point, int64_t axis, int64_t quant_min, int64_t quant_max)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor auto dispatch_fake_quantize_per_channel_affine = [](const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fake_quantize_per_channel_affine(self, scale, zero_point, axis, quant_min, quant_max); }; return wrap(dispatch_fake_quantize_per_channel_affine(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4), _r.toInt64(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fake_quantize_learnable_per_channel_affine static PyObject * THPVariable__fake_quantize_learnable_per_channel_affine(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fake_quantize_learnable_per_channel_affine(Tensor input, Tensor scale, Tensor zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor auto dispatch__fake_quantize_learnable_per_channel_affine = [](const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fake_quantize_learnable_per_channel_affine(self, scale, zero_point, axis, quant_min, quant_max, grad_factor); }; return wrap(dispatch__fake_quantize_learnable_per_channel_affine(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4), _r.toInt64(5), _r.toDouble(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fused_moving_avg_obs_fake_quant static PyObject * THPVariable_fused_moving_avg_obs_fake_quant(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fused_moving_avg_obs_fake_quant(Tensor input, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False)", }, /*traceable=*/true); ParsedArgs<13> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor auto dispatch_fused_moving_avg_obs_fake_quant = [](const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor running_min, at::Tensor running_max, at::Tensor scale, at::Tensor zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fused_moving_avg_obs_fake_quant(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); }; return wrap(dispatch_fused_moving_avg_obs_fake_quant(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.toDouble(7), _r.toInt64(8), _r.toInt64(9), _r.toInt64(10), _r.toBool(11), _r.toBool(12))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fused_moving_avg_obs_fq_helper static PyObject * THPVariable__fused_moving_avg_obs_fq_helper(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__fused_moving_avg_obs_fq_helper_structseq(); static PythonArgParser parser({ "_fused_moving_avg_obs_fq_helper(Tensor input, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False)", }, /*traceable=*/true); ParsedArgs<13> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fused_moving_avg_obs_fq_helper(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask) auto dispatch__fused_moving_avg_obs_fq_helper = [](const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor running_min, at::Tensor running_max, at::Tensor scale, at::Tensor zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_fused_moving_avg_obs_fq_helper(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); }; return wrap(NamedTuple, dispatch__fused_moving_avg_obs_fq_helper(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.toDouble(7), _r.toInt64(8), _r.toInt64(9), _r.toInt64(10), _r.toBool(11), _r.toBool(12))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _choose_qparams_per_tensor static PyObject * THPVariable__choose_qparams_per_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_choose_qparams_per_tensor(Tensor input, bool reduce_range=False)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int) auto dispatch__choose_qparams_per_tensor = [](const at::Tensor & self, bool reduce_range) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_choose_qparams_per_tensor(self, reduce_range); }; return wrap(dispatch__choose_qparams_per_tensor(_r.tensor(0), _r.toBool(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _saturate_weight_to_fp16 static PyObject * THPVariable__saturate_weight_to_fp16(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_saturate_weight_to_fp16(Tensor weight)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_saturate_weight_to_fp16(Tensor weight) -> Tensor auto dispatch__saturate_weight_to_fp16 = [](const at::Tensor & weight) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_saturate_weight_to_fp16(weight); }; return wrap(dispatch__saturate_weight_to_fp16(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // choose_qparams_optimized static PyObject * THPVariable_choose_qparams_optimized(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "choose_qparams_optimized(Tensor input, int64_t numel, int64_t n_bins, double ratio, int64_t bit_width)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::choose_qparams_optimized(Tensor input, int numel, int n_bins, float ratio, int bit_width) -> (Tensor, Tensor) auto dispatch_choose_qparams_optimized = [](const at::Tensor & input, int64_t numel, int64_t n_bins, double ratio, int64_t bit_width) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::choose_qparams_optimized(input, numel, n_bins, ratio, bit_width); }; return wrap(dispatch_choose_qparams_optimized(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toDouble(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // meshgrid static PyObject * THPVariable_meshgrid(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "meshgrid(TensorList tensors)", "meshgrid(TensorList tensors, *, c10::string_view indexing)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::meshgrid(Tensor[] tensors) -> Tensor[] auto dispatch_meshgrid = [](at::TensorList tensors) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::meshgrid(tensors); }; return wrap(dispatch_meshgrid(_r.tensorlist(0))); } case 1: { // aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[] auto dispatch_meshgrid = [](at::TensorList tensors, c10::string_view indexing) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::meshgrid(tensors, indexing); }; return wrap(dispatch_meshgrid(_r.tensorlist(0), _r.stringView(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cartesian_prod static PyObject * THPVariable_cartesian_prod(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cartesian_prod(TensorList tensors)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::cartesian_prod(Tensor[] tensors) -> Tensor auto dispatch_cartesian_prod = [](at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cartesian_prod(tensors); }; return wrap(dispatch_cartesian_prod(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // combinations static PyObject * THPVariable_combinations(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "combinations(Tensor input, int64_t r=2, bool with_replacement=False)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor auto dispatch_combinations = [](const at::Tensor & self, int64_t r, bool with_replacement) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::combinations(self, r, with_replacement); }; return wrap(dispatch_combinations(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // result_type static PyObject * THPVariable_result_type(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "result_type(Tensor tensor, Tensor other)", "result_type(Scalar scalar, Tensor tensor)", "result_type(Tensor tensor, Scalar other)", "result_type(Scalar scalar1, Scalar scalar2)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::result_type.Tensor(Tensor tensor, Tensor other) -> ScalarType auto dispatch_result_type = [](const at::Tensor & tensor, const at::Tensor & other) -> at::ScalarType { pybind11::gil_scoped_release no_gil; return at::result_type(tensor, other); }; return wrap(dispatch_result_type(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarType auto dispatch_result_type = [](const at::Scalar & scalar, const at::Tensor & tensor) -> at::ScalarType { pybind11::gil_scoped_release no_gil; return at::result_type(scalar, tensor); }; return wrap(dispatch_result_type(_r.scalar(0), _r.tensor(1))); } case 2: { // aten::result_type.Scalar(Tensor tensor, Scalar other) -> ScalarType auto dispatch_result_type = [](const at::Tensor & tensor, const at::Scalar & other) -> at::ScalarType { pybind11::gil_scoped_release no_gil; return at::result_type(tensor, other); }; return wrap(dispatch_result_type(_r.tensor(0), _r.scalar(1))); } case 3: { // aten::result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarType auto dispatch_result_type = [](const at::Scalar & scalar1, const at::Scalar & scalar2) -> at::ScalarType { pybind11::gil_scoped_release no_gil; return at::result_type(scalar1, scalar2); }; return wrap(dispatch_result_type(_r.scalar(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // can_cast static PyObject * THPVariable_can_cast(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "can_cast(ScalarType from_, ScalarType to)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::can_cast(ScalarType from_, ScalarType to) -> bool auto dispatch_can_cast = [](at::ScalarType from_, at::ScalarType to) -> bool { pybind11::gil_scoped_release no_gil; return at::can_cast(from_, to); }; return wrap(dispatch_can_cast(_r.scalartype(0), _r.scalartype(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // promote_types static PyObject * THPVariable_promote_types(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "promote_types(ScalarType type1, ScalarType type2)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::promote_types(ScalarType type1, ScalarType type2) -> ScalarType auto dispatch_promote_types = [](at::ScalarType type1, at::ScalarType type2) -> at::ScalarType { pybind11::gil_scoped_release no_gil; return at::promote_types(type1, type2); }; return wrap(dispatch_promote_types(_r.scalartype(0), _r.scalartype(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _lstm_mps static PyObject * THPVariable__lstm_mps(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_lstm_mps(Tensor input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) auto dispatch__lstm_mps = [](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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_lstm_mps(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); }; return wrap(dispatch__lstm_mps(_r.tensor(0), _r.tensorlist(1), _r.tensorlist(2), _r.toBool(3), _r.toInt64(4), _r.toDouble(5), _r.toBool(6), _r.toBool(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // lstm static PyObject * THPVariable_lstm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lstm(Tensor data, Tensor batch_sizes, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional)", "lstm(Tensor input, TensorList hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::lstm(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); }; return wrap(dispatch_lstm(_r.tensor(0), _r.tensor(1), _r.tensorlist(2), _r.tensorlist(3), _r.toBool(4), _r.toInt64(5), _r.toDouble(6), _r.toBool(7), _r.toBool(8))); } case 1: { // aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::lstm(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); }; return wrap(dispatch_lstm(_r.tensor(0), _r.tensorlist(1), _r.tensorlist(2), _r.toBool(3), _r.toInt64(4), _r.toDouble(5), _r.toBool(6), _r.toBool(7), _r.toBool(8))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // gru static PyObject * THPVariable_gru(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "gru(Tensor data, Tensor batch_sizes, Tensor hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional)", "gru(Tensor input, Tensor hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::gru(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); }; return wrap(dispatch_gru(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensorlist(3), _r.toBool(4), _r.toInt64(5), _r.toDouble(6), _r.toBool(7), _r.toBool(8))); } case 1: { // aten::gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::gru(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); }; return wrap(dispatch_gru(_r.tensor(0), _r.tensor(1), _r.tensorlist(2), _r.toBool(3), _r.toInt64(4), _r.toDouble(5), _r.toBool(6), _r.toBool(7), _r.toBool(8))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // rnn_tanh static PyObject * THPVariable_rnn_tanh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rnn_tanh(Tensor data, Tensor batch_sizes, Tensor hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional)", "rnn_tanh(Tensor input, Tensor hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::rnn_tanh(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); }; return wrap(dispatch_rnn_tanh(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensorlist(3), _r.toBool(4), _r.toInt64(5), _r.toDouble(6), _r.toBool(7), _r.toBool(8))); } case 1: { // aten::rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::rnn_tanh(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); }; return wrap(dispatch_rnn_tanh(_r.tensor(0), _r.tensor(1), _r.tensorlist(2), _r.toBool(3), _r.toInt64(4), _r.toDouble(5), _r.toBool(6), _r.toBool(7), _r.toBool(8))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // rnn_relu static PyObject * THPVariable_rnn_relu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rnn_relu(Tensor data, Tensor batch_sizes, Tensor hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional)", "rnn_relu(Tensor input, Tensor hx, TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::rnn_relu.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::rnn_relu(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); }; return wrap(dispatch_rnn_relu(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensorlist(3), _r.toBool(4), _r.toInt64(5), _r.toDouble(6), _r.toBool(7), _r.toBool(8))); } case 1: { // aten::rnn_relu.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) auto dispatch_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) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::rnn_relu(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); }; return wrap(dispatch_rnn_relu(_r.tensor(0), _r.tensor(1), _r.tensorlist(2), _r.toBool(3), _r.toInt64(4), _r.toDouble(5), _r.toBool(6), _r.toBool(7), _r.toBool(8))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // lstm_cell static PyObject * THPVariable_lstm_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lstm_cell(Tensor input, TensorList hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None)", }, /*traceable=*/false); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor) auto dispatch_lstm_cell = [](const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional & b_ih, const ::std::optional & b_hh) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::lstm_cell(input, hx, w_ih, w_hh, b_ih, b_hh); }; return wrap(dispatch_lstm_cell(_r.tensor(0), _r.tensorlist(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.optionalTensor(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // gru_cell static PyObject * THPVariable_gru_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None)", }, /*traceable=*/false); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor auto dispatch_gru_cell = [](const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional & b_ih, const ::std::optional & b_hh) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::gru_cell(input, hx, w_ih, w_hh, b_ih, b_hh); }; return wrap(dispatch_gru_cell(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.optionalTensor(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rnn_tanh_cell static PyObject * THPVariable_rnn_tanh_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None)", }, /*traceable=*/false); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor auto dispatch_rnn_tanh_cell = [](const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional & b_ih, const ::std::optional & b_hh) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rnn_tanh_cell(input, hx, w_ih, w_hh, b_ih, b_hh); }; return wrap(dispatch_rnn_tanh_cell(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.optionalTensor(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // rnn_relu_cell static PyObject * THPVariable_rnn_relu_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None)", }, /*traceable=*/false); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor auto dispatch_rnn_relu_cell = [](const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional & b_ih, const ::std::optional & b_hh) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::rnn_relu_cell(input, hx, w_ih, w_hh, b_ih, b_hh); }; return wrap(dispatch_rnn_relu_cell(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.optionalTensor(4), _r.optionalTensor(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_lstm_cell static PyObject * THPVariable_quantized_lstm_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_lstm_cell(Tensor input, TensorList hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh)", }, /*traceable=*/true); ParsedArgs<14> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> (Tensor, Tensor) auto dispatch_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, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::quantized_lstm_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); }; return wrap(dispatch_quantized_lstm_cell(_r.tensor(0), _r.tensorlist(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.tensor(7), _r.tensor(8), _r.tensor(9), _r.scalar(10), _r.scalar(11), _r.scalar(12), _r.scalar(13))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_gru_cell static PyObject * THPVariable_quantized_gru_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh)", }, /*traceable=*/true); ParsedArgs<14> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor auto dispatch_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, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantized_gru_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); }; return wrap(dispatch_quantized_gru_cell(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.tensor(7), _r.tensor(8), _r.tensor(9), _r.scalar(10), _r.scalar(11), _r.scalar(12), _r.scalar(13))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_rnn_relu_cell static PyObject * THPVariable_quantized_rnn_relu_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh)", }, /*traceable=*/true); ParsedArgs<14> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor auto dispatch_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, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantized_rnn_relu_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); }; return wrap(dispatch_quantized_rnn_relu_cell(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.tensor(7), _r.tensor(8), _r.tensor(9), _r.scalar(10), _r.scalar(11), _r.scalar(12), _r.scalar(13))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // quantized_rnn_tanh_cell static PyObject * THPVariable_quantized_rnn_tanh_cell(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh)", }, /*traceable=*/true); ParsedArgs<14> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor auto dispatch_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, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantized_rnn_tanh_cell(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); }; return wrap(dispatch_quantized_rnn_tanh_cell(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.tensor(7), _r.tensor(8), _r.tensor(9), _r.scalar(10), _r.scalar(11), _r.scalar(12), _r.scalar(13))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _pack_padded_sequence static PyObject * THPVariable__pack_padded_sequence(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) auto dispatch__pack_padded_sequence = [](const at::Tensor & input, const at::Tensor & lengths, bool batch_first) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_pack_padded_sequence(input, lengths, batch_first); }; return wrap(dispatch__pack_padded_sequence(_r.tensor(0), _r.tensor(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _pad_packed_sequence static PyObject * THPVariable__pad_packed_sequence(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int64_t total_length)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor) auto dispatch__pad_packed_sequence = [](const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_pad_packed_sequence(data, batch_sizes, batch_first, padding_value, total_length); }; return wrap(dispatch__pad_packed_sequence(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.scalar(3), _r.toInt64(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // masked_fill static PyObject * THPVariable_masked_fill(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "masked_fill(Tensor input, Tensor mask, Tensor value)", "masked_fill(Tensor input, Tensor mask, Scalar value)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor auto dispatch_masked_fill = [](const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.masked_fill(mask, value); }; return wrap(dispatch_masked_fill(_r.tensor(0), _r.tensor(1), _r.tensor(2))); } case 1: { // aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor auto dispatch_masked_fill = [](const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.masked_fill(mask, value); }; return wrap(dispatch_masked_fill(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // masked_scatter static PyObject * THPVariable_masked_scatter(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "masked_scatter(Tensor input, Tensor mask, Tensor source)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor auto dispatch_masked_scatter = [](const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.masked_scatter(mask, source); }; return wrap(dispatch_masked_scatter(_r.tensor(0), _r.tensor(1), _r.tensor(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _masked_softmax static PyObject * THPVariable__masked_softmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_masked_softmax(Tensor input, Tensor mask, int64_t? dim=None, int64_t? mask_type=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor auto dispatch__masked_softmax = [](const at::Tensor & self, const at::Tensor & mask, ::std::optional dim, ::std::optional mask_type) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_masked_softmax(self, mask, dim, mask_type); }; return wrap(dispatch__masked_softmax(_r.tensor(0), _r.tensor(1), _r.toInt64Optional(2), _r.toInt64Optional(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // put static PyObject * THPVariable_put(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "put(Tensor input, Tensor index, Tensor source, bool accumulate=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::put(Tensor self, Tensor index, Tensor source, bool accumulate=False) -> Tensor auto dispatch_put = [](const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.put(index, source, accumulate); }; return wrap(dispatch_put(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // index_add static PyObject * THPVariable_index_add(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "index_add(Tensor input, int64_t dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor out=None)", "index_add(Tensor input, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // aten::index_add(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor auto dispatch_index_add = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_add(dim, index, source, alpha); }; return wrap(dispatch_index_add(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.scalar(4))); } else { // aten::index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_index_add_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::index_add_out(out, self, dim, index, source, alpha); }; return wrap(dispatch_index_add_out(_r.tensor(5), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.scalar(4))); } } case 1: { // aten::index_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor auto dispatch_index_add = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_add(dim, index, source, alpha); }; return wrap(dispatch_index_add(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.tensor(3), _r.scalar(4))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // index_reduce static PyObject * THPVariable_index_reduce(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "index_reduce(Tensor input, int64_t dim, Tensor index, Tensor source, c10::string_view reduce, *, bool include_self=True, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(6)) { // aten::index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor auto dispatch_index_reduce = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_reduce(dim, index, source, reduce, include_self); }; return wrap(dispatch_index_reduce(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.stringView(4), _r.toBool(5))); } else { // aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) auto dispatch_index_reduce_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::index_reduce_out(out, self, dim, index, source, reduce, include_self); }; return wrap(dispatch_index_reduce_out(_r.tensor(6), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.stringView(4), _r.toBool(5))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // index_fill static PyObject * THPVariable_index_fill(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "index_fill(Tensor input, int64_t dim, Tensor index, Tensor value)", "index_fill(Tensor input, Dimname dim, Tensor index, Tensor value)", "index_fill(Tensor input, int64_t dim, Tensor index, Scalar value)", "index_fill(Tensor input, Dimname dim, Tensor index, Scalar value)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor auto dispatch_index_fill = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_fill(dim, index, value); }; return wrap(dispatch_index_fill(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3))); } case 1: { // aten::index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor auto dispatch_index_fill = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_fill(dim, index, value); }; return wrap(dispatch_index_fill(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.tensor(3))); } case 2: { // aten::index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor auto dispatch_index_fill = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_fill(dim, index, value); }; return wrap(dispatch_index_fill(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.scalar(3))); } case 3: { // aten::index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor auto dispatch_index_fill = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_fill(dim, index, value); }; return wrap(dispatch_index_fill(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.scalar(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // scatter static PyObject * THPVariable_scatter(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "scatter(Tensor input, int64_t dim, Tensor index, Tensor src, *, Tensor out=None)", "scatter(Tensor input, int64_t dim, Tensor index, Tensor src, *, c10::string_view reduce, Tensor out=None)", "scatter(Tensor input, Dimname dim, Tensor index, Tensor src)", "scatter(Tensor input, int64_t dim, Tensor index, Scalar value, *, Tensor out=None)", "scatter(Tensor input, int64_t dim, Tensor index, Scalar value, *, c10::string_view reduce, Tensor out=None)", "scatter(Tensor input, Dimname dim, Tensor index, Scalar value)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor auto dispatch_scatter = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter(dim, index, src); }; return wrap(dispatch_scatter(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3))); } else { // aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_scatter_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::scatter_out(out, self, dim, index, src); }; return wrap(dispatch_scatter_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3))); } } case 1: { if (_r.isNone(5)) { // aten::scatter.reduce(Tensor self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor auto dispatch_scatter = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter(dim, index, src, reduce); }; return wrap(dispatch_scatter(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.stringView(4))); } else { // aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!) auto dispatch_scatter_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::scatter_out(out, self, dim, index, src, reduce); }; return wrap(dispatch_scatter_out(_r.tensor(5), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.stringView(4))); } } case 2: { // aten::scatter.dimname_src(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor auto dispatch_scatter = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter(dim, index, src); }; return wrap(dispatch_scatter(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.tensor(3))); } case 3: { if (_r.isNone(4)) { // aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor auto dispatch_scatter = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter(dim, index, value); }; return wrap(dispatch_scatter(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.scalar(3))); } else { // aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_scatter_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::scatter_out(out, self, dim, index, value); }; return wrap(dispatch_scatter_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.scalar(3))); } } case 4: { if (_r.isNone(5)) { // aten::scatter.value_reduce(Tensor self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor auto dispatch_scatter = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter(dim, index, value, reduce); }; return wrap(dispatch_scatter(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.scalar(3), _r.stringView(4))); } else { // aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!) auto dispatch_scatter_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::scatter_out(out, self, dim, index, value, reduce); }; return wrap(dispatch_scatter_out(_r.tensor(5), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.scalar(3), _r.stringView(4))); } } case 5: { // aten::scatter.dimname_value(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor auto dispatch_scatter = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter(dim, index, value); }; return wrap(dispatch_scatter(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.scalar(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // scatter_add static PyObject * THPVariable_scatter_add(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "scatter_add(Tensor input, int64_t dim, Tensor index, Tensor src, *, Tensor out=None)", "scatter_add(Tensor input, Dimname dim, Tensor index, Tensor src)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor auto dispatch_scatter_add = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter_add(dim, index, src); }; return wrap(dispatch_scatter_add(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3))); } else { // aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_scatter_add_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::scatter_add_out(out, self, dim, index, src); }; return wrap(dispatch_scatter_add_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3))); } } case 1: { // aten::scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor auto dispatch_scatter_add = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter_add(dim, index, src); }; return wrap(dispatch_scatter_add(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.tensor(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // scatter_reduce static PyObject * THPVariable_scatter_reduce(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "scatter_reduce(Tensor input, int64_t dim, Tensor index, Tensor src, c10::string_view reduce, *, bool include_self=True, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(6)) { // aten::scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor auto dispatch_scatter_reduce = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.scatter_reduce(dim, index, src, reduce, include_self); }; return wrap(dispatch_scatter_reduce(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.stringView(4), _r.toBool(5))); } else { // aten::scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) auto dispatch_scatter_reduce_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::scatter_reduce_out(out, self, dim, index, src, reduce, include_self); }; return wrap(dispatch_scatter_reduce_out(_r.tensor(6), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.tensor(3), _r.stringView(4), _r.toBool(5))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bitwise_and static PyObject * THPVariable_bitwise_and(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bitwise_and(Tensor input, Tensor other, *, Tensor out=None)", "bitwise_and(Scalar self, Tensor other)", "bitwise_and(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_bitwise_and = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_and(other); }; return wrap(dispatch_bitwise_and(_r.tensor(0), _r.tensor(1))); } else { // aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_and_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_and_out(out, self, other); }; return wrap(dispatch_bitwise_and_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor auto dispatch_bitwise_and = [](const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_and(self, other); }; return wrap(dispatch_bitwise_and(_r.scalar(0), _r.tensor(1))); } case 2: { if (_r.isNone(2)) { // aten::bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_bitwise_and = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_and(other); }; return wrap(dispatch_bitwise_and(_r.tensor(0), _r.scalar(1))); } else { // aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_and_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_and_out(out, self, other); }; return wrap(dispatch_bitwise_and_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // __and__ static PyObject * THPVariable___and__(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "__and__(Tensor input, Tensor other)", "__and__(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch___and__ = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__and__(other); }; return wrap(dispatch___and__(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch___and__ = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__and__(other); }; return wrap(dispatch___and__(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bitwise_or static PyObject * THPVariable_bitwise_or(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bitwise_or(Tensor input, Tensor other, *, Tensor out=None)", "bitwise_or(Scalar self, Tensor other)", "bitwise_or(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_bitwise_or = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_or(other); }; return wrap(dispatch_bitwise_or(_r.tensor(0), _r.tensor(1))); } else { // aten::bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_or_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_or_out(out, self, other); }; return wrap(dispatch_bitwise_or_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::bitwise_or.Scalar_Tensor(Scalar self, Tensor other) -> Tensor auto dispatch_bitwise_or = [](const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_or(self, other); }; return wrap(dispatch_bitwise_or(_r.scalar(0), _r.tensor(1))); } case 2: { if (_r.isNone(2)) { // aten::bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_bitwise_or = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_or(other); }; return wrap(dispatch_bitwise_or(_r.tensor(0), _r.scalar(1))); } else { // aten::bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_or_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_or_out(out, self, other); }; return wrap(dispatch_bitwise_or_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // __or__ static PyObject * THPVariable___or__(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "__or__(Tensor input, Tensor other)", "__or__(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::__or__.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch___or__ = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__or__(other); }; return wrap(dispatch___or__(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::__or__.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch___or__ = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__or__(other); }; return wrap(dispatch___or__(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bitwise_xor static PyObject * THPVariable_bitwise_xor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bitwise_xor(Tensor input, Tensor other, *, Tensor out=None)", "bitwise_xor(Scalar self, Tensor other)", "bitwise_xor(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_bitwise_xor = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_xor(other); }; return wrap(dispatch_bitwise_xor(_r.tensor(0), _r.tensor(1))); } else { // aten::bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_xor_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_xor_out(out, self, other); }; return wrap(dispatch_bitwise_xor_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::bitwise_xor.Scalar_Tensor(Scalar self, Tensor other) -> Tensor auto dispatch_bitwise_xor = [](const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_xor(self, other); }; return wrap(dispatch_bitwise_xor(_r.scalar(0), _r.tensor(1))); } case 2: { if (_r.isNone(2)) { // aten::bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_bitwise_xor = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_xor(other); }; return wrap(dispatch_bitwise_xor(_r.tensor(0), _r.scalar(1))); } else { // aten::bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_xor_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_xor_out(out, self, other); }; return wrap(dispatch_bitwise_xor_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // __xor__ static PyObject * THPVariable___xor__(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "__xor__(Tensor input, Tensor other)", "__xor__(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch___xor__ = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__xor__(other); }; return wrap(dispatch___xor__(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch___xor__ = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__xor__(other); }; return wrap(dispatch___xor__(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // __lshift__ static PyObject * THPVariable___lshift__(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "__lshift__(Tensor input, Tensor other)", "__lshift__(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::__lshift__.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch___lshift__ = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__lshift__(other); }; return wrap(dispatch___lshift__(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch___lshift__ = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__lshift__(other); }; return wrap(dispatch___lshift__(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bitwise_left_shift static PyObject * THPVariable_bitwise_left_shift(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bitwise_left_shift(Tensor input, Tensor other, *, Tensor out=None)", "bitwise_left_shift(Scalar self, Tensor other)", "bitwise_left_shift(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::bitwise_left_shift.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_bitwise_left_shift = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_left_shift(other); }; return wrap(dispatch_bitwise_left_shift(_r.tensor(0), _r.tensor(1))); } else { // aten::bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_left_shift_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_left_shift_out(out, self, other); }; return wrap(dispatch_bitwise_left_shift_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::bitwise_left_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor auto dispatch_bitwise_left_shift = [](const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_left_shift(self, other); }; return wrap(dispatch_bitwise_left_shift(_r.scalar(0), _r.tensor(1))); } case 2: { if (_r.isNone(2)) { // aten::bitwise_left_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_bitwise_left_shift = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_left_shift(other); }; return wrap(dispatch_bitwise_left_shift(_r.tensor(0), _r.scalar(1))); } else { // aten::bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_left_shift_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_left_shift_out(out, self, other); }; return wrap(dispatch_bitwise_left_shift_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // __rshift__ static PyObject * THPVariable___rshift__(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "__rshift__(Tensor input, Tensor other)", "__rshift__(Tensor input, Scalar other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::__rshift__.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch___rshift__ = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__rshift__(other); }; return wrap(dispatch___rshift__(_r.tensor(0), _r.tensor(1))); } case 1: { // aten::__rshift__.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch___rshift__ = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.__rshift__(other); }; return wrap(dispatch___rshift__(_r.tensor(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bitwise_right_shift static PyObject * THPVariable_bitwise_right_shift(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bitwise_right_shift(Tensor input, Tensor other, *, Tensor out=None)", "bitwise_right_shift(Scalar self, Tensor other)", "bitwise_right_shift(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::bitwise_right_shift.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_bitwise_right_shift = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_right_shift(other); }; return wrap(dispatch_bitwise_right_shift(_r.tensor(0), _r.tensor(1))); } else { // aten::bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_right_shift_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_right_shift_out(out, self, other); }; return wrap(dispatch_bitwise_right_shift_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::bitwise_right_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor auto dispatch_bitwise_right_shift = [](const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_right_shift(self, other); }; return wrap(dispatch_bitwise_right_shift(_r.scalar(0), _r.tensor(1))); } case 2: { if (_r.isNone(2)) { // aten::bitwise_right_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_bitwise_right_shift = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.bitwise_right_shift(other); }; return wrap(dispatch_bitwise_right_shift(_r.tensor(0), _r.scalar(1))); } else { // aten::bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_bitwise_right_shift_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bitwise_right_shift_out(out, self, other); }; return wrap(dispatch_bitwise_right_shift_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // addbmm static PyObject * THPVariable_addbmm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "addbmm(Scalar beta, Tensor input, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor out=None)|deprecated", "addbmm(Scalar beta, Tensor input, Tensor batch1, Tensor batch2, *, Tensor out=None)|deprecated", "addbmm(Tensor input, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // [deprecated] aten::addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addbmm = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addbmm(batch1, batch2, beta, alpha); }; return wrap(dispatch_addbmm(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } else { // [deprecated] aten::addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addbmm_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Scalar & alpha, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addbmm_out(out, self, batch1, batch2, beta, alpha); }; return wrap(dispatch_addbmm_out(_r.tensor(5), _r.scalar(0), _r.tensor(1), _r.scalar(2), _r.tensor(3), _r.tensor(4))); } } case 1: { if (_r.isNone(4)) { // [deprecated] aten::addbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addbmm = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addbmm(batch1, batch2, beta, 1); }; return wrap(dispatch_addbmm(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } else { // [deprecated] aten::addbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addbmm_out = [](at::Tensor out, const at::Scalar & beta, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addbmm_out(out, self, batch1, batch2, beta, 1); }; return wrap(dispatch_addbmm_out(_r.tensor(4), _r.scalar(0), _r.tensor(1), _r.tensor(2), _r.tensor(3))); } } case 2: { if (_r.isNone(5)) { // aten::addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor auto dispatch_addbmm = [](const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addbmm(batch1, batch2, beta, alpha); }; return wrap(dispatch_addbmm(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } else { // aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_addbmm_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addbmm_out(out, self, batch1, batch2, beta, alpha); }; return wrap(dispatch_addbmm_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3), _r.scalar(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // diag static PyObject * THPVariable_diag(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "diag(Tensor input, int64_t diagonal=0, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::diag(Tensor self, int diagonal=0) -> Tensor auto dispatch_diag = [](const at::Tensor & self, int64_t diagonal) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.diag(diagonal); }; return wrap(dispatch_diag(_r.tensor(0), _r.toInt64(1))); } else { // aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_diag_out = [](at::Tensor out, const at::Tensor & self, int64_t diagonal) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::diag_out(out, self, diagonal); }; return wrap(dispatch_diag_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cross static PyObject * THPVariable_cross(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cross(Tensor input, Tensor other, int64_t? dim=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::cross(Tensor self, Tensor other, int? dim=None) -> Tensor auto dispatch_cross = [](const at::Tensor & self, const at::Tensor & other, ::std::optional dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cross(other, dim); }; return wrap(dispatch_cross(_r.tensor(0), _r.tensor(1), _r.toInt64Optional(2))); } else { // aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cross_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other, ::std::optional dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cross_out(out, self, other, dim); }; return wrap(dispatch_cross_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.toInt64Optional(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // triu static PyObject * THPVariable_triu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "triu(Tensor input, int64_t diagonal=0, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::triu(Tensor self, int diagonal=0) -> Tensor auto dispatch_triu = [](const at::Tensor & self, int64_t diagonal) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.triu(diagonal); }; return wrap(dispatch_triu(_r.tensor(0), _r.toInt64(1))); } else { // aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_triu_out = [](at::Tensor out, const at::Tensor & self, int64_t diagonal) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::triu_out(out, self, diagonal); }; return wrap(dispatch_triu_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tril static PyObject * THPVariable_tril(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tril(Tensor input, int64_t diagonal=0, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::tril(Tensor self, int diagonal=0) -> Tensor auto dispatch_tril = [](const at::Tensor & self, int64_t diagonal) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.tril(diagonal); }; return wrap(dispatch_tril(_r.tensor(0), _r.toInt64(1))); } else { // aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_tril_out = [](at::Tensor out, const at::Tensor & self, int64_t diagonal) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::tril_out(out, self, diagonal); }; return wrap(dispatch_tril_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // tril_indices static PyObject * THPVariable_tril_indices(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "tril_indices(int64_t row, int64_t col, int64_t offset=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(3, at::kLong)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_tril_indices = [](int64_t row, int64_t col, int64_t offset, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::tril_indices(row, col, offset, options); }; return wrap(dispatch_tril_indices(_r.toInt64(0), _r.toInt64(1), _r.toInt64(2), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // triu_indices static PyObject * THPVariable_triu_indices(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "triu_indices(int64_t row, int64_t col, int64_t offset=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeWithDefault(3, at::kLong)) .device(_r.deviceWithDefault(5, torch::tensors::get_default_device())) .layout(_r.layoutOptional(4)) .requires_grad(_r.toBool(7)) .pinned_memory(_r.toBool(6)); torch::utils::maybe_initialize_device(options); auto dispatch_triu_indices = [](int64_t row, int64_t col, int64_t offset, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::triu_indices(row, col, offset, options); }; return wrap(dispatch_triu_indices(_r.toInt64(0), _r.toInt64(1), _r.toInt64(2), options)); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // trace static PyObject * THPVariable_trace(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "trace(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::trace(Tensor self) -> Tensor auto dispatch_trace = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.trace(); }; return wrap(dispatch_trace(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // ne static PyObject * THPVariable_ne(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ne(Tensor input, Tensor other, *, Tensor out=None)", "ne(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::ne.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_ne = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ne(other); }; return wrap(dispatch_ne(_r.tensor(0), _r.tensor(1))); } else { // aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ne_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ne_out(out, self, other); }; return wrap(dispatch_ne_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::ne.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_ne = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ne(other); }; return wrap(dispatch_ne(_r.tensor(0), _r.scalar(1))); } else { // aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ne_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ne_out(out, self, other); }; return wrap(dispatch_ne_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // not_equal static PyObject * THPVariable_not_equal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "not_equal(Tensor input, Tensor other, *, Tensor out=None)", "not_equal(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::not_equal.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_not_equal = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.not_equal(other); }; return wrap(dispatch_not_equal(_r.tensor(0), _r.tensor(1))); } else { // aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_not_equal_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::not_equal_out(out, self, other); }; return wrap(dispatch_not_equal_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::not_equal.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_not_equal = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.not_equal(other); }; return wrap(dispatch_not_equal(_r.tensor(0), _r.scalar(1))); } else { // aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_not_equal_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::not_equal_out(out, self, other); }; return wrap(dispatch_not_equal_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // eq static PyObject * THPVariable_eq(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "eq(Tensor input, Tensor other, *, Tensor out=None)", "eq(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::eq.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_eq = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.eq(other); }; return wrap(dispatch_eq(_r.tensor(0), _r.tensor(1))); } else { // aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_eq_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::eq_out(out, self, other); }; return wrap(dispatch_eq_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::eq.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_eq = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.eq(other); }; return wrap(dispatch_eq(_r.tensor(0), _r.scalar(1))); } else { // aten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_eq_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::eq_out(out, self, other); }; return wrap(dispatch_eq_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // ge static PyObject * THPVariable_ge(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ge(Tensor input, Tensor other, *, Tensor out=None)", "ge(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::ge.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_ge = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ge(other); }; return wrap(dispatch_ge(_r.tensor(0), _r.tensor(1))); } else { // aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ge_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ge_out(out, self, other); }; return wrap(dispatch_ge_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::ge.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_ge = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ge(other); }; return wrap(dispatch_ge(_r.tensor(0), _r.scalar(1))); } else { // aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ge_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ge_out(out, self, other); }; return wrap(dispatch_ge_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // greater_equal static PyObject * THPVariable_greater_equal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "greater_equal(Tensor input, Tensor other, *, Tensor out=None)", "greater_equal(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::greater_equal.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_greater_equal = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.greater_equal(other); }; return wrap(dispatch_greater_equal(_r.tensor(0), _r.tensor(1))); } else { // aten::greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_greater_equal_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::greater_equal_out(out, self, other); }; return wrap(dispatch_greater_equal_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::greater_equal.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_greater_equal = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.greater_equal(other); }; return wrap(dispatch_greater_equal(_r.tensor(0), _r.scalar(1))); } else { // aten::greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_greater_equal_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::greater_equal_out(out, self, other); }; return wrap(dispatch_greater_equal_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // le static PyObject * THPVariable_le(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "le(Tensor input, Tensor other, *, Tensor out=None)", "le(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::le.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_le = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.le(other); }; return wrap(dispatch_le(_r.tensor(0), _r.tensor(1))); } else { // aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_le_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::le_out(out, self, other); }; return wrap(dispatch_le_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::le.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_le = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.le(other); }; return wrap(dispatch_le(_r.tensor(0), _r.scalar(1))); } else { // aten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_le_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::le_out(out, self, other); }; return wrap(dispatch_le_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // less_equal static PyObject * THPVariable_less_equal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "less_equal(Tensor input, Tensor other, *, Tensor out=None)", "less_equal(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::less_equal.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_less_equal = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.less_equal(other); }; return wrap(dispatch_less_equal(_r.tensor(0), _r.tensor(1))); } else { // aten::less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_less_equal_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::less_equal_out(out, self, other); }; return wrap(dispatch_less_equal_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::less_equal.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_less_equal = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.less_equal(other); }; return wrap(dispatch_less_equal(_r.tensor(0), _r.scalar(1))); } else { // aten::less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_less_equal_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::less_equal_out(out, self, other); }; return wrap(dispatch_less_equal_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // gt static PyObject * THPVariable_gt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "gt(Tensor input, Tensor other, *, Tensor out=None)", "gt(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::gt.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_gt = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.gt(other); }; return wrap(dispatch_gt(_r.tensor(0), _r.tensor(1))); } else { // aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_gt_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::gt_out(out, self, other); }; return wrap(dispatch_gt_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::gt.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_gt = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.gt(other); }; return wrap(dispatch_gt(_r.tensor(0), _r.scalar(1))); } else { // aten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_gt_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::gt_out(out, self, other); }; return wrap(dispatch_gt_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // greater static PyObject * THPVariable_greater(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "greater(Tensor input, Tensor other, *, Tensor out=None)", "greater(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::greater.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_greater = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.greater(other); }; return wrap(dispatch_greater(_r.tensor(0), _r.tensor(1))); } else { // aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_greater_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::greater_out(out, self, other); }; return wrap(dispatch_greater_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::greater.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_greater = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.greater(other); }; return wrap(dispatch_greater(_r.tensor(0), _r.scalar(1))); } else { // aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_greater_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::greater_out(out, self, other); }; return wrap(dispatch_greater_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // lt static PyObject * THPVariable_lt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lt(Tensor input, Tensor other, *, Tensor out=None)", "lt(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::lt.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_lt = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lt(other); }; return wrap(dispatch_lt(_r.tensor(0), _r.tensor(1))); } else { // aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_lt_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::lt_out(out, self, other); }; return wrap(dispatch_lt_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::lt.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_lt = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lt(other); }; return wrap(dispatch_lt(_r.tensor(0), _r.scalar(1))); } else { // aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_lt_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::lt_out(out, self, other); }; return wrap(dispatch_lt_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // less static PyObject * THPVariable_less(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "less(Tensor input, Tensor other, *, Tensor out=None)", "less(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::less.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_less = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.less(other); }; return wrap(dispatch_less(_r.tensor(0), _r.tensor(1))); } else { // aten::less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_less_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::less_out(out, self, other); }; return wrap(dispatch_less_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::less.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_less = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.less(other); }; return wrap(dispatch_less(_r.tensor(0), _r.scalar(1))); } else { // aten::less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_less_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::less_out(out, self, other); }; return wrap(dispatch_less_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // take static PyObject * THPVariable_take(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "take(Tensor input, Tensor index, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::take(Tensor self, Tensor index) -> Tensor auto dispatch_take = [](const at::Tensor & self, const at::Tensor & index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.take(index); }; return wrap(dispatch_take(_r.tensor(0), _r.tensor(1))); } else { // aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_take_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::take_out(out, self, index); }; return wrap(dispatch_take_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // take_along_dim static PyObject * THPVariable_take_along_dim(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "take_along_dim(Tensor input, Tensor indices, int64_t? dim=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor auto dispatch_take_along_dim = [](const at::Tensor & self, const at::Tensor & indices, ::std::optional dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.take_along_dim(indices, dim); }; return wrap(dispatch_take_along_dim(_r.tensor(0), _r.tensor(1), _r.toInt64Optional(2))); } else { // aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_take_along_dim_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & indices, ::std::optional dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::take_along_dim_out(out, self, indices, dim); }; return wrap(dispatch_take_along_dim_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.toInt64Optional(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // index_select static PyObject * THPVariable_index_select(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "index_select(Tensor input, int64_t dim, Tensor index, *, Tensor out=None)", "index_select(Tensor input, Dimname dim, Tensor index, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::index_select(Tensor self, int dim, Tensor index) -> Tensor auto dispatch_index_select = [](const at::Tensor & self, int64_t dim, const at::Tensor & index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_select(dim, index); }; return wrap(dispatch_index_select(_r.tensor(0), _r.toInt64(1), _r.tensor(2))); } else { // aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_index_select_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::index_select_out(out, self, dim, index); }; return wrap(dispatch_index_select_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.tensor(2))); } } case 1: { if (_r.isNone(3)) { // aten::index_select.dimname(Tensor self, Dimname dim, Tensor index) -> Tensor auto dispatch_index_select = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.index_select(dim, index); }; return wrap(dispatch_index_select(_r.tensor(0), _r.dimname(1), _r.tensor(2))); } else { // aten::index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_index_select_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim, const at::Tensor & index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::index_select_out(out, self, dim, index); }; return wrap(dispatch_index_select_out(_r.tensor(3), _r.tensor(0), _r.dimname(1), _r.tensor(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // masked_select static PyObject * THPVariable_masked_select(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "masked_select(Tensor input, Tensor mask, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::masked_select(Tensor self, Tensor mask) -> Tensor auto dispatch_masked_select = [](const at::Tensor & self, const at::Tensor & mask) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.masked_select(mask); }; return wrap(dispatch_masked_select(_r.tensor(0), _r.tensor(1))); } else { // aten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_masked_select_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & mask) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::masked_select_out(out, self, mask); }; return wrap(dispatch_masked_select_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // nonzero_static static PyObject * THPVariable_nonzero_static(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nonzero_static(Tensor input, *, SymInt size, int64_t fill_value=-1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::nonzero_static(Tensor self, *, SymInt size, int fill_value=-1) -> Tensor auto dispatch_nonzero_static = [](const at::Tensor & self, c10::SymInt size, int64_t fill_value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nonzero_static_symint(size, fill_value); }; return wrap(dispatch_nonzero_static(_r.tensor(0), _r.toSymInt(1), _r.toInt64(2))); } else { // aten::nonzero_static.out(Tensor self, *, SymInt size, int fill_value=-1, Tensor(a!) out) -> Tensor(a!) auto dispatch_nonzero_static_out = [](at::Tensor out, const at::Tensor & self, c10::SymInt size, int64_t fill_value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nonzero_static_symint_out(out, self, size, fill_value); }; return wrap(dispatch_nonzero_static_out(_r.tensor(3), _r.tensor(0), _r.toSymInt(1), _r.toInt64(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // argwhere static PyObject * THPVariable_argwhere(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "argwhere(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::argwhere(Tensor self) -> Tensor auto dispatch_argwhere = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.argwhere(); }; return wrap(dispatch_argwhere(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // gather static PyObject * THPVariable_gather(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "gather(Tensor input, int64_t dim, Tensor index, *, bool sparse_grad=False, Tensor out=None)", "gather(Tensor input, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor auto dispatch_gather = [](const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.gather(dim, index, sparse_grad); }; return wrap(dispatch_gather(_r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.toBool(3))); } else { // aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_gather_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::gather_out(out, self, dim, index, sparse_grad); }; return wrap(dispatch_gather_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.tensor(2), _r.toBool(3))); } } case 1: { if (_r.isNone(4)) { // aten::gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor auto dispatch_gather = [](const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.gather(dim, index, sparse_grad); }; return wrap(dispatch_gather(_r.tensor(0), _r.dimname(1), _r.tensor(2), _r.toBool(3))); } else { // aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_gather_out = [](at::Tensor out, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::gather_out(out, self, dim, index, sparse_grad); }; return wrap(dispatch_gather_out(_r.tensor(4), _r.tensor(0), _r.dimname(1), _r.tensor(2), _r.toBool(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // addcmul static PyObject * THPVariable_addcmul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "addcmul(Tensor input, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor out=None)|deprecated", "addcmul(Tensor input, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // [deprecated] aten::addcmul(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addcmul = [](at::Tensor out, const at::Tensor & self, const at::Scalar & value, const at::Tensor & tensor1, const at::Tensor & tensor2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addcmul(tensor1, tensor2, value); }; return wrap(dispatch_addcmul(_r.tensor(4), _r.tensor(0), _r.scalar(1), _r.tensor(2), _r.tensor(3))); } else { // [deprecated] aten::addcmul(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addcmul_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & value, const at::Tensor & tensor1, const at::Tensor & tensor2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addcmul_out(out, self, tensor1, tensor2, value); }; return wrap(dispatch_addcmul_out(_r.tensor(4), _r.tensor(0), _r.scalar(1), _r.tensor(2), _r.tensor(3))); } } case 1: { if (_r.isNone(4)) { // aten::addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor auto dispatch_addcmul = [](const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addcmul(tensor1, tensor2, value); }; return wrap(dispatch_addcmul(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3))); } else { // aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_addcmul_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addcmul_out(out, self, tensor1, tensor2, value); }; return wrap(dispatch_addcmul_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // addcdiv static PyObject * THPVariable_addcdiv(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "addcdiv(Tensor input, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor out=None)|deprecated", "addcdiv(Tensor input, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // [deprecated] aten::addcdiv(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addcdiv = [](at::Tensor out, const at::Tensor & self, const at::Scalar & value, const at::Tensor & tensor1, const at::Tensor & tensor2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addcdiv(tensor1, tensor2, value); }; return wrap(dispatch_addcdiv(_r.tensor(4), _r.tensor(0), _r.scalar(1), _r.tensor(2), _r.tensor(3))); } else { // [deprecated] aten::addcdiv(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_addcdiv_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & value, const at::Tensor & tensor1, const at::Tensor & tensor2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addcdiv_out(out, self, tensor1, tensor2, value); }; return wrap(dispatch_addcdiv_out(_r.tensor(4), _r.tensor(0), _r.scalar(1), _r.tensor(2), _r.tensor(3))); } } case 1: { if (_r.isNone(4)) { // aten::addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor auto dispatch_addcdiv = [](const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.addcdiv(tensor1, tensor2, value); }; return wrap(dispatch_addcdiv(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3))); } else { // aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) auto dispatch_addcdiv_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::addcdiv_out(out, self, tensor1, tensor2, value); }; return wrap(dispatch_addcdiv_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.scalar(3))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // triangular_solve static PyObject * THPVariable_triangular_solve(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_triangular_solve_out_structseq(); static PyTypeObject* NamedTuple1 = generated::get_triangular_solve_structseq(); static PythonArgParser parser({ "triangular_solve(Tensor input, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(5)) { // aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient) auto dispatch_triangular_solve = [](const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.triangular_solve(A, upper, transpose, unitriangular); }; return wrap(NamedTuple1, dispatch_triangular_solve(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3), _r.toBool(4))); } else { // aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient) auto out = _r.tensorlist_n<2>(5); auto dispatch_triangular_solve_out = [](at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::triangular_solve_out(X, M, self, A, upper, transpose, unitriangular); }; return wrap(NamedTuple, dispatch_triangular_solve_out(out[0], out[1], _r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3), _r.toBool(4))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _linalg_check_errors static PyObject * THPVariable__linalg_check_errors(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_linalg_check_errors(Tensor info, c10::string_view api_name, *, bool is_matrix)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> () auto dispatch__linalg_check_errors = [](const at::Tensor & info, c10::string_view api_name, bool is_matrix) -> void { pybind11::gil_scoped_release no_gil; at::_linalg_check_errors(info, api_name, is_matrix); }; dispatch__linalg_check_errors(_r.tensor(0), _r.stringView(1), _r.toBool(2)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // svd static PyObject * THPVariable_svd(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_svd_out_structseq(); static PyTypeObject* NamedTuple1 = generated::get_svd_structseq(); static PythonArgParser parser({ "svd(Tensor input, bool some=True, bool compute_uv=True, *, TensorList[3] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V) auto dispatch_svd = [](const at::Tensor & self, bool some, bool compute_uv) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.svd(some, compute_uv); }; return wrap(NamedTuple1, dispatch_svd(_r.tensor(0), _r.toBool(1), _r.toBool(2))); } else { // aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) auto out = _r.tensorlist_n<3>(3); auto dispatch_svd_out = [](at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some, bool compute_uv) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::svd_out(U, S, V, self, some, compute_uv); }; return wrap(NamedTuple, dispatch_svd_out(out[0], out[1], out[2], _r.tensor(0), _r.toBool(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // swapaxes static PyObject * THPVariable_swapaxes(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "swapaxes(Tensor input, int64_t axis0, int64_t axis1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a) auto dispatch_swapaxes = [](const at::Tensor & self, int64_t axis0, int64_t axis1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.swapaxes(axis0, axis1); }; return wrap(dispatch_swapaxes(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // swapdims static PyObject * THPVariable_swapdims(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "swapdims(Tensor input, int64_t dim0, int64_t dim1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a) auto dispatch_swapdims = [](const at::Tensor & self, int64_t dim0, int64_t dim1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.swapdims(dim0, dim1); }; return wrap(dispatch_swapdims(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cholesky static PyObject * THPVariable_cholesky(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cholesky(Tensor input, bool upper=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::cholesky(Tensor self, bool upper=False) -> Tensor auto dispatch_cholesky = [](const at::Tensor & self, bool upper) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cholesky(upper); }; return wrap(dispatch_cholesky(_r.tensor(0), _r.toBool(1))); } else { // aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cholesky_out = [](at::Tensor out, const at::Tensor & self, bool upper) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cholesky_out(out, self, upper); }; return wrap(dispatch_cholesky_out(_r.tensor(2), _r.tensor(0), _r.toBool(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cholesky_solve static PyObject * THPVariable_cholesky_solve(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cholesky_solve(Tensor input, Tensor input2, bool upper=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor auto dispatch_cholesky_solve = [](const at::Tensor & self, const at::Tensor & input2, bool upper) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cholesky_solve(input2, upper); }; return wrap(dispatch_cholesky_solve(_r.tensor(0), _r.tensor(1), _r.toBool(2))); } else { // aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cholesky_solve_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & input2, bool upper) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cholesky_solve_out(out, self, input2, upper); }; return wrap(dispatch_cholesky_solve_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // cholesky_inverse static PyObject * THPVariable_cholesky_inverse(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "cholesky_inverse(Tensor input, bool upper=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::cholesky_inverse(Tensor self, bool upper=False) -> Tensor auto dispatch_cholesky_inverse = [](const at::Tensor & self, bool upper) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.cholesky_inverse(upper); }; return wrap(dispatch_cholesky_inverse(_r.tensor(0), _r.toBool(1))); } else { // aten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_cholesky_inverse_out = [](at::Tensor out, const at::Tensor & self, bool upper) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::cholesky_inverse_out(out, self, upper); }; return wrap(dispatch_cholesky_inverse_out(_r.tensor(2), _r.tensor(0), _r.toBool(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // qr static PyObject * THPVariable_qr(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_qr_out_structseq(); static PyTypeObject* NamedTuple1 = generated::get_qr_structseq(); static PythonArgParser parser({ "qr(Tensor input, bool some=True, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R) auto dispatch_qr = [](const at::Tensor & self, bool some) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.qr(some); }; return wrap(NamedTuple1, dispatch_qr(_r.tensor(0), _r.toBool(1))); } else { // aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) auto out = _r.tensorlist_n<2>(2); auto dispatch_qr_out = [](at::Tensor & Q, at::Tensor & R, const at::Tensor & self, bool some) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::qr_out(Q, R, self, some); }; return wrap(NamedTuple, dispatch_qr_out(out[0], out[1], _r.tensor(0), _r.toBool(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // geqrf static PyObject * THPVariable_geqrf(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_geqrf_out_structseq(); static PyTypeObject* NamedTuple1 = generated::get_geqrf_structseq(); static PythonArgParser parser({ "geqrf(Tensor input, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::geqrf(Tensor self) -> (Tensor a, Tensor tau) auto dispatch_geqrf = [](const at::Tensor & self) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.geqrf(); }; return wrap(NamedTuple1, dispatch_geqrf(_r.tensor(0))); } else { // aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau) auto out = _r.tensorlist_n<2>(1); auto dispatch_geqrf_out = [](at::Tensor & a, at::Tensor & tau, const at::Tensor & self) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::geqrf_out(a, tau, self); }; return wrap(NamedTuple, dispatch_geqrf_out(out[0], out[1], _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // orgqr static PyObject * THPVariable_orgqr(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "orgqr(Tensor input, Tensor input2, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::orgqr(Tensor self, Tensor input2) -> Tensor auto dispatch_orgqr = [](const at::Tensor & self, const at::Tensor & input2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.orgqr(input2); }; return wrap(dispatch_orgqr(_r.tensor(0), _r.tensor(1))); } else { // aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_orgqr_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & input2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::orgqr_out(out, self, input2); }; return wrap(dispatch_orgqr_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ormqr static PyObject * THPVariable_ormqr(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ormqr(Tensor input, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(5)) { // aten::ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor auto dispatch_ormqr = [](const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ormqr(input2, input3, left, transpose); }; return wrap(dispatch_ormqr(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3), _r.toBool(4))); } else { // aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ormqr_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ormqr_out(out, self, input2, input3, left, transpose); }; return wrap(dispatch_ormqr_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toBool(3), _r.toBool(4))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _lu_with_info static PyObject * THPVariable__lu_with_info(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__lu_with_info_structseq(); static PythonArgParser parser({ "_lu_with_info(Tensor input, bool pivot=True, bool check_errors=True)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_lu_with_info(Tensor self, bool pivot=True, bool check_errors=True) -> (Tensor LU, Tensor pivots, Tensor info) auto dispatch__lu_with_info = [](const at::Tensor & self, bool pivot, bool check_errors) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_lu_with_info(self, pivot, check_errors); }; return wrap(NamedTuple, dispatch__lu_with_info(_r.tensor(0), _r.toBool(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // lu_solve static PyObject * THPVariable_lu_solve(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lu_solve(Tensor input, Tensor LU_data, Tensor LU_pivots, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor auto dispatch_lu_solve = [](const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lu_solve(LU_data, LU_pivots); }; return wrap(dispatch_lu_solve(_r.tensor(0), _r.tensor(1), _r.tensor(2))); } else { // aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_lu_solve_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::lu_solve_out(out, self, LU_data, LU_pivots); }; return wrap(dispatch_lu_solve_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.tensor(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // lu_unpack static PyObject * THPVariable_lu_unpack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_lu_unpack_structseq(); static PyTypeObject* NamedTuple1 = generated::get_lu_unpack_out_structseq(); static PythonArgParser parser({ "lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, TensorList[3] out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True) -> (Tensor P, Tensor L, Tensor U) auto dispatch_lu_unpack = [](const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::lu_unpack(LU_data, LU_pivots, unpack_data, unpack_pivots); }; return wrap(NamedTuple, dispatch_lu_unpack(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } else { // aten::lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) auto out = _r.tensorlist_n<3>(4); auto dispatch_lu_unpack_out = [](at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::lu_unpack_out(P, L, U, LU_data, LU_pivots, unpack_data, unpack_pivots); }; return wrap(NamedTuple1, dispatch_lu_unpack_out(out[0], out[1], out[2], _r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // multinomial static PyObject * THPVariable_multinomial(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "multinomial(Tensor input, int64_t num_samples, bool replacement=False, *, Generator? generator=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::multinomial(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor auto dispatch_multinomial = [](const at::Tensor & self, int64_t num_samples, bool replacement, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.multinomial(num_samples, replacement, generator); }; return wrap(dispatch_multinomial(_r.tensor(0), _r.toInt64(1), _r.toBool(2), _r.generator(3))); } else { // aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_multinomial_out = [](at::Tensor out, const at::Tensor & self, int64_t num_samples, bool replacement, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::multinomial_out(out, self, num_samples, replacement, generator); }; return wrap(dispatch_multinomial_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.toBool(2), _r.generator(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // lgamma static PyObject * THPVariable_lgamma(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lgamma(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::lgamma(Tensor self) -> Tensor auto dispatch_lgamma = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lgamma(); }; return wrap(dispatch_lgamma(_r.tensor(0))); } else { // aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_lgamma_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::lgamma_out(out, self); }; return wrap(dispatch_lgamma_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // digamma static PyObject * THPVariable_digamma(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "digamma(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::digamma(Tensor self) -> Tensor auto dispatch_digamma = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.digamma(); }; return wrap(dispatch_digamma(_r.tensor(0))); } else { // aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_digamma_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::digamma_out(out, self); }; return wrap(dispatch_digamma_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // polygamma static PyObject * THPVariable_polygamma(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "polygamma(int64_t n, Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::polygamma(int n, Tensor self) -> Tensor auto dispatch_polygamma = [](int64_t n, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.polygamma(n); }; return wrap(dispatch_polygamma(_r.toInt64(0), _r.tensor(1))); } else { // aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_polygamma_out = [](at::Tensor out, int64_t n, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::polygamma_out(out, n, self); }; return wrap(dispatch_polygamma_out(_r.tensor(2), _r.toInt64(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // erfinv static PyObject * THPVariable_erfinv(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "erfinv(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::erfinv(Tensor self) -> Tensor auto dispatch_erfinv = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.erfinv(); }; return wrap(dispatch_erfinv(_r.tensor(0))); } else { // aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_erfinv_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::erfinv_out(out, self); }; return wrap(dispatch_erfinv_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // i0 static PyObject * THPVariable_i0(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "i0(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::i0(Tensor self) -> Tensor auto dispatch_i0 = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.i0(); }; return wrap(dispatch_i0(_r.tensor(0))); } else { // aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_i0_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::i0_out(out, self); }; return wrap(dispatch_i0_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // i0_ static PyObject * THPVariable_i0_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "i0_(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::i0_(Tensor(a!) self) -> Tensor(a!) auto dispatch_i0_ = [](at::Tensor self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.i0_(); }; return wrap(dispatch_i0_(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // sign static PyObject * THPVariable_sign(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "sign(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::sign(Tensor self) -> Tensor auto dispatch_sign = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.sign(); }; return wrap(dispatch_sign(_r.tensor(0))); } else { // aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_sign_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::sign_out(out, self); }; return wrap(dispatch_sign_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // signbit static PyObject * THPVariable_signbit(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "signbit(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::signbit(Tensor self) -> Tensor auto dispatch_signbit = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.signbit(); }; return wrap(dispatch_signbit(_r.tensor(0))); } else { // aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_signbit_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::signbit_out(out, self); }; return wrap(dispatch_signbit_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // dist static PyObject * THPVariable_dist(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "dist(Tensor input, Tensor other, Scalar p=2)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::dist(Tensor self, Tensor other, Scalar p=2) -> Tensor auto dispatch_dist = [](const at::Tensor & self, const at::Tensor & other, const at::Scalar & p) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.dist(other, p); }; return wrap(dispatch_dist(_r.tensor(0), _r.tensor(1), _r.scalar(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // atan2 static PyObject * THPVariable_atan2(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "atan2(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::atan2(Tensor self, Tensor other) -> Tensor auto dispatch_atan2 = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.atan2(other); }; return wrap(dispatch_atan2(_r.tensor(0), _r.tensor(1))); } else { // aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_atan2_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::atan2_out(out, self, other); }; return wrap(dispatch_atan2_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // arctan2 static PyObject * THPVariable_arctan2(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "arctan2(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::arctan2(Tensor self, Tensor other) -> Tensor auto dispatch_arctan2 = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.arctan2(other); }; return wrap(dispatch_arctan2(_r.tensor(0), _r.tensor(1))); } else { // aten::arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_arctan2_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::arctan2_out(out, self, other); }; return wrap(dispatch_arctan2_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // lerp static PyObject * THPVariable_lerp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "lerp(Tensor input, Tensor end, Tensor weight, *, Tensor out=None)", "lerp(Tensor input, Tensor end, Scalar weight, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor auto dispatch_lerp = [](const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lerp(end, weight); }; return wrap(dispatch_lerp(_r.tensor(0), _r.tensor(1), _r.tensor(2))); } else { // aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_lerp_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::lerp_out(out, self, end, weight); }; return wrap(dispatch_lerp_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.tensor(2))); } } case 1: { if (_r.isNone(3)) { // aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor auto dispatch_lerp = [](const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.lerp(end, weight); }; return wrap(dispatch_lerp(_r.tensor(0), _r.tensor(1), _r.scalar(2))); } else { // aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_lerp_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::lerp_out(out, self, end, weight); }; return wrap(dispatch_lerp_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.scalar(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // histc static PyObject * THPVariable_histc(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "histc(Tensor input, int64_t bins=100, Scalar min=0, Scalar max=0, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor auto dispatch_histc = [](const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.histc(bins, min, max); }; return wrap(dispatch_histc(_r.tensor(0), _r.toInt64(1), _r.scalar(2), _r.scalar(3))); } else { // aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_histc_out = [](at::Tensor out, const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::histc_out(out, self, bins, min, max); }; return wrap(dispatch_histc_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.scalar(2), _r.scalar(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // histogram static PyObject * THPVariable_histogram(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_histogram_out_structseq(); static PyTypeObject* NamedTuple1 = generated::get_histogram_structseq(); static PythonArgParser parser({ "histogram(Tensor input, Tensor bins, *, Tensor? weight=None, bool density=False, TensorList[2] out=None)", "histogram(Tensor input, int64_t bins=100, *, ArrayRef? range=None, Tensor? weight=None, bool density=False, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::histogram.bins_tensor(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges) auto dispatch_histogram = [](const at::Tensor & self, const at::Tensor & bins, const ::std::optional & weight, bool density) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.histogram(bins, weight, density); }; return wrap(NamedTuple1, dispatch_histogram(_r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.toBool(3))); } else { // aten::histogram.bins_tensor_out(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) auto out = _r.tensorlist_n<2>(4); auto dispatch_histogram_out = [](at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, const at::Tensor & bins, const ::std::optional & weight, bool density) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::histogram_out(hist, bin_edges, self, bins, weight, density); }; return wrap(NamedTuple, dispatch_histogram_out(out[0], out[1], _r.tensor(0), _r.tensor(1), _r.optionalTensor(2), _r.toBool(3))); } } case 1: { if (_r.isNone(5)) { // aten::histogram.bin_ct(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges) auto dispatch_histogram = [](const at::Tensor & self, int64_t bins, ::std::optional> range, const ::std::optional & weight, bool density) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.histogram(bins, range, weight, density); }; return wrap(NamedTuple1, dispatch_histogram(_r.tensor(0), _r.toInt64(1), _r.doublelistOptional(2), _r.optionalTensor(3), _r.toBool(4))); } else { // aten::histogram.bin_ct_out(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) auto out = _r.tensorlist_n<2>(5); auto dispatch_histogram_out = [](at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, int64_t bins, ::std::optional> range, const ::std::optional & weight, bool density) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::histogram_out(hist, bin_edges, self, bins, range, weight, density); }; return wrap(NamedTuple, dispatch_histogram_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.doublelistOptional(2), _r.optionalTensor(3), _r.toBool(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _histogramdd_bin_edges static PyObject * THPVariable__histogramdd_bin_edges(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_histogramdd_bin_edges(Tensor input, IntArrayRef bins, *, ArrayRef? range=None, Tensor? weight=None, bool density=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[] auto dispatch__histogramdd_bin_edges = [](const at::Tensor & self, at::IntArrayRef bins, ::std::optional> range, const ::std::optional & weight, bool density) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_histogramdd_bin_edges(self, bins, range, weight, density); }; return wrap(dispatch__histogramdd_bin_edges(_r.tensor(0), _r.intlist(1), _r.doublelistOptional(2), _r.optionalTensor(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _histogramdd_from_bin_cts static PyObject * THPVariable__histogramdd_from_bin_cts(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_histogramdd_from_bin_cts(Tensor input, IntArrayRef bins, *, ArrayRef? range=None, Tensor? weight=None, bool density=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_histogramdd_from_bin_cts(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor auto dispatch__histogramdd_from_bin_cts = [](const at::Tensor & self, at::IntArrayRef bins, ::std::optional> range, const ::std::optional & weight, bool density) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_histogramdd_from_bin_cts(self, bins, range, weight, density); }; return wrap(dispatch__histogramdd_from_bin_cts(_r.tensor(0), _r.intlist(1), _r.doublelistOptional(2), _r.optionalTensor(3), _r.toBool(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _histogramdd_from_bin_tensors static PyObject * THPVariable__histogramdd_from_bin_tensors(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_histogramdd_from_bin_tensors(Tensor input, TensorList bins, *, Tensor? weight=None, bool density=False)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor auto dispatch__histogramdd_from_bin_tensors = [](const at::Tensor & self, at::TensorList bins, const ::std::optional & weight, bool density) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_histogramdd_from_bin_tensors(self, bins, weight, density); }; return wrap(dispatch__histogramdd_from_bin_tensors(_r.tensor(0), _r.tensorlist(1), _r.optionalTensor(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // histogramdd static PyObject * THPVariable_histogramdd(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_histogramdd_structseq(); static PythonArgParser parser({ "histogramdd(Tensor input, int64_t bins, ArrayRef? range=None, Tensor? weight=None, bool density=False)", "histogramdd(Tensor input, IntArrayRef bins, ArrayRef? range=None, Tensor? weight=None, bool density=False)", "histogramdd(Tensor input, TensorList bins, ArrayRef? range=None, Tensor? weight=None, bool density=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::histogramdd.int_bins(Tensor self, int bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) auto dispatch_histogramdd = [](const at::Tensor & self, int64_t bins, ::std::optional> range, const ::std::optional & weight, bool density) -> ::std::tuple> { pybind11::gil_scoped_release no_gil; return at::histogramdd(self, bins, range, weight, density); }; return wrap(NamedTuple, dispatch_histogramdd(_r.tensor(0), _r.toInt64(1), _r.doublelistOptional(2), _r.optionalTensor(3), _r.toBool(4))); } case 1: { // aten::histogramdd(Tensor self, int[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) auto dispatch_histogramdd = [](const at::Tensor & self, at::IntArrayRef bins, ::std::optional> range, const ::std::optional & weight, bool density) -> ::std::tuple> { pybind11::gil_scoped_release no_gil; return at::histogramdd(self, bins, range, weight, density); }; return wrap(NamedTuple, dispatch_histogramdd(_r.tensor(0), _r.intlist(1), _r.doublelistOptional(2), _r.optionalTensor(3), _r.toBool(4))); } case 2: { // aten::histogramdd.TensorList_bins(Tensor self, Tensor[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) auto dispatch_histogramdd = [](const at::Tensor & self, at::TensorList bins, ::std::optional> range, const ::std::optional & weight, bool density) -> ::std::tuple> { pybind11::gil_scoped_release no_gil; return at::histogramdd(self, bins, range, weight, density); }; return wrap(NamedTuple, dispatch_histogramdd(_r.tensor(0), _r.tensorlist(1), _r.doublelistOptional(2), _r.optionalTensor(3), _r.toBool(4))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // fmod static PyObject * THPVariable_fmod(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fmod(Tensor input, Tensor other, *, Tensor out=None)", "fmod(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::fmod.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_fmod = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fmod(other); }; return wrap(dispatch_fmod(_r.tensor(0), _r.tensor(1))); } else { // aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_fmod_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fmod_out(out, self, other); }; return wrap(dispatch_fmod_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::fmod.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_fmod = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fmod(other); }; return wrap(dispatch_fmod(_r.tensor(0), _r.scalar(1))); } else { // aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_fmod_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fmod_out(out, self, other); }; return wrap(dispatch_fmod_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // hypot static PyObject * THPVariable_hypot(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "hypot(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::hypot(Tensor self, Tensor other) -> Tensor auto dispatch_hypot = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.hypot(other); }; return wrap(dispatch_hypot(_r.tensor(0), _r.tensor(1))); } else { // aten::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_hypot_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::hypot_out(out, self, other); }; return wrap(dispatch_hypot_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // igamma static PyObject * THPVariable_igamma(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "igamma(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::igamma(Tensor self, Tensor other) -> Tensor auto dispatch_igamma = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.igamma(other); }; return wrap(dispatch_igamma(_r.tensor(0), _r.tensor(1))); } else { // aten::igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_igamma_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::igamma_out(out, self, other); }; return wrap(dispatch_igamma_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // igammac static PyObject * THPVariable_igammac(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "igammac(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::igammac(Tensor self, Tensor other) -> Tensor auto dispatch_igammac = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.igammac(other); }; return wrap(dispatch_igammac(_r.tensor(0), _r.tensor(1))); } else { // aten::igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_igammac_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::igammac_out(out, self, other); }; return wrap(dispatch_igammac_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // nextafter static PyObject * THPVariable_nextafter(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nextafter(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::nextafter(Tensor self, Tensor other) -> Tensor auto dispatch_nextafter = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nextafter(other); }; return wrap(dispatch_nextafter(_r.tensor(0), _r.tensor(1))); } else { // aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_nextafter_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nextafter_out(out, self, other); }; return wrap(dispatch_nextafter_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // remainder static PyObject * THPVariable_remainder(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "remainder(Tensor input, Tensor other, *, Tensor out=None)", "remainder(Scalar self, Tensor other)", "remainder(Tensor input, Scalar other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::remainder.Tensor(Tensor self, Tensor other) -> Tensor auto dispatch_remainder = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.remainder(other); }; return wrap(dispatch_remainder(_r.tensor(0), _r.tensor(1))); } else { // aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_remainder_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::remainder_out(out, self, other); }; return wrap(dispatch_remainder_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { // aten::remainder.Scalar_Tensor(Scalar self, Tensor other) -> Tensor auto dispatch_remainder = [](const at::Scalar & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::remainder(self, other); }; return wrap(dispatch_remainder(_r.scalar(0), _r.tensor(1))); } case 2: { if (_r.isNone(2)) { // aten::remainder.Scalar(Tensor self, Scalar other) -> Tensor auto dispatch_remainder = [](const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.remainder(other); }; return wrap(dispatch_remainder(_r.tensor(0), _r.scalar(1))); } else { // aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_remainder_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::remainder_out(out, self, other); }; return wrap(dispatch_remainder_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fmin static PyObject * THPVariable_fmin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fmin(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::fmin(Tensor self, Tensor other) -> Tensor auto dispatch_fmin = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fmin(other); }; return wrap(dispatch_fmin(_r.tensor(0), _r.tensor(1))); } else { // aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_fmin_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fmin_out(out, self, other); }; return wrap(dispatch_fmin_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // fmax static PyObject * THPVariable_fmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "fmax(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::fmax(Tensor self, Tensor other) -> Tensor auto dispatch_fmax = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.fmax(other); }; return wrap(dispatch_fmax(_r.tensor(0), _r.tensor(1))); } else { // aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_fmax_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::fmax_out(out, self, other); }; return wrap(dispatch_fmax_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // maximum static PyObject * THPVariable_maximum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "maximum(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::maximum(Tensor self, Tensor other) -> Tensor auto dispatch_maximum = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.maximum(other); }; return wrap(dispatch_maximum(_r.tensor(0), _r.tensor(1))); } else { // aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_maximum_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::maximum_out(out, self, other); }; return wrap(dispatch_maximum_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // minimum static PyObject * THPVariable_minimum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "minimum(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::minimum(Tensor self, Tensor other) -> Tensor auto dispatch_minimum = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.minimum(other); }; return wrap(dispatch_minimum(_r.tensor(0), _r.tensor(1))); } else { // aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_minimum_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::minimum_out(out, self, other); }; return wrap(dispatch_minimum_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // quantile static PyObject * THPVariable_quantile(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "quantile(Tensor input, Tensor q, int64_t? dim=None, bool keepdim=False, *, c10::string_view interpolation=\"linear\", Tensor out=None)", "quantile(Tensor input, double q, int64_t? dim=None, bool keepdim=False, *, c10::string_view interpolation=\"linear\", Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor auto dispatch_quantile = [](const at::Tensor & self, const at::Tensor & q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.quantile(q, dim, keepdim, interpolation); }; return wrap(dispatch_quantile(_r.tensor(0), _r.tensor(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } else { // aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) auto dispatch_quantile_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantile_out(out, self, q, dim, keepdim, interpolation); }; return wrap(dispatch_quantile_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } } case 1: { if (_r.isNone(5)) { // aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor auto dispatch_quantile = [](const at::Tensor & self, double q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.quantile(q, dim, keepdim, interpolation); }; return wrap(dispatch_quantile(_r.tensor(0), _r.toDouble(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } else { // aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) auto dispatch_quantile_out = [](at::Tensor out, const at::Tensor & self, double q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::quantile_out(out, self, q, dim, keepdim, interpolation); }; return wrap(dispatch_quantile_out(_r.tensor(5), _r.tensor(0), _r.toDouble(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // nanquantile static PyObject * THPVariable_nanquantile(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "nanquantile(Tensor input, Tensor q, int64_t? dim=None, bool keepdim=False, *, c10::string_view interpolation=\"linear\", Tensor out=None)", "nanquantile(Tensor input, double q, int64_t? dim=None, bool keepdim=False, *, c10::string_view interpolation=\"linear\", Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(5)) { // aten::nanquantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor auto dispatch_nanquantile = [](const at::Tensor & self, const at::Tensor & q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nanquantile(q, dim, keepdim, interpolation); }; return wrap(dispatch_nanquantile(_r.tensor(0), _r.tensor(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } else { // aten::nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) auto dispatch_nanquantile_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nanquantile_out(out, self, q, dim, keepdim, interpolation); }; return wrap(dispatch_nanquantile_out(_r.tensor(5), _r.tensor(0), _r.tensor(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } } case 1: { if (_r.isNone(5)) { // aten::nanquantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor auto dispatch_nanquantile = [](const at::Tensor & self, double q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.nanquantile(q, dim, keepdim, interpolation); }; return wrap(dispatch_nanquantile(_r.tensor(0), _r.toDouble(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } else { // aten::nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) auto dispatch_nanquantile_out = [](at::Tensor out, const at::Tensor & self, double q, ::std::optional dim, bool keepdim, c10::string_view interpolation) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::nanquantile_out(out, self, q, dim, keepdim, interpolation); }; return wrap(dispatch_nanquantile_out(_r.tensor(5), _r.tensor(0), _r.toDouble(1), _r.toInt64Optional(2), _r.toBool(3), _r.stringView(4))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // sort static PyObject * THPVariable_sort(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_sort_out_structseq(); static PyTypeObject* NamedTuple1 = generated::get_sort_structseq(); static PythonArgParser parser({ "sort(Tensor input, *, bool? stable, int64_t dim=-1, bool descending=False, TensorList[2] out=None)", "sort(Tensor input, int64_t dim=-1, bool descending=False, *, TensorList[2] out=None)", "sort(Tensor input, *, bool? stable, Dimname dim, bool descending=False, TensorList[2] out=None)", "sort(Tensor input, Dimname dim, bool descending=False, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::sort.stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) auto dispatch_sort = [](const at::Tensor & self, ::std::optional stable, int64_t dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.sort(stable, dim, descending); }; return wrap(NamedTuple1, dispatch_sort(_r.tensor(0), _r.toBoolOptional(1), _r.toInt64(2), _r.toBool(3))); } else { // aten::sort.values_stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(4); auto dispatch_sort_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, ::std::optional stable, int64_t dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::sort_out(values, indices, self, stable, dim, descending); }; return wrap(NamedTuple, dispatch_sort_out(out[0], out[1], _r.tensor(0), _r.toBoolOptional(1), _r.toInt64(2), _r.toBool(3))); } } case 1: { if (_r.isNone(3)) { // aten::sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) auto dispatch_sort = [](const at::Tensor & self, int64_t dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.sort(dim, descending); }; return wrap(NamedTuple1, dispatch_sort(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_sort_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::sort_out(values, indices, self, dim, descending); }; return wrap(NamedTuple, dispatch_sort_out(out[0], out[1], _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } } case 2: { if (_r.isNone(4)) { // aten::sort.dimname_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices) auto dispatch_sort = [](const at::Tensor & self, ::std::optional stable, at::Dimname dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.sort(stable, dim, descending); }; return wrap(NamedTuple1, dispatch_sort(_r.tensor(0), _r.toBoolOptional(1), _r.dimname(2), _r.toBool(3))); } else { // aten::sort.dimname_values_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(4); auto dispatch_sort_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, ::std::optional stable, at::Dimname dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::sort_out(values, indices, self, stable, dim, descending); }; return wrap(NamedTuple, dispatch_sort_out(out[0], out[1], _r.tensor(0), _r.toBoolOptional(1), _r.dimname(2), _r.toBool(3))); } } case 3: { if (_r.isNone(3)) { // aten::sort.dimname(Tensor self, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices) auto dispatch_sort = [](const at::Tensor & self, at::Dimname dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.sort(dim, descending); }; return wrap(NamedTuple1, dispatch_sort(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } else { // aten::sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(3); auto dispatch_sort_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool descending) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::sort_out(values, indices, self, dim, descending); }; return wrap(NamedTuple, dispatch_sort_out(out[0], out[1], _r.tensor(0), _r.dimname(1), _r.toBool(2))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // msort static PyObject * THPVariable_msort(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "msort(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::msort(Tensor self) -> Tensor auto dispatch_msort = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.msort(); }; return wrap(dispatch_msort(_r.tensor(0))); } else { // aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_msort_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::msort_out(out, self); }; return wrap(dispatch_msort_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // argsort static PyObject * THPVariable_argsort(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "argsort(Tensor input, *, bool stable, int64_t dim=-1, bool descending=False, Tensor out=None)", "argsort(Tensor input, int64_t dim=-1, bool descending=False)", "argsort(Tensor input, Dimname dim, bool descending=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor auto dispatch_argsort = [](const at::Tensor & self, bool stable, int64_t dim, bool descending) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.argsort(stable, dim, descending); }; return wrap(dispatch_argsort(_r.tensor(0), _r.toBool(1), _r.toInt64(2), _r.toBool(3))); } else { // aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_argsort_out = [](at::Tensor out, const at::Tensor & self, bool stable, int64_t dim, bool descending) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::argsort_out(out, self, stable, dim, descending); }; return wrap(dispatch_argsort_out(_r.tensor(4), _r.tensor(0), _r.toBool(1), _r.toInt64(2), _r.toBool(3))); } } case 1: { // aten::argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor auto dispatch_argsort = [](const at::Tensor & self, int64_t dim, bool descending) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.argsort(dim, descending); }; return wrap(dispatch_argsort(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } case 2: { // aten::argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor auto dispatch_argsort = [](const at::Tensor & self, at::Dimname dim, bool descending) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.argsort(dim, descending); }; return wrap(dispatch_argsort(_r.tensor(0), _r.dimname(1), _r.toBool(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // topk static PyObject * THPVariable_topk(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_topk_out_structseq(); static PyTypeObject* NamedTuple1 = generated::get_topk_structseq(); static PythonArgParser parser({ "topk(Tensor input, SymInt k, int64_t dim=-1, bool largest=True, bool sorted=True, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(5)) { // aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) auto dispatch_topk = [](const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.topk_symint(k, dim, largest, sorted); }; return wrap(NamedTuple1, dispatch_topk(_r.tensor(0), _r.toSymInt(1), _r.toInt64(2), _r.toBool(3), _r.toBool(4))); } else { // aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) auto out = _r.tensorlist_n<2>(5); auto dispatch_topk_out = [](at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::topk_symint_out(values, indices, self, k, dim, largest, sorted); }; return wrap(NamedTuple, dispatch_topk_out(out[0], out[1], _r.tensor(0), _r.toSymInt(1), _r.toInt64(2), _r.toBool(3), _r.toBool(4))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // renorm static PyObject * THPVariable_renorm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "renorm(Tensor input, Scalar p, int64_t dim, Scalar maxnorm, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor auto dispatch_renorm = [](const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.renorm(p, dim, maxnorm); }; return wrap(dispatch_renorm(_r.tensor(0), _r.scalar(1), _r.toInt64(2), _r.scalar(3))); } else { // aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_renorm_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::renorm_out(out, self, p, dim, maxnorm); }; return wrap(dispatch_renorm_out(_r.tensor(4), _r.tensor(0), _r.scalar(1), _r.toInt64(2), _r.scalar(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // equal static PyObject * THPVariable_equal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "equal(Tensor input, Tensor other)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::equal(Tensor self, Tensor other) -> bool auto dispatch_equal = [](const at::Tensor & self, const at::Tensor & other) -> bool { pybind11::gil_scoped_release no_gil; return self.equal(other); }; return wrap(dispatch_equal(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // pow static PyObject * THPVariable_pow(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "pow(Tensor input, Tensor exponent, *, Tensor out=None)", "pow(Scalar self, Tensor exponent, *, Tensor out=None)", "pow(Tensor input, Scalar exponent, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor auto dispatch_pow = [](const at::Tensor & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.pow(exponent); }; return wrap(dispatch_pow(_r.tensor(0), _r.tensor(1))); } else { // aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_pow_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pow_out(out, self, exponent); }; return wrap(dispatch_pow_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::pow.Scalar(Scalar self, Tensor exponent) -> Tensor auto dispatch_pow = [](const at::Scalar & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pow(self, exponent); }; return wrap(dispatch_pow(_r.scalar(0), _r.tensor(1))); } else { // aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_pow_out = [](at::Tensor out, const at::Scalar & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pow_out(out, self, exponent); }; return wrap(dispatch_pow_out(_r.tensor(2), _r.scalar(0), _r.tensor(1))); } } case 2: { if (_r.isNone(2)) { // aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor auto dispatch_pow = [](const at::Tensor & self, const at::Scalar & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.pow(exponent); }; return wrap(dispatch_pow(_r.tensor(0), _r.scalar(1))); } else { // aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_pow_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::pow_out(out, self, exponent); }; return wrap(dispatch_pow_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // float_power static PyObject * THPVariable_float_power(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "float_power(Tensor input, Tensor exponent, *, Tensor out=None)", "float_power(Scalar self, Tensor exponent, *, Tensor out=None)", "float_power(Tensor input, Scalar exponent, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::float_power.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor auto dispatch_float_power = [](const at::Tensor & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.float_power(exponent); }; return wrap(dispatch_float_power(_r.tensor(0), _r.tensor(1))); } else { // aten::float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_float_power_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::float_power_out(out, self, exponent); }; return wrap(dispatch_float_power_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } } case 1: { if (_r.isNone(2)) { // aten::float_power.Scalar(Scalar self, Tensor exponent) -> Tensor auto dispatch_float_power = [](const at::Scalar & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::float_power(self, exponent); }; return wrap(dispatch_float_power(_r.scalar(0), _r.tensor(1))); } else { // aten::float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_float_power_out = [](at::Tensor out, const at::Scalar & self, const at::Tensor & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::float_power_out(out, self, exponent); }; return wrap(dispatch_float_power_out(_r.tensor(2), _r.scalar(0), _r.tensor(1))); } } case 2: { if (_r.isNone(2)) { // aten::float_power.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor auto dispatch_float_power = [](const at::Tensor & self, const at::Scalar & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.float_power(exponent); }; return wrap(dispatch_float_power(_r.tensor(0), _r.scalar(1))); } else { // aten::float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_float_power_out = [](at::Tensor out, const at::Tensor & self, const at::Scalar & exponent) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::float_power_out(out, self, exponent); }; return wrap(dispatch_float_power_out(_r.tensor(2), _r.tensor(0), _r.scalar(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // normal static PyObject * THPVariable_normal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "normal(Tensor mean, Tensor std, *, Generator? generator=None, Tensor out=None)", "normal(Tensor mean, double std=1, *, Generator? generator=None, Tensor out=None)", "normal(double mean, Tensor std, *, Generator? generator=None, Tensor out=None)", "normal(double mean, double std, SymIntArrayRef size, *, Generator? generator=None, Tensor out=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? requires_grad=False)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(3)) { // aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor auto dispatch_normal = [](const at::Tensor & mean, const at::Tensor & std, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::normal(mean, std, generator); }; return wrap(dispatch_normal(_r.tensor(0), _r.tensor(1), _r.generator(2))); } else { // aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_normal_out = [](at::Tensor out, const at::Tensor & mean, const at::Tensor & std, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::normal_out(out, mean, std, generator); }; return wrap(dispatch_normal_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.generator(2))); } } case 1: { if (_r.isNone(3)) { // aten::normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor auto dispatch_normal = [](const at::Tensor & mean, double std, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::normal(mean, std, generator); }; return wrap(dispatch_normal(_r.tensor(0), _r.toDouble(1), _r.generator(2))); } else { // aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_normal_out = [](at::Tensor out, const at::Tensor & mean, double std, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::normal_out(out, mean, std, generator); }; return wrap(dispatch_normal_out(_r.tensor(3), _r.tensor(0), _r.toDouble(1), _r.generator(2))); } } case 2: { if (_r.isNone(3)) { // aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor auto dispatch_normal = [](double mean, const at::Tensor & std, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::normal(mean, std, generator); }; return wrap(dispatch_normal(_r.toDouble(0), _r.tensor(1), _r.generator(2))); } else { // aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_normal_out = [](at::Tensor out, double mean, const at::Tensor & std, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::normal_out(out, mean, std, generator); }; return wrap(dispatch_normal_out(_r.tensor(3), _r.toDouble(0), _r.tensor(1), _r.generator(2))); } } case 3: { if (_r.isNone(4)) { // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor const auto options = TensorOptions() .dtype(_r.scalartypeOptional(5)) .device(_r.deviceWithDefault(7, torch::tensors::get_default_device())) .layout(_r.layoutOptional(6)) .requires_grad(_r.toBool(9)) .pinned_memory(_r.toBool(8)); torch::utils::maybe_initialize_device(options); auto dispatch_normal = [](double mean, double std, c10::SymIntArrayRef size, ::std::optional generator, at::TensorOptions options) -> at::Tensor { pybind11::gil_scoped_release no_gil; return torch::normal_symint(mean, std, size, generator, options); }; return wrap(dispatch_normal(_r.toDouble(0), _r.toDouble(1), _r.symintlist(2), _r.generator(3), options)); } else { // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) check_out_type_matches(_r.tensor(4), _r.scalartypeOptional(5), _r.isNone(5), _r.layoutOptional(6), _r.deviceWithDefault(7, torch::tensors::get_default_device()), _r.isNone(7)); auto dispatch_normal_out = [](at::Tensor out, double mean, double std, c10::SymIntArrayRef size, ::std::optional generator) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::normal_symint_out(out, mean, std, size, generator); }; return wrap(dispatch_normal_out(_r.tensor(4), _r.toDouble(0), _r.toDouble(1), _r.symintlist(2), _r.generator(3)).set_requires_grad(_r.toBool(9))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _amp_foreach_non_finite_check_and_unscale_ static PyObject * THPVariable__amp_foreach_non_finite_check_and_unscale_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_amp_foreach_non_finite_check_and_unscale_(TensorList self, Tensor found_inf, Tensor inv_scale)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_amp_foreach_non_finite_check_and_unscale_(Tensor(a!)[] self, Tensor(b!) found_inf, Tensor inv_scale) -> () auto dispatch__amp_foreach_non_finite_check_and_unscale_ = [](at::TensorList self, at::Tensor found_inf, const at::Tensor & inv_scale) -> void { pybind11::gil_scoped_release no_gil; at::_amp_foreach_non_finite_check_and_unscale_(self, found_inf, inv_scale); }; dispatch__amp_foreach_non_finite_check_and_unscale_(_r.tensorlist(0), _r.tensor(1), _r.tensor(2)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _amp_update_scale_ static PyObject * THPVariable__amp_update_scale_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_amp_update_scale_(Tensor input, Tensor growth_tracker, Tensor found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_amp_update_scale_(Tensor(a!) self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> Tensor(a!) auto dispatch__amp_update_scale_ = [](at::Tensor self, at::Tensor growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_amp_update_scale_(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval); }; return wrap(dispatch__amp_update_scale_(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toDouble(3), _r.toDouble(4), _r.toInt64(5))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_add static PyObject * THPVariable__foreach_add(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_add(TensorList self, Scalar scalar)", "_foreach_add(TensorList self, ScalarList scalars)", "_foreach_add(TensorList self, Tensor other, *, Scalar alpha=1)", "_foreach_add(TensorList self, TensorList other, *, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_add.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_add = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_add(self, scalar); }; return wrap(dispatch__foreach_add(_r.tensorlist(0), _r.scalar(1))); } case 1: { // aten::_foreach_add.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_add = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_add(self, scalars); }; return wrap(dispatch__foreach_add(_r.tensorlist(0), _r.scalarlist(1))); } case 2: { // aten::_foreach_add.Tensor(Tensor[] self, Tensor other, *, Scalar alpha=1) -> Tensor[] auto dispatch__foreach_add = [](at::TensorList self, const at::Tensor & other, const at::Scalar & alpha) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_add(self, other, alpha); }; return wrap(dispatch__foreach_add(_r.tensorlist(0), _r.tensor(1), _r.scalar(2))); } case 3: { // aten::_foreach_add.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] auto dispatch__foreach_add = [](at::TensorList self, at::TensorList other, const at::Scalar & alpha) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_add(self, other, alpha); }; return wrap(dispatch__foreach_add(_r.tensorlist(0), _r.tensorlist(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_add_ static PyObject * THPVariable__foreach_add_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_add_(TensorList self, Scalar scalar)", "_foreach_add_(TensorList self, ScalarList scalars)", "_foreach_add_(TensorList self, Tensor other, *, Scalar alpha=1)", "_foreach_add_(TensorList self, TensorList other, *, Scalar alpha=1)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_add_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_add_(self, scalar); }; dispatch__foreach_add_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_add_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_add_(self, scalars); }; dispatch__foreach_add_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_add_.Tensor(Tensor(a!)[] self, Tensor other, *, Scalar alpha=1) -> () auto dispatch__foreach_add_ = [](at::TensorList self, const at::Tensor & other, const at::Scalar & alpha) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_add_(self, other, alpha); }; dispatch__foreach_add_(_r.tensorlist(0), _r.tensor(1), _r.scalar(2)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 3: { // aten::_foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () auto dispatch__foreach_add_ = [](at::TensorList self, at::TensorList other, const at::Scalar & alpha) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_add_(self, other, alpha); }; dispatch__foreach_add_(_r.tensorlist(0), _r.tensorlist(1), _r.scalar(2)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_sub static PyObject * THPVariable__foreach_sub(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sub(TensorList self, Scalar scalar)", "_foreach_sub(TensorList self, ScalarList scalars)", "_foreach_sub(TensorList self, TensorList other, *, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_sub = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sub(self, scalar); }; return wrap(dispatch__foreach_sub(_r.tensorlist(0), _r.scalar(1))); } case 1: { // aten::_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_sub = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sub(self, scalars); }; return wrap(dispatch__foreach_sub(_r.tensorlist(0), _r.scalarlist(1))); } case 2: { // aten::_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] auto dispatch__foreach_sub = [](at::TensorList self, at::TensorList other, const at::Scalar & alpha) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sub(self, other, alpha); }; return wrap(dispatch__foreach_sub(_r.tensorlist(0), _r.tensorlist(1), _r.scalar(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_sub_ static PyObject * THPVariable__foreach_sub_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sub_(TensorList self, Scalar scalar)", "_foreach_sub_(TensorList self, ScalarList scalars)", "_foreach_sub_(TensorList self, TensorList other, *, Scalar alpha=1)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_sub_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sub_(self, scalar); }; dispatch__foreach_sub_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_sub_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sub_(self, scalars); }; dispatch__foreach_sub_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () auto dispatch__foreach_sub_ = [](at::TensorList self, at::TensorList other, const at::Scalar & alpha) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sub_(self, other, alpha); }; dispatch__foreach_sub_(_r.tensorlist(0), _r.tensorlist(1), _r.scalar(2)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_mul static PyObject * THPVariable__foreach_mul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_mul(TensorList self, ScalarList scalars)", "_foreach_mul(TensorList self, Tensor other)", "_foreach_mul(TensorList self, TensorList other)", "_foreach_mul(TensorList self, Scalar scalar)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_mul = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_mul(self, scalars); }; return wrap(dispatch__foreach_mul(_r.tensorlist(0), _r.scalarlist(1))); } case 1: { // aten::_foreach_mul.Tensor(Tensor[] self, Tensor other) -> Tensor[] auto dispatch__foreach_mul = [](at::TensorList self, const at::Tensor & other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_mul(self, other); }; return wrap(dispatch__foreach_mul(_r.tensorlist(0), _r.tensor(1))); } case 2: { // aten::_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[] auto dispatch__foreach_mul = [](at::TensorList self, at::TensorList other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_mul(self, other); }; return wrap(dispatch__foreach_mul(_r.tensorlist(0), _r.tensorlist(1))); } case 3: { // aten::_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_mul = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_mul(self, scalar); }; return wrap(dispatch__foreach_mul(_r.tensorlist(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_mul_ static PyObject * THPVariable__foreach_mul_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_mul_(TensorList self, ScalarList scalars)", "_foreach_mul_(TensorList self, Tensor other)", "_foreach_mul_(TensorList self, TensorList other)", "_foreach_mul_(TensorList self, Scalar scalar)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_mul_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_mul_(self, scalars); }; dispatch__foreach_mul_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_mul_.Tensor(Tensor(a!)[] self, Tensor other) -> () auto dispatch__foreach_mul_ = [](at::TensorList self, const at::Tensor & other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_mul_(self, other); }; dispatch__foreach_mul_(_r.tensorlist(0), _r.tensor(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> () auto dispatch__foreach_mul_ = [](at::TensorList self, at::TensorList other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_mul_(self, other); }; dispatch__foreach_mul_(_r.tensorlist(0), _r.tensorlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 3: { // aten::_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_mul_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_mul_(self, scalar); }; dispatch__foreach_mul_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_div static PyObject * THPVariable__foreach_div(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_div(TensorList self, ScalarList scalars)", "_foreach_div(TensorList self, Tensor other)", "_foreach_div(TensorList self, TensorList other)", "_foreach_div(TensorList self, Scalar scalar)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_div = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_div(self, scalars); }; return wrap(dispatch__foreach_div(_r.tensorlist(0), _r.scalarlist(1))); } case 1: { // aten::_foreach_div.Tensor(Tensor[] self, Tensor other) -> Tensor[] auto dispatch__foreach_div = [](at::TensorList self, const at::Tensor & other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_div(self, other); }; return wrap(dispatch__foreach_div(_r.tensorlist(0), _r.tensor(1))); } case 2: { // aten::_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[] auto dispatch__foreach_div = [](at::TensorList self, at::TensorList other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_div(self, other); }; return wrap(dispatch__foreach_div(_r.tensorlist(0), _r.tensorlist(1))); } case 3: { // aten::_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_div = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_div(self, scalar); }; return wrap(dispatch__foreach_div(_r.tensorlist(0), _r.scalar(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_div_ static PyObject * THPVariable__foreach_div_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_div_(TensorList self, ScalarList scalars)", "_foreach_div_(TensorList self, Tensor other)", "_foreach_div_(TensorList self, TensorList other)", "_foreach_div_(TensorList self, Scalar scalar)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_div_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_div_(self, scalars); }; dispatch__foreach_div_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_div_.Tensor(Tensor(a!)[] self, Tensor other) -> () auto dispatch__foreach_div_ = [](at::TensorList self, const at::Tensor & other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_div_(self, other); }; dispatch__foreach_div_(_r.tensorlist(0), _r.tensor(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> () auto dispatch__foreach_div_ = [](at::TensorList self, at::TensorList other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_div_(self, other); }; dispatch__foreach_div_(_r.tensorlist(0), _r.tensorlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 3: { // aten::_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_div_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_div_(self, scalar); }; dispatch__foreach_div_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_clamp_max static PyObject * THPVariable__foreach_clamp_max(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_clamp_max(TensorList self, Scalar scalar)", "_foreach_clamp_max(TensorList self, ScalarList scalars)", "_foreach_clamp_max(TensorList self, TensorList other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_clamp_max.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_clamp_max = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_clamp_max(self, scalar); }; return wrap(dispatch__foreach_clamp_max(_r.tensorlist(0), _r.scalar(1))); } case 1: { // aten::_foreach_clamp_max.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_clamp_max = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_clamp_max(self, scalars); }; return wrap(dispatch__foreach_clamp_max(_r.tensorlist(0), _r.scalarlist(1))); } case 2: { // aten::_foreach_clamp_max.List(Tensor[] self, Tensor[] other) -> Tensor[] auto dispatch__foreach_clamp_max = [](at::TensorList self, at::TensorList other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_clamp_max(self, other); }; return wrap(dispatch__foreach_clamp_max(_r.tensorlist(0), _r.tensorlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_clamp_max_ static PyObject * THPVariable__foreach_clamp_max_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_clamp_max_(TensorList self, Scalar scalar)", "_foreach_clamp_max_(TensorList self, ScalarList scalars)", "_foreach_clamp_max_(TensorList self, TensorList other)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_clamp_max_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_clamp_max_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_clamp_max_(self, scalar); }; dispatch__foreach_clamp_max_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_clamp_max_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_clamp_max_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_clamp_max_(self, scalars); }; dispatch__foreach_clamp_max_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_clamp_max_.List(Tensor(a!)[] self, Tensor[] other) -> () auto dispatch__foreach_clamp_max_ = [](at::TensorList self, at::TensorList other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_clamp_max_(self, other); }; dispatch__foreach_clamp_max_(_r.tensorlist(0), _r.tensorlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_clamp_min static PyObject * THPVariable__foreach_clamp_min(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_clamp_min(TensorList self, Scalar scalar)", "_foreach_clamp_min(TensorList self, ScalarList scalars)", "_foreach_clamp_min(TensorList self, TensorList other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_clamp_min.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_clamp_min = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_clamp_min(self, scalar); }; return wrap(dispatch__foreach_clamp_min(_r.tensorlist(0), _r.scalar(1))); } case 1: { // aten::_foreach_clamp_min.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_clamp_min = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_clamp_min(self, scalars); }; return wrap(dispatch__foreach_clamp_min(_r.tensorlist(0), _r.scalarlist(1))); } case 2: { // aten::_foreach_clamp_min.List(Tensor[] self, Tensor[] other) -> Tensor[] auto dispatch__foreach_clamp_min = [](at::TensorList self, at::TensorList other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_clamp_min(self, other); }; return wrap(dispatch__foreach_clamp_min(_r.tensorlist(0), _r.tensorlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_clamp_min_ static PyObject * THPVariable__foreach_clamp_min_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_clamp_min_(TensorList self, Scalar scalar)", "_foreach_clamp_min_(TensorList self, ScalarList scalars)", "_foreach_clamp_min_(TensorList self, TensorList other)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_clamp_min_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_clamp_min_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_clamp_min_(self, scalar); }; dispatch__foreach_clamp_min_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_clamp_min_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_clamp_min_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_clamp_min_(self, scalars); }; dispatch__foreach_clamp_min_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_clamp_min_.List(Tensor(a!)[] self, Tensor[] other) -> () auto dispatch__foreach_clamp_min_ = [](at::TensorList self, at::TensorList other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_clamp_min_(self, other); }; dispatch__foreach_clamp_min_(_r.tensorlist(0), _r.tensorlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_maximum static PyObject * THPVariable__foreach_maximum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_maximum(TensorList self, Scalar scalar)", "_foreach_maximum(TensorList self, ScalarList scalars)", "_foreach_maximum(TensorList self, TensorList other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_maximum = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_maximum(self, scalar); }; return wrap(dispatch__foreach_maximum(_r.tensorlist(0), _r.scalar(1))); } case 1: { // aten::_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_maximum = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_maximum(self, scalars); }; return wrap(dispatch__foreach_maximum(_r.tensorlist(0), _r.scalarlist(1))); } case 2: { // aten::_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[] auto dispatch__foreach_maximum = [](at::TensorList self, at::TensorList other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_maximum(self, other); }; return wrap(dispatch__foreach_maximum(_r.tensorlist(0), _r.tensorlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_maximum_ static PyObject * THPVariable__foreach_maximum_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_maximum_(TensorList self, Scalar scalar)", "_foreach_maximum_(TensorList self, ScalarList scalars)", "_foreach_maximum_(TensorList self, TensorList other)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_maximum_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_maximum_(self, scalar); }; dispatch__foreach_maximum_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_maximum_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_maximum_(self, scalars); }; dispatch__foreach_maximum_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> () auto dispatch__foreach_maximum_ = [](at::TensorList self, at::TensorList other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_maximum_(self, other); }; dispatch__foreach_maximum_(_r.tensorlist(0), _r.tensorlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_minimum static PyObject * THPVariable__foreach_minimum(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_minimum(TensorList self, Scalar scalar)", "_foreach_minimum(TensorList self, ScalarList scalars)", "_foreach_minimum(TensorList self, TensorList other)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_minimum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] auto dispatch__foreach_minimum = [](at::TensorList self, const at::Scalar & scalar) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_minimum(self, scalar); }; return wrap(dispatch__foreach_minimum(_r.tensorlist(0), _r.scalar(1))); } case 1: { // aten::_foreach_minimum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_minimum = [](at::TensorList self, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_minimum(self, scalars); }; return wrap(dispatch__foreach_minimum(_r.tensorlist(0), _r.scalarlist(1))); } case 2: { // aten::_foreach_minimum.List(Tensor[] self, Tensor[] other) -> Tensor[] auto dispatch__foreach_minimum = [](at::TensorList self, at::TensorList other) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_minimum(self, other); }; return wrap(dispatch__foreach_minimum(_r.tensorlist(0), _r.tensorlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_minimum_ static PyObject * THPVariable__foreach_minimum_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_minimum_(TensorList self, Scalar scalar)", "_foreach_minimum_(TensorList self, ScalarList scalars)", "_foreach_minimum_(TensorList self, TensorList other)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_minimum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () auto dispatch__foreach_minimum_ = [](at::TensorList self, const at::Scalar & scalar) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_minimum_(self, scalar); }; dispatch__foreach_minimum_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_minimum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () auto dispatch__foreach_minimum_ = [](at::TensorList self, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_minimum_(self, scalars); }; dispatch__foreach_minimum_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_minimum_.List(Tensor(a!)[] self, Tensor[] other) -> () auto dispatch__foreach_minimum_ = [](at::TensorList self, at::TensorList other) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_minimum_(self, other); }; dispatch__foreach_minimum_(_r.tensorlist(0), _r.tensorlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_addcdiv static PyObject * THPVariable__foreach_addcdiv(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_addcdiv(TensorList self, TensorList tensor1, TensorList tensor2, ScalarList scalars)", "_foreach_addcdiv(TensorList self, TensorList tensor1, TensorList tensor2, Tensor scalars)", "_foreach_addcdiv(TensorList self, TensorList tensor1, TensorList tensor2, Scalar value=1)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_addcdiv.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_addcdiv = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_addcdiv(self, tensor1, tensor2, scalars); }; return wrap(dispatch__foreach_addcdiv(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalarlist(3))); } case 1: { // aten::_foreach_addcdiv.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] auto dispatch__foreach_addcdiv = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_addcdiv(self, tensor1, tensor2, scalars); }; return wrap(dispatch__foreach_addcdiv(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensor(3))); } case 2: { // aten::_foreach_addcdiv.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] auto dispatch__foreach_addcdiv = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_addcdiv(self, tensor1, tensor2, value); }; return wrap(dispatch__foreach_addcdiv(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalar(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_addcdiv_ static PyObject * THPVariable__foreach_addcdiv_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_addcdiv_(TensorList self, TensorList tensor1, TensorList tensor2, ScalarList scalars)", "_foreach_addcdiv_(TensorList self, TensorList tensor1, TensorList tensor2, Tensor scalars)", "_foreach_addcdiv_(TensorList self, TensorList tensor1, TensorList tensor2, Scalar value=1)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () auto dispatch__foreach_addcdiv_ = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_addcdiv_(self, tensor1, tensor2, scalars); }; dispatch__foreach_addcdiv_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalarlist(3)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_addcdiv_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () auto dispatch__foreach_addcdiv_ = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_addcdiv_(self, tensor1, tensor2, scalars); }; dispatch__foreach_addcdiv_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensor(3)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () auto dispatch__foreach_addcdiv_ = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_addcdiv_(self, tensor1, tensor2, value); }; dispatch__foreach_addcdiv_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalar(3)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_addcmul static PyObject * THPVariable__foreach_addcmul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_addcmul(TensorList self, TensorList tensor1, TensorList tensor2, ScalarList scalars)", "_foreach_addcmul(TensorList self, TensorList tensor1, TensorList tensor2, Tensor scalars)", "_foreach_addcmul(TensorList self, TensorList tensor1, TensorList tensor2, Scalar value=1)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] auto dispatch__foreach_addcmul = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_addcmul(self, tensor1, tensor2, scalars); }; return wrap(dispatch__foreach_addcmul(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalarlist(3))); } case 1: { // aten::_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] auto dispatch__foreach_addcmul = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_addcmul(self, tensor1, tensor2, scalars); }; return wrap(dispatch__foreach_addcmul(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensor(3))); } case 2: { // aten::_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] auto dispatch__foreach_addcmul = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_addcmul(self, tensor1, tensor2, value); }; return wrap(dispatch__foreach_addcmul(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalar(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_addcmul_ static PyObject * THPVariable__foreach_addcmul_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_addcmul_(TensorList self, TensorList tensor1, TensorList tensor2, ScalarList scalars)", "_foreach_addcmul_(TensorList self, TensorList tensor1, TensorList tensor2, Tensor scalars)", "_foreach_addcmul_(TensorList self, TensorList tensor1, TensorList tensor2, Scalar value=1)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () auto dispatch__foreach_addcmul_ = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_addcmul_(self, tensor1, tensor2, scalars); }; dispatch__foreach_addcmul_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalarlist(3)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () auto dispatch__foreach_addcmul_ = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_addcmul_(self, tensor1, tensor2, scalars); }; dispatch__foreach_addcmul_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensor(3)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () auto dispatch__foreach_addcmul_ = [](at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_addcmul_(self, tensor1, tensor2, value); }; dispatch__foreach_addcmul_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.scalar(3)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_abs static PyObject * THPVariable__foreach_abs(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_abs(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_abs(Tensor[] self) -> Tensor[] auto dispatch__foreach_abs = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_abs(self); }; return wrap(dispatch__foreach_abs(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_abs_ static PyObject * THPVariable__foreach_abs_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_abs_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_abs_(Tensor(a!)[] self) -> () auto dispatch__foreach_abs_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_abs_(self); }; dispatch__foreach_abs_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_acos static PyObject * THPVariable__foreach_acos(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_acos(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_acos(Tensor[] self) -> Tensor[] auto dispatch__foreach_acos = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_acos(self); }; return wrap(dispatch__foreach_acos(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_acos_ static PyObject * THPVariable__foreach_acos_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_acos_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_acos_(Tensor(a!)[] self) -> () auto dispatch__foreach_acos_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_acos_(self); }; dispatch__foreach_acos_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_asin static PyObject * THPVariable__foreach_asin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_asin(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_asin(Tensor[] self) -> Tensor[] auto dispatch__foreach_asin = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_asin(self); }; return wrap(dispatch__foreach_asin(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_asin_ static PyObject * THPVariable__foreach_asin_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_asin_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_asin_(Tensor(a!)[] self) -> () auto dispatch__foreach_asin_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_asin_(self); }; dispatch__foreach_asin_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_atan static PyObject * THPVariable__foreach_atan(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_atan(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_atan(Tensor[] self) -> Tensor[] auto dispatch__foreach_atan = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_atan(self); }; return wrap(dispatch__foreach_atan(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_atan_ static PyObject * THPVariable__foreach_atan_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_atan_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_atan_(Tensor(a!)[] self) -> () auto dispatch__foreach_atan_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_atan_(self); }; dispatch__foreach_atan_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_ceil static PyObject * THPVariable__foreach_ceil(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_ceil(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_ceil(Tensor[] self) -> Tensor[] auto dispatch__foreach_ceil = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_ceil(self); }; return wrap(dispatch__foreach_ceil(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_ceil_ static PyObject * THPVariable__foreach_ceil_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_ceil_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_ceil_(Tensor(a!)[] self) -> () auto dispatch__foreach_ceil_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_ceil_(self); }; dispatch__foreach_ceil_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_cos static PyObject * THPVariable__foreach_cos(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_cos(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_cos(Tensor[] self) -> Tensor[] auto dispatch__foreach_cos = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_cos(self); }; return wrap(dispatch__foreach_cos(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_cos_ static PyObject * THPVariable__foreach_cos_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_cos_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_cos_(Tensor(a!)[] self) -> () auto dispatch__foreach_cos_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_cos_(self); }; dispatch__foreach_cos_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_cosh static PyObject * THPVariable__foreach_cosh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_cosh(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_cosh(Tensor[] self) -> Tensor[] auto dispatch__foreach_cosh = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_cosh(self); }; return wrap(dispatch__foreach_cosh(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_cosh_ static PyObject * THPVariable__foreach_cosh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_cosh_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_cosh_(Tensor(a!)[] self) -> () auto dispatch__foreach_cosh_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_cosh_(self); }; dispatch__foreach_cosh_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_erf static PyObject * THPVariable__foreach_erf(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_erf(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_erf(Tensor[] self) -> Tensor[] auto dispatch__foreach_erf = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_erf(self); }; return wrap(dispatch__foreach_erf(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_erf_ static PyObject * THPVariable__foreach_erf_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_erf_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_erf_(Tensor(a!)[] self) -> () auto dispatch__foreach_erf_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_erf_(self); }; dispatch__foreach_erf_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_erfc static PyObject * THPVariable__foreach_erfc(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_erfc(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_erfc(Tensor[] self) -> Tensor[] auto dispatch__foreach_erfc = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_erfc(self); }; return wrap(dispatch__foreach_erfc(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_erfc_ static PyObject * THPVariable__foreach_erfc_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_erfc_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_erfc_(Tensor(a!)[] self) -> () auto dispatch__foreach_erfc_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_erfc_(self); }; dispatch__foreach_erfc_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_exp static PyObject * THPVariable__foreach_exp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_exp(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_exp(Tensor[] self) -> Tensor[] auto dispatch__foreach_exp = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_exp(self); }; return wrap(dispatch__foreach_exp(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_exp_ static PyObject * THPVariable__foreach_exp_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_exp_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_exp_(Tensor(a!)[] self) -> () auto dispatch__foreach_exp_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_exp_(self); }; dispatch__foreach_exp_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_expm1 static PyObject * THPVariable__foreach_expm1(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_expm1(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_expm1(Tensor[] self) -> Tensor[] auto dispatch__foreach_expm1 = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_expm1(self); }; return wrap(dispatch__foreach_expm1(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_expm1_ static PyObject * THPVariable__foreach_expm1_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_expm1_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_expm1_(Tensor(a!)[] self) -> () auto dispatch__foreach_expm1_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_expm1_(self); }; dispatch__foreach_expm1_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_floor static PyObject * THPVariable__foreach_floor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_floor(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_floor(Tensor[] self) -> Tensor[] auto dispatch__foreach_floor = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_floor(self); }; return wrap(dispatch__foreach_floor(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_floor_ static PyObject * THPVariable__foreach_floor_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_floor_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_floor_(Tensor(a!)[] self) -> () auto dispatch__foreach_floor_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_floor_(self); }; dispatch__foreach_floor_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_frac static PyObject * THPVariable__foreach_frac(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_frac(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_frac(Tensor[] self) -> Tensor[] auto dispatch__foreach_frac = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_frac(self); }; return wrap(dispatch__foreach_frac(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_frac_ static PyObject * THPVariable__foreach_frac_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_frac_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_frac_(Tensor(a!)[] self) -> () auto dispatch__foreach_frac_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_frac_(self); }; dispatch__foreach_frac_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_lerp static PyObject * THPVariable__foreach_lerp(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_lerp(TensorList self, TensorList tensors1, Scalar weight)", "_foreach_lerp(TensorList self, TensorList tensors1, ScalarList weight)", "_foreach_lerp(TensorList self, TensorList tensors1, TensorList weights)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_lerp.Scalar(Tensor[] self, Tensor[] tensors1, Scalar weight) -> Tensor[] auto dispatch__foreach_lerp = [](at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_lerp(self, tensors1, weight); }; return wrap(dispatch__foreach_lerp(_r.tensorlist(0), _r.tensorlist(1), _r.scalar(2))); } case 1: { // aten::_foreach_lerp.ScalarList(Tensor[] self, Tensor[] tensors1, Scalar[] weight) -> Tensor[] auto dispatch__foreach_lerp = [](at::TensorList self, at::TensorList tensors1, at::ArrayRef weight) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_lerp(self, tensors1, weight); }; return wrap(dispatch__foreach_lerp(_r.tensorlist(0), _r.tensorlist(1), _r.scalarlist(2))); } case 2: { // aten::_foreach_lerp.List(Tensor[] self, Tensor[] tensors1, Tensor[] weights) -> Tensor[] auto dispatch__foreach_lerp = [](at::TensorList self, at::TensorList tensors1, at::TensorList weights) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_lerp(self, tensors1, weights); }; return wrap(dispatch__foreach_lerp(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_lerp_ static PyObject * THPVariable__foreach_lerp_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_lerp_(TensorList self, TensorList tensors1, Scalar weight)", "_foreach_lerp_(TensorList self, TensorList tensors1, ScalarList weight)", "_foreach_lerp_(TensorList self, TensorList tensors1, TensorList weights)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_lerp_.Scalar(Tensor(a!)[] self, Tensor[] tensors1, Scalar weight) -> () auto dispatch__foreach_lerp_ = [](at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_lerp_(self, tensors1, weight); }; dispatch__foreach_lerp_(_r.tensorlist(0), _r.tensorlist(1), _r.scalar(2)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_lerp_.ScalarList(Tensor(a!)[] self, Tensor[] tensors1, Scalar[] weight) -> () auto dispatch__foreach_lerp_ = [](at::TensorList self, at::TensorList tensors1, at::ArrayRef weight) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_lerp_(self, tensors1, weight); }; dispatch__foreach_lerp_(_r.tensorlist(0), _r.tensorlist(1), _r.scalarlist(2)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_lerp_.List(Tensor(a!)[] self, Tensor[] tensors1, Tensor[] weights) -> () auto dispatch__foreach_lerp_ = [](at::TensorList self, at::TensorList tensors1, at::TensorList weights) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_lerp_(self, tensors1, weights); }; dispatch__foreach_lerp_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_lgamma static PyObject * THPVariable__foreach_lgamma(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_lgamma(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_lgamma(Tensor[] self) -> Tensor[] auto dispatch__foreach_lgamma = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_lgamma(self); }; return wrap(dispatch__foreach_lgamma(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_lgamma_ static PyObject * THPVariable__foreach_lgamma_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_lgamma_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_lgamma_(Tensor(a!)[] self) -> () auto dispatch__foreach_lgamma_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_lgamma_(self); }; dispatch__foreach_lgamma_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log static PyObject * THPVariable__foreach_log(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log(Tensor[] self) -> Tensor[] auto dispatch__foreach_log = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_log(self); }; return wrap(dispatch__foreach_log(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log_ static PyObject * THPVariable__foreach_log_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log_(Tensor(a!)[] self) -> () auto dispatch__foreach_log_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_log_(self); }; dispatch__foreach_log_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log10 static PyObject * THPVariable__foreach_log10(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log10(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log10(Tensor[] self) -> Tensor[] auto dispatch__foreach_log10 = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_log10(self); }; return wrap(dispatch__foreach_log10(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log10_ static PyObject * THPVariable__foreach_log10_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log10_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log10_(Tensor(a!)[] self) -> () auto dispatch__foreach_log10_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_log10_(self); }; dispatch__foreach_log10_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log1p static PyObject * THPVariable__foreach_log1p(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log1p(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log1p(Tensor[] self) -> Tensor[] auto dispatch__foreach_log1p = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_log1p(self); }; return wrap(dispatch__foreach_log1p(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log1p_ static PyObject * THPVariable__foreach_log1p_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log1p_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log1p_(Tensor(a!)[] self) -> () auto dispatch__foreach_log1p_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_log1p_(self); }; dispatch__foreach_log1p_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log2 static PyObject * THPVariable__foreach_log2(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log2(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log2(Tensor[] self) -> Tensor[] auto dispatch__foreach_log2 = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_log2(self); }; return wrap(dispatch__foreach_log2(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_log2_ static PyObject * THPVariable__foreach_log2_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_log2_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_log2_(Tensor(a!)[] self) -> () auto dispatch__foreach_log2_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_log2_(self); }; dispatch__foreach_log2_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_max static PyObject * THPVariable__foreach_max(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_max(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_max(Tensor[] self) -> Tensor[] auto dispatch__foreach_max = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_max(self); }; return wrap(dispatch__foreach_max(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_neg static PyObject * THPVariable__foreach_neg(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_neg(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_neg(Tensor[] self) -> Tensor[] auto dispatch__foreach_neg = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_neg(self); }; return wrap(dispatch__foreach_neg(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_neg_ static PyObject * THPVariable__foreach_neg_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_neg_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_neg_(Tensor(a!)[] self) -> () auto dispatch__foreach_neg_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_neg_(self); }; dispatch__foreach_neg_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_norm static PyObject * THPVariable__foreach_norm(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_norm(TensorList self, Scalar ord=2, ScalarType? dtype=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_norm.Scalar(Tensor[] self, Scalar ord=2, ScalarType? dtype=None) -> Tensor[] auto dispatch__foreach_norm = [](at::TensorList self, const at::Scalar & ord, ::std::optional dtype) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_norm(self, ord, dtype); }; return wrap(dispatch__foreach_norm(_r.tensorlist(0), _r.scalar(1), _r.scalartypeOptional(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_pow static PyObject * THPVariable__foreach_pow(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_pow(Scalar self, TensorList exponent)", "_foreach_pow(TensorList self, Scalar exponent)", "_foreach_pow(TensorList self, ScalarList exponent)", "_foreach_pow(TensorList self, TensorList exponent)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_pow.ScalarAndTensor(Scalar self, Tensor[] exponent) -> Tensor[] auto dispatch__foreach_pow = [](const at::Scalar & self, at::TensorList exponent) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_pow(self, exponent); }; return wrap(dispatch__foreach_pow(_r.scalar(0), _r.tensorlist(1))); } case 1: { // aten::_foreach_pow.Scalar(Tensor[] self, Scalar exponent) -> Tensor[] auto dispatch__foreach_pow = [](at::TensorList self, const at::Scalar & exponent) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_pow(self, exponent); }; return wrap(dispatch__foreach_pow(_r.tensorlist(0), _r.scalar(1))); } case 2: { // aten::_foreach_pow.ScalarList(Tensor[] self, Scalar[] exponent) -> Tensor[] auto dispatch__foreach_pow = [](at::TensorList self, at::ArrayRef exponent) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_pow(self, exponent); }; return wrap(dispatch__foreach_pow(_r.tensorlist(0), _r.scalarlist(1))); } case 3: { // aten::_foreach_pow.List(Tensor[] self, Tensor[] exponent) -> Tensor[] auto dispatch__foreach_pow = [](at::TensorList self, at::TensorList exponent) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_pow(self, exponent); }; return wrap(dispatch__foreach_pow(_r.tensorlist(0), _r.tensorlist(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _foreach_pow_ static PyObject * THPVariable__foreach_pow_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_pow_(TensorList self, Scalar exponent)", "_foreach_pow_(TensorList self, ScalarList exponent)", "_foreach_pow_(TensorList self, TensorList exponent)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_foreach_pow_.Scalar(Tensor(a!)[] self, Scalar exponent) -> () auto dispatch__foreach_pow_ = [](at::TensorList self, const at::Scalar & exponent) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_pow_(self, exponent); }; dispatch__foreach_pow_(_r.tensorlist(0), _r.scalar(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 1: { // aten::_foreach_pow_.ScalarList(Tensor(a!)[] self, Scalar[] exponent) -> () auto dispatch__foreach_pow_ = [](at::TensorList self, at::ArrayRef exponent) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_pow_(self, exponent); }; dispatch__foreach_pow_(_r.tensorlist(0), _r.scalarlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } case 2: { // aten::_foreach_pow_.List(Tensor(a!)[] self, Tensor[] exponent) -> () auto dispatch__foreach_pow_ = [](at::TensorList self, at::TensorList exponent) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_pow_(self, exponent); }; dispatch__foreach_pow_(_r.tensorlist(0), _r.tensorlist(1)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_reciprocal static PyObject * THPVariable__foreach_reciprocal(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_reciprocal(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_reciprocal(Tensor[] self) -> Tensor[] auto dispatch__foreach_reciprocal = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_reciprocal(self); }; return wrap(dispatch__foreach_reciprocal(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_reciprocal_ static PyObject * THPVariable__foreach_reciprocal_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_reciprocal_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_reciprocal_(Tensor(a!)[] self) -> () auto dispatch__foreach_reciprocal_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_reciprocal_(self); }; dispatch__foreach_reciprocal_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_round static PyObject * THPVariable__foreach_round(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_round(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_round(Tensor[] self) -> Tensor[] auto dispatch__foreach_round = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_round(self); }; return wrap(dispatch__foreach_round(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_round_ static PyObject * THPVariable__foreach_round_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_round_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_round_(Tensor(a!)[] self) -> () auto dispatch__foreach_round_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_round_(self); }; dispatch__foreach_round_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_rsqrt static PyObject * THPVariable__foreach_rsqrt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_rsqrt(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_rsqrt(Tensor[] self) -> Tensor[] auto dispatch__foreach_rsqrt = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_rsqrt(self); }; return wrap(dispatch__foreach_rsqrt(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_rsqrt_ static PyObject * THPVariable__foreach_rsqrt_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_rsqrt_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_rsqrt_(Tensor(a!)[] self) -> () auto dispatch__foreach_rsqrt_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_rsqrt_(self); }; dispatch__foreach_rsqrt_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sigmoid static PyObject * THPVariable__foreach_sigmoid(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sigmoid(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sigmoid(Tensor[] self) -> Tensor[] auto dispatch__foreach_sigmoid = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sigmoid(self); }; return wrap(dispatch__foreach_sigmoid(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sigmoid_ static PyObject * THPVariable__foreach_sigmoid_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sigmoid_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sigmoid_(Tensor(a!)[] self) -> () auto dispatch__foreach_sigmoid_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sigmoid_(self); }; dispatch__foreach_sigmoid_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sign static PyObject * THPVariable__foreach_sign(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sign(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sign(Tensor[] self) -> Tensor[] auto dispatch__foreach_sign = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sign(self); }; return wrap(dispatch__foreach_sign(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sign_ static PyObject * THPVariable__foreach_sign_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sign_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sign_(Tensor(a!)[] self) -> () auto dispatch__foreach_sign_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sign_(self); }; dispatch__foreach_sign_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sin static PyObject * THPVariable__foreach_sin(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sin(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sin(Tensor[] self) -> Tensor[] auto dispatch__foreach_sin = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sin(self); }; return wrap(dispatch__foreach_sin(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sin_ static PyObject * THPVariable__foreach_sin_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sin_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sin_(Tensor(a!)[] self) -> () auto dispatch__foreach_sin_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sin_(self); }; dispatch__foreach_sin_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sinh static PyObject * THPVariable__foreach_sinh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sinh(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sinh(Tensor[] self) -> Tensor[] auto dispatch__foreach_sinh = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sinh(self); }; return wrap(dispatch__foreach_sinh(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sinh_ static PyObject * THPVariable__foreach_sinh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sinh_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sinh_(Tensor(a!)[] self) -> () auto dispatch__foreach_sinh_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sinh_(self); }; dispatch__foreach_sinh_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sqrt static PyObject * THPVariable__foreach_sqrt(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sqrt(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sqrt(Tensor[] self) -> Tensor[] auto dispatch__foreach_sqrt = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_sqrt(self); }; return wrap(dispatch__foreach_sqrt(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_sqrt_ static PyObject * THPVariable__foreach_sqrt_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_sqrt_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_sqrt_(Tensor(a!)[] self) -> () auto dispatch__foreach_sqrt_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_sqrt_(self); }; dispatch__foreach_sqrt_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_tan static PyObject * THPVariable__foreach_tan(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_tan(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_tan(Tensor[] self) -> Tensor[] auto dispatch__foreach_tan = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_tan(self); }; return wrap(dispatch__foreach_tan(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_tan_ static PyObject * THPVariable__foreach_tan_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_tan_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_tan_(Tensor(a!)[] self) -> () auto dispatch__foreach_tan_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_tan_(self); }; dispatch__foreach_tan_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_tanh static PyObject * THPVariable__foreach_tanh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_tanh(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_tanh(Tensor[] self) -> Tensor[] auto dispatch__foreach_tanh = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_tanh(self); }; return wrap(dispatch__foreach_tanh(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_tanh_ static PyObject * THPVariable__foreach_tanh_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_tanh_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_tanh_(Tensor(a!)[] self) -> () auto dispatch__foreach_tanh_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_tanh_(self); }; dispatch__foreach_tanh_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_trunc static PyObject * THPVariable__foreach_trunc(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_trunc(TensorList self)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_trunc(Tensor[] self) -> Tensor[] auto dispatch__foreach_trunc = [](at::TensorList self) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::_foreach_trunc(self); }; return wrap(dispatch__foreach_trunc(_r.tensorlist(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_trunc_ static PyObject * THPVariable__foreach_trunc_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_trunc_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_trunc_(Tensor(a!)[] self) -> () auto dispatch__foreach_trunc_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_trunc_(self); }; dispatch__foreach_trunc_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_zero_ static PyObject * THPVariable__foreach_zero_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_zero_(TensorList self)", }, /*traceable=*/false); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_zero_(Tensor(a!)[] self) -> () auto dispatch__foreach_zero_ = [](at::TensorList self) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_zero_(self); }; dispatch__foreach_zero_(_r.tensorlist(0)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foreach_copy_ static PyObject * THPVariable__foreach_copy_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foreach_copy_(TensorList self, TensorList src, bool non_blocking=False)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foreach_copy_(Tensor(a!)[] self, Tensor[] src, bool non_blocking=False) -> () auto dispatch__foreach_copy_ = [](at::TensorList self, at::TensorList src, bool non_blocking) -> void { pybind11::gil_scoped_release no_gil; at::_foreach_copy_(self, src, non_blocking); }; dispatch__foreach_copy_(_r.tensorlist(0), _r.tensorlist(1), _r.toBool(2)); PyObject* self_tensorlist = _r.args[0]; Py_INCREF(self_tensorlist); return self_tensorlist; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // bucketize static PyObject * THPVariable_bucketize(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "bucketize(Tensor input, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor out=None)", "bucketize(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(4)) { // aten::bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor auto dispatch_bucketize = [](const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bucketize(self, boundaries, out_int32, right); }; return wrap(dispatch_bucketize(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } else { // aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_bucketize_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bucketize_out(out, self, boundaries, out_int32, right); }; return wrap(dispatch_bucketize_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } } case 1: { // aten::bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor auto dispatch_bucketize = [](const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::bucketize(self, boundaries, out_int32, right); }; return wrap(dispatch_bucketize(_r.scalar(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // searchsorted static PyObject * THPVariable_searchsorted(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "searchsorted(Tensor sorted_sequence, Tensor input, *, bool out_int32=False, bool right=False, c10::string_view? side=None, Tensor? sorter=None, Tensor out=None)", "searchsorted(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, c10::string_view? side=None, Tensor? sorter=None, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(6)) { // aten::searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor auto dispatch_searchsorted = [](const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, ::std::optional side, const ::std::optional & sorter) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::searchsorted(sorted_sequence, self, out_int32, right, side, sorter); }; return wrap(dispatch_searchsorted(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3), _r.stringViewOptional(4), _r.optionalTensor(5))); } else { // aten::searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_searchsorted_out = [](at::Tensor out, const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, ::std::optional side, const ::std::optional & sorter) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::searchsorted_out(out, sorted_sequence, self, out_int32, right, side, sorter); }; return wrap(dispatch_searchsorted_out(_r.tensor(6), _r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3), _r.stringViewOptional(4), _r.optionalTensor(5))); } } case 1: { if (_r.isNone(6)) { // aten::searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor auto dispatch_searchsorted = [](const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, ::std::optional side, const ::std::optional & sorter) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::searchsorted(sorted_sequence, self, out_int32, right, side, sorter); }; return wrap(dispatch_searchsorted(_r.tensor(0), _r.scalar(1), _r.toBool(2), _r.toBool(3), _r.stringViewOptional(4), _r.optionalTensor(5))); } else { // aten::searchsorted.Scalar_out(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) auto dispatch_searchsorted_out = [](at::Tensor out, const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, ::std::optional side, const ::std::optional & sorter) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::searchsorted_out(out, sorted_sequence, self, out_int32, right, side, sorter); }; return wrap(dispatch_searchsorted_out(_r.tensor(6), _r.tensor(0), _r.scalar(1), _r.toBool(2), _r.toBool(3), _r.stringViewOptional(4), _r.optionalTensor(5))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _convert_indices_from_coo_to_csr static PyObject * THPVariable__convert_indices_from_coo_to_csr(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_convert_indices_from_coo_to_csr(Tensor input, int64_t size, *, bool out_int32=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::_convert_indices_from_coo_to_csr(Tensor self, int size, *, bool out_int32=False) -> Tensor auto dispatch__convert_indices_from_coo_to_csr = [](const at::Tensor & self, int64_t size, bool out_int32) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convert_indices_from_coo_to_csr(self, size, out_int32); }; return wrap(dispatch__convert_indices_from_coo_to_csr(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); } else { // aten::_convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!) auto dispatch__convert_indices_from_coo_to_csr_out = [](at::Tensor out, const at::Tensor & self, int64_t size, bool out_int32) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convert_indices_from_coo_to_csr_out(out, self, size, out_int32); }; return wrap(dispatch__convert_indices_from_coo_to_csr_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _convert_indices_from_csr_to_coo static PyObject * THPVariable__convert_indices_from_csr_to_coo(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor auto dispatch__convert_indices_from_csr_to_coo = [](const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convert_indices_from_csr_to_coo(crow_indices, col_indices, out_int32, transpose); }; return wrap(dispatch__convert_indices_from_csr_to_coo(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } else { // aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!) auto dispatch__convert_indices_from_csr_to_coo_out = [](at::Tensor out, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_convert_indices_from_csr_to_coo_out(out, crow_indices, col_indices, out_int32, transpose); }; return wrap(dispatch__convert_indices_from_csr_to_coo_out(_r.tensor(4), _r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // mkldnn_adaptive_avg_pool2d static PyObject * THPVariable_mkldnn_adaptive_avg_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "mkldnn_adaptive_avg_pool2d(Tensor input, IntArrayRef[2] output_size, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor auto dispatch_mkldnn_adaptive_avg_pool2d = [](const at::Tensor & self, at::IntArrayRef output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mkldnn_adaptive_avg_pool2d(self, output_size); }; return wrap(dispatch_mkldnn_adaptive_avg_pool2d(_r.tensor(0), _r.intlist(1))); } else { // aten::mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_mkldnn_adaptive_avg_pool2d_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::mkldnn_adaptive_avg_pool2d_out(out, self, output_size); }; return wrap(dispatch_mkldnn_adaptive_avg_pool2d_out(_r.tensor(2), _r.tensor(0), _r.intlist(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _adaptive_avg_pool2d static PyObject * THPVariable__adaptive_avg_pool2d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_adaptive_avg_pool2d(Tensor input, SymIntArrayRef[2] output_size)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor auto dispatch__adaptive_avg_pool2d = [](const at::Tensor & self, c10::SymIntArrayRef output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_adaptive_avg_pool2d_symint(self, output_size); }; return wrap(dispatch__adaptive_avg_pool2d(_r.tensor(0), _r.symintlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _adaptive_avg_pool3d static PyObject * THPVariable__adaptive_avg_pool3d(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_adaptive_avg_pool3d(Tensor input, SymIntArrayRef[3] output_size)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor auto dispatch__adaptive_avg_pool3d = [](const at::Tensor & self, c10::SymIntArrayRef output_size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_adaptive_avg_pool3d_symint(self, output_size); }; return wrap(dispatch__adaptive_avg_pool3d(_r.tensor(0), _r.symintlist(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // column_stack static PyObject * THPVariable_column_stack(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "column_stack(TensorList tensors, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::column_stack(Tensor[] tensors) -> Tensor auto dispatch_column_stack = [](at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::column_stack(tensors); }; return wrap(dispatch_column_stack(_r.tensorlist(0))); } else { // aten::column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_column_stack_out = [](at::Tensor out, at::TensorList tensors) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::column_stack_out(out, tensors); }; return wrap(dispatch_column_stack_out(_r.tensor(1), _r.tensorlist(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // isfinite static PyObject * THPVariable_isfinite(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isfinite(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::isfinite(Tensor self) -> Tensor auto dispatch_isfinite = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.isfinite(); }; return wrap(dispatch_isfinite(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // isinf static PyObject * THPVariable_isinf(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isinf(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::isinf(Tensor self) -> Tensor auto dispatch_isinf = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.isinf(); }; return wrap(dispatch_isinf(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // isposinf static PyObject * THPVariable_isposinf(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isposinf(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::isposinf(Tensor self) -> Tensor auto dispatch_isposinf = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.isposinf(); }; return wrap(dispatch_isposinf(_r.tensor(0))); } else { // aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_isposinf_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isposinf_out(out, self); }; return wrap(dispatch_isposinf_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // isneginf static PyObject * THPVariable_isneginf(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "isneginf(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::isneginf(Tensor self) -> Tensor auto dispatch_isneginf = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.isneginf(); }; return wrap(dispatch_isneginf(_r.tensor(0))); } else { // aten::isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_isneginf_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::isneginf_out(out, self); }; return wrap(dispatch_isneginf_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _add_batch_dim static PyObject * THPVariable__add_batch_dim(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_add_batch_dim(Tensor input, int64_t batch_dim, int64_t level)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_add_batch_dim(Tensor self, int batch_dim, int level) -> Tensor auto dispatch__add_batch_dim = [](const at::Tensor & self, int64_t batch_dim, int64_t level) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_add_batch_dim(self, batch_dim, level); }; return wrap(dispatch__add_batch_dim(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _remove_batch_dim static PyObject * THPVariable__remove_batch_dim(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_remove_batch_dim(Tensor input, int64_t level, SymInt batch_size, int64_t out_dim)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_remove_batch_dim(Tensor self, int level, SymInt batch_size, int out_dim) -> Tensor auto dispatch__remove_batch_dim = [](const at::Tensor & self, int64_t level, c10::SymInt batch_size, int64_t out_dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_remove_batch_dim_symint(self, level, batch_size, out_dim); }; return wrap(dispatch__remove_batch_dim(_r.tensor(0), _r.toInt64(1), _r.toSymInt(2), _r.toInt64(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _linalg_det static PyObject * THPVariable__linalg_det(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__linalg_det_structseq(); static PyTypeObject* NamedTuple1 = generated::get__linalg_det_out_structseq(); static PythonArgParser parser({ "_linalg_det(Tensor A, *, TensorList[3] out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots) auto dispatch__linalg_det = [](const at::Tensor & A) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_det(A); }; return wrap(NamedTuple, dispatch__linalg_det(_r.tensor(0))); } else { // aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) auto out = _r.tensorlist_n<3>(1); auto dispatch__linalg_det_out = [](at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_det_out(result, LU, pivots, A); }; return wrap(NamedTuple1, dispatch__linalg_det_out(out[0], out[1], out[2], _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // det static PyObject * THPVariable_det(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "det(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::det(Tensor self) -> Tensor auto dispatch_det = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.det(); }; return wrap(dispatch_det(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _linalg_slogdet static PyObject * THPVariable__linalg_slogdet(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__linalg_slogdet_structseq(); static PyTypeObject* NamedTuple1 = generated::get__linalg_slogdet_out_structseq(); static PythonArgParser parser({ "_linalg_slogdet(Tensor A, *, TensorList[4] out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet, Tensor LU, Tensor pivots) auto dispatch__linalg_slogdet = [](const at::Tensor & A) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_slogdet(A); }; return wrap(NamedTuple, dispatch__linalg_slogdet(_r.tensor(0))); } else { // aten::_linalg_slogdet.sign(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) -> (Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) auto out = _r.tensorlist_n<4>(1); auto dispatch__linalg_slogdet_out = [](at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_slogdet_out(sign, logabsdet, LU, pivots, A); }; return wrap(NamedTuple1, dispatch__linalg_slogdet_out(out[0], out[1], out[2], out[3], _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // slogdet static PyObject * THPVariable_slogdet(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get_slogdet_structseq(); static PyTypeObject* NamedTuple1 = generated::get_slogdet_out_structseq(); static PythonArgParser parser({ "slogdet(Tensor input, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet) auto dispatch_slogdet = [](const at::Tensor & self) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return self.slogdet(); }; return wrap(NamedTuple, dispatch_slogdet(_r.tensor(0))); } else { // aten::slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) auto out = _r.tensorlist_n<2>(1); auto dispatch_slogdet_out = [](at::Tensor & sign, at::Tensor & logabsdet, const at::Tensor & self) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::slogdet_out(sign, logabsdet, self); }; return wrap(NamedTuple1, dispatch_slogdet_out(out[0], out[1], _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // logdet static PyObject * THPVariable_logdet(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "logdet(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::logdet(Tensor self) -> Tensor auto dispatch_logdet = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.logdet(); }; return wrap(dispatch_logdet(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _linalg_eigh static PyObject * THPVariable__linalg_eigh(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__linalg_eigh_structseq(); static PyTypeObject* NamedTuple1 = generated::get__linalg_eigh_out_structseq(); static PythonArgParser parser({ "_linalg_eigh(Tensor A, c10::string_view UPLO=\"L\", bool compute_v=True, *, TensorList[2] out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::_linalg_eigh(Tensor A, str UPLO="L", bool compute_v=True) -> (Tensor eigenvalues, Tensor eigenvectors) auto dispatch__linalg_eigh = [](const at::Tensor & A, c10::string_view UPLO, bool compute_v) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_eigh(A, UPLO, compute_v); }; return wrap(NamedTuple, dispatch__linalg_eigh(_r.tensor(0), _r.stringView(1), _r.toBool(2))); } else { // aten::_linalg_eigh.eigenvalues(Tensor A, str UPLO="L", bool compute_v=True, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) auto out = _r.tensorlist_n<2>(3); auto dispatch__linalg_eigh_out = [](at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & A, c10::string_view UPLO, bool compute_v) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_eigh_out(eigenvalues, eigenvectors, A, UPLO, compute_v); }; return wrap(NamedTuple1, dispatch__linalg_eigh_out(out[0], out[1], _r.tensor(0), _r.stringView(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // inverse static PyObject * THPVariable_inverse(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "inverse(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::inverse(Tensor self) -> Tensor auto dispatch_inverse = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.inverse(); }; return wrap(dispatch_inverse(_r.tensor(0))); } else { // aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_inverse_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::inverse_out(out, self); }; return wrap(dispatch_inverse_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // inner static PyObject * THPVariable_inner(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "inner(Tensor input, Tensor other, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::inner(Tensor self, Tensor other) -> Tensor auto dispatch_inner = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.inner(other); }; return wrap(dispatch_inner(_r.tensor(0), _r.tensor(1))); } else { // aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_inner_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & other) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::inner_out(out, self, other); }; return wrap(dispatch_inner_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // outer static PyObject * THPVariable_outer(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "outer(Tensor input, Tensor vec2, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::outer(Tensor self, Tensor vec2) -> Tensor auto dispatch_outer = [](const at::Tensor & self, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.outer(vec2); }; return wrap(dispatch_outer(_r.tensor(0), _r.tensor(1))); } else { // aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_outer_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::outer_out(out, self, vec2); }; return wrap(dispatch_outer_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ger static PyObject * THPVariable_ger(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ger(Tensor input, Tensor vec2, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::ger(Tensor self, Tensor vec2) -> Tensor auto dispatch_ger = [](const at::Tensor & self, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return self.ger(vec2); }; return wrap(dispatch_ger(_r.tensor(0), _r.tensor(1))); } else { // aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ger_out = [](at::Tensor out, const at::Tensor & self, const at::Tensor & vec2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ger_out(out, self, vec2); }; return wrap(dispatch_ger_out(_r.tensor(2), _r.tensor(0), _r.tensor(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _linalg_svd static PyObject * THPVariable__linalg_svd(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__linalg_svd_structseq(); static PyTypeObject* NamedTuple1 = generated::get__linalg_svd_out_structseq(); static PythonArgParser parser({ "_linalg_svd(Tensor A, bool full_matrices=False, bool compute_uv=True, *, c10::string_view? driver=None, TensorList[3] out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_linalg_svd(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh) auto dispatch__linalg_svd = [](const at::Tensor & A, bool full_matrices, bool compute_uv, ::std::optional driver) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_svd(A, full_matrices, compute_uv, driver); }; return wrap(NamedTuple, dispatch__linalg_svd(_r.tensor(0), _r.toBool(1), _r.toBool(2), _r.stringViewOptional(3))); } else { // aten::_linalg_svd.U(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) auto out = _r.tensorlist_n<3>(4); auto dispatch__linalg_svd_out = [](at::Tensor & U, at::Tensor & S, at::Tensor & Vh, const at::Tensor & A, bool full_matrices, bool compute_uv, ::std::optional driver) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_svd_out(U, S, Vh, A, full_matrices, compute_uv, driver); }; return wrap(NamedTuple1, dispatch__linalg_svd_out(out[0], out[1], out[2], _r.tensor(0), _r.toBool(1), _r.toBool(2), _r.stringViewOptional(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _linalg_solve_ex static PyObject * THPVariable__linalg_solve_ex(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__linalg_solve_ex_structseq(); static PyTypeObject* NamedTuple1 = generated::get__linalg_solve_ex_out_structseq(); static PythonArgParser parser({ "_linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, TensorList[4] out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::_linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor LU, Tensor pivots, Tensor info) auto dispatch__linalg_solve_ex = [](const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_solve_ex(A, B, left, check_errors); }; return wrap(NamedTuple, dispatch__linalg_solve_ex(_r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } else { // aten::_linalg_solve_ex.result(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) auto out = _r.tensorlist_n<4>(4); auto dispatch__linalg_solve_ex_out = [](at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_linalg_solve_ex_out(result, LU, pivots, info, A, B, left, check_errors); }; return wrap(NamedTuple1, dispatch__linalg_solve_ex_out(out[0], out[1], out[2], out[3], _r.tensor(0), _r.tensor(1), _r.toBool(2), _r.toBool(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _test_serialization_subcmul static PyObject * THPVariable__test_serialization_subcmul(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_test_serialization_subcmul(Tensor input, Tensor other, Scalar alpha=1)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor auto dispatch__test_serialization_subcmul = [](const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_serialization_subcmul(self, other, alpha); }; return wrap(dispatch__test_serialization_subcmul(_r.tensor(0), _r.tensor(1), _r.scalar(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _test_parallel_materialize static PyObject * THPVariable__test_parallel_materialize(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_test_parallel_materialize(Tensor input, int64_t num_parallel, bool skip_first=False)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_test_parallel_materialize(Tensor self, int num_parallel, bool skip_first=False) -> Tensor auto dispatch__test_parallel_materialize = [](const at::Tensor & self, int64_t num_parallel, bool skip_first) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_parallel_materialize(self, num_parallel, skip_first); }; return wrap(dispatch__test_parallel_materialize(_r.tensor(0), _r.toInt64(1), _r.toBool(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _test_autograd_multiple_dispatch static PyObject * THPVariable__test_autograd_multiple_dispatch(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_test_autograd_multiple_dispatch(Tensor input)", "_test_autograd_multiple_dispatch(Tensor input, bool b)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_test_autograd_multiple_dispatch.fullcoverage(Tensor self) -> Tensor auto dispatch__test_autograd_multiple_dispatch = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_autograd_multiple_dispatch(self); }; return wrap(dispatch__test_autograd_multiple_dispatch(_r.tensor(0))); } case 1: { // aten::_test_autograd_multiple_dispatch.ntonly(Tensor self, bool b) -> Tensor auto dispatch__test_autograd_multiple_dispatch = [](const at::Tensor & self, bool b) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_autograd_multiple_dispatch(self, b); }; return wrap(dispatch__test_autograd_multiple_dispatch(_r.tensor(0), _r.toBool(1))); } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _test_autograd_multiple_dispatch_view static PyObject * THPVariable__test_autograd_multiple_dispatch_view(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_test_autograd_multiple_dispatch_view(Tensor input)", }, /*traceable=*/true); ParsedArgs<1> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_test_autograd_multiple_dispatch_view(Tensor(a) self) -> Tensor(a) auto dispatch__test_autograd_multiple_dispatch_view = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_autograd_multiple_dispatch_view(self); }; return wrap(dispatch__test_autograd_multiple_dispatch_view(_r.tensor(0))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _test_autograd_multiple_dispatch_view_copy static PyObject * THPVariable__test_autograd_multiple_dispatch_view_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_test_autograd_multiple_dispatch_view_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor auto dispatch__test_autograd_multiple_dispatch_view_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_autograd_multiple_dispatch_view_copy(self); }; return wrap(dispatch__test_autograd_multiple_dispatch_view_copy(_r.tensor(0))); } else { // aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__test_autograd_multiple_dispatch_view_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_test_autograd_multiple_dispatch_view_copy_out(out, self); }; return wrap(dispatch__test_autograd_multiple_dispatch_view_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // segment_reduce static PyObject * THPVariable_segment_reduce(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "segment_reduce(Tensor data, c10::string_view reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int64_t axis=0, bool unsafe=False, Scalar? initial=None)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None) -> Tensor auto dispatch_segment_reduce = [](const at::Tensor & data, c10::string_view reduce, const ::std::optional & lengths, const ::std::optional & indices, const ::std::optional & offsets, int64_t axis, bool unsafe, const ::std::optional & initial) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::segment_reduce(data, reduce, lengths, indices, offsets, axis, unsafe, initial); }; return wrap(dispatch_segment_reduce(_r.tensor(0), _r.stringView(1), _r.optionalTensor(2), _r.optionalTensor(3), _r.optionalTensor(4), _r.toInt64(5), _r.toBool(6), _r.scalarOptional(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_tensor_from_tensor_list static PyObject * THPVariable__nested_tensor_from_tensor_list(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_tensor_from_tensor_list(TensorList list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_tensor_from_tensor_list(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor auto dispatch__nested_tensor_from_tensor_list = [](at::TensorList list, ::std::optional dtype, ::std::optional layout, ::std::optional device, ::std::optional pin_memory) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_tensor_from_tensor_list(list, dtype, layout, device, pin_memory); }; return wrap(dispatch__nested_tensor_from_tensor_list(_r.tensorlist(0), _r.scalartypeOptional(1), _r.layoutOptional(2), _r.deviceOptional(3), _r.toBoolOptional(4))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fw_primal_copy static PyObject * THPVariable__fw_primal_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fw_primal_copy(Tensor input, int64_t level, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::_fw_primal_copy(Tensor self, int level) -> Tensor auto dispatch__fw_primal_copy = [](const at::Tensor & self, int64_t level) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fw_primal_copy(self, level); }; return wrap(dispatch__fw_primal_copy(_r.tensor(0), _r.toInt64(1))); } else { // aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__fw_primal_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t level) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fw_primal_copy_out(out, self, level); }; return wrap(dispatch__fw_primal_copy_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _make_dual_copy static PyObject * THPVariable__make_dual_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_make_dual_copy(Tensor primal, Tensor tangent, int64_t level, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::_make_dual_copy(Tensor primal, Tensor tangent, int level) -> Tensor auto dispatch__make_dual_copy = [](const at::Tensor & primal, const at::Tensor & tangent, int64_t level) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_make_dual_copy(primal, tangent, level); }; return wrap(dispatch__make_dual_copy(_r.tensor(0), _r.tensor(1), _r.toInt64(2))); } else { // aten::_make_dual_copy.out(Tensor primal, Tensor tangent, int level, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__make_dual_copy_out = [](at::Tensor out, const at::Tensor & primal, const at::Tensor & tangent, int64_t level) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_make_dual_copy_out(out, primal, tangent, level); }; return wrap(dispatch__make_dual_copy_out(_r.tensor(3), _r.tensor(0), _r.tensor(1), _r.toInt64(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // view_as_real_copy static PyObject * THPVariable_view_as_real_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "view_as_real_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::view_as_real_copy(Tensor self) -> Tensor auto dispatch_view_as_real_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_as_real_copy(self); }; return wrap(dispatch_view_as_real_copy(_r.tensor(0))); } else { // aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_view_as_real_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_as_real_copy_out(out, self); }; return wrap(dispatch_view_as_real_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // view_as_complex_copy static PyObject * THPVariable_view_as_complex_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "view_as_complex_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::view_as_complex_copy(Tensor self) -> Tensor auto dispatch_view_as_complex_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_as_complex_copy(self); }; return wrap(dispatch_view_as_complex_copy(_r.tensor(0))); } else { // aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_view_as_complex_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_as_complex_copy_out(out, self); }; return wrap(dispatch_view_as_complex_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _conj_copy static PyObject * THPVariable__conj_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_conj_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_conj_copy(Tensor self) -> Tensor auto dispatch__conj_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_conj_copy(self); }; return wrap(dispatch__conj_copy(_r.tensor(0))); } else { // aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__conj_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_conj_copy_out(out, self); }; return wrap(dispatch__conj_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _neg_view_copy static PyObject * THPVariable__neg_view_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_neg_view_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_neg_view_copy(Tensor self) -> Tensor auto dispatch__neg_view_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_neg_view_copy(self); }; return wrap(dispatch__neg_view_copy(_r.tensor(0))); } else { // aten::_neg_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__neg_view_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_neg_view_copy_out(out, self); }; return wrap(dispatch__neg_view_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // as_strided_copy static PyObject * THPVariable_as_strided_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "as_strided_copy(Tensor input, SymIntArrayRef size, SymIntArrayRef stride, SymInt? storage_offset=None, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor auto dispatch_as_strided_copy = [](const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional storage_offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::as_strided_copy_symint(self, size, stride, storage_offset); }; return wrap(dispatch_as_strided_copy(_r.tensor(0), _r.symintlist(1), _r.symintlist(2), _r.toSymIntOptional(3))); } else { // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_as_strided_copy_out = [](at::Tensor out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional storage_offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::as_strided_copy_symint_out(out, self, size, stride, storage_offset); }; return wrap(dispatch_as_strided_copy_out(_r.tensor(4), _r.tensor(0), _r.symintlist(1), _r.symintlist(2), _r.toSymIntOptional(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _sparse_broadcast_to_copy static PyObject * THPVariable__sparse_broadcast_to_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_sparse_broadcast_to_copy(Tensor input, IntArrayRef size, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::_sparse_broadcast_to_copy(Tensor self, int[] size) -> Tensor auto dispatch__sparse_broadcast_to_copy = [](const at::Tensor & self, at::IntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_broadcast_to_copy(self, size); }; return wrap(dispatch__sparse_broadcast_to_copy(_r.tensor(0), _r.intlist(1))); } else { // aten::_sparse_broadcast_to_copy.out(Tensor self, int[] size, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__sparse_broadcast_to_copy_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_sparse_broadcast_to_copy_out(out, self, size); }; return wrap(dispatch__sparse_broadcast_to_copy_out(_r.tensor(2), _r.tensor(0), _r.intlist(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // diagonal_copy static PyObject * THPVariable_diagonal_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "diagonal_copy(Tensor input, int64_t offset=0, int64_t dim1=0, int64_t dim2=1, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::diagonal_copy(Tensor self, int offset=0, int dim1=0, int dim2=1) -> Tensor auto dispatch_diagonal_copy = [](const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::diagonal_copy(self, offset, dim1, dim2); }; return wrap(dispatch_diagonal_copy(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toInt64(3))); } else { // aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_diagonal_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::diagonal_copy_out(out, self, offset, dim1, dim2); }; return wrap(dispatch_diagonal_copy_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toInt64(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // expand_copy static PyObject * THPVariable_expand_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "expand_copy(Tensor input, SymIntArrayRef size, *, bool implicit=False, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor auto dispatch_expand_copy = [](const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::expand_copy_symint(self, size, implicit); }; return wrap(dispatch_expand_copy(_r.tensor(0), _r.symintlist(1), _r.toBool(2))); } else { // aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!) auto dispatch_expand_copy_out = [](at::Tensor out, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::expand_copy_symint_out(out, self, size, implicit); }; return wrap(dispatch_expand_copy_out(_r.tensor(3), _r.tensor(0), _r.symintlist(1), _r.toBool(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // permute_copy static PyObject * THPVariable_permute_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "permute_copy(Tensor input, IntArrayRef dims, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::permute_copy(Tensor self, int[] dims) -> Tensor auto dispatch_permute_copy = [](const at::Tensor & self, at::IntArrayRef dims) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::permute_copy(self, dims); }; return wrap(dispatch_permute_copy(_r.tensor(0), _r.intlist(1))); } else { // aten::permute_copy.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_permute_copy_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dims) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::permute_copy_out(out, self, dims); }; return wrap(dispatch_permute_copy_out(_r.tensor(2), _r.tensor(0), _r.intlist(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _reshape_alias_copy static PyObject * THPVariable__reshape_alias_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_reshape_alias_copy(Tensor input, SymIntArrayRef size, SymIntArrayRef stride, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor auto dispatch__reshape_alias_copy = [](const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_reshape_alias_copy_symint(self, size, stride); }; return wrap(dispatch__reshape_alias_copy(_r.tensor(0), _r.symintlist(1), _r.symintlist(2))); } else { // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__reshape_alias_copy_out = [](at::Tensor out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_reshape_alias_copy_symint_out(out, self, size, stride); }; return wrap(dispatch__reshape_alias_copy_out(_r.tensor(3), _r.tensor(0), _r.symintlist(1), _r.symintlist(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // select_copy static PyObject * THPVariable_select_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "select_copy(Tensor input, int64_t dim, SymInt index, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::select_copy.int(Tensor self, int dim, SymInt index) -> Tensor auto dispatch_select_copy = [](const at::Tensor & self, int64_t dim, c10::SymInt index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::select_copy_symint(self, dim, index); }; return wrap(dispatch_select_copy(_r.tensor(0), _r.toInt64(1), _r.toSymInt(2))); } else { // aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_select_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, c10::SymInt index) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::select_copy_symint_out(out, self, dim, index); }; return wrap(dispatch_select_copy_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.toSymInt(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // detach_copy static PyObject * THPVariable_detach_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "detach_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::detach_copy(Tensor self) -> Tensor auto dispatch_detach_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::detach_copy(self); }; return wrap(dispatch_detach_copy(_r.tensor(0))); } else { // aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_detach_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::detach_copy_out(out, self); }; return wrap(dispatch_detach_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // slice_copy static PyObject * THPVariable_slice_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "slice_copy(Tensor input, int64_t dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<6> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(5)) { // aten::slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor auto dispatch_slice_copy = [](const at::Tensor & self, int64_t dim, ::std::optional start, ::std::optional end, c10::SymInt step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::slice_copy_symint(self, dim, start, end, step); }; return wrap(dispatch_slice_copy(_r.tensor(0), _r.toInt64(1), _r.toSymIntOptional(2), _r.toSymIntOptional(3), _r.toSymInt(4))); } else { // aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_slice_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dim, ::std::optional start, ::std::optional end, c10::SymInt step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::slice_copy_symint_out(out, self, dim, start, end, step); }; return wrap(dispatch_slice_copy_out(_r.tensor(5), _r.tensor(0), _r.toInt64(1), _r.toSymIntOptional(2), _r.toSymIntOptional(3), _r.toSymInt(4))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // split_copy static PyObject * THPVariable_split_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "split_copy(Tensor input, SymInt split_size, int64_t dim=0, *, TensorList out=None)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] auto dispatch_split_copy = [](const at::Tensor & self, c10::SymInt split_size, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::split_copy_symint(self, split_size, dim); }; return wrap(dispatch_split_copy(_r.tensor(0), _r.toSymInt(1), _r.toInt64(2))); } else { // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () auto dispatch_split_copy_out = [](at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim) -> void { pybind11::gil_scoped_release no_gil; at::split_copy_symint_out(out, self, split_size, dim); }; dispatch_split_copy_out(_r.tensorlist(3), _r.tensor(0), _r.toSymInt(1), _r.toInt64(2)); Py_RETURN_NONE; } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // split_with_sizes_copy static PyObject * THPVariable_split_with_sizes_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "split_with_sizes_copy(Tensor input, SymIntArrayRef split_sizes, int64_t dim=0, *, TensorList out=None)", }, /*traceable=*/false); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] auto dispatch_split_with_sizes_copy = [](const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::split_with_sizes_copy_symint(self, split_sizes, dim); }; return wrap(dispatch_split_with_sizes_copy(_r.tensor(0), _r.symintlist(1), _r.toInt64(2))); } else { // aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () auto dispatch_split_with_sizes_copy_out = [](at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) -> void { pybind11::gil_scoped_release no_gil; at::split_with_sizes_copy_symint_out(out, self, split_sizes, dim); }; dispatch_split_with_sizes_copy_out(_r.tensorlist(3), _r.tensor(0), _r.symintlist(1), _r.toInt64(2)); Py_RETURN_NONE; } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // squeeze_copy static PyObject * THPVariable_squeeze_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "squeeze_copy(Tensor input, *, Tensor out=None)", "squeeze_copy(Tensor input, int64_t dim, *, Tensor out=None)", "squeeze_copy(Tensor input, IntArrayRef dim, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(1)) { // aten::squeeze_copy(Tensor self) -> Tensor auto dispatch_squeeze_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::squeeze_copy(self); }; return wrap(dispatch_squeeze_copy(_r.tensor(0))); } else { // aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_squeeze_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::squeeze_copy_out(out, self); }; return wrap(dispatch_squeeze_copy_out(_r.tensor(1), _r.tensor(0))); } } case 1: { if (_r.isNone(2)) { // aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor auto dispatch_squeeze_copy = [](const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::squeeze_copy(self, dim); }; return wrap(dispatch_squeeze_copy(_r.tensor(0), _r.toInt64(1))); } else { // aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_squeeze_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::squeeze_copy_out(out, self, dim); }; return wrap(dispatch_squeeze_copy_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } } case 2: { if (_r.isNone(2)) { // aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor auto dispatch_squeeze_copy = [](const at::Tensor & self, at::IntArrayRef dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::squeeze_copy(self, dim); }; return wrap(dispatch_squeeze_copy(_r.tensor(0), _r.intlist(1))); } else { // aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_squeeze_copy_out = [](at::Tensor out, const at::Tensor & self, at::IntArrayRef dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::squeeze_copy_out(out, self, dim); }; return wrap(dispatch_squeeze_copy_out(_r.tensor(2), _r.tensor(0), _r.intlist(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // t_copy static PyObject * THPVariable_t_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "t_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::t_copy(Tensor self) -> Tensor auto dispatch_t_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::t_copy(self); }; return wrap(dispatch_t_copy(_r.tensor(0))); } else { // aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_t_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::t_copy_out(out, self); }; return wrap(dispatch_t_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // transpose_copy static PyObject * THPVariable_transpose_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "transpose_copy(Tensor input, int64_t dim0, int64_t dim1, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(3)) { // aten::transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor auto dispatch_transpose_copy = [](const at::Tensor & self, int64_t dim0, int64_t dim1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::transpose_copy(self, dim0, dim1); }; return wrap(dispatch_transpose_copy(_r.tensor(0), _r.toInt64(1), _r.toInt64(2))); } else { // aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_transpose_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dim0, int64_t dim1) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::transpose_copy_out(out, self, dim0, dim1); }; return wrap(dispatch_transpose_copy_out(_r.tensor(3), _r.tensor(0), _r.toInt64(1), _r.toInt64(2))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unsqueeze_copy static PyObject * THPVariable_unsqueeze_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unsqueeze_copy(Tensor input, int64_t dim, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::unsqueeze_copy(Tensor self, int dim) -> Tensor auto dispatch_unsqueeze_copy = [](const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::unsqueeze_copy(self, dim); }; return wrap(dispatch_unsqueeze_copy(_r.tensor(0), _r.toInt64(1))); } else { // aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_unsqueeze_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dim) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::unsqueeze_copy_out(out, self, dim); }; return wrap(dispatch_unsqueeze_copy_out(_r.tensor(2), _r.tensor(0), _r.toInt64(1))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _indices_copy static PyObject * THPVariable__indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_indices_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_indices_copy(Tensor self) -> Tensor auto dispatch__indices_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_indices_copy(self); }; return wrap(dispatch__indices_copy(_r.tensor(0))); } else { // aten::_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__indices_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_indices_copy_out(out, self); }; return wrap(dispatch__indices_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _values_copy static PyObject * THPVariable__values_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_values_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::_values_copy(Tensor self) -> Tensor auto dispatch__values_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_values_copy(self); }; return wrap(dispatch__values_copy(_r.tensor(0))); } else { // aten::_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch__values_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_values_copy_out(out, self); }; return wrap(dispatch__values_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // indices_copy static PyObject * THPVariable_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "indices_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::indices_copy(Tensor self) -> Tensor auto dispatch_indices_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::indices_copy(self); }; return wrap(dispatch_indices_copy(_r.tensor(0))); } else { // aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_indices_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::indices_copy_out(out, self); }; return wrap(dispatch_indices_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // values_copy static PyObject * THPVariable_values_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "values_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::values_copy(Tensor self) -> Tensor auto dispatch_values_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::values_copy(self); }; return wrap(dispatch_values_copy(_r.tensor(0))); } else { // aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_values_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::values_copy_out(out, self); }; return wrap(dispatch_values_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // crow_indices_copy static PyObject * THPVariable_crow_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "crow_indices_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::crow_indices_copy(Tensor self) -> Tensor auto dispatch_crow_indices_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::crow_indices_copy(self); }; return wrap(dispatch_crow_indices_copy(_r.tensor(0))); } else { // aten::crow_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_crow_indices_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::crow_indices_copy_out(out, self); }; return wrap(dispatch_crow_indices_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // col_indices_copy static PyObject * THPVariable_col_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "col_indices_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::col_indices_copy(Tensor self) -> Tensor auto dispatch_col_indices_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::col_indices_copy(self); }; return wrap(dispatch_col_indices_copy(_r.tensor(0))); } else { // aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_col_indices_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::col_indices_copy_out(out, self); }; return wrap(dispatch_col_indices_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // ccol_indices_copy static PyObject * THPVariable_ccol_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "ccol_indices_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::ccol_indices_copy(Tensor self) -> Tensor auto dispatch_ccol_indices_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ccol_indices_copy(self); }; return wrap(dispatch_ccol_indices_copy(_r.tensor(0))); } else { // aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_ccol_indices_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::ccol_indices_copy_out(out, self); }; return wrap(dispatch_ccol_indices_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // row_indices_copy static PyObject * THPVariable_row_indices_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "row_indices_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::row_indices_copy(Tensor self) -> Tensor auto dispatch_row_indices_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::row_indices_copy(self); }; return wrap(dispatch_row_indices_copy(_r.tensor(0))); } else { // aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_row_indices_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::row_indices_copy_out(out, self); }; return wrap(dispatch_row_indices_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unbind_copy static PyObject * THPVariable_unbind_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unbind_copy(Tensor input, int64_t dim=0, *, TensorList out=None)", }, /*traceable=*/false); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(2)) { // aten::unbind_copy.int(Tensor self, int dim=0) -> Tensor[] auto dispatch_unbind_copy = [](const at::Tensor & self, int64_t dim) -> ::std::vector { pybind11::gil_scoped_release no_gil; return at::unbind_copy(self, dim); }; return wrap(dispatch_unbind_copy(_r.tensor(0), _r.toInt64(1))); } else { // aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> () auto dispatch_unbind_copy_out = [](at::TensorList out, const at::Tensor & self, int64_t dim) -> void { pybind11::gil_scoped_release no_gil; at::unbind_copy_out(out, self, dim); }; dispatch_unbind_copy_out(_r.tensorlist(2), _r.tensor(0), _r.toInt64(1)); Py_RETURN_NONE; } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // view_copy static PyObject * THPVariable_view_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "view_copy(Tensor input, ScalarType dtype, *, Tensor out=None)", "view_copy(Tensor input, SymIntArrayRef size, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { if (_r.isNone(2)) { // aten::view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor auto dispatch_view_copy = [](const at::Tensor & self, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_copy(self, dtype); }; return wrap(dispatch_view_copy(_r.tensor(0), _r.scalartype(1))); } else { // aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_view_copy_out = [](at::Tensor out, const at::Tensor & self, at::ScalarType dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_copy_out(out, self, dtype); }; return wrap(dispatch_view_copy_out(_r.tensor(2), _r.tensor(0), _r.scalartype(1))); } } case 1: { if (_r.isNone(2)) { // aten::view_copy(Tensor self, SymInt[] size) -> Tensor auto dispatch_view_copy = [](const at::Tensor & self, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_copy_symint(self, size); }; return wrap(dispatch_view_copy(_r.tensor(0), _r.symintlist(1))); } else { // aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_view_copy_out = [](at::Tensor out, const at::Tensor & self, c10::SymIntArrayRef size) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::view_copy_symint_out(out, self, size); }; return wrap(dispatch_view_copy_out(_r.tensor(2), _r.tensor(0), _r.symintlist(1))); } } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // unfold_copy static PyObject * THPVariable_unfold_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "unfold_copy(Tensor input, int64_t dimension, int64_t size, int64_t step, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<5> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(4)) { // aten::unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor auto dispatch_unfold_copy = [](const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::unfold_copy(self, dimension, size, step); }; return wrap(dispatch_unfold_copy(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toInt64(3))); } else { // aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_unfold_copy_out = [](at::Tensor out, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::unfold_copy_out(out, self, dimension, size, step); }; return wrap(dispatch_unfold_copy_out(_r.tensor(4), _r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.toInt64(3))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // alias_copy static PyObject * THPVariable_alias_copy(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "alias_copy(Tensor input, *, Tensor out=None)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } if (_r.isNone(1)) { // aten::alias_copy(Tensor self) -> Tensor auto dispatch_alias_copy = [](const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::alias_copy(self); }; return wrap(dispatch_alias_copy(_r.tensor(0))); } else { // aten::alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) auto dispatch_alias_copy_out = [](at::Tensor out, const at::Tensor & self) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::alias_copy_out(out, self); }; return wrap(dispatch_alias_copy_out(_r.tensor(1), _r.tensor(0))); } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_from_padded_tensor static PyObject * THPVariable__nested_from_padded_tensor(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_from_padded_tensor(Tensor padded, Tensor offsets, Tensor dummy, int64_t ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, SymInt? sum_S=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_from_padded_tensor(Tensor padded, Tensor offsets, Tensor dummy, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, SymInt? sum_S=None) -> Tensor auto dispatch__nested_from_padded_tensor = [](const at::Tensor & padded, const at::Tensor & offsets, const at::Tensor & dummy, int64_t ragged_idx, const ::std::optional & min_seqlen, const ::std::optional & max_seqlen, ::std::optional sum_S) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_from_padded_tensor_symint(padded, offsets, dummy, ragged_idx, min_seqlen, max_seqlen, sum_S); }; return wrap(dispatch__nested_from_padded_tensor(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.optionalTensor(4), _r.optionalTensor(5), _r.toSymIntOptional(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _nested_tensor_softmax_with_shape static PyObject * THPVariable__nested_tensor_softmax_with_shape(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_nested_tensor_softmax_with_shape(Tensor input, Tensor query)", }, /*traceable=*/true); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_nested_tensor_softmax_with_shape(Tensor self, Tensor query) -> Tensor auto dispatch__nested_tensor_softmax_with_shape = [](const at::Tensor & self, const at::Tensor & query) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_nested_tensor_softmax_with_shape(self, query); }; return wrap(dispatch__nested_tensor_softmax_with_shape(_r.tensor(0), _r.tensor(1))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _safe_softmax static PyObject * THPVariable__safe_softmax(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_safe_softmax(Tensor input, int64_t dim, ScalarType? dtype=None)", }, /*traceable=*/true); ParsedArgs<3> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_safe_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor auto dispatch__safe_softmax = [](const at::Tensor & self, int64_t dim, ::std::optional dtype) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_safe_softmax(self, dim, dtype); }; return wrap(dispatch__safe_softmax(_r.tensor(0), _r.toInt64(1), _r.scalartypeOptional(2))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _transformer_encoder_layer_fwd static PyObject * THPVariable__transformer_encoder_layer_fwd(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_transformer_encoder_layer_fwd(Tensor src, int64_t embed_dim, int64_t num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, double eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int64_t? mask_type=None)", }, /*traceable=*/true); ParsedArgs<20> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_transformer_encoder_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None) -> Tensor auto dispatch__transformer_encoder_layer_fwd = [](const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const ::std::optional & mask, ::std::optional mask_type) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_transformer_encoder_layer_fwd(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type); }; return wrap(dispatch__transformer_encoder_layer_fwd(_r.tensor(0), _r.toInt64(1), _r.toInt64(2), _r.tensor(3), _r.tensor(4), _r.tensor(5), _r.tensor(6), _r.toBool(7), _r.toBool(8), _r.toDouble(9), _r.tensor(10), _r.tensor(11), _r.tensor(12), _r.tensor(13), _r.tensor(14), _r.tensor(15), _r.tensor(16), _r.tensor(17), _r.optionalTensor(18), _r.toInt64Optional(19))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _native_multi_head_attention static PyObject * THPVariable__native_multi_head_attention(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int64_t embed_dim, int64_t num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int64_t? mask_type=None)", }, /*traceable=*/true); ParsedArgs<13> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor) auto dispatch__native_multi_head_attention = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional & mask, bool need_weights, bool average_attn_weights, ::std::optional mask_type) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_native_multi_head_attention(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type); }; return wrap(dispatch__native_multi_head_attention(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4), _r.tensor(5), _r.tensor(6), _r.tensor(7), _r.tensor(8), _r.optionalTensor(9), _r.toBool(10), _r.toBool(11), _r.toInt64Optional(12))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fused_sdp_choice static PyObject * THPVariable__fused_sdp_choice(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, double dropout_p=0.0, bool is_causal=False, *, double? scale=None, bool enable_gqa=False)", }, /*traceable=*/false); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, *, float? scale=None, bool enable_gqa=False) -> int auto dispatch__fused_sdp_choice = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional & attn_mask, double dropout_p, bool is_causal, ::std::optional scale, bool enable_gqa) -> int64_t { pybind11::gil_scoped_release no_gil; return at::_fused_sdp_choice(query, key, value, attn_mask, dropout_p, is_causal, scale, enable_gqa); }; return wrap(dispatch__fused_sdp_choice(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toDouble(4), _r.toBool(5), _r.toDoubleOptional(6), _r.toBool(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_dot_product_attention_math static PyObject * THPVariable__scaled_dot_product_attention_math(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_scaled_dot_product_attention_math(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, double dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, double? scale=None, bool enable_gqa=False)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_scaled_dot_product_attention_math(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None, bool enable_gqa=False) -> (Tensor, Tensor) auto dispatch__scaled_dot_product_attention_math = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional & attn_mask, double dropout_p, bool is_causal, const ::std::optional & dropout_mask, ::std::optional scale, bool enable_gqa) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_scaled_dot_product_attention_math(query, key, value, attn_mask, dropout_p, is_causal, dropout_mask, scale, enable_gqa); }; return wrap(dispatch__scaled_dot_product_attention_math(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toDouble(4), _r.toBool(5), _r.optionalTensor(6), _r.toDoubleOptional(7), _r.toBool(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_dot_product_attention_math_for_mps static PyObject * THPVariable__scaled_dot_product_attention_math_for_mps(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_scaled_dot_product_attention_math_for_mps(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, double dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, double? scale=None)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_scaled_dot_product_attention_math_for_mps(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None) -> (Tensor, Tensor) auto dispatch__scaled_dot_product_attention_math_for_mps = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional & attn_mask, double dropout_p, bool is_causal, const ::std::optional & dropout_mask, ::std::optional scale) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_scaled_dot_product_attention_math_for_mps(query, key, value, attn_mask, dropout_p, is_causal, dropout_mask, scale); }; return wrap(dispatch__scaled_dot_product_attention_math_for_mps(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toDouble(4), _r.toBool(5), _r.optionalTensor(6), _r.toDoubleOptional(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_dot_product_flash_attention static PyObject * THPVariable__scaled_dot_product_flash_attention(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__scaled_dot_product_flash_attention_structseq(); static PythonArgParser parser({ "_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, double dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, double? scale=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor rng_state, Tensor unused, Tensor debug_attn_mask) auto dispatch__scaled_dot_product_flash_attention = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional scale) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_scaled_dot_product_flash_attention(query, key, value, dropout_p, is_causal, return_debug_mask, scale); }; return wrap(NamedTuple, dispatch__scaled_dot_product_flash_attention(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toDouble(3), _r.toBool(4), _r.toBool(5), _r.toDoubleOptional(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_dot_product_flash_attention_for_cpu static PyObject * THPVariable__scaled_dot_product_flash_attention_for_cpu(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__scaled_dot_product_flash_attention_for_cpu_structseq(); static PythonArgParser parser({ "_scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, double dropout_p=0.0, bool is_causal=False, *, Tensor? attn_mask=None, double? scale=None)", }, /*traceable=*/true); ParsedArgs<7> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor output, Tensor logsumexp) auto dispatch__scaled_dot_product_flash_attention_for_cpu = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p, bool is_causal, const ::std::optional & attn_mask, ::std::optional scale) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_scaled_dot_product_flash_attention_for_cpu(query, key, value, dropout_p, is_causal, attn_mask, scale); }; return wrap(NamedTuple, dispatch__scaled_dot_product_flash_attention_for_cpu(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toDouble(3), _r.toBool(4), _r.optionalTensor(5), _r.toDoubleOptional(6))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_dot_product_efficient_attention static PyObject * THPVariable__scaled_dot_product_efficient_attention(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__scaled_dot_product_efficient_attention_structseq(); static PythonArgParser parser({ "_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, double dropout_p=0.0, bool is_causal=False, *, double? scale=None)", }, /*traceable=*/true); ParsedArgs<8> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, *, float? scale=None) -> (Tensor output, Tensor log_sumexp, Tensor philox_seed, Tensor philox_offset) auto dispatch__scaled_dot_product_efficient_attention = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional & attn_bias, bool compute_log_sumexp, double dropout_p, bool is_causal, ::std::optional scale) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_scaled_dot_product_efficient_attention(query, key, value, attn_bias, compute_log_sumexp, dropout_p, is_causal, scale); }; return wrap(NamedTuple, dispatch__scaled_dot_product_efficient_attention(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toBool(4), _r.toDouble(5), _r.toBool(6), _r.toDoubleOptional(7))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _scaled_dot_product_cudnn_attention static PyObject * THPVariable__scaled_dot_product_cudnn_attention(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PyTypeObject* NamedTuple = generated::get__scaled_dot_product_cudnn_attention_structseq(); static PythonArgParser parser({ "_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, double dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, double? scale=None)", }, /*traceable=*/true); ParsedArgs<9> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask) auto dispatch__scaled_dot_product_cudnn_attention = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional & attn_bias, bool compute_log_sumexp, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional scale) -> ::std::tuple { pybind11::gil_scoped_release no_gil; return at::_scaled_dot_product_cudnn_attention(query, key, value, attn_bias, compute_log_sumexp, dropout_p, is_causal, return_debug_mask, scale); }; return wrap(NamedTuple, dispatch__scaled_dot_product_cudnn_attention(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.optionalTensor(3), _r.toBool(4), _r.toDouble(5), _r.toBool(6), _r.toBool(7), _r.toDoubleOptional(8))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _triton_scaled_dot_attention static PyObject * THPVariable__triton_scaled_dot_attention(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, double dropout_p=0.0)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, float dropout_p=0.0) -> Tensor auto dispatch__triton_scaled_dot_attention = [](const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_triton_scaled_dot_attention(q, k, v, dropout_p); }; return wrap(dispatch__triton_scaled_dot_attention(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toDouble(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fill_mem_eff_dropout_mask_ static PyObject * THPVariable__fill_mem_eff_dropout_mask_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fill_mem_eff_dropout_mask_(Tensor input, double dropout_p, int64_t seed, int64_t offset)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fill_mem_eff_dropout_mask_(Tensor(a!) self, float dropout_p, int seed, int offset) -> Tensor(a!) auto dispatch__fill_mem_eff_dropout_mask_ = [](at::Tensor self, double dropout_p, int64_t seed, int64_t offset) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_fill_mem_eff_dropout_mask_(self, dropout_p, seed, offset); }; return wrap(dispatch__fill_mem_eff_dropout_mask_(_r.tensor(0), _r.toDouble(1), _r.toInt64(2), _r.toInt64(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _triton_multi_head_attention static PyObject * THPVariable__triton_multi_head_attention(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int64_t embed_dim, int64_t num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None)", }, /*traceable=*/true); ParsedArgs<10> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor auto dispatch__triton_multi_head_attention = [](const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional & mask) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_triton_multi_head_attention(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask); }; return wrap(dispatch__triton_multi_head_attention(_r.tensor(0), _r.tensor(1), _r.tensor(2), _r.toInt64(3), _r.toInt64(4), _r.tensor(5), _r.tensor(6), _r.tensor(7), _r.tensor(8), _r.optionalTensor(9))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _foobar static PyObject * THPVariable__foobar(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_foobar(Tensor input, bool arg1=True, bool arg2=True, *, bool arg3=True)", }, /*traceable=*/true); ParsedArgs<4> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_foobar(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True) -> Tensor auto dispatch__foobar = [](const at::Tensor & self, bool arg1, bool arg2, bool arg3) -> at::Tensor { pybind11::gil_scoped_release no_gil; return at::_foobar(self, arg1, arg2, arg3); }; return wrap(dispatch__foobar(_r.tensor(0), _r.toBool(1), _r.toBool(2), _r.toBool(3))); Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _fused_adam_ static PyObject * THPVariable__fused_adam_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fused_adam_(TensorList self, TensorList grads, TensorList exp_avgs, TensorList exp_avg_sqs, TensorList max_exp_avg_sqs, TensorList state_steps, *, Tensor lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None)", "_fused_adam_(TensorList self, TensorList grads, TensorList exp_avgs, TensorList exp_avg_sqs, TensorList max_exp_avg_sqs, TensorList state_steps, *, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None)", }, /*traceable=*/false); ParsedArgs<15> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_fused_adam_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () auto dispatch__fused_adam_ = [](at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional & grad_scale, const ::std::optional & found_inf) -> void { pybind11::gil_scoped_release no_gil; at::_fused_adam_(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); }; dispatch__fused_adam_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensorlist(3), _r.tensorlist(4), _r.tensorlist(5), _r.tensor(6), _r.toDouble(7), _r.toDouble(8), _r.toDouble(9), _r.toDouble(10), _r.toBool(11), _r.toBool(12), _r.optionalTensor(13), _r.optionalTensor(14)); Py_RETURN_NONE; } case 1: { // aten::_fused_adam_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () auto dispatch__fused_adam_ = [](at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional & grad_scale, const ::std::optional & found_inf) -> void { pybind11::gil_scoped_release no_gil; at::_fused_adam_(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); }; dispatch__fused_adam_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensorlist(3), _r.tensorlist(4), _r.tensorlist(5), _r.toDouble(6), _r.toDouble(7), _r.toDouble(8), _r.toDouble(9), _r.toDouble(10), _r.toBool(11), _r.toBool(12), _r.optionalTensor(13), _r.optionalTensor(14)); Py_RETURN_NONE; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _fused_adamw_ static PyObject * THPVariable__fused_adamw_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fused_adamw_(TensorList self, TensorList grads, TensorList exp_avgs, TensorList exp_avg_sqs, TensorList max_exp_avg_sqs, TensorList state_steps, *, Tensor lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None)", "_fused_adamw_(TensorList self, TensorList grads, TensorList exp_avgs, TensorList exp_avg_sqs, TensorList max_exp_avg_sqs, TensorList state_steps, *, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None)", }, /*traceable=*/false); ParsedArgs<15> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_fused_adamw_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () auto dispatch__fused_adamw_ = [](at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional & grad_scale, const ::std::optional & found_inf) -> void { pybind11::gil_scoped_release no_gil; at::_fused_adamw_(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); }; dispatch__fused_adamw_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensorlist(3), _r.tensorlist(4), _r.tensorlist(5), _r.tensor(6), _r.toDouble(7), _r.toDouble(8), _r.toDouble(9), _r.toDouble(10), _r.toBool(11), _r.toBool(12), _r.optionalTensor(13), _r.optionalTensor(14)); Py_RETURN_NONE; } case 1: { // aten::_fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () auto dispatch__fused_adamw_ = [](at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional & grad_scale, const ::std::optional & found_inf) -> void { pybind11::gil_scoped_release no_gil; at::_fused_adamw_(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); }; dispatch__fused_adamw_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensorlist(3), _r.tensorlist(4), _r.tensorlist(5), _r.toDouble(6), _r.toDouble(7), _r.toDouble(8), _r.toDouble(9), _r.toDouble(10), _r.toBool(11), _r.toBool(12), _r.optionalTensor(13), _r.optionalTensor(14)); Py_RETURN_NONE; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } \ // _fused_sgd_ static PyObject * THPVariable__fused_sgd_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fused_sgd_(TensorList self, TensorList grads, TensorList momentum_buffer_list, *, double weight_decay, double momentum, Tensor lr, double dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None)", "_fused_sgd_(TensorList self, TensorList grads, TensorList momentum_buffer_list, *, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None)", }, /*traceable=*/false); ParsedArgs<12> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } switch (_r.idx) { case 0: { // aten::_fused_sgd_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> () auto dispatch__fused_sgd_ = [](at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional & grad_scale, const ::std::optional & found_inf) -> void { pybind11::gil_scoped_release no_gil; at::_fused_sgd_(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); }; dispatch__fused_sgd_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.toDouble(3), _r.toDouble(4), _r.tensor(5), _r.toDouble(6), _r.toBool(7), _r.toBool(8), _r.toBool(9), _r.optionalTensor(10), _r.optionalTensor(11)); Py_RETURN_NONE; } case 1: { // aten::_fused_sgd_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> () auto dispatch__fused_sgd_ = [](at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional & grad_scale, const ::std::optional & found_inf) -> void { pybind11::gil_scoped_release no_gil; at::_fused_sgd_(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); }; dispatch__fused_sgd_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.toDouble(3), _r.toDouble(4), _r.toDouble(5), _r.toDouble(6), _r.toBool(7), _r.toBool(8), _r.toBool(9), _r.optionalTensor(10), _r.optionalTensor(11)); Py_RETURN_NONE; } } Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _fused_adagrad_ static PyObject * THPVariable__fused_adagrad_(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_fused_adagrad_(TensorList self, TensorList grads, TensorList state_sums, TensorList state_steps, *, double lr, double lr_decay, double weight_decay, double eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None)", }, /*traceable=*/false); ParsedArgs<11> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_fused_adagrad_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor(d!)[] state_steps, *, float lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () auto dispatch__fused_adagrad_ = [](at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, double lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional & grad_scale, const ::std::optional & found_inf) -> void { pybind11::gil_scoped_release no_gil; at::_fused_adagrad_(self, grads, state_sums, state_steps, lr, lr_decay, weight_decay, eps, maximize, grad_scale, found_inf); }; dispatch__fused_adagrad_(_r.tensorlist(0), _r.tensorlist(1), _r.tensorlist(2), _r.tensorlist(3), _r.toDouble(4), _r.toDouble(5), _r.toDouble(6), _r.toDouble(7), _r.toBool(8), _r.optionalTensor(9), _r.optionalTensor(10)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } // _propagate_xla_data static PyObject * THPVariable__propagate_xla_data(PyObject* self_, PyObject* args, PyObject* kwargs) { HANDLE_TH_ERRORS static PythonArgParser parser({ "_propagate_xla_data(Tensor input, Tensor output)", }, /*traceable=*/false); ParsedArgs<2> parsed_args; auto _r = parser.parse(nullptr, args, kwargs, parsed_args); if(_r.has_torch_function()) { return handle_torch_function(_r, nullptr, args, kwargs, THPVariableFunctionsModule, "torch"); } // aten::_propagate_xla_data(Tensor input, Tensor output) -> () auto dispatch__propagate_xla_data = [](const at::Tensor & input, const at::Tensor & output) -> void { pybind11::gil_scoped_release no_gil; at::_propagate_xla_data(input, output); }; dispatch__propagate_xla_data(_r.tensor(0), _r.tensor(1)); Py_RETURN_NONE; Py_RETURN_NONE; END_HANDLE_TH_ERRORS } } // namespace torch::autograd